dataesr/scientific-paragraphs-categorization huggingface dataset:
The multi-type birth-death model with sampling is a phylodynamic model which enables the quantification of past population dynamics in structured populations based on phylogenetic trees. The BEAST 2 package bdmm implements an algorithm for numerically computing the probability density of a phylogenetic tree given the population dynamic parameters under this model. In the initial release of bdmm, analyses were computationally limited to trees consisting of up to approximately 250 genetic samples. We implemented important algorithmic changes to bdmm which dramatically increased the number of genetic samples that could be analyzed and which improved the numerical robustness and efficiency of the calculations. Including more samples led to the improved precision of parameter estimates, particularly for structured models with a high number of inferred parameters.
Furthermore, we report on several model extensions to bdmm, inspired by properties common to empirical datasets. We applied this improved algorithm to two partly overlapping datasets of the Influenza A virus HA sequences sampled around the world-one with 500 samples and the other with only 175-for comparison. We report and compare the global migration patterns and seasonal dynamics inferred from each dataset. In this way, we show the information that is gained by analyzing the bigger dataset, which became possible with the presented algorithmic changes to bdmm. In summary, bdmm allows for the robust, faster, and more general phylodynamic inference of larger datasets.
Introduction Genetic sequencing data taken from a measurably evolving population contain fingerprints of past population dynamics  [1]. In particular, the phylogeny spanning the sampled genetic data contains information about the mixing pattern of different populations and thus contains information beyond what is encoded in classic occurrence data; see, e. g. , Hey and Machado  [2] , Stadler and Bonhoeffer  [3]. Phylodynamic methods  [4, 5]  aim at quantifying past population dynamic parameters, such as migration rates, from genetic sequencing data. Such tools have been widely used to study the spread of infectious diseases in structured populations; see, e. g. , Dudas et al. [6] , Faria et al. [7]  as examples of analyses of recent epidemic outbreaks. The Bayesian phylodynamic inference framework BEAST2  [8]  is one of the software frameworks within which such analyses can be carried out.
The use of this model within a phylodynamic setting and the associated computational approach were initially proposed for the analysis of species phylogenies  [15]  and later for the analysis of pathogen phylogenies  [3, 13]. The BEAST2 package bdmm generalizes the assumptions of these two initial approaches  [16]. It further allows for co-inferring phylogenetic trees together with the model parameters and thus explicitly takes phylogenetic uncertainty into account. Datasets containing more than 250 genetic sequences could not be analyzed using the original bdmm package  [16]  due to numerical instability. This limitation was a strong impediment to obtaining reliable results, particularly for the analysis of structured populations as the quantification of parameters which characterize each subpopulation requires a significant amount of samples from each of them.
The instability was due to numerical underflow in the probability density calculations, which meant that probability values extremely close to zero could not be accurately calculated and stored. We were able to solve the numerical instability issue of bdmm, thereby lifting the hard limit on the number of samples that could be analyzed. In addition, the practical usefulness of the bdmm package had previously been restricted by the amount of computation time required to carry out the analyses. Here, we report on significant improvements in computation efficiency. As a result, bdmm can now handle datasets containing several hundred genetic samples. Finally, we made the multi-type birth-death model more general in several ways: homochronous sampling events can now occur at multiple time points (not only the present), individuals are no longer necessarily removed upon sampling, and the migration rate specification has been made more flexible by allowing for piecewise-constant changes through time.
Evaluation of Numerical Improvements We compared the robustness and efficiency of the improved bdmm package against its original version. We considered tree sizes varying between 10 and 1000 samples. For each tree size, we simulated 50 branch-typed and 50 sample-typed trees under the multi-type birth-death model using randomly drawn parameter values from the distributions shown in Table  A1. The distributions from which the parameters were drawn were selected to reflect a wide range of scenarios. For each simulated tree, we measured the time taken to perform the calculation of the probability density, given the parameter values under which the tree was simulated, using the updated and the original bdmm implementation. We report here the wall-clock time taken to perform this calculation 5000 times (Figure  2 ).
