With the rapid development of high-throughput sequencing technology, the analysis of metagenomic sequencing data and the accurate and efficient estimation of relative microbial abundance have become important ways to explore the microbial composition and function of microbes. In addition, the accuracy and efficiency of the relative microbial abundance estimation are closely related to the algorithm and the selection of the reference sequence for sequence alignment. We introduced the microbial core genome as the reference sequence for potential microbes in a metagenomic sample, and we constructed a finite mixture and latent Dirichlet models and used the Gibbs sampling algorithm to estimate the relative abundance of microorganisms. The simulation results showed that our approach can improve the efficiency while maintaining high accuracy and is more suitable for high-throughput metagenomic data. The new approach was implemented in our CoreProbe package which provides a pipeline for an accurate and efficient estimation of the relative abundance of microbes in a community. This tool is available free of charge from the CoreProbe’s website: Access the Docker image with the following instruction: sudo docker pull panhongfei/coreprobe:1.0. Microbial organisms are ubiquitous in virtually all the natural environments of the earth’s biosphere. They play integral and unique roles in ecosystems , they arRecent innovations in metagenomic shotgun sequencing have made it possible to characterize microbial contents in uncultured samples by yielding billions of short reads from metagenomes. Many algorithms have been merged to estimate a community’s taxonomic composition by analyzing the metagenomics sequencing data. These algorithms can be classified into two categories, alignment-based or composition-based, according to their different resolutions of taxonomic binning.The composition-based algorithms classify metagenomic reads mostly according to their k-mer frequencies. A number of unsupervised methods of this kind have been employed for clustering the reads generated from similar taxonomies. For example, TETRA  clustersOld composition-based methods such as TETRA and TACOA show sharply reduced accuracy during the abundance estimation when the lengths of the reads are less than 800 bp or the community complexity of the samples is high. Although it is claimed that newly merged composition-based methods can be applied quickly and accurately to metagenomic datasets from next-generation sequencing with read lengths of less than 300 bp, these methods still do not appear to be widely used in real metagenomic analyses because there is currently an open question about how k-mer compositions are associated with microbial phylogeny. More evidence must be discovered until the compositional characteristics of DNA sequences become solid phylogenetic signals.In the alignment-based approaches, alignment and mapping tools, such as BLAST , BWA 2727 and BoAnother way to perform microbial abundance estimations other than introducing the probabilistic model to alignment-based approaches is to substitute traditional complete genome sequences with more condensed and informative marker sequences as references. MetaPhlAn  and mOTUHere, we recommend using core genomes as the reference sequences of potential species and estimating the microbial abundances in metagenomic analysis by using the probabilistic model. According to the pan-genome concepts motivated by Tettelin , a cladehttps://portal.hmpdacc.org/), yielding new insights into microbiomes from different human body sites. Finally, we implemented CoreProbe in C++, and we accessed the Docker image with the following instruction: sudo docker pull panhongfei/coreprobe:1.0.In this paper, we introduce CoreProbe, a relative abundance estimation framework for microbes that employs microbial core-genomes as references for metagenomic analysis. In addition, CoreProbe takes advantage of the mixture model theory and describes the sequencing procedure for metagenomic reads as a generative model to accurately estimate microbial abundances with the Gibbs sampling algorithm ,46,47. WTo estimate the relative abundance of reference species accurately according to a metagenomic dataset, we describe the sampling and sequencing procedure of metagenomic reads as a generative model: First, in using a et al. . Here, tBased on the assumptions in the metagenomic generative model, we can easily induce the formula of the relative abundances for reference species when their reference sequence lengths The procedure in the metagenomic generative model will be repeated ritchard  and usedAlgorithm 1 (A finite mixture and latent Dirichlet model for metagenomics).Require the hyperparameter, the total number of reads, the species set, the read-composition distributionsEnsurethe read dataset Rsample the species mixture parametersfor a metagenomerepeat1. sample species2. sample readuntilthe total number of metagenomic readsis reachedreturnthe read datasetIdeally, we would need exact knowledge about the occurrence counts of the read-origin species for each read in a metagenomic dataset to estimate the mixture parameters In this section, we introduce hidden variables be shown  that undNext, we derive the full conditional Then, by using the above two formulas, and integrating out Second, we obtain the probability of reads conditioned on the hidden variables Thus, we have Here, we leave the priors out to simplify the notations. The relation Similarly, Finally, we can infer the genome mixture parameters Algorithm 2 (Gibbs Sampling Algorithm for the Metagenomic Model).Require:references species, metagenomic reads, hyperparameterGlobal data:count statistics, read-composition distributions, memory for full conditionalsEnsure:mixture parameters//initialization: obtain read-composition distributionsaccording to alignment results zero all count statisticsfortodo sample the species indexincrement sampled species countend for//Gibbs samplingwhiledonot finished fortododecrement target species countsample a new species indexincrement sampled species’ countend forifthenconverged and a given number of samples generated returnmixture parameteraccording to the equationend ifend whileThe primary contents of this article can be summarized as shown in We then created a brief introduction to the specific CoreProbe process, which can be found in To evaluate the performance of the CoreProbe framework and compare it with the existing methods, we generated 90 simulated metagenomic datasets. We chose 40 microbial species as embed microorganisms in simulated metagenomic datasets. We aimed to estimate the relative abundances of 10 species, leaving the others as “unknown” species, and we compared the precision of the estimations among the existing methods. We downloaded the current completely sequenced microbial genomes of those species in which sequences may coexist, representing different strains of one species. We then employed MetaSim  to generWhen applying CoreProbe to those simulated datasets, we first downloaded all the comprehensive non-redundant reference core gene catalogs, which contained classification information and their corresponding sequences, and they also included over 2800 microbial species. Second, we selected certain microbial species as reference species, and we extracted all the core gene sequences for each reference species in the downloaded core gene catalogue. Third, we concatenated the core gene sequences of a reference species together into one FASTA file as a reference sequence of the species. We then used BWA MEM to map the simulated metagenomic reads against the reference core-genome sequences. All parameters were set as the defaults, and the output consisted of all the alignments in a SAM file.The output SAM files were then passed to our program to estimate the relative abundances. In addition, we applied the three widely used algorithms for an abundance estimation, including GRAMMy , MetaPhlIt should be noted that when estimating the relative abundance of microorganisms, two types of whole genome sequenced strains were selected as the reference genome of the above algorithm, namely, (1) the selected strain used as a reference genome is contained in a sequence of strains used to generate simulated metagenomic datasets as We compare the accuracy by using the relative mean square error of the relative abundance of each algorithm, and the results are shown in There are some common characteristics of the two graphs. With the increased capacity of the metagenome reads , the mean variance of the relative abundance estimation of each algorithm decreases, indicating that the increase in the sequencing depth can improve the accuracy of each algorithm. MetaPhlAn  is ratheHowever, by comparing The various algorithms mentioned in this paper are used in different algorithms, and during the process of dealing with data, there are different sequence mapping algorithms; for example, Kraken , MetaPhlThe overall situation of the two figures is similar. As shown in As an immediate application, we applied this CoreProbe pipeline to a set of actual data. These data come from the HMP, and its sub-database HMIWGS contains 764 groups of high-throughput metagenomic sample data from 16 different human tissues via the Illumina platforms. In this paper, five different human tissues were selected, including the anterior nares, buccal mucosa, posterior fornix, right retro-auricular crease, stool, and five different genomic samples that were randomly selected from each organization. The metagenome data in this paper have passed the quality control (QC) test of the HMP. We intended to use these data to estimate the relative abundance of microorganisms and to classify the metagenomic data. Thus, no additional preprocessing of the above metagenome data was performed.In this section, 150 common microbes were selected, and the gene sequences belonging to their core genomes were selected from the MetaRef database. Their core gene sequences were grouped together to form a FASTA formatted file as the reference genome sequence of the corresponding microorganism. In this paper, we used the BWA MEM program to compare the sequence of the actual genome reading with the reference genome. This parameter was used to locate the default value and output all the matching results. Finally, the CoreProbe algorithm implemented in this paper was used to estimate the relative abundance of microbes in each sample.On this basis, the relative abundance of the obtained microbes was analyzed by heat map. In Through the above analysis, we can see that the CoreProbe algorithm can accurately and quickly estimate the relative abundance of microbes and analyze them by heat map, which can distinguish among the different genomic samples from different tissues, and it can be used to find the differences from and links in microbial distributions between different tissues. It is of great significance to understand the relationship between human health and microorganisms.We developed the CoreProbe framework to estimate the relative abundance of genomes. This approach has three unique advantages. First, a complete probability model is established for the relative abundance of microbes. The distribution and structure of microbes are simulated on the basis of this finding; thus, the relative abundance of various microorganisms can be estimated more accurately.Second, CoreProbe uses the stochastic algorithm of the Gibbs sampling algorithm as an alternative to the deterministic algorithm of statistical reasoning  to achieve a better fuzzy allocation of the readings, which makes the method particularly suitable for short read data sets. The distribution of ambiguous sources may be sequencing errors, genetic variation, horizontal gene transfer or closely related genomes. The Gibbs sampling algorithm is a good solution to this series of problems.Third, due to the changes in microorganisms in the actual environment to adapt to the changes in the surrounding ecology, an abnormal mutation mechanism is evolved, and the genomic polymorphism at the strain level is not uncommon. An environment often contains different strains of a certain microorganism, and the relative abundance of these strains is inconsistent. For the strains that have never been found and sequenced, these factors have greatly affected the accuracy of the relative abundance statistics algorithm for the genome sequence of the few sequenced strains as the reference genome. The core genome is a collection of all the genes of all the strains, which maintain high stability during microbial variation .In using the microbial core genome as a reference genome, the effect of genomic polymorphism and strain diversity on the sequence alignment process can be eliminated to a greater extent, and the accuracy of the relative abundance estimation can be improved. In addition, the core genome can be used as a common gene set of all the strains, accounting for only approximately 8% of all the gene sets of the microbial species. Compared with the algorithm that uses the whole genome of the sequenced strain as a reference gene, the efficiency of the comparison is greatly improved.In summary, the CoreProbe method we provide is likely to provide a more accurate and efficient estimate of the relative abundance estimates of microbes and to uncover a new direction for estimating abundance.


