dataesr/scientific-paragraphs-categorization huggingface dataset:
On other strains found in Enterobase Of the 334 strains present in Table  S2 , 311 are E. coli sensu stricto and the members of species E. albertii, E. fergusonii and E. clades II-V are under-represented. Because they are far more distant than the E. coli sensu stricto strains, they might prove challenging for our method so we gathered a second test dataset consisting only of strains of non-sensu stricto Escherichia. These strains were found using Enterobase  [25]  based on multiple research criteria, resulting in the 180 strains listed in Table  S3. For these strains, we achieved 100 % agreement between the in silico PCR typing and the Mash typing.
CONCLUSION We have developed ClermonTyping, a user-friendly, open source and freely available method, and its web counterpart, the ClermonTyper, which mimics the in vitro PCR assays previously used by the scientific community. This tool allows, with high accuracy, a given strain of the genus Escherichia to be assigned to a specific species, and for E. coli sensu stricto strains to be assigned to a phylogroup. It has the advantage, in epidemiological studies, to allow comparison with the data obtained using in vitro typing assays. Five reasons to publish your next article with a Microbiology Society journal 1. The Microbiology Society is a not-for-profit organization. 2. We offer fast and rigorous peer reviewaverage time to first decision is 4-6 weeks. 3. Our journals have a global readership with subscriptions held in research institutions around the world.
The code used in the methods described here is deposited in a github repository at the following address: https://github. com/A-BN/ClermonTyping We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. We provide three supplementary tables. This material and corresponding links to the files are available for download in the online version of this article (Supplementary Material). All supporting data, code and protocols have been provided within the article or through supplementary data files. Three supplementary tables are available with the online version of this article.
We developed Convis, a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just-in-time optimization and compilation of the model onto CPU or GPU architectures. An alternative implementation based on Theano (Theano Development Team, 2016)  is also available, although not fully supported. Through automatic differentiation, any parameter of a specified model can be optimized to approach a desired output which is a significant improvement over e. g. , Monte Carlo or particle optimizations without gradients.
We show that a number of models including even complex non-linearities such as contrast gain control and spiking mechanisms can be implemented easily. We show in this paper that we can in particular recreate the simulation results of a popular retina simulation software VirtualRetina  (Wohrer and Kornprobst, 2009) , with the added benefit of providing (1) arbitrary linear filters instead of the product of Gaussian and exponential filters and (2) optimization routines utilizing the gradients of the model. We demonstrate the utility of 3d convolution filters with a simple direction selective filter. Also we show that it is possible to optimize the input for a certain goal, rather than the parameters, which can aid the design of experiments as well as closed-loop online stimulus generation. Yet, Convis is more than a retina simulator.
, 2009). To simulate arbitrary, non-round, non-Gaussian and non-continuous spatiotemporal filters we used 1d, 2d or 3d convolutions, which have the drawback of being very inefficient when implemented in single threaded programs. To counter this flaw and to keep the code comprehensible and maintainable, we used the PyTorch library to create models and run optimized code on a GPU. Spatio-temporal filters with finite impulse responses can be implemented effectively with either a recursive definition that keeps N previous states or higher moments in memory (as used in VirtualRetina and COREM) or as a convolution with a 3D kernel. We go further into the advantages and disadvantages of the two methods in section 2. 3. 3. Although a convolution is computationally costly because it has to calculate the crossproduct of filter and image at each position of the 3D image, the use of GPUs for cheap parallel computations can speed up this process dramatically.
General Usability We recommend using jupyter notebooks or IPython console to work with Convis  (Pérez and Granger, 2007). This way, models can be built interactively, auto completion is available and data can be inspected directly and plotted. For code examples we provide, we assume that the necessary packages are installed and a python interpreter is running with the following code already executed: import numpy as np import matplotlib. pylab as plt import torch import convis Convis aims to be compatible with Python 2. 7 and Python 3. 6. Installation instructions and a more in depth documentation of features and usage can be found at https://jahuth. github. io/ convis/.
