bluuebunny/crossref_metadata_2025_split huggingface dataset:
Devising Hyperthermia Dose of NIR-Irradiated Cs0.33WO3 Nanoparticles for HepG2 Hepatic Cancer Cells
Authors: Hu Po-Sheng , Chou Hsiu-Jen , Chen Chi-An , Wu Po-Yi , Hsiao Kai-Hsien , Kuo Yu-Min 
Published: June 2021

AbstractHyperthermia is one of the most patient-friendly methods to cure cancer diseases owing to its noninvasiveness, minimally induced side-effects and toxicity, and easy implementation, prompting the development of novel therapeutic methods like photothermally triggering dose system. This research herein interrogates the variables of photothermal effects of Cs0.33WO3 nanoparticles (NPs), the duration of irradiation, optical power density and NP concentration, upon HepG2 liver cancer cell line in vitro, leading to the formulation of a near-infrared (NIR)-irradiated thermal dose. Expressly, the NPs with particulate feature sizes of 120 nm were synthesized through a series of oxidation–reduction (REDOX) reaction, thermal annealing and wet-grinding processes, and the subsequent characterization of physical, compositional, optical, photothermal properties were examined using dynamic light scattering (DLS), energy-dispersive X-ray spectroscopy (EDS), scanning and tunneling electron microscopies (SEM and TEM), X-ray diffraction (XRD) and visible-near-infrared (VIS–NIR) photospectroscopy. Cytotoxicity of the NPs and its irradiation parameters were obtained for the HepG2 cells. By incubating the cells with the NPs, the state of endocytosis was verified, and the dependence of cellular survival rate on the variable parameters of photothermal dose was determined while maintaining the medium temperature of the cell-containing culture dish at human body temperature around 36.5 °C.
Proteomic and Transcriptomic Analyses Provide Novel Insights into the Crucial Roles of Host-Induced Carbohydrate Metabolism Enzymes in Xanthomonas oryzae pv. oryzae Virulence and Rice-Xoo Interaction
Authors: Wu Guichun , Zhang Yuqiang , Wang Bo , Li Kaihuai , Lou Yuanlai , Zhao Yancun , Liu Fengquan 
Published: June 2021

Abstract
Background
Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial leaf blight, a devastating rice disease. The Xoo-rice interaction, wherein wide ranging host- and pathogen-derived proteins and genes wage molecular arms race, is a research hotspot. Hence, the identification of novel rice-induced Xoo virulence factors and characterization of their roles affecting rice global gene expression profiles will provide an integrated and better understanding of Xoo-rice interactions from the molecular perspective.

Results
Using comparative proteomics and an in vitro interaction system, we revealed that 5 protein spots from Xoo exhibited significantly different expression patterns (|fold change| > 1.5) at 3, 6, 12 h after susceptible rice leaf extract (RLX) treatment. MALDI-TOF MS analysis and pathogenicity tests showed that 4 host-induced proteins, including phosphohexose mutase, inositol monophosphatase, arginase and septum site-determining protein, affected Xoo virulence. Among them, mutants of two host-induced carbohydrate metabolism enzyme-encoding genes, ΔxanA and Δimp, elicited enhanced defense responses and nearly abolished Xoo virulence in rice. To decipher rice differentially expressed genes (DEGs) associated with xanA and imp, transcriptomic responses of ΔxanA-treated and Δimp-treated susceptible rice were compared to those in rice treated with PXO99A at 1 and 3 dpi. A total of 1521 and 227 DEGs were identified for PXO99A vs Δimp at 1 and 3 dpi, while for PXO99A vs ΔxanA, there were 131 and 106 DEGs, respectively. GO, KEGG and MapMan analyses revealed that the DEGs for PXO99A vs Δimp were mainly involved in photosynthesis, signal transduction, transcription, oxidation-reduction, hydrogen peroxide catabolism, ion transport, phenylpropanoid biosynthesis and metabolism of carbohydrates, lipids, amino acids, secondary metabolites, hormones, and nucleotides, while the DEGs from PXO99A vs ΔxanA were predominantly associated with photosynthesis, signal transduction, oxidation-reduction, phenylpropanoid biosynthesis, cytochrome P450 and metabolism of carbohydrates, lipids, amino acids, secondary metabolites and hormones. Although most pathways were associated with both the Δimp and ΔxanA treatments, the underlying genes were not the same.