Influenza A Virus (H3N2) Analysis As an example of a biological question that can be investigated with the use of bdmm, we analyzed 500 H3N2 influenza virus HA sequences sampled around the globe from 2000 to 2006; we aimed to recover the seasonal dynamics of the global epidemics. The dataset is a random subset of the data analyzed by Vaughan et al. [18] , taken from three different regions around the globe: New York (North, n = 167), New Zealand (South, n = 215), and Hong Kong (Tropics, n = 118). The dataset of 980 samples assembled by Vaughan et al. [18]  was built with the aim of gathering samples from three locations with relatively similar population sizes, each representative of the northern, southern, or equatorial regions. As a comparison, we performed an identical analysis of the H3N2 influenza dataset with 175 sequences sampled between 2003 and 2006 that was used in  [16].
This dataset of 175 sequences was also a subset of the data by Vaughan et al. [18] , and it also grouped samples from three locations denoted as North (for the northern hemisphere), South (for the southern hemisphere), and Tropic (for tropical regions). Note that the latter dataset came from more geographically spread samples, and thus we did not expect results from both analyses to be perfectly comparable. As we were dealing with pathogen sequence data, we adopted the epidemiological parametrization of the multi-type birth-death model, as detailed in Kühnert et al. [16]. The epidemiological parametrization substitutes birth, death, and sampling rates with effective reproduction numbers within types, rate at which hosts become noninfectious, and sampling proportions. To study the seasonal dynamics of the global epidemic, we allowed the effective reproduction number R e to vary through time.
To do so, we subdivided time into six-month intervals (starting April 1st and October 1st), and we constrained the effective reproduction number values corresponding to the same season across different years to be equal for each particular location, assuming that the R e values were the same in the summer seasons and the same in the winter seasons. The testing of this hypothesis' validity by estimating the R e values that varied in each six-month period was not performed as we expected little information from the data for the additional parameters. For the same reason, the migration rate was not varied through time. Further details on the data analysis configuration can be found in Appendix D. The analysis of the larger dataset (500 samples) allowed for the reconstruction of the phylogenetic tree encompassing a longer time period, and therefore gave a more long-term and detailed view of the evolution of the global epidemic (see Figure  4  for the Maximum Clade Credibility trees).
As can be expected for the tropical location, in both analyses, the effective reproduction numbers for H3N2 influenza A were inferred to be close to one throughout the year (Figure  5A ). Conversely, strong seasonal variations can be observed in the Northern and Southern hemisphere locations, where the effective reproduction number was close to one in winter and was much lower in summer. Inferences from the small and large datasets are mostly in agreement. A subtle difference appears: in the larger dataset, the effective reproduction numbers in the winter seasons and in the tropical location are closer to one, with less variation across estimates. This seems to indicate that the variations between estimates observed with the smaller dataset, including samples from 2003 to 2006 (for instance, R e in winter in the North compared to R e in winter in the South), are due to stochastic fluctuations, which are averaged out when considering a longer period of transmission dynamics in the larger dataset covering the years 2000-2006.
However, we still observed that the uncertainty was generally reduced for the inference performed with the larger dataset, as expected. A significant difference between the migration rates inferred from the Southern to Tropical locations arose between the two analyses. With the larger dataset, the estimated rate was much lower than that with the smaller one, and it was more in range with the other migration rate estimates. Detailed results of all the parameter estimates for both analyses are available in Table  A4. Most notably, the estimates of the root locations for both datasets are very similar. In both cases, the tropical location is most likely to be the location of the root; however, neither of the two other locations can be entirely excluded.