However, following that the French highest legislative body for administrative justice, the Conseil d’État, has sought advice from The Court of Justice of the European Union (CJEU) in interpreting the scope of the genetically modified organisms (GMO) Directive, CJEU in a decision from 2018, stated that organisms modified by these new techniques are not exempted from the current EU GMO legislation. The decision was based in a context of conventional plant breeding using mutagenesis of crop plants by physical or chemical treatments. These plants are explicitly exempted from the EU GMO legislation, based on the long-termed use of mutagenesis. Following its decision, the EU Court considers that the NBTs operate “at a rate out of all proportion to those resulting from the application of conventional methods of mutagenesis.” In this paper, we argue that in fact this is not the case anymore; instead, a convergence has taken place between conventional mutagenesis and NBTs, in particular due to the possibilities of TILLING methods that allow the fast detection of mutations in any gene of a genome. Thus, by both strategies mutations in any gene across the genome can be obtained at a rather high speed. However, the differences between the strategies are 1) the precision of the exact site of mutation in a target gene, and 2) the number of off-target mutations affecting other genes than the target gene. Both aspects favour the NBT methods, which provide more precision and fewer off-target mutations. This is in stark contrast to the different status of the two technologies with respect to EU GMO legislation. In the future, this situation is not sustainable for the European plant breeding industry, since it is expected that restrictions on the use of NBTs will be weaker outside Europe. This calls for reconsiderations of the EU legislation of plants generated  Plant breeding is a discipline for targeted and continuous development of new plant varieties. It utilizes the genetic variation between individuals within a plant species and combines the desired properties into new and improved varieties. Plant breeding is dependent on genetic variation, and new variation is fundamentally important for introduction of new traits in breeding programs. However, in cases where a specific genetic trait is not immediately available to be crossed into breeding materials, the genetic variation in a crop species can be expanded by other means. For decades this has been achieved by, e.g., chemical or physical treatments, translocation breeding, synthetic hexaploids, etc; techniques that involve comprehensive changes of the plant’s genome. Due to its long safety record, organisms obtained by physical and chemical mutagenesis are exempt from the provisions of the GMO legislation in the EU. Nevertheless, the methods incite hundreds or even thousands of random mutations with unknown effects and consequences.New Breeding Techniques (NBT) include several new technologies for introduction of variation into crop plants. NBT comprises a number of technologies that have emerged since the current Directive 2001/18/EC on GM plants was implemented. At the request of the member states, the European Commission set up a working group in 2007 to assess whether or not a number of new breeding techniques should fall within the scope of GMO legislation. The working group prepared a list of seven new plant breeding techniques: zinc finger nuclease (ZFN) technology, oligonucleotide-directed mutagenesis (ODM), cisgenesis and intragenesis, grafting on GM-rootstock, RNA-dependent DNAmethylation, agro-infiltration “sensu stricto,” and reverse breeding. The ZFN technique is a site-directed nuclease (SDN) tool that can be designed to produce a mutation at a predetermined position in the plant genome. Since 2007, a number of new SDN tools have emerged, such as the TALEN and CRISPR/Cas techniques, of which, in particular, the latter is now widely used. It is beyond the scope of this paper to describe all the different NBTs in detail. Here we will focus on the two techniques involved in the generation of mutations at pre-determined sites in a plant genome, i.e., ODM and especially the SDN-tools. We will refer to these as NBT mutations and use the term precision breeding to describe the use of NBT mutations in plant breeding. The other NBTs are described in detail in  and Sch. Common Early after their emergence, the SDN technologies were adopted to improve mutations already available from traditional mutagenesis. For example, in order to improve the quality of soy oil and avoid non-ideal mutations induced by traditional mutation, two target genes FAD2-1A and FAD2-1B were simultaneously mutated using TALENs . FunctioOnly a few crops have been improved through the use of ODM, whereas the SDN tools are widely used. Without doubt, NBT mutations represents a significant progress for the breeding of crops for a challenging future. Speed and precision are often mentioned as key beneficial properties of the NBT mutations. However, on July 25, 2018, The Court of Justice of the European Union (CJEU) ruled that organisms obtained by these new mutagenesis technologies are not exempted from the current EU GMO legislation. Mutation breeding has been used by plant breeders world-wide since the discovery in the 1920s that heritable mutations could be induced in plants by means of irradiation or chemical treatments . The exp3, EMS, MNU) almost exclusively cause single base substitutions  techniques . PreviouFor the major European crops, barley and wheat, good TILLING population resources are already existing , and forhttps://curia.europa.eu/jcms/upload/docs/application/pdf/2018-07/cp180111en.pdf). The situation now is that mutations can be acquired at a high rate as well with the conventional techniques. This sets a new scene for comparing the new precision breeding techniques, in particular those based on CRISPR/Cas9, with the conventional mutation breeding techniques. Hence, with this development there has been a convergence between the new breeding techniques and the efficient use of TILLING principles in reverse genetics approaches. This applies to the general ability to achieve mutations in any gene of interest, but also to the speed of the process.Overall, the development of TILLING, stable mutant populations, and efficient backcrossing in principle now makes the acquisition of mutations in specific genes very efficient and fast for seed propagated crop species. This contrasts with the statement that were made by the CJEU in its 2018 decision on NBTs, namely that the new breeding “techniques make it possible to produce genetically modified varieties at a rate out of all proportion to those resulting from the application of conventional methods of mutagenesis”  precision – genome editing has only few constraints with respect to selection of the exact site of mutation in a gene, whereas TILLING is based on random mutations across the entire genome; 2) off-target mutations – genome editing can result in a few off-target mutations, whereas a high load of off-target mutations is an intrinsic property of conventional mutagenesis that has to be dealt with in plant breeding As previously mentioned, the most widely used NBT mutation tool in plants is CRISPR/Cas9. This is mainly because it is highly efficient and easy to design and because it is possible by multiplexing to make more than one targeted mutation at a time e.g. . HoweverThe mechanism behind the precision of the ODM and SDN mutation tools are very different. ODM makes use of oligonucleotides (between 20 to 100 nucleotides in length) designed to be identical to a corresponding sequence in the plant genome except for one or a few altered nucleotides corresponding to the intended mutation . The oliSDNs are tools that can be designed to recognize and cleave at specific sites within a genome and thereby create a double strand break (DSB) at the targeted site. Mutations can then, prone to some error rate, be generated in the subsequent repair of the DSB performed by the cell’s own DNA repair systems . The DSBThe most commonly used repair pathway of DSBs is non-homologous end-joining (NHEJ) in which the broken DNA strands are just simply rejoined. When the rejoining is imprecise, deletions or insertions are introduced at the site of the DSB. If the SDN-tool is designed to make a DSB in a gene sequence, imprecise rejoining can inactivate the gene by changing the amino acid sequence reading frame.The other main repair system of DSBs in cells is homologous recombination (HR). This repair requires the presence of a DNA fragment with sequence homology to either site of the DSB which can be used as a template for the HR repair. Specific nucleotide changes in the genomic sequence at the site of the DSB can then be achieved through HR by designing a DNA repair template with homologous sequences to either side of the DSB, but with the desired nucleotide changes at the site of the DSB. When the DNA repair template is delivered to the cell along with the SDN-tool, the template can be used for HR repair of the DSB and the template with the nucleotide changes will be incorporated into the chromosome, thereby specifically replacing one or a few nucleotides to other desired nucleotides. In this way, the genetic code of an amino acid can be changed to the code of another amino acid. Replacing a single amino acid in an enzyme often can alter the activity or specificity of the enzyme. Therefore, if already known which amino acid that has to be replace to achieve an improvement, SDN2 can be used to induce the corresponding specific nucleotide change.Changing a single or a few nucleotides using SDN2 is, however, difficult as the delivery of the SDN-tool to the cell must be coordinated with the delivery of the DNA repair template. Thus, new approaches to overcome this hurdle are currently developed. Two base editing systems based on the CRISPR/Cas9 tool have recently been developed which can alter a particular nucleotide in a DNA sequence without the use of a DNA repair template  where the first 20 nucleotides (the guide sequence) can be made complementary to a 20-nucleotide genomic sequence located where the mutation is intended . The gRN desired .A key difference between conventional mutation breeding and NBT mutations is that the NBT mutation tools have to be delivered into the cells. For ODM, the oligonucleotides are transiently delivered to the cell and are degraded in the cells after induction of the specific mutation. The SDN-tools can be delivered to the plant cells as DNA constructs either using stable or transient transformation techniques. Although mutated primary transformants generated by stable transformation contains the SDN DNA-construct there is most frequently no linkage between the site of insertion of the construct and the site of the mutation. Mutants without the DNA construct can therefore be selected in the subsequent generation after segregation. For CRISPR/Cas, it is also possible to deliver the CRISPR and the Cas as mRNA and guide RNA, respectively  or to deThe lack of foreign DNA in the NBT mutated plants complicates the traceability, which is required when NBT mutations are regulated as GMOs. Traditional GM plants normally holds a large piece of foreign DNA inserted randomly in the plant genome. Today, GM plants are detected by standard or real-time PCR that, depending on the primers used, can identify specific gene elements, gene constructs, and transformation events present in the plants. Knowledge about the sequences to be identified is a prerequisite for design of the primers. With respect to traceability of NBT mutations, it will not be possible to separate mutations resulting from SDN1, SDN2, or ODM from mutations