Species Tree Estimation for the Late Blight Pathogen, Phytophthora infestans, and Close Relatives

To better understand the evolutionary history of a group of organisms, an accurate estimate of the species phylogeny must be known. Traditionally, gene trees have served as a proxy for the species tree, although it was acknowledged early on that these trees represented different evolutionary processes. Discordances among gene trees and between the gene trees and the species tree are also expected in closely related species that have rapidly diverged, due to processes such as the incomplete sorting of ancestral polymorphisms. Recently, methods have been developed for the explicit estimation of species trees, using information from multilocus gene trees while accommodating heterogeneity among them. Here we have used three distinct approaches to estimate the species tree for five Phytophthora pathogens, including P. infestans, the causal agent of late blight disease in potato and tomato. Our concatenation-based “supergene” approach was unable to resolve relationships even with data from both the nuclear and mitochondrial genomes, and from multiple isolates per species. Our multispecies coalescent approach using both Bayesian and maximum likelihood methods was able to estimate a moderately supported species tree showing a close relationship among P. infestans, P. andina, and P. ipomoeae. The topology of the species tree was also identical to the dominant phylogenetic history estimated in our third approach, Bayesian concordance analysis. Our results support previous suggestions that P. andina is a hybrid species, with P. infestans representing one parental lineage. The other parental lineage is not known, but represents an independent evolutionary lineage more closely related to P. ipomoeae. While all five species likely originated in the New World, further study is needed to determine when and under what conditions this hybridization event may have occurred

The Plant Pathogen Phytophthora andina Emerged via Hybridization of an Unknown Phytophthora Species and the Irish Potato Famine Pathogen, P. infestans

Emerging plant pathogens have largely been a consequence of the movement of pathogens to new geographic regions. Another documented mechanism for the emergence of plant pathogens is hybridization between individuals of different species or subspecies, which may allow rapid evolution and adaptation to new hosts or environments. Hybrid plant pathogens have traditionally been difficult to detect or confirm, but the increasing ease of cloning and sequencing PCR products now makes the identification of species that consistently have genes or alleles with phylogenetically divergent origins relatively straightforward. We investigated the genetic origin of Phytophthora andina, an increasingly common pathogen of Andean crops Solanum betaceum, S. muricatum, S. quitoense, and several wild Solanum spp. It has been hypothesized that P. andina is a hybrid between the potato late blight pathogen P. infestans and another Phytophthora species. We tested this hypothesis by cloning four nuclear loci to obtain haplotypes and using these loci to infer the phylogenetic relationships of P. andina to P. infestans and other related species. Sequencing of cloned PCR products in every case revealed two distinct haplotypes for each locus in P. andina, such that each isolate had one allele derived from a P. infestans parent and a second divergent allele derived from an unknown species that is closely related but distinct from P. infestans, P. mirabilis, and P. ipomoeae. To the best of our knowledge, the unknown parent has not yet been collected. We also observed sequence polymorphism among P. andina isolates at three of the four loci, many of which segregate between previously described P. andina clonal lineages. These results provide strong support that P. andina emerged via hybridization between P. infestans and another unknown Phytophthora species also belonging to Phytophthora clade 1c

Comparación de distintas estrategias para la predicción de muerte a corto plazo en el paciente anciano infectado

Objective. The aim of this study was to determine the utility of a post hoc lactate added to SIRS and qSOFA score to predict 30-day mortality in older non-severely dependent patients attended for infection in the Emergency Department (ED). Methods. We performed an analytical, observational, prospective cohort study including patients of 75 years of age or older, without severe functional dependence, attended for an infectious disease in 69 Spanish ED for 2-day three seasonal periods. Demographic, clinical and analytical data were collected. The primary outcome was 30-day mortality after the index event. Results. We included 739 patients with a mean age of 84.9 (SD 6.0) years; 375 (50.7%) were women. Ninety-one (12.3%) died within 30 days. The AUC was 0.637 (IC 95% 0.587-0.688; p= 2 and 0.698 (IC 95% 0.635- 0.761; p= 2. Comparing receiver operating characteristic (ROC) there was a better accuracy of qSOFA vs SIRS (p=0.041). Both scales improve the prognosis accuracy with lactate inclusion. The AUC was 0.705 (IC95% 0.652-0.758; p<0.001) for SIRS plus lactate and 0.755 (IC95% 0.696-0.814; p<0.001) for qSOFA plus lactate, showing a trend to statistical significance for the second strategy (p=0.0727). Charlson index not added prognosis accuracy to SIRS (p=0.2269) or qSOFA (p=0.2573). Conclusions. Lactate added to SIRS and qSOFA score improve the accuracy of SIRS and qSOFA to predict short-term mortality in older non-severely dependent patients attended for infection. There is not effect in adding Charlson index

Spatial positioning token (SPToken) for smart mobility

We introduce a permissioned distributed ledger technology (DLT) design for crowdsourced smart mobility applications. This architecture is based on a directed acyclic graph architecture (similar to the IOTA tangle) and uses both Proof-of-Work and Proof-of-Position mechanisms to provide protection against spam attacks and malevolent actors. In addition to enabling individuals to retain ownership of their data and to monetize it, the architecture is also suitable for distributed privacy-preserving machine learning algorithms, is lightweight, and can be implemented in simple internet-of-things (IoT) devices. To demonstrate its efficacy, we apply this framework to reinforcement learning settings where a third party is interested in acquiring information from agents. In particular, one may be interested in sampling an unknown vehicular traffic flow in a city, using a DLT-type architecture and without perturbing the density, with the idea of realizing a set of virtual tokens as surrogates of real vehicles to explore geographical areas of interest. These tokens, whose authenticated position determines write access to the ledger, are thus used to emulate the probing actions of commanded (real) vehicles on a given planned route by ``jumping'' from a passing-by vehicle to another to complete the planned trajectory. Consequently, the environment stays unaffected (i.e., the autonomy of participating vehicles is not influenced by the algorithm), regardless of the number of emitted tokens. The design of such a DLT architecture is presented, and numerical results from large-scale simulations are provided to validate the proposed approach