Genomic consequences associated with Agrobacterium-mediated transformation of plants.

Attenuated strains of the naturally occurring plant pathogen Agrobacterium tumefaciens can transfer virtually any DNA sequence of interest to model plants and crops. This has made Agrobacterium-mediated transformation (AMT) one of the most commonly used tools in agricultural biotechnology. Understanding AMT, and its functional consequences, is of fundamental importance given that it sits at the intersection of many fundamental fields of study, including plant-microbe interactions, DNA repair/genome stability, and epigenetic regulation of gene expression. Despite extensive research and use of AMT over the last 40 years, the extent of genomic disruption associated with integrating exogenous DNA into plant genomes using this method remains underappreciated. However, new technologies like long-read sequencing make this disruption more apparent, complementing previous findings from multiple research groups that have tackled this question in the past. In this review, we cover progress on the molecular mechanisms involved in Agrobacterium-mediated DNA integration into plant genomes. We also discuss localized mutations at the site of insertion and describe the structure of these DNA insertions, which can range from single copy insertions to large concatemers, consisting of complex DNA originating from different sources. Finally, we discuss the prevalence of large-scale genomic rearrangements associated with the integration of DNA during AMT with examples. Understanding the intended and unintended effects of AMT on genome stability is critical to all plant researchers who use this methodology to generate new genetic variants.

A Simplified Label-Free Method for Proteotyping Sets of Six Isolates in a Single Liquid Chromatography-High-Resolution Tandem Mass Spectrometry Analysis.

Clinical diagnostics and microbiology require high-throughput identification of microorganisms. Sample multiplexing prior to detection is an attractive means to reduce analysis costs and time-to-result. Recent studies have demonstrated the discriminative power of tandem mass spectrometry-based proteotyping. This technology can rapidly identify the most likely taxonomical position of any microorganism, even uncharacterized organisms. Here, we present a simplified label-free multiplexing method to proteotype isolates by tandem mass spectrometry that can identify six microorganisms in a single 20 min analytical run. The strategy involves the production of peptide fractions with distinct hydrophobicity profiles using spin column fractionation. Assemblages of different fractions can then be analyzed using mass spectrometry. Results are subsequently interpreted based on the hydrophobic characteristics of the peptides detected, which make it possible to link each taxon identified to the initial sample. The methodology was tested on 32 distinct sets of six organisms including several worst-scenario assemblages-with differences in sample quantities or the presence of the same organisms in multiple fractions-and proved to be robust. These results pave the way for the deployment of tandem mass spectrometry-based proteotyping in microbiology laboratories.

Full-length isoform concatenation sequencing to resolve cancer transcriptome complexity.

BACKGROUND: Cancers exhibit complex transcriptomes with aberrant splicing that induces isoform-level differential expression compared to non-diseased tissues. Transcriptomic profiling using short-read sequencing has utility in providing a cost-effective approach for evaluating isoform expression, although short-read assembly displays limitations in the accurate inference of full-length transcripts. Long-read RNA sequencing (Iso-Seq), using the Pacific Biosciences (PacBio) platform, can overcome such limitations by providing full-length isoform sequence resolution which requires no read assembly and represents native expressed transcripts. A constraint of the Iso-Seq protocol is due to fewer reads output per instrument run, which, as an example, can consequently affect the detection of lowly expressed transcripts. To address these deficiencies, we developed a concatenation workflow, PacBio Full-Length Isoform Concatemer Sequencing (PB_FLIC-Seq), designed to increase the number of unique, sequenced PacBio long-reads thereby improving overall detection of unique isoforms. In addition, we anticipate that the increase in read depth will help improve the detection of moderate to low-level expressed isoforms. RESULTS: In sequencing a commercial reference (Spike-In RNA Variants; SIRV) with known isoform complexity we demonstrated a 3.4-fold increase in read output per run and improved SIRV recall when using the PB_FLIC-Seq method compared to the same samples processed with the Iso-Seq protocol. We applied this protocol to a translational cancer case, also demonstrating the utility of the PB_FLIC-Seq method for identifying differential full-length isoform expression in a pediatric diffuse midline glioma compared to its adjacent non-malignant tissue. Our data analysis revealed increased expression of extracellular matrix (ECM) genes within the tumor sample, including an isoform of the Secreted Protein Acidic and Cysteine Rich (SPARC) gene that was expressed 11,676-fold higher than in the adjacent non-malignant tissue. Finally, by using the PB_FLIC-Seq method, we detected several cancer-specific novel isoforms. CONCLUSION: This work describes a concatenation-based methodology for increasing the number of sequenced full-length isoform reads on the PacBio platform, yielding improved discovery of expressed isoforms. We applied this workflow to profile the transcriptome of a pediatric diffuse midline glioma and adjacent non-malignant tissue. Our findings of cancer-specific novel isoform expression further highlight the importance of long-read sequencing for characterization of complex tumor transcriptomes.