induced spontaneously or by conventional mutagenesis even if information about the gene sequences is available. The same goes for base editing. This will complicate the control of crops imported from countries that do not regulate NBT mutated crops.An often-mentioned concern about the ODM and SDN-tools is if mutations are generated at places in the genome where the tools were not intended to mutate. These so-called off-target mutations occur when the tool is capable of binding and inducing DSBs within sequences similar to the sequence which the tool was designed for (off-target sequences). Both ODM and all the SDN-tools may induce off-target mutations but the frequency is higher with the CRISPR/Cas9 tool . The reaIt is difficult to make a general estimate of the off-target mutation frequency induced by the CRISPR/Cas9 tool in plants. In most of the CRISPR/Cas9 mutated crops developed, no analyzes have been performed for off-targets. Out of 1328 studies using CRISPR/Cas, TALENs, base editing, ZFN, and ODM, 252 of them investigated off-target mutations. In around 3% of the analyzed potential of-target sites, unintended mutations were detected .Examples of studies where the CRISPR/Cas9 off-target mutation frequencies have been investigated by PCR amplification of the off-target sequences, restriction fragment analysis and/or sequencing and where off-target mutations have been identified are shown in Off-target mutation frequencies can also be estimated by whole-genome sequencing (WGS). In order to get maximum information from this method, the appropriate controls need to be included, revealing also the mutagenesis effect of tissue culture and CRISPR/Cas.Agrobacterium-mediated transformation from off-target mutations  with respect to the EU GMO legislation is important for the possibilities to exploit the potentials of the technologies in future European plant breeding. The ruling of the EU Court of Justice in July 2018 stating that organisms obtained by the new mutation techniques are not exempt from the legislation on the deliberate release of GMOs, makes it difficult, if not impossible, for plant breeders to make use of the new techniques, due to heavy costs associated with the approval of GM varieties . RealistParadoxically, mutant plants can now be achieved with mutagenesis methods exempted from the GM legislation just as fast as with the SDN1 techniques. Hence, with respect to targeting and speed a convergence between conventional mutagenesis and the SDN1 techniques has occurred. The two major differences are the precision and the number of off-target mutations, both of which favour the SDN1 methods.With the current status of the EU legislation of plants with NBT mutations, reluctance by private industry to embark on projects implementing these techniques in generation of new varieties will probably persist. Already now, Europe falls behind with respect to patenting within the area of CRISPR-based plant biotechnology . HoweverThe development of crop varieties usually takes many years and, thus, the effects on the market and in agriculture will have an equivalent lag. For proper exploitation, it is therefore important that implementation of the new precision breeding techniques is started now, maybe in strategies in combination with TILLING approaches as outlined above. The fear, however, could be that the decision by the EU court will still make the industry reluctant to go into research and development directed toward the use of the new techniques. This would only aggravate the long term weakening of the European breeding industry in the global competition.For some crops and traits, in particular ornamentals and garden/vegetable seeds, new cultivars have a short developmental horizon, e.g., when it comes to developing new flower colours. Since the market of these is global, the impact of the new techniques on Europe can come on rather short terms, if restrictions on their use, as expected, will be limited in Asia, South America, Canada, and the US. This raises the issues of detection of genetic modifications introduced by the new techniques. In Europe, they are regulated according to the current EU GMO legislation and, hence, subject to strict approval, but it is close to impossible to make unambiguous tests for the introduced mutations, since there are no marks distinguishing them from natural mutations/variants. This situation will get even worse on longer terms, when varieties of major European crops with new NBT mutation induced traits can enter the European market from the surrounding world and be crossed with locally developed varieties. From the regulatory aspect, this situation will not be sustainable and thus unacceptable for the European plant breeding industry. Long-term stability in this area calls for clarifications at the political level of EU legislation.IH, PG and HB contributed equally to the manuscript.The study was supported by Innovation Fund Denmark, grant 8055-00038B, ReTraQue.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This paper presents a system based on pedestrian dead reckoning (PDR) for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a novel cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple devices. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth’s magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. Compared to other methods in the literature, the method has the advantage of being infrastructure-free, fully distributed and robust to sensor failures thanks to the pre-filtering of perturbed measurements. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance. As technology advances, computers and mobile devices are becoming the leading digital platforms in our everyday life. Currently, ubiquitous computing is an emerging field that infers the contextual information of the environment from connected smart devices ,2. As thGlobal Navigation Satellite Systems (GNSS) are the most widely used positioning solutions , howeverRecently, new promising solutions have been emerging in the context of Internet of Things (IoT), where sophisticated sensing, computing and networking technologies are used to interconnect large numbers of smart devices and let them cooperate for monitoring and control goals, included location sensing ,12,13,14Although an individual magnetic sensor may experience perturbation, multiple pedestrians walking in the same direction could in principle collaborate to overcome the effect of isolated perturbations. Emerging device-to-device (D2D) communication standards, such as WiFi-Direct , could bIn this paper, a new PDR-based localization for an IoT-enabled smart-cities’ paradigm is proposed. We assume the mobile users carry smart devices , which are equipped with inertial sensors and they are interconnected via WiFi-Direct. We propose a novel distributed and collaborative PDR system based on cooperative machine learning (CML) that enhances magnetic sensors embedded in the smart phones and accurately estimates the heading of pedestrians that are closely located. Performance analysis indicates an error reduction of Indoor navigation is highly challenging due to the complex propagation conditions, the density of objects or installation of equipment and infrastructure. The proposed algorithms for Indoor Positioning System (IPS) can be categorized into two groups: network-based and non-network-based systems. The former systems rely on network and infrastructure and need reference points  for identifying user locations and the latter rely on inertial sensors and PDR algorithms.Network-based systems utilize multi-lateration/angulation and fingerprinting techniques to localize the user ,8,24. GeNon-network-based systems are usually based on PDR algorithms and inertial sensors. Unlike network-based localization systems, they do not require any pre-arranged infrastructure, and the user’s location estimate is updated sequentially at each step . PDR sysAs PDR alone is not a promising solution in complex indoor environments, hybrid and collaborative solutions have been proposed to improve the performance of IPS. The hybrid solutions rely on the combination of several technologies to localize a user ,42, wherDue to the highly localized nature of indoor magnetic anomalies, multiple pedestrians walking in the same direction but stepping on different locations are likely to experience different level of magnetic disturbance. Despite an individual magnetic sensor may experience some perturbations, pedestrians who are walking in the same direction should have similar heading measurements, therefore we can combine their heading estimates to overcome the effect of isolated perturbations and improve the heading accuracy.The proposed cooperative machine learning PDR (CML-PDR) system consists of three main components: Magnetic Perturbation Detection (MPD), Pedestrian Clustering (PC) and Distributed Data Fusion (DDF). A machine learning algorithm is first designed for MPD to detect whether a heading estimate is perturbed by magnetic anomalies or not. Only estimates within a selected error margin are retained and used for heading estimation. The second component, PC uses a machine learning algorithm to detect groups of users heading in the same direction based on Received Signal Strength (RSS) measurements exchanged over D2D links. Finally DDF combines the retained heading measurements from users in the same group in a fully distributed manner by a consensus algorithm. We assess the performance of CML by an extensive experimental campaign carried out in complex localization scenarios, for varying configurations of user density and network connectivity.To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. With respect to other infrastructure-free methods ,44 that We propose a machine learning algorithm to detect whether magnetometer measurements collected by the device-embedded sensors are severely perturbed. The algorithm is completely self-sufficient, i.e., it detects perturbed measurements using only features extracted from magnetic readings. We show that this algorithm can detect perturbation with an accuracy of 95%.We design an algorithm to detect pedestrians walking in the same direction, using a machine learning approach. The algorithm exploits RSS data extracted from WiFi beacons exchanged by pedestrians’ mobile devices. Our experiments indicate that the proposed algorithm can detect pedestrians walking in the same direction with an accuracy of 90%.We prove that fusion of heading data can be carried out in a fully distributed way, with negligible performance loss compared to the centralized fusion. Different consensus approaches are considered for data fusion. The performances of the algorithms are analyzed in terms of localization accuracy and convergence rate, for varying number of users and connectivity graphs.We evaluate the performance of the proposed architecture utilizing experimental data. We find that the proposed architecture achieves an error reduction of 86% in the heading estimation and 79% in user localization compared to PDR legacy architecture. In order to show the reliability of the proposed approach, we evaluated the performance for different experiments at different locations. Our result indicates that the error reduction is consistent for different settings and independent of the location and time of the experiments.To summarize, our contributions are as follows:The paper is organized as follows. We first introduce the CML-PDR system in We introduce a PDR system that utilizes only sensors embedded in the user device  and D2D connectivity between pedestrians. D2D communications are used for data fusion between multiple pedestrians to take benefit from the spatial diversity of magnetic measurements in indoor environment. Given