Creating and Running a Model Models can be created by instantiating an instance of one of the model classes available in Convis, e. g. , the convis. retina. Retina() model or the models found in convis. models. *. Keeping with PyTorch, each model can be called as a function to process some input. Additionally, a run method can be called with an argument dt to automatically break down the input into chunks of length dt and processing one after another to limit memory usage. This code sample will run the Retina model on a moving grating stimulus: retina = convis. retina. Retina() # creating a model some_input = convis. samples. moving_grating (t=2000,x=20,y=20) o = retina(some_input[0:100,:,:]) # or o = retina. run(some_input,dt=100) By default the model will generate on and off spiking responses.
The Computational Graph and Automatic Optimization A computational graph can represent mathematical operations on data by combining symbols for variables and operations in an acyclic graph. In deep learning frameworks, such as Theano, Tensorflow or PyTorch, this graph is used to automatically derive gradients by applying back-propagation rules. In an earlier iteration of the toolbox, which is still available on github https://github. com/jahuth/convis_theano, we used Theano (Theano Development Team, 2016) to create models that are fully implemented as computational graphs. In the current version, which uses PyTorch, the computational graph is build during computation and only used for automatically deriving the gradients, while the forward computations are just-in-time compiled either to for GPU or CPU.
Usability Goals Getting familiar with a new tool is always a time investment, especially for experimentalists. We chose to design Convis to be used in an interactive Python environment, even though this increases the learning curve compared to e. g. , a GUI centered application. The advantages of interactive Python sessions is that there is no additional configuration language to create models. Completely new models can be created and tested interactively, giving immediate feedback and existing models can be inspected and modified e. g. , by replacing non-linearities or by replacing a full numerical convolution filter with a recursive filter. One goal in the design of Convis is that during runtime all parameters and configuration options are discoverable-only a minimum of names should have to be memorized to use the software (e.
g. , that models can be found under convis. models). All other names can be discovered by (1) tab completion in IPython  (Pérez and Granger, 2007) , and other environments that inspect the attributes of Python objects, (2) printing a model or layer, revealing its layers and sub-layers and (3) accessing the doc strings of the underlying classes. We chose to implement our toolbox in PyTorch to benefit from its just-in-time compilation and automatic differentiation mechanisms. Just-in-time compilation allows for fast execution of complex operations, while still using the flexibility of the Python interpreter. PyTorch can trace the execution of a Python function and compile it efficiently using one of the compiler backends, combining native C or CUDA functions with on-the-fly created code to execute python for-loops as C for-loops.
Parameters and Modules We build on the torch. nn module and provide a Layer class that adds a few convenience functions on top of the torch. nn Modules (see the documentation at https://jahuth. github. io/convis/). Similar to a torch. nn. Module, a Layer has to define two functions: an initializer (__init__), which sets up all the variables and computational modules the Layer requires, and a function that does the actual computation (forward). Parameters that are added as attributes to a Layer are automatically collected by PyTorch, such that optimization algorithms can find and optimize all available parameters of a model. Also the values of all variables of the Layer can be exported or imported. Since our models run not only once, but on a sequence of input, we need to distinguish between variables that signify a Parameter or a State.
Simulating a Population of RGCs To verify that our new implementation of the VirtualRetina model still replicates old results we made one-to-one comparisons using different stimuli and retina configurations. Our implementation of the VirtualRetina model using Convis is available in the convis. retina submodule. To verify that the temporal responses are identical to the same model implemented in VirtualRetina, we used a full field chirp stimulus as used in  Baden et al. (2016)  (see Figure  1 ) to characterize the temporal characteristics of retinal ganglion cells. The chirp features an OFF-ON-OFF pulse followed by oscillations increasing in frequency and then oscillations increasing in amplitude. The configuration was supplied as an xml file, as is typically used for VirtualRetina. Both simulations were configured with the same configuration file.