A solution to achieve sequencing from SARS-CoV-2 specimens with low viral loads: concatenation of reads from independent reactions.

BACKGROUND: During the pandemic, whole genome sequencing was critical to characterize SARS-CoV-2 for surveillance, clinical and therapeutical purposes. However, low viral loads in specimens often led to suboptimal sequencing, making lineage assignment and phylogenetic analysis difficult. We propose an alternative approach to sequencing these specimens that involves sequencing in triplicate and concatenation of the reads obtained using bioinformatics. This proposal is based on the hypothesis that the uncovered regions in each replicate differ and that concatenation would compensate for these gaps and recover a larger percentage of the sequenced genome. RESULTS: Whole genome sequencing was performed in triplicate on 30 samples with Ct > 32 and the benefit of replicate read concatenation was assessed. After concatenation: i) 28% of samples reached the standard quality coverage threshold (> 90% genome covered > 30x); ii) 39% of samples did not reach the coverage quality thresholds but coverage improved by more than 40%; and iii) SARS-CoV-2 lineage assignment was possible in 68.7% of samples where it had been impaired. CONCLUSIONS: Concatenation of reads from replicate sequencing reactions provides a simple way to access hidden information in the large proportion of SARS-CoV-2-positive specimens eliminated from analysis in standard sequencing schemes. This approach will enhance our potential to rule out involvement in outbreaks, to characterize reinfections and to identify lineages of concern for surveillance or therapeutical purposes.

Using all Gene Families Vastly Expands Data Available for Phylogenomic Inference.

Traditionally, single-copy orthologs have been the gold standard in phylogenomics. Most phylogenomic studies identify putative single-copy orthologs using clustering approaches and retain families with a single sequence per species. This limits the amount of data available by excluding larger families. Recent advances have suggested several ways to include data from larger families. For instance, tree-based decomposition methods facilitate the extraction of orthologs from large families. Additionally, several methods for species tree inference are robust to the inclusion of paralogs and could use all of the data from larger families. Here, we explore the effects of using all families for phylogenetic inference by examining relationships among 26 primate species in detail and by analyzing five additional data sets. We compare single-copy families, orthologs extracted using tree-based decomposition approaches, and all families with all data. We explore several species tree inference methods, finding that identical trees are returned across nearly all subsets of the data and methods for primates. The relationships among Platyrrhini remain contentious; however, the species tree inference method matters more than the subset of data used. Using data from larger gene families drastically increases the number of genes available and leads to consistent estimates of branch lengths, nodal certainty and concordance, and inferences of introgression in primates. For the other data sets, topological inferences are consistent whether single-copy families or orthologs extracted using decomposition approaches are analyzed. Using larger gene families is a promising approach to include more data in phylogenomics without sacrificing accuracy, at least when high-quality genomes are available.

Chemometric and sensometric techniques in enological data analysis.