Discussion The multi-type birth-death model with its updated implementation in the bdmm package for BEAST 2 provides a flexible method for taking into account the effect of population structure when performing a phylodynamic genetic sequence analysis. Compared to the original implementation, we now prevent the underflow of numerical calculations and speed up calculations by almost an order of magnitude. The size limit of around 250 samples for datasets that could be handled by bdmm is thus lifted, and significantly larger datasets can now be analyzed. Now, the bottleneck lies in the search for tree space with MCMC rather than with bdmm. We demonstrate this improvement by analyzing two datasets of Influenza A virus H3N2 genetic data from around the globe. One dataset has 500 samples and could not have been analyzed with the original version of bdmm, while the other one contains 175 samples and is the original sample dataset analyzed in  [16].
Overall, we observed that analyzing a dataset with more samples, as expected, gives a more long-term picture of the global transmission patterns and reduces the general uncertainty concerning parameter estimates. With the addition of the so-called ρ-sampling events in the past, intense sampling efforts limited to short periods of time (leading to many samples being taken nearly simultaneously) can be easily modeled as instantaneous sampling events across the entire population (or subpopulation) rather than as non-instantaneous sampling over small sampling intervals. This simplifies the modeling of intense pathogen sequencing efforts in very short time windows. By allowing the removal probability r (the probability for an individual to be removed from the infectious population upon sampling) to be typedependent and to vary across time intervals, as well as allowing migration rates between types to vary across time intervals, we further increase the generality and flexibility of the multi-type birth-death model.
A sample bdmm analysis with a ρ-sampling event in the past was added to the software package to guide users who may want to set up such an analysis with their own data. We focused on an epidemiological application of bdmm, where we co-infer the phylogenetic trees to take into account the phylogenetic uncertainty. However, the bdmm modeling assumptions are equally applicable to the analysis of macroevolutionary data, in which context bdmm allows for the joint inference of trees with fossil samples under structured models  [22]. When using a multi-type birth-death model in the macroevolutionary framework, ρ-sampling can be used to model fossil samples originating from the same rock layer. In the context of the exploration of trait-dependent speciation, structured birth-death models such as the binary-state speciation and extinction model (BiSSE)  [23, 24]  have been shown to possibly produce spurious associations between character state and speciation rate when applied to empirical phylogenies  [25].
When used in this fashion, users of bdmm should assess the propensity of their dataset analysis for Type I errors through neutral trait simulations, as suggested by  Rabosky and Goldberg [25]. In summary, the new release of bdmm overcomes several constraints when analyzing sequencing data in BEAST2. As it stands, the main constraint now is given by the efficiency of the BEAST2 MCMC tree space sampler rather than bdmm itself. We expect the new release of bdmm to become a standard tool for the phylodynamic analysis of sequencing data and phylogenetic trees from structured populations.
Supplementary Materials: The following are available at https://www. mdpi. com/article/10. 339 0/v14081648/s1. The XML files for the replication of the BEAST2 analyses of the two datasets with 175 and 500 samples of the H3N2 influenza virus HA sequences are available as supplementary files. To run these XML files using BEAST2, the bdmm and feast packages must be installed. denote the more recent node with n e , occurring at time t k-1 < t e ≤ t k. Node n e represents either a sampling event (Figure  A1A ), a branching event (Figure  A1B ), or a degree-2 node at t e = t k with or without sampling (Figure  A1C, D ). One can show that g e i,k (t) for t < t e satisfies the ODE - d dt g e i,k (t) = - d ∑ j=1 (λ ij,k + m ij,k ) + µ i,k + ψ i,k g e i,k (t) + d ∑ j=1 m ij,k g e j,k (t) + d ∑ j=1 λ ij,k p j,k (t)g e i,k (t) + d ∑ j=1 λ ij,k p i,k (t)g e j,k (t), (A3) with the derivation being analogous to that of p i,k (t).