that the Earth’s magnetic field value is stationary and known in any given spot of the world ,51, in oThe MPD component detects highly corrupted magnetometer readings based on a ML approach. Perturbed measurements have to be excluded from data fusion to avoid large positioning errors. For this reason, only heading readings that are believed to fall within a selected error margin are retained and passed to the subsequent processing steps.The PC component identifies groups of pedestrians walking in the same direction. This detection has a key role as it aggregates users that can benefit from measurements fusion. ML approach is used to perform this clustering, based on RSS measurements exchanged among users.The DDF component aggregates the heading measurements that have been filtered by MPD and PC in a fully distributed way. The outcome is shared with all the users belonging to the same group, including those that were excluded from data fusion.The CML-PDR system architecture is displayed in We used two different sets of experiments to analyze the performance of CML-PDR referred to as the Small- and Large-Scale Scenarios.The Small-Scale Scenario was carried out to validate the individual system components and the overall localization performance for different user configurations, i.e., with pedestrians walking in the same direction, crossing or turning and increased complexity of trajectories. A small group of users (up to 4 pedestrians) in the validation was considered. The experiments of Small-Scale Scenario were conducted in indoor environment in Tyree Building at UNSW in Sydney, Australia. As represented in The Large-Scale Scenario is considered This campaign involved four pedestrians  walking in a corridor of the Tyree Building at UNSW.Two different corridors were considered, one in the lower ground (Corridor LG) and the other at the ground level (Corridor G) of the Tyree Building. The two corridors have the same dimension (27 m long and 4 m wide) and orientation   for SydnSince the reference IGRF is known at Sydney as the reference location, we computed the deviation to the reference values and obtained the error in the IGRF components as: Since the magnetometer readings are affected by random error, the instantaneous heading estimate fluctuates over time. Therefore we computed the mean of heading estimate one step . Let htrus study , we showe is below the error threshold L observations of In order to build a model that detects whether the heading measurement is corrupted or not, we first need to create our dataset. Based on our previous study , statistj-th row is For the Tyree Building dataset, we collected are Weka : SupportAs we mentioned earlier, for each experiment, we used cross validation to evaluate the performance of the classifiers. Therefore, for each experiment, we first randomly splitted the data into training and test sets. Then the model is learnt from the training data of the experiment and exploited to classify test data from the same experiment. This has been repeated 10 times and the average of accuracy is reported as the classification accuracy.In our previous study , we haveThe PC component uses WiFi-Direct RSS data to determine whether pedestrians are walking in the same direction. WiFi-Direct  is a varTo investigate whether WiFi-Direct RSS data can be useful to detect pedestrians walking in the same direction, we carried out a set of experiments as a proof of concept in three different buildings at the UNSW and at the Australian Technology Park (ATP) in Sydney. For each building, we asked two volunteers to walk in the same, opposite, and perpendicular directions as shown in Rate of change (RoC) of RSS values over two consequent time windows.Slope of the line of best fit (SoP) of RSS values in a given time window.Root mean square (RMS) of the RSS values in a given time window.Minimum (Min) RSS value recorded in a given time window.Maximum (Max) RSS value recorded in a given time window.We proposed several time-domain statistical features for RSS measurements and employed the Principal Component Analysis (PCA) to identify the most informative features. After an extensive training and testing, we selected the following five features:To calculate these features, we use again a window size of For the analysis of the PC performance in a larger group of users, we use the dataset of the Tyree Building Scenario in Using Weka, we evaluated the features listed above with six different classifiers and utilising 10-fold cross validation. From the accuracy of the classifiers reported in In the DDF, heading estimates from multiple people walking in the same direction—filtered by MPD and PC—are aggregated to provide a more accurate estimate of the common heading. This purpose is accomplished by averaging the estimates, so as to take into account the different degree of reliability of measurements. As we mentioned earlier, the rationale of this approach is based on the observation that magnetic perturbations are highly localized in space. Thus, if a number of users are moving with the same heading, it is likely that only a subset of their sensor measurements is corrupted.The heading estimate Cartesian components Fusion is carried out according to the Best Linear Unbiased Estimator (BLUE) :(3)x^=∑ifusion over time and fusion between users.We accomplish the above fusion in a distributed way as a cascade of two steps, L different time windows asAt first, each user performs a data fusion over time by performing a weighted average of the measurements he collected in j.Note that weighting by the variance inverse allows to account for the different degree of reliability at different windows. For practical implementation, the variances N different users,Time averaging is then followed by a data fusion between users, which consists in merging the estimates  collecteity with , the weij communicates with node i); i are denoted by A main issue is the distributed implementation of . We recan the estimate at user i is updated as follows:In the consensus approach each node updates at each iteration its local estimate and exchanges it with its neighbors until an agreement is reached. At iteration W1,⋯,WN) .weighted average consensus (WAC) and average consensus (AC), respectively and shown by DDF-W and DDF in the evaluation section.At the first step, each user utilizes its own estimate, i.e., for the first iteration at rding to , if weigution in  and x^i, users can employ their previous estimate as the current heading until at least one of them is able to overcome the MPD and provide a new heading estimate for the group. If this scenario happens in the first time instant of the algorithm the users fuse their estimates even if they are all corrupted.Conventional PDR: only magnetometer readings are used to compute the heading without any further processing.MPD: only perturbation filtering is used and the simple average is carried out on the filtered heading.DDF: only distributed fusion is performed using the AC algorithm.DDF-W: only distributed fusion is applied using WAC.MPD & DDF: cascade of MPD and DDF.MPD & DDF-W: cascade of MPD and DDF-W.MPD & CDF: the centralized unweighted computation of  is emploMPD & CDF-W: the centralized weighted computation of  is emploThe performance of CML-PDR is evaluated in terms of heading and location accuracy in the following, by using the dataset of the real indoor scenarios presented in To validate CML-PDR, we utilize the experiments of the described in Compared to the conventional PDR, we can notice that the proposed cooperative method significantly reduces the average heading error. The lowest error is achieved by the combination of MPD and DDF with an average value of 4.55ith step.We examine now the system performance in terms of location accuracy. In the experiments, the subjects were instructed to walk at a constant speed (1 m/s on average) taking constant step lengths (0.6 m approximately). User positions are estimated at each pedestrian step, assuming perfect knowledge of the step length. The position estimate is thus computed every 0.6 s (at each pedestrian step) by combining the heading estimate with the known step length. We evaluate the localization performance in terms of Root Mean Square Error (RMSE) of the location estimate by averaging over different users and paths. In every experiment, the RMSE, over the entire trajectory of M steps, has been computed as:By means of PC, MPD and DDF our system achieves a RMSE of 1.38 m. This result represents a remarkable improvement of 79% if it is compared to the conventional PDR, which in turn achieves an accuracy of 6.57 m.In order to verify the robustness of CML-PDR, we evaluated it in a more practical scenario, where users make several turns and do not walk at the same velocity. We repeated the same procedures described in In We show the position estimates for both CML-PDR and conventional PDR approaches in In this section, the performances of data fusion algorithms are analyzed in terms of convergence time and degree of cooperation among large group of users. This analysis is necessary because unlike the centralized approach, the performance of the distributed solution depends on the connectivity degree of the network used for sharing. To perform this evaluation, we use the Large-Scale Scenario with 15 and 18 users described in We examine the effect of the network connectivity by using cooperation radius In In order to analyze the effect of cooperation radius As we mentioned earlier, one objective of performance analysis for Large-Scale Scenario is to verify the heading error reduction of our proposed method for a larger group of pedestrians. Similar to ous work .The main purpose of this paper is to overcome one of the main limits of PDR indoor localization systems: heading estimation. This work provides an accurate and efficient method to assess the direction of motion of pedestrians relying only on magnetometers embedded in smart devices and peer-to-peer connectivity. We evaluated the performance of our approach using real experiments in indoor environments and the performance exhibits a significant heading error reduction (86%) with respect to the conventional PDR, corresponding to an average heading error of CML-PDR relies on the spatial diversity of magnetic perturbation inside a building and it is necessary to train magnetometer measurements for MPD in advance. Comparing to fingerprinting database, the data collection for this training does not require exhaustive labour work and substantial cost, however the magnetometer data training is needed for every building. This is one of the limitation of CML-PDR. Another disadvantage of this method is that the initial position should be shared with the smartphone to create the trajectory. This is due to the fact that PDR estimates the location at each step and based on the previous location. Therefore it is necessary to provide the initial position at the entrance of the building with the device.In our evaluations, we show that the performance of CML-PDR is robust in a complex environment with several turns and a long distance 60 m) while the pedestrian holds the smartphone horizontally. Future work will address real-time testing in large indoor spaces, such as airport and museum, by considering more complicated paths, longer distances and as a crowded environment. We also verify the performance of CML-PDR when the pedestrian holds the smartphone at different position and focus on the enhancement of the PC component to further improve the localization accuracy. Other D2D communication, such as Bluetooth iBeacon  m while , will be