Enological evaluations capture the chemical and sensory space of wine using different techniques; many sensory methods as well as a variety of analytical chemistry techniques contribute to the amount of information generated. Data fusion, especially integrating data sets, is important when working with complex systems. The success reported when trying to integrate different modalities is generally low and has been attributed to the lack of statistically considerate strategies focusing on the data handling process. Multiple stages of data handling must be carefully considered when dealing with multi-modal data. In this review, the different stages in the data analysis process were examined. The study revealed misconceptions surrounding the process and elucidated rules for purpose-driven approaches by examining the complexities of each stage and the impact the decisions made at each stage have on the resulting models. The two major modeling approaches are either supervised (discrimination, classification, prediction) or unsupervised (exploration). Supervised approaches were emphatic on the pre-processing steps and prioritized increasing performance. Unsupervised approaches were mostly used for preliminary steps. The review found aspects often neglected when it came to the data collection and capturing which in the end contributed to the low success in combining sensory and chemistry data.

An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning.

This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured through the steering wheel during driving, the data are always exposed to noise caused by vehicle vibrations, bumpy road conditions, and the driver's steering wheel gripping force. The proposed scheme extracts stable ECG signals and transforms them into full 10 s ECG signals to classify arrhythmias using convolutional neural networks (CNN). Before the ECG stitching algorithm is applied, data preprocessing is performed. To extract the cycle from the collected ECG data, the R peaks are found and the TP interval segmentation is applied. An abnormal P peak is very difficult to find. Therefore, this study also introduces a P peak estimation method. Finally, 4 × 2.5 s ECG segments are collected. To classify arrhythmias with stitched ECG data, each time series' ECG signal is transformed via the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and transfer learning is performed for classification using CNNs. Finally, the parameters of the networks that provide the best performance are investigated. According to the classification accuracy, GoogleNet with the CWT image set shows the best results. The classification accuracy is 82.39% for the stitched ECG data, while it is 88.99% for the original ECG data.

Complexity of the simplest species tree problem.

The multispecies coalescent model provides a natural framework for species tree estimation accounting for gene-tree conflicts. Although a number of species tree methods under the multispecies coalescent have been suggested and evaluated using simulation, their statistical properties remain poorly understood. Here, we use mathematical analysis aided by computer simulation to examine the identifiability, consistency, and efficiency of different species tree methods in the case of three species and three sequences under the molecular clock. We consider four major species-tree methods including concatenation, two-step, independent-sites maximum likelihood, and maximum likelihood. We develop approximations that predict that the probit transform of the species tree estimation error decreases linearly with the square root of the number of loci. Even in this simplest case, major differences exist among the methods. Full-likelihood methods are considerably more efficient than summary methods such as concatenation and two-step. They also provide estimates of important parameters such as species divergence times and ancestral population sizes,whereas these parameters are not identifiable by summary methods. Our results highlight the need to improve the statistical efficiency of summary methods and the computational efficiency of full likelihood methods of species tree estimation.

Gauging ages of tiger swallowtail butterflies using alternate SNP analyses.

Divergence times underpin diverse evolutionary hypotheses, but conflicting age estimates across studies diminish the validity of such hypotheses. These conflicts have continued to grow as large genomics datasets become commonplace and analytical approaches proliferate. To provide more stable temporal intervals, age estimations should be interpreted in the context of both the type of data and analysis being used. Here, we use multispecies coalescent (MSC), concatenation-based, and categorical data transformation approaches on genome-wide SNP data to infer divergence ages within the Papilio glaucus group of tiger swallowtail butterflies in North America. While the SNP data supported previously recognized relationships within the group (P. multicaudata, ((P. eurymedon, P. rutulus), (P. appalachiensis, P. canadensis, P. glaucus))), estimated ages of divergence between the major lineages varied substantially among analyses. MSC produced wide credibility intervals particularly for deeper nodes, reflecting uncertainty in the coalescence times as a possible result of conflicting signal across gene trees. Concatenation, in contrast, gave narrower and more well-defined posterior distributions for the node ages; however, the higher precision of these time estimates is a likely artefact due to more simplistic underlying assumptions of this approach that do not account for conflict among gene trees. Transformed categorical data analysis gave the least precise and the most variable results, with its simple substitution model coupled with a relaxed clock tending to produce spurious results from large genome-wide datasets. While median node ages differed considerably between analyses (∼2 Mya between MSC and concatenation-based results), their corresponding credibility intervals nonetheless highlight common temporal patterns for deeper divergences in the group as well as finer-scale phylogeography. Age distributions across analyses support an origin of the group during the warm period of the early to mid-Pliocene. Late Pliocene climate aridification and cooling drove divergence between eastern and western groups that further diversified during the period of repeated Pleistocene glaciations. Our results provide a structured comparative assessment of divergence time estimates and evolutionary relationships in a well-studied group of butterflies, and support better understanding of analytical biases in divergence time estimation.

