Search

MOTIVATION: Advancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations. RESULTS: We benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable. AVAILABILITY AND IMPLEMENTATION: We provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).

Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via "automatic differentiation" implemented in general-purpose machine-learning libraries such as TensorFlow and PyTorch. Although these libraries are highly optimized, it is not clear if their general-purpose nature will limit their algorithmic complexity or implementation speed for the phylogenetic case compared to phylogenetics-specific code. In this paper, we compare six gradient implementations of the phylogenetic likelihood functions, in isolation and also as part of a variational inference procedure. We find that although automatic differentiation can scale approximately linearly in tree size, it is much slower than the carefully implemented gradient calculation for tree likelihood and ratio transformation operations. We conclude that a mixed approach combining phylogenetic libraries with machine learning libraries will provide the optimal combination of speed and model flexibility moving forward.

Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.

This article summarizes the current understanding of epigenetic regulation in grapevines, emphasising their significance in a clonally propagated plant with limited genetic diversity. Key epigenetic processes, including DNA methylation and histone modifications, shape chromatin structure, influencing gene expression. The grapevine leaf methylome reveals similarities with other clonally propagated plants, emphasizing low methylation levels in specific contexts. Epigenetic regulation contributes to grapevine phenotypic plasticity, clonal diversity, and an intriguing dialogue between grafted partners. These mechanisms form a vital part of plant memory, especially in the face of climate change. Despite the potential resetting during plant regeneration, recent evidence suggests the persistence of parental epigenetic imprints in progeny. Understanding how environmental conditions affect epigenetic imprints in grapevine clones is crucial. In Uruguay, where the wine industry faces climate challenges, Tannat stands as an emblematic variety adapted for our environmental production systems. However, climate change predictions in the region include rising temperatures, altered precipitation patterns, and increased extreme events, which could impact some aspects of its adaptation (yields, berry quality and typicity, among others). Vineyard management strategies, along with plant breeding, are essential for adaptation. Adding epigenetic diversity for breeding strategies enhances adaptability, contributing to sustainable viticulture in the face of climate change. The article calls for urgently developing innovative strategies utilizing heritable epigenetic variations, presenting a faster and more efficient approach to grapevine breeding for stress tolerance in the era of climate change.

Plasticity is typically used in biology to describe the ability of an organism to adapt to changes in its environment. Within a viticulture production system under similar management practices, studying the plasticity of a specific cultivar under different local climatic conditions can enhance our understanding of the grape typicity and, consequently, the wine style of a region. This work aims to assess the performance of Albariño vines grown in different mesoclimates within the emerging wine region along Uruguay’s Atlantic coast. The study was conducted in a commercial vineyard in Garzón, Maldonado Department, a key location in this developing wine region. Temperature sensors were installed in each of the four Albariño plots, situated in two contrasting topographic positions relative to Atlantic Ocean exposure, to assess mesoclimate and the ocean’s influences on grapevine plasticity. Agronomic measurements were taken to assess the performance of Albariño under the local climatic conditions. The multivariate analysis of yield and berry composition variables with ocean exposure and seasonal climate has identified three key findings: 1) the Albariño cultivar has shown strong potential for cultivation in eastern Uruguay due to its favourable agronomic and oenological performance, 2) vineyard ocean exposure has a greater effect on berry composition at harvest than on yield, contributing 70.7 % and 63.4 % of the total variability, respectively as observed from the Principal Component Analysis, 3) the correlations between berry composition and ocean exposure are stronger under warmer and drier conditions. Understanding the plasticity of Albariño under different local climatic conditions is valuable for sustainable production systems, provided both production and high quality are maintained. This study highlights the importance of selecting appropriate cultivars and understanding the local climate for the future success of vineyards.

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here, we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real data sets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators.

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.

Bayesian inference for phylogenetics is a gold standard for computing distributions of phylogenies. However, Bayesian phylogenetics faces the challenging computational problem of moving throughout the high-dimensional space of trees. Fortunately, hyperbolic space offers a low dimensional representation of tree-like data. In this paper, we embed genomic sequences as points in hyperbolic space and perform hyperbolic Markov Chain Monte Carlo for Bayesian inference in this space. The posterior probability of an embedding is computed by decoding a neighbour-joining tree from the embedding locations of the sequences. We empirically demonstrate the fidelity of this method on eight data sets. We systematically investigated the effect of embedding dimension and hyperbolic curvature on the performance in these data sets. The sampled posterior distribution recovers the splits and branch lengths to a high degree over a range of curvatures and dimensions. We systematically investigated the effects of the embedding space's curvature and dimension on the Markov Chain's performance, demonstrating the suitability of hyperbolic space for phylogenetic inference.

Abstract Humans appear to have regularly worn footwear since at least the Early Upper Palaeolithic. However, due to the perishable nature of footwear, the archaeological record of its presence during the Pleistocene is poor. While footwear would have played an essential role in protecting the foot, it could also have been used as ornamentation and/or as a social marker. Footprints may provide the most relevant insight regarding the origin and function of footwear. Here we report the discovery of footprints in Cussac Cave (southwest France) at 28–31 ka cal BP and the results of a multi-focal approach, including experimentation, that demonstrate that Gravettian people most likely wore footwear while moving through the cave. These singular footprints would constitute one of the oldest cases of indirect evidence for this unusual practice in decorated Palaeolithic caves and reinforce the exceptional nature of Cussac already attested by the presence of monumental engravings and funerary deposits.