Bacterial and archaeal evolution involve extensive gene gain and loss. Thus, phylogenetic trees of prokaryotes can be constructed both by traditional sequence-based methods (gene trees) and by comparison of gene compositions (genome trees). Comparing the branch lengths in gene and genome trees with identical topologies for 34 clusters of closely related bacterial and archaeal genomes, we show here that terminal branches of gene trees are systematically compressed compared to those of genome trees. Thus, sequence evolution is delayed compared to genome evolution by gene gain and loss. The extent of this delay differs widely among bacteria and archaea. Mathematical modeling shows that the divergence delay can result from sequence homogenization by homologous recombination. The model explains how homologous recombination maintains the cohesiveness of the core genome of a species while allowing extensive gene gain and loss within the accessory genome. Once evolving genomes become isolated by barriers impeding homologous recombination, gene and genome evolution processes settle into parallel trajectories, and genomes diverge, resulting in speciation. A significant proportion of the molecular evolution of bacteria and archaea occurs through gene gain and loss. Here Iranzo et al. develop a mathematical model that explains observed differential patterns of sequence evolution vs. gene content evolution as a consequence of homologous recombination. Gene gain and loss occur through insertion and deletion of genome segments of variable size, including large genomic islands, via mechanisms of non-homologous recombination, often involving mobile genetic elements5. The gene gain and loss events can be used to generate ‘gene content trees’ that reflect the evolution of microbial pangenomes and complement traditional phylogenetic trees constructed from sequence alignments of highly conserved marker genes. From the gene content trees, a gene turnover clock can be defined. The gene turnover clock ticks at a rate that does not necessarily correlate with the rate of the traditional, sequence-based molecular clock. Actually, as demonstrated below, substantial non-linearity between the two types of trees is observed, and in this work we analyze its likely causes.Evolution of bacterial and archaeal genomes is a highly dynamic process that involves extensive gain and loss of genes, with turnover rates comparable to if not exceeding the rate of nucleotide substitution9. Efficient homologous recombination between two genomes requires the presence of (nearly) identical nucleotide sequences flanking the exchanged genomic regions. The minimum length of these flanks depends on the species, with typical values around 25–100 nucleotides11. As genomes diverge, the probability to find fully conserved flanking sequences decreases, and so does the efficiency of recombination13. The existence of genetic barriers to homologous recombination was initially observed in experimental studies which have shown that sequence divergence of over 5% can prevent most recombination events in some bacteria16. Subsequently, comparative genomic analyses have confirmed that barriers to homologous recombination are widespread and can be used to define biological species in bacteria and archaea19. Mathematical modeling of the molecular processes involved in homologous recombination has shown that barriers to recombination can build up spontaneously and lead to speciation if (i) recombination efficiency decays sufficiently fast with sequence divergence and (ii) the balance of mutation and recombination favors the accumulation of sequence variability in the population24. Barriers to recombination can also arise after the acquisition of new genes27, especially if the newly acquired genes are involved in niche specialization30. In this case, the barriers seem to result, in part, from selection against gene conversion events that would lead to the loss of the recently acquired, beneficial genes27. Because barriers to recombination do not necessarily affect the whole genome, prokaryotic genomes can diverge at some loci while remaining cohesive at others25. Thus, the rates of population divergence and, eventually, speciation depend on the dynamics of recombination barrier emergence and spread across genomes.Evolution of prokaryotic populations is strongly affected by homologous recombination, which is regarded as a major contributor to maintaining genetic cohesion by preventing sequence divergence via gene conversionHere, we combine comparative genomics and phylogenetic analysis to investigate how the fraction of genes shared by closely related bacterial and archaeal genomes decays with the phylogenetic distance and show that the sequence-based molecular clock and the gene turnover clock are incongruent at short evolutionary times. To elucidate the origin of this discrepancy, we develop a mathematical model of genome evolution that describes the dynamics of sequence divergence in the presence of gene conversion. The model predicts the existence of a recombination-driven delay in the molecular clock, the magnitude of which corresponds to the time required for barriers to recombination to spread across the genome. By fitting the model to genomic data, we obtain estimates of such recombination-driven delays in 34 groups of closely related bacteria and archaea, each covering the whole range of divergence, from early differentiating strains to incipient species. We show that the incongruence between the molecular and gene turnover clocks disappears if the former is corrected to account for the estimated delay. Finally, we investigate the tempo and the factors that contribute to the establishment of barriers to recombination and, eventually, speciation in the populations of diverse bacteria and archaea.39. These analyses have shown that the fraction of genes shared by a pair of genomes decays exponentially with time as the genomes diverge, and that genes can be classified in two categories based on their turnover rates32. Here, we extend these approaches to sets of 3 and more genomes. For illustration, let us consider a simple scenario in which all genes have the same turnover rate λ. The fraction of genes shared by a pair of genomes is thenD2 is the total evolutionary tree distance, which in the case of 2 genomes is equal to the sum of the distances from each genome to their last common ancestor. We show in the Methods that a similar formula describes the divergence in gene content for groups of 3 or more genomes. Specifically, the fraction of genes shared by k genomes decays asDk is the total evolutionary distance spanned by those k genomes. Given a phylogenetic tree, the total distance Dk is the sum of branch lengths of the subtree that includes the k genomes. The most notable aspect of this result is that the dynamics of gene content divergence is independent of the number of genomes considered (k). As a result, plots of the fraction of shared genes (Ik) as a function of the total evolutionary distance (Dk) for different sample sizes collapse into a single curve  and multiple strains per species, from almost identical (<0.001 nucleotide substitutions per site in the core genes) to well differentiated . As a proxy for the evolutionary time, we used the branch lengths of high-resolution sequence-based phylogenetic trees built from concatenated alignments of single-copy core genes. At least at short evolutionary distances, the branch lengths are supposed to accurately reflect evolutionary time because they are determined, primarily, by synonymous substitutions that are supposed to be effectively neutral and accumulate in a clock-like manner40. Then, we sampled subsets of genomes and represented the fraction of shared genes as a function of the total tree distance. At odds with the theoretical expectation, we found that gene-sharing decay curves depend on the number of sampled genomes: as more genomes are added, the curves shift down and the fraction of shared genes becomes smaller than expected , indicating that this phenomenon affects not only rare genes that are present in a small fraction of the genomes, but also common genes that comprise the core genome  do not accumulate, motivated us to model the dynamics of sequence divergence in the presence of homologous recombination. Rather than focusing on the mechanistic details of recombination, we formulated a phenomenological model for the fraction of loci within a genome that become isolated with respect to another genome from the same original population (see Methods section). Loci that are not affected by barriers to recombination experience periodical gene conversion that reverts them to the population averaget is the time since the last common ancestor. Mathematically, the delay τ(t) is a concave and saturating function, the detailed form of which depends on the dynamics of the evolution of recombination barriers , whereas internal branches are shortened by a distance μτ(tA)-μτ(tA), where tA and tB are consecutive branching times measured from the tips towards the root. Because both τ(tA) and τ(tB) tend to the same value τ∞ as time passes, the recombination-driven delays cancel out at long evolutionary times and deep internal branches remain approximately unchanged.A notable consequence of the delay in the molecular clock is that the terminal branches of phylogenetic trees inferred from sequence analysis appear shorter than expected given the actual evolutionary times. Specifically, terminal branches are shortened by a distance p = 0.012; Supplementary Fig. To test whether the recombination-driven delay is a plausible cause for the lack of linearity between the molecular (sequence divergence) and gene turnover clocks, we used the recombination barrier model to correct the branches of sequence-based trees, accounting for the effects of recombination (see Methods section). Then, we sampled subsets of genomes and reassessed the divergence in gene content as a function of the corrected tree distances. Remarkably, correction of the sequence trees led to gene-sharing decay curves that do not depend on the sample size, as predicted by the theory  sequence similarity trees Fig.  and geneτ∞ range from less than 0.01 to more than 0.5 underreported substitutions per site, with broad variation among prokaryotic groups and sometimes even within the same genus  divergence, and the estimation of the upper 95% confidence bound for the long-term evolutionary delay becomes unfeasible. The 5 groups of Firmicutes included in the analysis  belong to this category. In contrast, representatives of the genus Pseudomonas are characterized by a linear divergence regime, with little or no signs of recombination-driven delay. As already noted, these results cannot be explained by recent gene acquisition or deletion events and are robust to the removal of mobile genetic elements and genes with no orthologs (ORFans) that are likely to evolve under a different regime than the rest of the genome . Moreover, when comparing taxa with long and short delays (relative to the evolutionary depth), we found that variance of relative evolutionary rates is significantly greater in the taxa with long delays .The magnitude of the recombination-driven delay is tightly linked to the time frame over which sequence divergence takes place. In taxa with little or no delay, variations in the time at which different genes cross the recombination barrier are negligible; from the perspective of divergence times, all genes in these taxa start diverging roughly at the same time Fig. , left. Cp < 0.001; the exact p-value associated with the correlation coefficient is calculated in Matlab based on the bivariate distribution), which indicates that fast gene turnover is associated with a rapid spread of barriers to gene conversion. The statistical power for the analysis of ecological traits (lifestyle and