A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth.

BACKGROUND: Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar tooth using deep learning. The accuracy of the model was evaluated by comparing the extraction time predicted by deep learning with the actual time required for extraction. METHODS: A total of 724 panoramic X-ray images and clinical data were used for artificial intelligence (AI) prediction of extraction time. Clinical data such as age, sex, maximum mouth opening, body weight, height, the time from the start of incision to the start of suture, and surgeon's experience were recorded. Data augmentation and weight balancing were used to improve learning abilities of AI models. Extraction time predicted by the concatenated AI model was compared with the actual extraction time. RESULTS: The final combined model (CNN + MLP) model achieved an R value of 0.8315, an R-squared value of 0.6839, a p-value of less than 0.0001, and a mean absolute error (MAE) of 2.95 min with the test dataset. CONCLUSIONS: Our proposed model for predicting time to extract the mandibular third molar tooth performs well with a high accuracy in clinical practice.

Densely Connected Neural Networks for Nonlinear Regression.

Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimensions of the proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models, including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation with observations, which indicates that our model could advance environmental data science.

Impact of inter-individual variability on the estimation of default mode network in temporal concatenation group ICA.

Temporal concatenation group ICA (TC-GICA) is a widely used data-driven method to extract common functional brain networks among individuals. TC-GICA concatenates the time series of individual fMRI data and applies dimension reduction and ICA algorithms to decompose the data into group-level components. The default mode network (DMN) estimated using TC-GICA at relatively high model orders (i.e., large numbers of components) is split into multiple components. The split DMNs are topographically different from those estimated using other methods (e.g., seed-based correlation, clustering, graph theoretical analysis, and other ICA methods like gRAICAR and IVA-GL) and are inconsistent with the existing knowledge of DMN. We hypothesize that the "DMN-splitting'' phenomenon reflects the impact of inter-individual variability in data, which is propagated into the ICA decomposition via the data-concatenation step of TC-GICA. By systematically manipulating the amount of variability involved in the temporal concatenation in both simulated and several realistic datasets, we observed that as more variability was involved, the estimated DMN became less similar to the averaged functional connectivity (FC) pattern obtained using seed-based correlation analysis. The performance of the DMN estimation in TC-GICA also exhibited remarkable dependence on the model order settings. Further analyses revealed that the "DMN-splitting" in TC-GICA could be reproduced when involving large variability in the data-concatenation and performing ICA at high model orders. These results were replicated across multiple datasets and various software implementations. When applying ICA approaches that avoid temporal concatenation, such as gRAICAR and IVA-GL, to the same datasets, the estimated group-level DMN was more consistent with the seed-based FC pattern and was more robust to various model order settings. This study calls for caution when applying TC-GICA to datasets expected to have large inter-individual variability, such as pooling different experimental groups of subjects.

Assessing topological congruence among concatenation-based phylogenomic approaches in empirical datasets.