The BEAST 2 analysis infers the birth-death model parameters, the substitution model parameters, and the clock model parameters together with the phylogenetic tree. In our analysis, the inferred phylogenetic trees are sample-typed trees. Thus, we do not attempt to reconstruct the history of migrations; rather, we marginalize over all the possible migration histories. This is realized in order to allow the MCMC chain to converge in a shorter time as compared to an analysis inferring branch-typed trees. For each data analysis, we ran 10 parallel MCMC chains for 9 × 10 7 steps, each using the new implementation of bdmm as a package of BEAST 2. 6  [8, 30]. The XML files providing all analyses specifications are available as Supplementary Materials. The computations were run on the Euler computation cluster at ETH Zürich.
Each MCMC chain in the 175-sample analysis required 150 computation hours on average (1500 h in total for ten chains). In the 500-sample analysis, each MCMC required 360 computation hours on average (3600 h for ten chains). We used Tracer v1. 6  [31]  to check for convergence. The effective sample size was above 200 for all parameters. We combined the chains run in parallel into one using LogCombiner. We obtained the MCC tree using TreeAnnotator. Both LogCombiner and TreeAnnotator are available as part of BEAST 2. 6. Finally, we plotted the numerical results with the R package ggplot2  [32]  and the MCC tree with ggtree  [33]. LogNormal(0, 0. 5) s Exp(0. 001) truncated on [0 , 1] r Beta(10. 0, 1. 5) T LogNormal(2. 0, 1. 0) Table  A4. Inferred parameter values for Influenza A virus analysis under the multi-type birth-death model.
The SEM examination was performed as previously illustrated (Figure  5C ). Confocal laser scanning microscopy (CLSM) was used to acquire 6 × 6 tile scans of immunostained slides after using a primary anti-SARS Coronavirus Spike protein and a secondary fluorescent antibody (Figure  5D ). Negative controls were performed in order to check the specificity of our immunostainings, by using the supernatant of non-infected cells and by omitting primary anti-SARS-CoV-2 antibodies in our staining protocol of SARS-CoV-2-infected cell supernatants. Neither case showed significant staining (not shown). The fluorescence signal of background pixels was indeed negligible in negative controls, with an intensity around 6000 (16-bit images), corresponding to the threshold value that we used for signal quantification from our datasets.
In parallel, using SEM we found the following mean numbers of particles per field of view: without centrifugation, 12. 33 ± 2. 08 with no ConA-coating, 18. 00 ± 4. 58 (+46% increase) with 1XConA-coating, 43. 50 ± 10. 61 (+253%) with 5XConA-coating, 45. 67 ± 8. 96 (+270%, significant) with 10XConA-coating and 36 ± 4. 58 (+192%, significant) with 25XConA-coating; with centrifugation, 22. 33 ± 16. 17 (+81% increase) with no ConA-coating, 181 ± 51. 03 (+1368%, significant) with 1XConA-coating, 253 ± 38. 43 (+1951%, significant) with 5XConA-coating, 185 ± 70. 71 (+1400%) with 10XConAcoating and 360 ± 42. 04 (+2819%, significant) with 25XConA-coating (n = 3; Student's ttest) (Figure  5B ). Thus, CLSM datasets confirmed that SARS-CoV-2 virus-like particles in our SEM images that we counted in our experiments were indeed likely to be SARS-CoV-2 virus particles.
Perspectives for Automated Processing of SEM and CLSM Images of SARS-CoV-2-Infected Cell Culture Supernatants Our SEM images of SARS-CoV-2 virus particles from infected cell culture supernatants provide very suitable images for automatic particle analysis due to the high contrast. One potential issue would be the aggregation of particles, which may pose difficulties for Thus, CLSM datasets confirmed that SARS-CoV-2 virus-like particles in our SEM images that we counted in our experiments were indeed likely to be SARS-CoV-2 virus particles.