La adaptación del cultivo de la vid al cambio y la variabilidad climática implica conocer cómo los factores ambientales locales impactan en la fisiología de la vid, repercutiendo en el rendimiento, y la composición final de la uva. La composición de la uva en cosecha está determinada por las condiciones climáticas de la temporada ya que tienen un efecto sobre la fisiología y el desarrollo de la vid. El objetivo de este trabajo es estudiar el impacto de la técnica de viñedo de prepoda post envero sobre los componentes de rendimiento y composición de la uva en cosecha. Las evaluaciones se realizaron sobre un viñedo comercial en la zona sur de Uruguay de los cultivares Tannat y Merlot durante la vendimia 2021. La prepoda en envero no afectó el rendimiento, el número de racimos por planta y peso de racimo en los dos cultivares, pero si afectó el balance fisiológico de estos. Sobre la composición primaria de la uva en vendimia, Tannat alcanzó diferencias en sólidos solubles y Merlot en acidez total. Al alterar la canopia post envero se afecta el equilibrio fisiológico de las plantas. Esta tendencia al desequilibrio fisiológico debe continuar siendo estudiada a lo largo de varios ciclos productivos.

Climate is one of the main components that define the development and behaviour of the plant, conditioning the health status and the final quality of the grapes. The objective was to describe the precipitation (PP) variability during the last 32 years and how Tannat and Albariño responded in three growing seasons (GS). We analyzed PP data from two agro-meteorological stations and calculated descriptive indexes. In addition, we measured vine water potential, plant physiological parameters, grapes’ final composition and health status at harvest in 2019, 2020 and 2021. In the last 32 years, PP during GS ranged from 133-1154 mm and 10-599 mm during the grape ripening period. An average of 51 days had PP, which means one event every four days. However, when analyzing the dry periods (moving average of 15 days with PP

Resumen El objetivo de esta investigación fue evaluar el efecto de la sustitución de mosto Tannat por mosto Ugni Blanc, sobre el contenido de alcohol, pH, color y composición fenólica de los vinos. Para ello, se realizaron vinificaciones por triplicado de mostos Tannat (VT) y mostos Tannat sustuidos con un 20% de mosto Ugni blanc (SM) en tres temporadas consecutivas. Se realizó un análisis climático de la región durante la maduración. Se evaluó la composición de la uva en cosecha y el color y la composición de los vinos a los cuatro meses del descube. Las características climáticas de las temporadas fueron contrastantes debido a la cantidad y distribución de las precipitaciones. Las uvas Tannat cosechada en el 2020 presentaron mayor concentración de azúcares, pH y riqueza fenólica. Las lluvias durante la temporada 2022 provocaron una mayor incidencia de Botrytis spp., afectando su potencial enológico. Los vinos SM presentaron menor contenido de etanol respecto a los VT. Los vinos SM y VT elaborados en la vendimia 2020, no presentaron diferencias en el pH, en tanto que para las demás temporadas los resultados fueron discordantes. La sustitución de mostos no afectó el color del vino, en tanto que las concentraciones de polifenoles totales, antocianos y taninos de los vinos SM fueron iguales o superiores a la de los vinos VT. La sustitución de mosto, parece ser una alternativa válida para la elaboración de vinos tintos con menos contenidos de alcohol, sin afectar negativamente su color ni su composición fenólica.

ABSTRACTThe H30Rx subclade of Escherichia coli ST131 is a clinically important, globally dispersed pathogenic lineage that typically displays resistance to fluoroquinolones and extended spectrum β-lactams. Isolates EC233 and EC234, variants of ST131-H30Rx with a novel sequence type (ST) 8196, isolated from unrelated patients presenting with bacteraemia at a Sydney Hospital in 2014 are characterised here. EC233 and EC234 are phylogroup B2, serotype O25:H4A, and resistant to ampicillin, amoxicillin, cefoxitin, ceftazidime, ceftriaxone, ciprofloxacin, norfloxacin and gentamicin and are likely clonal. Both harbour an IncFII_2 plasmid (pSPRC_Ec234-FII) that carries most of the resistance genes on an IS26 associated translocatable unit, two small plasmids and a novel IncI1 plasmid (pSPRC_Ec234-I). SNP-based phylogenetic analysis of the core genome of representatives within the ST131 clonal complex places both isolates in a subclade with three clinical Australian ST131-H30Rx clade-C isolates. A MrBayes phylogeny analysis of EC233 and EC234 indicates ST8196 share a most recent common ancestor with ST131-H30Rx strain EC70 isolated from the same hospital in 2013. Our study identified genomic hallmarks that define the ST131-H30Rx subclade in the ST8196 isolates and highlights a need for unbiased genomic surveillance approaches to identify novel high-risk MDR E. coli pathogens that impact healthcare facilities.

The recently discovered (2000) Cussac Cave is one of the major elaborately decorated caves for which the Ministry of Culture immediately invoked protective measures, including climatic controls and land and equipment purchases. A multi- and inter-disciplinary research programme was launched in 2009 as part of a Projet collectif de recherche (PCR) (A Collaborative Research Project) that provides for integrated conservation with study. Aside from its large size (1.6 km) and—due to the efforts of the speleologists who found the cave—its excellent state of preservation, Cussac Cave is distinctive because of the co-occurrence of uniquely engraved decorated panels, often of a monumental size, and contemporaneous human remains found in three locations. As in all other recently discovered decorated caves, the current study project is ambitious and holistic. The current research is, however, very focussed in scope, concentrating principally on ichnology (fossilised animal and human imprints), biological anthropology, and archaeothanatology. After the first few years of the initial stages of this multi-disciplinary project, the last few years have seen the successful integration of the work of geoscientists, anthropologists, ichnologists, experts in parietal art, and prehistorians with the application of new technological innovations, such as topographic mapping, 3D imaging, and geographic information systems. The following text provides an overview of some of these research endeavours.