Assessing the effect of methodological decisions on the resulting hypotheses is critical in phylogenetics. Recent studies have focused on evaluating how model selection, orthology definition and confounding factors affect phylogenomic results. Here, we compare the results of three concatenated phylogenetic methods (Maximum Likelihood, ML; Bayesian Inference, BI; Maximum Parsimony, MP) in 157 empirical phylogenomic datasets. The resulting trees were very similar, with 96.7% of all nodes shared between BI and ML (90.6% for ML-MP and 89.1% for BI-MP). Differing nodes were predominantly those of lower support. The main conclusions of most of the studies agreed for the three phylogenetic methods and the discordance involved nodes considered as recalcitrant problems in systematics. The differences between methods were proportionally larger in datasets that analyze the relationships at higher taxonomic levels (particularly phyla and kingdoms), and independent of the number of characters included in the datasets. Note: a spanish version of this article is available in the Supplementary material (Supplementary material online).

Phylogenomics of manakins (Aves: Pipridae) using alternative locus filtering strategies based on informativeness.

Target capture sequencing effectively generates molecular marker arrays useful for molecular systematics. These extensive data sets are advantageous where previous studies using a few loci have failed to resolve relationships confidently. Moreover, target capture is well-suited to fragmented source DNA, allowing data collection from species that lack fresh tissues. Herein we use target capture to generate data for a phylogeny of the avian family Pipridae (manakins), a group that has been the subject of many behavioral and ecological studies. Most manakin species feature lek mating systems, where males exhibit complex behavioral displays including mechanical and vocal sounds, coordinated movements of multiple males, and high speed movements. We analyzed thousands of ultraconserved element (UCE) loci along with a smaller number of coding exons and their flanking regions from all but one species of Pipridae. We examined three different methods of phylogenetic estimation (concatenation and two multispecies coalescent methods). Phylogenetic inferences using UCE data yielded strongly supported estimates of phylogeny regardless of analytical method. Exon probes had limited capability to capture sequence data and resulted in phylogeny estimates with reduced support and modest topological differences relative to the UCE trees, although these conflicts had limited support. Two genera were paraphyletic among all analyses and data sets, with Antilophia nested within Chiroxiphia and Tyranneutes nested within Neopelma. The Chiroxiphia-Antilophia clade was an exception to the generally high support we observed; the topology of this clade differed among analyses, even those based on UCE data. To further explore relationships within this group, we employed two filtering strategies to remove low-information loci. Those analyses resulted in distinct topologies, suggesting that the relationships we identified within Chiroxiphia-Antilophia should be interpreted with caution. Despite the existence of a few continuing uncertainties, our analyses resulted in a robust phylogenetic hypothesis of the family Pipridae that provides a comparative framework for future ecomorphological and behavioral studies.

Model Choice, Missing Data, and Taxon Sampling Impact Phylogenomic Inference of Deep Basidiomycota Relationships.

Resolving deep divergences in the tree of life is challenging even for analyses of genome-scale phylogenetic data sets. Relationships between Basidiomycota subphyla, the rusts and allies (Pucciniomycotina), smuts and allies (Ustilaginomycotina), and mushroom-forming fungi and allies (Agaricomycotina) were found particularly recalcitrant both to traditional multigene and genome-scale phylogenetics. Here, we address basal Basidiomycota relationships using concatenated and gene tree-based analyses of various phylogenomic data sets to examine the contribution of several potential sources of bias. We evaluate the contribution of biological causes (hard polytomy, incomplete lineage sorting) versus unmodeled evolutionary processes and factors that exacerbate their effects (e.g., fast-evolving sites and long-branch taxa) to inferences of basal Basidiomycota relationships. Bayesian Markov Chain Monte Carlo and likelihood mapping analyses reject the hard polytomy with confidence. In concatenated analyses, fast-evolving sites and oversimplified models of amino acid substitution favored the grouping of smuts with mushroom-forming fungi, often leading to maximal bootstrap support in both concatenation and coalescent analyses. On the contrary, the most conserved data subsets grouped rusts and allies with mushroom-forming fungi, although this relationship proved labile, sensitive to model choice, to different data subsets and to missing data. Excluding putative long-branch taxa, genes with high proportions of missing data and/or with strong signal failed to reveal a consistent trend toward one or the other topology, suggesting that additional sources of conflict are at play. While concatenated analyses yielded strong but conflicting support, individual gene trees mostly provided poor support for any resolution of rusts, smuts, and mushroom-forming fungi, suggesting that the true Basidiomycota tree might be in a part of tree space that is difficult to access using both concatenation and gene tree-based approaches. Inference-based assessments of absolute model fit strongly reject best-fit models for the vast majority of genes, indicating a poor fit of even the most commonly used models. While this is consistent with previous assessments of site-homogenous models of amino acid evolution, this does not appear to be the sole source of confounding signal. Our analyses suggest that topologies uniting smuts with mushroom-forming fungi can arise as a result of inappropriate modeling of amino acid sites that might be prone to systematic bias. We speculate that improved models of sequence evolution could shed more light on basal splits in the Basidiomycota, which, for now, remain unresolved despite the use of whole genome data.