effective population size) was low, and therefore, it cannot be ruled out that these factors contribute to the spread of recombination barriers as well.To elucidate potential causes for the large variation in the recombination-driven delays found in the data, we searched for associations with genomic and ecological features, such as genome size, number of mobile genetic elements, gene turnover rate, lifestyle (free-living or host-associated), natural competence for transformation, and effective population size  which determines metabolic capacities, ecological properties and pathogenicity of bacterial strains47. By studying sequence and gene content divergence in groups of closely related prokaryotes, we found that the onset of sequence evolution is often delayed with respect to genome evolution, although the magnitude of the delay broadly varies across bacterial and archaeal lineages. It has to be stressed that this finding demonstrates distinct dynamics of sequence and gene content divergence and is independent of the difference in the actual rates of these processes. We show that the delay in sequence evolution is likely to result from gene conversion caused by homologous recombination that homogenizes the core genome while, at the same time, many accessory genes can be gained and lost, resulting in gene content divergence and pangenome expansion. These results are fully compatible with our previous findings indicating that archaeal genomes contain a subset of genes that turn over extremely rapidly, before detectable sequence divergence occurs32. The delay of sequence divergence caused by homologous recombination provides a plausible mechanistic explanation for the previously observed instantaneous gene turnover in prokaryotes48. These results are also compatible with the observation that genes are gained and lost at higher rates on the tips of phylogenetic trees2.Evolution of prokaryotic genomes occurs at two levels: at the sequence level, through substitutions and small indels, and at the genome level, through the transfer and loss of genes and groups of genes49. The establishment of barriers to recombination is a pivotal step in the early divergence of closely related prokaryotic strains that eventually leads to speciation50. In the absence of such barriers, sequence divergence is prevented by the cohesive effect of intra-strain homologous recombination; only after the barriers arise and recombination ceases, substitutions start to accumulate at an appreciable rate. By modeling sequence evolution in the presence of gene conversion, we show here that the temporal dynamics for the spread of barriers to recombination directly affects the molecular clock. Specifically, homologous recombination sets back the molecular clock by an amount of time that, in the long-term, equals the average waiting time for the establishment of recombination barriers. Homologous recombination during early divergence also leads to the compression of the tips of phylogenetic trees. Our model provides a framework for correcting such trees, to make them consistent with the dynamics of gene turnover.Notwithstanding the long-term debate on the applicability of the species concept in prokaryotes, several recent studies strongly suggest that speciation does occur in bacteria and is a crucial factor shaping the earth microbiome25. The groups of genomes used in this study cover this whole range of divergences, which is what allows us to detect the transition from genomes connected by recombination to freely evolving ones and to quantify the delay in sequence divergence. Furthermore, the autocatalytic (feed-forward loop) dynamics that we detected for the spread of recombination barriers can lead to a relatively sharp transition from strains within a species, in which most genes can recombine, to separate species, in which recombination has ceased in most of the genome. Therefore, the autocatalytic spread of barriers to recombination could contribute to explaining the apparent discrete nature of many prokaryotic species, all the caveats associated with the species concept in the case of prokaryotes notwithstanding53.Escape from recombination does not occur in all genes at once, so in principle, a continuum could exist, from closely related strains that can recombine in nearly all shared genes to separate species that are isolated in most if not all genes24. However, it is a matter of debate whether mutation and recombination alone can explain the formation of non-recombining species in nature, and in particular, in prokaryotes22. The strong, negative correlation between the magnitude of the recombination-driven delay of sequence divergence and the rate of gene turnover that we observed here supports the hypothesis that gene gain and loss accelerate the establishment of barriers to homologous recombination, by promoting niche differentiation19 and/or by interfering with gene conversion at flanking loci29. The autocatalytic, sigmoidal dynamics that best describes the fraction of recombination-free loci in our model is consistent with the previous findings indicating that barriers to recombination are initiated by the acquisition of lineage-specific genes and subsequently spread from the vicinity of those genes towards the rest of the genome26. More generally, our findings imply that gene gain and loss drive speciation in prokaryotes by promoting the establishment of recombination barriers. In this context, speciation appears to be an emergent phenomenon in which a “macroscopic” discontinuity (a sharp barrier to recombination between different species) builds up from a continuous “microscopic” dynamics .The causes that underlie the spread of barriers to recombination are complex and likely involve both genomic and ecological factors. Theoretical models show that barriers can simply emerge if mutations generate diversity faster than recombination erodes itOne might argue that the correlation between the rates of recombination barrier establishment and gene turnover does not unambiguously define the causal relationship, and that reverse causality, whereby recombination barriers would accelerate gene turnover, could be a viable alternative to the scenario proposed here. Although we acknowledge this possibility, we argue that the role of gene turnover as a driver of the recombination barrier emergence is far more likely. Indeed, gain and loss of genes creates non-homologous regions between diverging genomes, thus, directly promoting the emergence of homologous recombination barriers. We are unaware of any reciprocal mechanism whereby elimination of homologous recombination would promote gene turnover.55.It should be emphasized that our model is purely phenomenological and as such is not contingent on the specific mechanisms behind the emergence of the recombination barriers. In particular, the model is applicable both to sympatric speciation, whereby prokaryotic strains diverge within the same habitats, and to allopatric speciation when sequence divergence is facilitated by physical  isolationEscherichia and Salmonella took place around 140 million years ago and spanned over 70 million years, which is in a close agreement with our finding that the delay in the Escherichia/Salmonella group is approximately half of its evolutionary depth. The high variability of the sequence divergence delays across taxa implies that the time required for lineages to split and form new species is strongly taxon-dependent. For taxa with long delays, it appears natural to think of speciation events being driven by the emergence of strong recombination barriers, whereas taxa with short delays would follow a more continuous speciation dynamics, with new species forming as the genomes gradually diverge22. Although our analysis focused on prokaryotic genomes, the conclusions appear to be general and can be extended to the evolution of other genomes that are substantially affected by horizontal gene transfer and homologous recombination, for example, viruses.Recombination-driven delays are indicative of the time frame over which different genes start to diverge during the split of prokaryotic lineages. Lineages with short delays are characterized by low variance in the gene-specific (gene family-corrected and taxon-corrected) evolutionary rates, which implies that most of their genes began diverging over a brief period of time. In contrast, the higher variances observed in lineages with long delays imply that, in those lineages, divergence of genes occurred in an asynchronous way and spanned longer periods of time. To illustrate this point, it has been estimated that the split between 22. The dynamics of such populations is driven by occasional outbreaks of subclones that accumulate divergence to a point where homologous recombination with the rest of the population ceases. Under the assumptions of Dixit et al.22, the probability that a subclone reaches a state of nearly irreversible divergence is roughly constant in time and depends on molecular and population parameters, such as the effective population size, mutation rate, and the dependency of the recombination rate on sequence divergence.Previous theoretical work has investigated the dynamics of a population subject to mutation and homologous recombination, with recombination rates that decay exponentially with sequence divergenceInspired by those results, we adopted a phenomenological approach to model the effect of intra-population homologous recombination on the genetic diversity of a population, taking the probability of escaping recombination (which in our case is not necessarily constant in time) as the main parameter of the model. Under this approach, each genomic region is either subject to periodical recombination with other members of the population or has reached a point where homologous recombination is not possible anymore. In the latter case, the respective genomic region is assumed to have crossed (diverged beyond) the recombination barrier. There was no attempt to explicitly model the molecular mechanisms that underlie barriers to recombination, and therefore, the present model not only captures the main feature of prior divergence-recombination models but also becomes applicable to scenarios in which ecological factors drive the isolation of evolving populations.f(t) to describe the fraction of each genome that is subject to recombination. The probability that a region of the genome crosses the recombination barrier exactly at time t (where the time starts counting at the last common ancestor of the two genomes) isGiven a pair of genomes, we used a general function μ is the average substitution rate. Integration by parts leads to the final resultConsidering that only the regions that have crossed the recombination barrier make a long-term contribution to sequence divergence, and that the number of substitutions in those regions is proportional to the time elapsed since the recombination barrier was crossed, the overall sequence divergence between a pair of genomes becomesR, the fraction of the genome subject to recombination is obtained by solving the differential equation To contrast the model with genomic data, we explored three more specific scenarios by assigning explicit functional forms to the rate at which regions of the genome cross the recombination barrier. Thus, we considered a power law time-dependency of the rate entclass1pt{minimax, in a prokaryotic genome was modeled as a stochastic birth-death process, with genome-wide gain rate P+ and loss rate P−31. Under this model, the number of genes in a genome is described by the differential equationThe number of genes, X with elements that assume values of 1 or 0. Each entry represents a gene, i.e., an ATGC-COG, where 1 or 0 indicate