Conclusion
Our study identified two novel host-induced virulence factors XanA and Imp in Xoo, and revealed their roles in global gene expression in susceptible rice. These results provide valuable insights into the molecular mechanisms of pathogen infection strategies and plant immunity.
Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers
Authors: Muller Ittai B. , Meijers Stijn , Kampstra Peter , van Dijk Steven , van Elswijk Michel , Lin Marry , Wojtuszkiewicz Anna M. , Jansen Gerrit , de Jonge Robert , Cloos Jacqueline 
Published: June 2021

Abstract
Background
Computational tools analyzing RNA-sequencing data have boosted alternative splicing research by identifying and assessing differentially spliced genes. However, common alternative splicing analysis tools differ substantially in their statistical analyses and general performance. This report compares the computational performance (CPU utilization and RAM usage) of three event-level splicing tools; rMATS, MISO, and SUPPA2. Additionally, concordance between tool outputs was investigated.

Results
Log-linear relations were found between job times and dataset size in all splicing tools and all virtual machine (VM) configurations. MISO had the highest job times for all analyses, irrespective of VM size, while MISO analyses also exceeded maximum CPU utilization on all VM sizes. rMATS and SUPPA2 load averages were relatively low in both size and replicate comparisons, not nearing maximum CPU utilization in the VM simulating the lowest computational power (D2 VM). RAM usage in rMATS and SUPPA2 did not exceed 20% of maximum RAM in both size and replicate comparisons while MISO reached maximum RAM usage in D2 VM analyses for input size. Correlation coefficients of differential splicing analyses showed high correlation (β > 80%) between different tool outputs with the exception of comparisons of retained intron (RI) events between rMATS/MISO and rMATS/SUPPA2 (β < 60%).

Conclusions
Prior to RNA-seq analyses, users should consider job time, amount of replicates and splice event type of interest to determine the optimal alternative splicing tool. In general, rMATS is superior to both MISO and SUPPA2 in computational performance. Analysis outputs show high concordance between tools, with the exception of RI events.
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
Authors: Pradier Léa , Tissot Tazzio , Fiston-Lavier Anna-Sophie , Bedhomme Stéphanie 
Published: June 2021

Abstract
Background
Plasmids are mobile genetic elements that often carry accessory genes, and are vectors for horizontal transfer between bacterial genomes. Plasmid detection in large genomic datasets is crucial to analyze their spread and quantify their role in bacteria adaptation and particularly in antibiotic resistance propagation. Bioinformatics methods have been developed to detect plasmids. However, they suffer from low sensitivity (i.e., most plasmids remain undetected) or low precision (i.e., these methods identify chromosomes as plasmids), and are overall not adapted to identify plasmids in whole genomes that are not fully assembled (contigs and scaffolds).

Results
We developed PlasForest, a homology-based random forest classifier identifying bacterial plasmid sequences in partially assembled genomes. Without knowing the taxonomical origin of the samples, PlasForest identifies contigs as plasmids or chromosomes with a F1 score of 0.950. Notably, it can detect 77.4% of plasmid contigs below 1 kb with 2.8% of false positives and 99.9% of plasmid contigs over 50 kb with 2.2% of false positives.

Conclusions
PlasForest outperforms other currently available tools on genomic datasets by being both sensitive and precise. The performance of PlasForest on metagenomic assemblies are currently well below those of other k-mer-based methods, and we discuss how homology-based approaches could improve plasmid detection in such datasets.
UniBind: maps of high-confidence direct TF-DNA interactions across nine species
Authors: Puig Rafael Riudavets , Boddie Paul , Khan Aziz , Castro-Mondragon Jaime Abraham , Mathelier Anthony 
Published: June 2021

Abstract
Background
Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. It is critical to locate these TF-DNA interactions to understand transcriptional regulation. Efforts to predict bona fide TFBSs benefit from the availability of experimental data mapping DNA binding regions of TFs (chromatin immunoprecipitation followed by sequencing - ChIP-seq).

Results
In this study, we processed ~ 10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~ 56 million TFBSs with experimental and computational support for direct TF-DNA interactions for 644 TFs in > 1000 cell lines and tissues. These TFBSs were used to predict > 197,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface (https://unibind.uio.no/), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions.

Conclusions
UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.
Construction of a high-density linkage map and graphical representation of the arrangement of transcriptome-based unigene markers on the chromosomes of onion, Allium cepa L.
Authors: Fujito Satoshi , Akyol Turgut Yigit , Mukae Takuya , Wako Tadayuki , Yamashita Ken-ichiro , Tsukazaki Hikaru , Hirakawa Hideki , Tanaka Keisuke , Mine Yoko , Sato Shusei , Shigyo Masayoshi 
Published: June 2021

Abstract
Background
Genomic information for Allium cepa L. is limited as it is heterozygous and its genome is very large. To elucidate potential SNP markers obtained by NGS, we used a complete set of A. fistulosum L.-A. cepa monosomic addition lines (MALs) and doubled haploids (DHs). These were the parental lines of an A. cepa mapping population for transcriptome-based SNP genotyping.