Acknowledgments: We sincerely thank  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara ,  Taku Sakazume ,  Toshihide Agemura ,  Yusuke Ominami  and the  Hitachi team in Japan from Hitachi High-Tech Corporation and Hitachi Ltd. for the installation of an SU5000 microscope at the  IHU Méditerranée Infection facility.
together with the connectivity between code and stored data, registered through specific version metadata members. The framework development was originally motivated to cover the needs of Rare Event Searches experiments (experiments looking for phenomena having extremely low occurrence probability, like dark matter or neutrino interactions or rare nuclear decays). The framework components naturally implement tools to address the challenges in these kinds of experiments. The integration of a detector physics response, the implementation of signal processing routines, or topological algorithms for physical event identification are some examples. Despite this specialization, the framework was conceived thinking in scalability. Other event-oriented applications could benefit from the data processing routines and/or metadata description implemented in REST, being the generic framework tools completely decoupled from dedicated libraries.
REST-for-Physics is a consolidated piece of software already serving the needs of different physics experiments -using gaseous Time Projection Chambers (TPCs) as detection technology -for detector data analysis and characterization, as well as generic R&D. Even though REST has been exploited mainly with gaseous TPCs, the code could be easily applied or adapted to other detector technologies. We present in this work an overview of REST-for-Physics, providing a broad perspective to the infrastructure and organization of the project as a whole. The framework and its different components will be described in the text.
Introduction REST-for-Physics 1 (Rare Event Searches Toolkit for Physics) is a collaborative software effort providing a common framework and tools for acquisition, simulation, generic data analysis, and detector response in experimental particle physics. An ambitious feature of REST-for-Physics is its capability to analyze and compare both Monte Carlo and experimental data using the same event processing routines upon a unified event data -metadata architecture. The framework was born to bring together different software requirements related to gaseous Time Projection Chambers (TPCs) in the context of Rare Event Searches, and to unify and coordinate various independent developments in a common modular infrastructure with potential for scalability and reusability. Special care has been taken to ensure the traceability and reproducibility of the results obtained after the data processing, linking the code version with the metadata version stored on disk, and protecting such relation.
Any user local changes to the code are identified at compilation time. This is used to guarantee that the executed version and the results written to disk correspond to an unmodified official public release. This fact is extremely relevant when planning to register official experimental data and preserve it for historical reasons, such as covering the data management plan of scientific collaborations, including the release of data to be publicly exploited outside the collaboration domain. The code updates are periodically published in the Zenodo citations system, where a reference to the latest official release is found  [1]. REST-for-Physics is the result of several years of experience on detector physics and research, motivated originally to cover the software needs of the T-REX project for neutrino and dark matter searches  [2, 3].
The REST-for-Physics code has benefited from several academic works, as it becomes apparent in several PhD thesis publications  [4, 5, 6, 7, 8, 9, 10]  that have contributed to shape and define the final project that is described in this manuscript. This project has contributed to the development of different but interconnected research activities in a coherent way, unifying common tools that are regularly used today not only in research but also at all the academic levels, from undergraduate to master students. Different experimental projects have seen REST-for-Physics growing from its preliminary stages to the mature project we present in this work. RESTfor-Physics has been evolving within, and it is being used to produce results at, CAST  [11] , TREX-DM  [12] , PandaX-III  [13, 14, 15] , and IAXO  [16].
Those projects have benefited from the consolidation of REST as a common tool widely used among collaborators to process, register and analyze detector data. The use and development of REST in other experiments is encouraged in a community effort to maintain appropriate tools for related tasks. In addition to sharing the know-how and experience in our physics domain, the motivation to release a public common framework resides in providing the possibility to distribute the experimental data following a unique format readable with REST-for-Physics, or any other ROOT I/O compatible code, in a future open-data program of the experiments. The code is open-source and it is distributed under a GNU public license at GitHub  [17]. The aim of this document is to give the reader a broad perspective of the purpose of the software project, its organization and contents, and the basic instruments that shape the whole infrastructure, giving an idea of its scalability potential, and in addition, showing the code validation strategy and continuous integration philosophy.