the presence or absence, respectively, of that ATGC-COG in the genome. Genome size x is then given by the sum of all elements in X.To facilitate comparison of gene content across genomes, each genome was represented by a vector I2 is defined asX1 and X2 are vectors that represent the two genomes, the angled brackets indicate averaging over multiple realizations of the stochastic process, and the dot operation stands for a scalar product. The dynamics of pairwise intersections is given by56, we haveThe number of shared genes in a pair of genomes, or pairwise intersection, t can be translated into tree pairwise distance as D2 asThe solution of this equation shows that pairwise genome intersections decay exponentially ask genomes, Ik, is the number of orthologous genes that are shared by all k genomes. It is formally defined asThe intersection of k-intersections is given bySimilar to pairwise genome intersections, the time derivative of k-intersectionsSolving the differential equation, we obtain an exponential decay for the λ is proportional to Dk is the sum of branch lengths in the tree that encompasses the k genomes . We selected 36 ATGCs that matched the following criteria: (i) maximum pairwise tree distance is at least 0.1 substitutions per site, to ensure that the ATGC includes well differentiated strains (the presence of closely related strains in an ATGC is guaranteed by construction), such that we could compare the rates of sequence and gene content evolution beyond closely related strains; and (ii) the phylogenetic tree contains more than two clades, such that pairwise tree distances are centered around more than two typical values, to ensure that there were at least two major clusters , such that the study of that ATGC was informative with regard to the stage of potential incipient speciation. Two of the 36 genome clusters were identified as outliers and were excluded from the dataset. The 34 genome clusters analyzed in this study are listed in Supplementary Data We used the Alignable Tight Genomic Clusters (ATGC) database57. We refer to these trees as sequence similarity-based trees. The phylogenomic reconstruction software Gloome58 was used to obtain trees based on the gene content similarity among the members of each ATGC. As the input for Gloome, we used the phyletic profiles for the presence or absence of each ATGC-COG, and the sequence similarity-based trees from the ATGC database; options were set to optimize the tree branch lengths under a genome evolution model with 4 categories of gamma-distributed gain and loss rates. This procedure resulted in 2 trees per ATGC, both with the same topology but with different branch lengths . All trees were inspected for extremely long and short branches, and clades responsible for such branches  were manually removed to avoid possible artifacts in the following steps.High-resolution phylogenetic trees based on the concatenated alignments of single-copy core genes were downloaded from the ATGC databasey = bxα. To compare the goodness of fit provided by different models, we calculated the Akaike Information Criterion (AIC) as k is the number of parameters, n is the number of observations, and RSS is the residual sum of squares59. The 95% confidence interval for the delay parameter τ∞ was obtained by finding the values of τ∞ such that the residual sum of squares becomes RSS* is the residual sum of squares produced by the best fit of the model60.For each ATGC, we computed all pairwise distances among tree leaves in the sequence similarity-based and gene content-based trees. Then, we compared the observed relationship between both sets of distances with the expectations of the recombination barrier model under four scenarios for the recombination barrier crossing rate . For each scenario, we fitted the model parameters using non-linear least-squares optimization (implemented in Matlab R2018b), with sequence similarity-based tree distances as independent variable and gene content-tree distances as dependent variable. The choice of sequence similarity-based tree distances as the independent variable was motivated by the need to fulfill the assumption of homoscedasticity in the non-linear regression model. In addition, we studied the fit of a heuristic power law model To account for unobserved variation, tree branch lengths were corrected by using the autocatalytic barrier spread model with the parameters inferred in the previous step. In that model, the observable divergence increases with time according to the functioni, hi, that isIn the simplest case of an ultrametric tree, the corrected height (the distance from the tip) of a node Hi, as the distance between the node’s parent and the tip, that is, Hi = hi + bi, where bi is the length of the branch that connects node i to its parent node. Parental heights were calculated as weighted averages from the tips to the root, such that Hi = bi for the leaves andi1 and i2 refer to the child nodes of node i; weights were computed as wi = bi for leaves and wi = wi1 + wi2 + bi for internal nodes . Next, corrected values of the parental heights were obtained by computing, numerically (MATLAB R2018b), the inverse function Branch lengths would then be obtained by subtracting the depths of the parent and child nodes. Extending this idea to non-ultrametric trees, we first defined the parental height of a node, k genomes that belong to the same ATGC, the gene content overlap was calculated as the number of ATGC-COGs shared by all the genomes divided by the mean number of ATGC-COGs per genome. The total sequence divergence of a set of k genomes was calculated as the sum of all branch lengths in the sequence similarity subtree that results from selecting the corresponding leaves in the whole-ATGC tree. Curves for the temporal decay of the fraction of shared genes were obtained by plotting the gene content overlap against the total sequence divergence for all possible combinations of k genomes within the ATGC. Smooth curves were obtained by fitting a cubic spline model with 5 knots  using the SLM tool ) in MATLAB R2018b. The mean separation among the curves obtained for different values of k was calculated asFor a set of n is the number of genomes in the ATGC, fk and k and x-axis interval in which both fk and 61. A value of S = 0 corresponds to the exact coincidence of the curves for all values of k, which is expected in the absence of evolutionary delays. To assess whether correction of branch lengths for unobserved variation reduces the separation among curves, we calculated the relative change in separation as 6 randomized datasets in which the ‘original’ and ‘corrected’ labels were randomly reassigned in each ATGC.where 62, defined as clusters of orthologous genes or COGs63. To minimize possible confounding effects due to paralogy, we identified all the ATGC-COGs that match any of the universal COGs and restricted the analysis to those COGs that are represented by a single ATGC-COG in at least 30 of the 34 analyzed ATGCs. Multiple sequence alignments for the selected ATGC-COGs were downloaded from the ATGC database and processed to extract all pairwise distances (Nei-Tamura method). Only index orthologs from the ATGC database, i.e., a single sequence per ATGC-COG per genome, were included. For each ATGC-COG, pairwise distances between sequences were plotted against pairwise distances in the phylogenetic tree, and a linear regression model with zero intercept was applied to obtain the relative evolutionary rate of the ATGC-COG with respect to the ATGC average. To minimize the impact of rare instances of gene replacement, which manifest as a non-linear relationship between sequence and tree pairwise distances, we discarded the ATGC-COGs with the fit to the regression model R2 < 0.9. To account for COG-specific evolutionary rates, the relative evolutionary rate of each ATGC-COG was divided by the mean of all ATGC-COGs that match the same COG. The result is the ATGC-COG relative evolutionary rate, that is, the ATGC-COG evolutionary rate corrected by COG-wise and ATGC-wise averages. For each ATGC, the dispersion of evolutionary rates was quantified as the standard deviation of the relative evolutionary rates of its ATGC-COGs.For the analysis of evolutionary rates, we focused on a previously published list of 100 nearly universal gene familiesFurther information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1


Mesenchymal stem cells (MSCs) are currently being tested in preclinical and clinical trials for their ability to foster wound healing and tissue regeneration .They are well known to show a therapeutic potential largely depending on their ability to secrete proregenerative cytokines, making these cells an attractive option for improving the treatment of chronic wounds. The wound microenvironment is a miscellaneous key factor in the local management of the healing process: players such as the extracellular matrix or the resident and recruited cells with paracrine activity are able to determine the way and the appropriateness of the regenerative processes .Dental-derived mesenchymal stem cells (D-dMSCs) are an intriguing milestone of the regenerative medicine, with regard to their potential of differentiating into osteogenic, adipogenic, and chondrogenic lineages –5, posseDespite the multiple barriers to their clinical use, MSCs or D-dMSCs have shown sufficient promise to garner a primary place in the field of translational medicine. In fact, MSC and D-dMSC therapies have significant implications for human health: clinical studies are greatly needed to confirm or stimulate the basic and translational researches aimed at reaching cutting-edge results –13.The special issue has reported articles on MSCs used as a therapeutic aid in clinical and surgical applications. The topics in translational medicine reported were the MSC therapy for intravertebral disc regeneration  and the cell therapy as a promising aid for cerebral vasculature , as well as for the proper management of thin endometrium .β1 that enhances MSC commitment to either the osteogenic or adipogenic lineages by reorganizing the actin cytoskeleton , as well as on the use of a PRP blood clot stabilizer to treat infrabony periodontal defects  and the use of vitamin D in dental-derived MSCs that promote osteogenic differentiation through the modulation of αVβ3 , the role played by the ganglioside GM1 in the osteogenic differentiation of human tendon stem cells , or via low-frequency pulsed electromagnetic fields The most reported translational use of MSC/D-dMSC therapy is related to bone tissue regeneration: in fact, many authors have investigated on the osteogenic ability of different stem cell types and genes, such as TGFSome authors have focused their researches on umbilical cord stem cells, due to their large application on translational medicine , as well on miRNA-132 MSC-derived exosomes in the treatment of myocardial infarction .in vitro loading model (2D and 3D in combination with different scaffolds) represents a simple and very efficient way to investigate molecular events during orthodontic tooth movement .Finally, experimental findings from in silico studies, on the one hand, highlighted the promotive role of hypoxia in MSC proliferation ; on the other hand, it was reported that an In this special issue, the editors together with the involved authors have well described the MSCs and D-dMSCs in their different but fundamental roles as promoters, enhancers, and playmakers of the translational regenerative medicine. Starting from the contents of our issue, the scientific community will be stimulated to experiment new ideas, to improve the knowledge of the MSCs/D-dMSCs, and to speed up their clinical application, so as to improve the future therapies.