Phylogenomics Uncovers Confidence and Conflict in the Rapid Radiation of Australo-Papuan Rodents.

The estimation of robust and accurate measures of branch support has proven challenging in the era of phylogenomics. In data sets of potentially millions of sites, bootstrap support for bifurcating relationships around very short internal branches can be inappropriately inflated. Such overestimation of branch support may be particularly problematic in rapid radiations, where phylogenetic signal is low and incomplete lineage sorting severe. Here, we explore this issue by comparing various branch support estimates under both concatenated and coalescent frameworks, in the recent radiation Australo-Papuan murine rodents (Muridae: Hydromyini). Using nucleotide sequence data from 1245 independent loci and several phylogenomic inference methods, we unequivocally resolve the majority of genus-level relationships within Hydromyini. However, at four nodes we recover inconsistency in branch support estimates both within and among concatenated and coalescent approaches. In most cases, concatenated likelihood approaches using standard fast bootstrap algorithms did not detect any uncertainty at these four nodes, regardless of partitioning strategy. However, we found this could be overcome with two-stage resampling, that is, across genes and sites within genes (using -bsam GENESITE in IQ-TREE). In addition, low confidence at recalcitrant nodes was recovered using UFBoot2, a recent revision to the bootstrap protocol in IQ-TREE, but this depended on partitioning strategy. Summary coalescent approaches also failed to detect uncertainty under some circumstances. For each of four recalcitrant nodes, an equivalent (or close to equivalent) number of genes were in strong support ($>$ 75% bootstrap) of both the primary and at least one alternative topological hypothesis, suggesting notable phylogenetic conflict among loci not detected using some standard branch support metrics. Recent debate has focused on the appropriateness of concatenated versus multigenealogical approaches to resolving species relationships, but less so on accurately estimating uncertainty in large data sets. Our results demonstrate the importance of employing multiple approaches when assessing confidence and highlight the need for greater attention to the development of robust measures of uncertainty in the era of phylogenomics.

SSD-EMB: An Improved SSD Using Enhanced Feature Map Block for Object Detection.

The development of deep learning has achieved great success in object detection, but small object detection is still a difficult and challenging task in computer vision. To address the problem, we propose an improved single-shot multibox detector (SSD) using enhanced feature map blocks (SSD-EMB). The enhanced feature map block (EMB) consists of attention stream and feature map concatenation stream. The attention stream allows the proposed model to focus on the object regions rather than background owing to channel averaging and the effectiveness of the normalization. The feature map concatenation stream provides additional semantic information to the model without degrading the detection speed. By combining the output of these two streams, the enhanced feature map, which improves the detection of a small object, is generated. Experimental results show that the proposed model has high accuracy in small object detection. The proposed model not only achieves good detection accuracy, but also has a good detection speed. The SSD-EMB achieved a mean average precision (mAP) of 80.4% on the PASCAL VOC 2007 dataset at 30 frames per second on an RTX 2080Ti graphics processing unit, an mAP of 79.9% on the VOC 2012 dataset, and an mAP of 26.6% on the MS COCO dataset.

Multi-Zone Authentication and Privacy-Preserving Protocol (MAPP) Based On the Bilinear Pairing Cryptography for 5G-V2X.