Results
We mapped the transcriptome sequence reads from a series of A. fistulosum-A. cepa MALs onto the unigene sequence of the doubled haploid shallot A. cepa Aggregatum group (DHA) and compared the MAL genotype call for parental bunching onion and shallot transcriptome mapping data. We identified SNP sites with at least four reads on 25,462 unigenes. They were anchored on eight A. cepa chromosomes. A single SNP site was identified on 3,278 unigenes and multiple SNPs were identified on 22,184 unigenes. The chromosome marker information was made public via the web database Allium TDB (http://alliumtdb.kazusa.or.jp/). To apply transcriptome based genotyping approach for genetic mapping, we gathered RNA sequence data from 96 lines of a DHA × doubled haploid bulb onion A. cepa common onion group (DHC) mapping population. After selecting co-dominant SNP sites, 16,872 SNPs were identified in 5,339 unigenes. Of these, at least two SNPs with identical genotypes were found in 1,435 unigenes. We developed a linkage map using genotype information from these unigenes. All unigene markers mapped onto the eight chromosomes and graphical genotyping was conducted based on the unigene order information. Another 2,963 unigenes were allocated onto the eight chromosomes. To confirm the accuracy of this transcriptome-based genetic linkage map, conventional PCR-based markers were used for linkage analysis. All SNP - and PCR-based markers were mapped onto the expected linkage groups and no inconsistency was found among these chromosomal locations.

Conclusions
Effective transcriptome analysis with unique Allium resources successfully associated numerous chromosome markers with unigene information and a high-density A. cepa linkage map. The information on these unigene markers is valuable in genome sequencing and useful trait detection in Allium.
Immunization with Pneumocystis carinii A121–85 antigen activates immune function against P. carinii
Authors: Tong Tong , Wang Zhongxin , Xu Yuanhong , Shen Jilu 
Published: June 2021

Abstract
Background
Pneumocystis pneumonia (PcP), which is caused by Pneumocystis carinii, is a life-threatening infection that affects immunocompromised individuals. Unfortunately, chemoprophylaxis and dapsone are only effective for half of the patients with PcP, indicating that additional preventive methods are needed. We predicated the pneumocystis surface protein A12 sequence 1–85 by DNAStar software and BepiPred, and identified it as a potential vaccine candidate by bioresearch.

Methods
We used recombinant A121–85 as antigen to immunized mice and detected serum titer of IgG, expression of inflammatory factors by EILSA, qRT-PCR and flow cytometry.

Results
Our results showed that immunization with recombinant A121–85 increased the serum titer of IgG, promoted the secretion of T lymphocytes, increased the expression of inflammatory factors, and elevated lung inflammatory injury in mice.

Conclusions
Our findings suggest that A121–85 is a potential vaccine target for preventing Pneumocystis carinii. The evaluation of A121–85-elicited antibodies in the prevention of PcP in humans deserves further investigation.
Application of machine learning in bacteriophage research
Authors: Nami Yousef , Imeni Nazila , Panahi Bahman 
Published: June 2021

AbstractPhages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.
The characterization of key physiological traits of medicinal cannabis (Cannabis sativa L.) as a tool for precision breeding
Authors: Naim-Feil Erez , Pembleton Luke W. , Spooner Laura E. , Malthouse Alix L. , Miner Amy , Quinn Melinda , Polotnianka Renata M. , Baillie Rebecca C. , Spangenberg German C. , Cogan Noel O. I. 
Published: June 2021

AbstractBackgroundFor millennia, drug-type cannabis strains were extensively used for various medicinal, ritual, and inebriant applications. However, cannabis prohibition during the last century led to cultivation and breeding activities being conducted under clandestine conditions, while scientific development of the crop ceased. Recently, the potential of medicinal cannabis has been reacknowledged and the now expanding industry requires optimal and scientifically characterized varieties. However, scientific knowledge that can propel this advancement is sorely lacking. To address this issue, the current study aims to provide a better understanding of key physiological and phenological traits that can facilitate the breeding of advanced cultivars.ResultsA diverse population of 121 genotypes of high-THC or balanced THC-CBD ratio was cultivated under a controlled environment facility and 13 plant parameters were measured. No physiological association across genotypes attributed to the same vernacular classification was observed. Floral bud dry weight was found to be positively associated with plant height and stem diameter but not with days to maturation. Furthermore, the heritability of both plant height and days to maturation was relatively high, but for plant height it decreased during the vegetative growth phase. To advance breeding efficacy, a prediction equation for forecasting floral bud dry weight was generated, driven by parameters that can be detected during the vegetative growth phase solely.ConclusionsOur findings suggest that selection for taller and fast-growing genotypes is likely to lead to an increase in floral bud productivity. It was also found that the final plant height and stem diameter are determined by 5 independent factors that can be used to maximize productivity through cultivation adjustments. The proposed prediction equation can facilitate the selection of prolific genotypes without the completion of a full cultivation cycle. Future studies that will associate genome-wide variation with plants morphological traits and cannabinoid profile will enable precise and accelerated breeding through genomic selection approaches.
Genome-wide identification of AP2/EREBP in Fragaria vesca and expression pattern analysis of the FvDREB subfamily under drought stress
Authors: Dong Chao , Xi Yue , Chen Xinlu , Cheng Zong-Ming 
Published: June 2021