A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way. Sensor technologies are widely used in our everyday lives. Wearable devices such as electronic clothes , watchesOffice workers, the elderly and students spend most of the day sitting on chairs ,10. AccoHeart rate variability (HRV) is highly related to stress, anxiety, diabetes, hypertension, fatigue, and depression symptoms ,14,15. DA unipolar approach for ECG signal acquisition is experimentally tested in this paper. Different from the conventional differential measurement, the capacitively coupled sensing electrodes in our approaches operate in the unipolar mode. The outputs of each sensing electrodes are amplified and sampled separately. Since the unipolar electrodes are independent, the measured ECG signals will not affect each other, problems caused by impedance imbalance can be overcome. Four unipolar electrodes are mounted on the backrest of a regular chair to get four-channel ECG signals. The experimental results indicate that such an approach gives robust performance to sitting postures and body types of the tested subjects.d is about 0.3 mm according to cotton shirt thickness. Therefore, the value of EC is about 37 pF by Equation (1). The transfer function of the front circuit around A1 in H1: (ε ≈ 1) . The strThe transfer function of the inverting amplifier A2 isBecause the entire circuit is cascade-connected by these two parts, and the transfer function of the electrode isThe equivalent capacitance of the diode is about 1 pF, which is far less than the coupling capacitance The electrodes are fixed on the chair. During the measurement, the subjects may perform slight changes in sitting postures and do some daily work. The capacitive electrode is hard and can slip over the clothes, the output signal is therefore susceptible to artifacts because of the poor contact and charging effects . In addi(2)Most clothes are soft and flexible. It is easy to produce random cloth folds when subjects are sitting on the chair with their back leaning on the backrest. The uneven distribution of clothes wrinkles may cause inconsistent distances between differential measurement electrodes and the skin.(3)Because the shape of the human back is irregular, during the measurement, the electrodes at different positions are subjected to different pressures. The pressure on the electrodes affects the contact conditions and therefore the impedances between the electrodes and the skin. The quality of the measured ECG signal is then be affected .The vertical and the horizontal distance between the sensing electrodes is 10 cm and 12 cm, respectively. The sensing material of the reference electrode is made of conductive fabric and mounted on the seat of the chair. The signal acquisition system is shown in Based on the abovementioned experiments, it can be concluded that the capacitively coupled ECG signal acquisition system with unipolar measurement should be more suitable for the chair-based ECG applications. Compared with the conventional differential measurement, under the same number of sensing electrodes, the unipolar measurement can obtain more signal channels and can provide more information. Furthermore, because the sensing electrodes in the unipolar measurement are mutually independent, the ECG signals measured by the unipolar electrodes do not affect each other. These advantages enable the proposed system to accommodate to uneven thickness of clothes and different kinds of sitting postures.The working principle of the proposed ECG signal acquisition system is shown in As mentioned above, due to the different sitting posture and body size, not all the channels could get reliable ECG signals. An effective algorithm for evaluating the signal quality is needed. Frequency domain features of the ECG signals are generally used to evaluate the signal quality ,32,33. g method . m is thG signal . Based oIn order to evaluate the availability of the above mentioned signal quality index, simulation is firstly carried out with four segments of ECG signal selected from the MIT-BIH Database . The selThe calculated The scaling factor The synthetic result One example of the signal synthesis is given in The contact conditions between the sensing electrodes and the human body are the most important factor for capacitively coupled ECG measurements. Because the sensing electrodes are mounted on the backrest of the chair, two backrests with different softness are experimentally tested. In the first case, which is indicated as firm configuration, the electrode-attached pad is placed directly on the chair. The tested backrest is relatively firm. In the second case, which is indicated as soft configuration, a sofa cushion with thickness 10 cm is inserted between the electrode—attached pad and the backrest of the chair as shown in There is no standard location for human back ECG signal measurement . VariousThe main advantage of the capacitively coupled electrode is the ability to work through insulation materials, such as fabric and clothing. In daily life, the subjects may wear clothes of different thickness. The influences of various clothing thickness on the system performance are evaluated. One subject  is asked to wear different cotton T-shirt layers, and sit on the proposed ECG signal acquisition system. The thickness of the single-layer cotton T-shirt is about 0.3 mm. The multi-channel ECG signals are measured under different layers of cotton T-shirt. A healthy subject  was experimentally tested. The subject was asked to sit on the proposed ECG signal acquisition system. To measure the standard lead-I ECG signals, the LA, RA and LL extremity electrodes of the clinical ECG instrument  are placed on the subject’s left arm, right arm and left leg respectively. One minute of ECG signals are simultaneously measured by our device and the clinical ECG instrument. The ECG signals obtained by the clinical ECG instrument are used as the reference ECG signals. During the experiment, the subject is asked to change the sitting posture at the 20 s and 40 s. The recorded ECG signals are shown in Six subjects were asked to sit on the proposed ECG signal acquisition system and their ECG signals were measured within three minutes. During the experiment, the slight movements  are allowed. All of the subjects are different in body shape and wear clothes of different thickness. The body mass index (BMI) is used to indicate their body shape. In the meantime, the standard lead-I ECG signals measured by the clinical ECG instrument are recorded as the reference ECG signals. One student with moderate training in ECG analysis annotated these measured ECG signals. The heart beat measurement results are listed in A prototype of an ECG signal acquisition system with multiple capacitively coupled unipolar electrodes is described. Compared with those differential measurement approaches, in the proposed system, the ECG signals are directly obtained from the outputs of each unipolar electrodes. Because the measured signal from one sensing electrode is affected by the electrode-skin coupling impedance, the differential ECG signal obtained from outputs of one pair of electrodes may be ruined by the impedance imbalance. This is especially true when the ECG signal is measured in a capacitive-coupled way. On the other hand, since the signals are amplified and sampled separately in unipolar approach, problems caused by such imbalance can be suppressed. Experiments are carried out with four unipolar electrodes attached on the backrest of a chair. The reference electrode is mounted on the seat of the chair. The measurement ECG signals are likely to be less affected by its location. One important phenomenon is that the signal quality is affected by the softness of the backrest. An index for quality evaluation is proposed based on phase space reconstruction. Using the index as the weight value for weighted synthesis, a fast signal synthetic method is designed for obtaining a single-channel ECG signal from the multiple-channel unipolar signals. Although the obtained ECG signal quality is not higher than that by the differential measurement, the experimental results indicate that available ECG signals can be obtained in case of slightly human body motions. Future work will be aimed at the long-term evaluation under real-life conditions and new measurement configurations.