5G-Vehicle-to-Everything (5G-V2X) supports high-reliability and low latency autonomous services and applications. Proposing an efficient security solution that supports multi-zone broadcast authentication and satisfies the 5G requirement is a critical challenge. In The 3rd Generation Partnership Project (3GPP) Release 16 standard, for Cellular- Vehicle-to-Everything (C-V2X) single-cell communication is suggested to reuse the IEEE1609.2 security standard that utilizes the Public Key Infrastructure (PKI) cryptography. PKI-based solutions provide a high-security level, however, it suffers from high communication and computation overhead, due to the large size of the attached certificate and signature. In this study, we propose a light-weight Multi-Zone Authentication and Privacy-Preserving Protocol (MAPP) based on the bilinear pairing cryptography and short-size signature. MAPP protocol provides three different authentication methods that enable a secure broadcast authentication over multiple zones of large-scale base stations, using a single message and a single short signature. We also propose a centralized dynamic key generation method for multiple zones. We implemented and analyzed the proposed key generation and authentication methods using an authentication simulator and a bilinear pairing library. The proposed methods significantly reduce the signature generation time by 16 times-80 times, as compared to the previous methods. Additionally, the proposed methods significantly reduced the signature verification time by 10 times-16 times, as compared to the two previous methods. The three proposed authentication methods achieved substantial speed-up in the signature generation time and verification time, using a short bilinear pairing signature.

Species Phylogeny versus Gene Trees: A Case Study of an Incongruent Data Matrix Based on Paphiopedilum Pfitz. (Orchidaceae).

The phylogeny of the genus Paphiopedilum based on the plastome is consistent with morphological analysis. However, to date, none of the analyzed nuclear markers has confirmed this. Topology incongruence among the trees of different nuclear markers concerns entire sections of the subgenus Paphiopedilum. The low-copy nuclear protein-coding gene PHYC was obtained for 22 species representing all sections and subgenera of Paphiopedilum. The nuclear-based phylogeny is supported by morphological characteristics and plastid data analysis. We assumed that an incongruence in nuclear gene trees is caused by ancestral homoploid hybridization. We present a model for inferring the phylogeny of the species despite the incongruence of the different tree topologies. Our analysis, based on six low-copy nuclear genes, is congruent with plastome phylogeny and has been confirmed by phylogenetic network analysis.

Phylogeny and highland adaptation of Chinese species in Allium section Daghestanica (Amaryllidaceae) revealed by transcriptome sequencing.

Allium L. is one of the largest monocotyledonous genera with extensive distribution in the Northern Hemisphere. The fundamental phylogenies of Allium have been investigated using many morphological and molecular characters. However, the morphological characters may not agree with the molecular results in some Allium groups or sections (such as the Chinese Allium section Daghestanica), which may result in ambiguous species relationships and hinder further evolutionary and adaptive researches. Here, transcriptome sequences of the six Chinese endemics from Allium section Daghestanica were collected, with their single-copy genes (SCGs) were extracted. The interspecies relationships were analyzed using concatenation and coalescent methods. The branch-site model (BSM) was conducted to detect the positively selected genes (PSGs) in five highland species of this section. Based on 1644, 1281 and 1580 SCGs in flowers, leaves, and flowers-leaves combination respectively, a robust consistent and well-resolved phylogeny was generated from the concatenation method. Strong conflicts among individual gene trees were detected in the coalescent method, and morphological characters were incongruent with molecular relationships to some degree. Many PSGs were involved in responses of various stresses and stimuli (e.g. hypoxia, low temperature, aridity), DNA repair, metabolism, nutrient or energy intake, photosynthesis, and signal transduction. Our study revealed a clear interspecies relationship of Chinese endemics in Allium section Daghestanica and suggested that the discordance between morphological characters and molecular relationships might result from that the former are more susceptible to convergence compared with the latter. PSGs detected in our study may provide some insights into highland adaptation in Allium species.