Abstract
Background
Drought is a common phenomenon worldwide. It is also one of the main abiotic factors that affect the growth and quality of strawberry. The dehydration-responsive element binding protein (DREB) members that belong to the APETALA2/ethylene-responsive element binding protein (AP2/EREBP) superfamily are unique transcription factors in plants that play important roles in the abiotic stress response.

Results
Here, a total of 119 AP2/EREBP genes were identified in Fragaria vesca, and the AP2/EREBP superfamily was divided into AP2, RAV, ERF, DREB, and soloist subfamilies, containing 18, 7, 61, 32, and one member(s), respectively. The DREB subfamily was further divided into six subgroups (A-1 to A-6) based on phylogenetic analysis. Gene structure, conserved motifs, chromosomal location, and synteny analysis were conducted to comprehensively investigate the characteristics of FvDREBs. Furthermore, transcriptome analysis revealed distinctive expression patterns among the FvDREB genes in strawberry plants exposed to drought stress. The expression of FvDREB6 of the A-2 subgroup was down-regulated in old leaves and up-regulated in young leaves in response to drought. Furthermore, qRT-PCR analysis found that FvDREB8 from the A-2 subgroup had the highest expression level under drought stress. Together, analyses with the expression pattern, phylogenetic relationship, motif, and promoter suggest that FvDREB18 may play a critical role in the regulation of FvDREB1 and FvDREB2 expression.

Conclusions
Our findings provide new insights into the characteristics and potential functions of FvDREBs. These FvDREB genes should be further studied as they appear to be excellent candidates for drought tolerance improvement of strawberry.
Multiple imputation to quantify misclassification in observational studies of the cognitively impaired: an application for pain assessment in nursing home residents
Authors: Nunes Anthony P. , Zhao Danni , Jesdale William M. , Lapane Kate L. 
Published: June 2021

Abstract
Background
Despite experimental evidence suggesting that pain sensitivity is not impaired by cognitive impairment, observational studies in nursing home residents have observed an inverse association between cognitive impairment and resident-reported or staff-assessed pain. Under the hypothesis that the inverse association may be partially attributable to differential misclassification due to recall and communication limitations, this study implemented a missing data approach to quantify the absolute magnitude of misclassification of pain, pain frequency, and pain intensity by level of cognitive impairment.

Methods
Using the 2016 Minimum Data Set 3.0, we conducted a cross-sectional study among newly admitted US nursing home residents. Pain presence, severity, and frequency is assessed via resident-reported measures. For residents unable to communicate their pain, nursing home staff document pain based on direct resident observation and record review. We estimate a counterfactual expected level of pain in the absence of cognitive impairment by multiply imputing modified pain indicators for which the values were retained for residents with no/mild cognitive impairment and set to missing for residents with moderate/severe cognitive impairment. Absolute differences (∆) in the presence and magnitude of pain were calculated as the difference between documented pain and the expected level of pain.

Results
The difference between observed and expected resident reported pain was greater in residents with severe cognitive impairment (∆ = -10.2%, 95% Confidence Interval (CI): -10.9% to -9.4%) than those with moderate cognitive impairment (∆ = -4.5%, 95% CI: -5.4% to -3.6%). For staff-assessed pain, the magnitude of apparent underreporting was similar between residents with moderate impairment (∆ = -7.2%, 95% CI: -8.3% to -6.0%) and residents with severe impairment (∆ = -7.2%, 95% CI: -8.0% to -6.3%). Pain characterized as “mild” had the highest magnitude of apparent underreporting.

Conclusions
In residents with moderate to severe cognitive impairment, documentation of any pain was lower than expected in the absence of cognitive impairment. This finding supports the hypothesis that an inverse association between pain and cognitive impairment may be explained by differential misclassification. This study highlights the need to develop analytic and/or procedural solutions to correct for recall/reporter bias resulting from cognitive impairment.