Fitting a Recursive Exponential Filter To show a simple example of optimization in the temporal domain we created a Poisson spike train which is convolved with an exponential filter of a certain time constant. Then we used the gradient with respect to the time constant of the recursively defined exponential filter to optimize this variable. As this is a   (Wohrer and Kornprobst, 2009) , Topographica  (Bednar, 2012) , Virtual Retina++ (continued development of VR in the ENAS/PRANAS package  Cessac and Palacios, 2013) ,  (Lorach et al. , 2012), and RetinaStudio (Martínez-Álvarez et al. , 2013). Recursive or convolution Yes Yes ≈VR signifies that the gain control was implemented similarly to VirtualRetina: Contrast gain control through shunting inhibition, using a local estimate of spatio-temporal contrast  (Wohrer et al.
Comparison to Other Simulation Software As we have shown in section 3. 1, the response of Convis is identical to the VirtualRetina if an identical configuration is used. The linear filtering and gain control properties are identical within numerical errors the magnitude of which is configurable. In addition to those response characteristics, Convis can also simulate non-circular, non-Gaussian and noncontinuous receptive field shapes. Similarly to COREM, Convis can be configured more flexibly, combining basic filters. Table  1  shows a comparison between different retina simulation software.
To assess how useful Convis can be for specific applications, we compared VirtualRetina and a set of Convis models over a range on stimulus sizes (see Figure  6 ). The comparison was done on a Dell PC with a 6-core Intel(R) Xeon(R) CPU E5 v3 running at 2. 40 GHz, 32 gb of RAM and a NVIDIA Quadro K620 GPU. For all models small stimuli could be processed faster than real-time and larger stimuli increased the running time polynomially. The Convis Retina model is for small stimulus sizes the slowest model and also has the smallest stimulus size that still allows real-time processing (20x20 pixel on our machine). The original VirtualRetina program outperformed the Convis model for all stimulus sizes and could still process 40x40 pixel images in close to real-time. However, this is only the case if the only relevant output are the produced spikes.
Features of VirtualRetina Not Implemented We did not implement code to parse radially varying parameters. While the general idea of retinotopic varying parameters can be useful, we found that we were interested in rather local retinal populations (<2 deg) which will be fairly uniform. Radially varying blur can still be used when filtering with recursive spatial filters, but the configuration values for radially varying blurs in VirtualRetina xml files will be ignored by default. Also, we did not implement cell sampling schemes. One pixel is one cell for us, however selecting a subset of cells from the output (before compiling the model) is possible and it can reduce the computations actually performed by the model due to the optimization mechanisms.
Taxon sampling and molecular dataset The latest effort to resolve phylogenetic relationships in Parnassiinae was  Condamine et al. (2012) , but that study did not include all species. For our study, DNA sequences were obtained from GenBank using   (Omoto et al. 2004 (Omoto et al. , 2009;; Katoh et al. 2005; Nazari et al. 2007; Nazari and Sperling 2007; Michel et al. 2008; Condamine et al. 2012) , and includes all currently described species within the family, except for Bhutanitis ludlowi  (Möhn 2005; Weiss 2005; Churkin 2006 ). The dataset also includes the majority of described subspecies or geographic races  (Nazari and Sperling 2007; Gratton et al. 2008; Schoville and Roderick 2009; Dapporto 2010; Todisco et al. 2010 Todisco et al. , 2012;; Zinetti et al. 2013). Additionally, sequences of nine species of swallowtails were added to the dataset as outgroups, with Baronia brevicornis considered as the sister group to all remaining Papilionidae, and two species for each of the four tribes included within Papilioninae, the sister subfamily of Parnassiinae  (Condamine et al.
2012). Sequences were aligned using MAFFT 7. 110  (Katoh and Standley 2013)  with default settings (E-INS-i algorithm). Reading frames of coding genes were checked in Mesquite 3. 03 (www. mesquite. org). Datasets (species/subspecies names and GenBank accession numbers) are available on Dryad (Appendices 1 and 2).
Assessment of fossil positions and calibrations Constraints on clade ages were enforced through fossil calibrations, whose systematic position was assessed using phylogenetic analyses  (Donoghue et al. 1989; Sauquet et al. 2012). Phylogenetic analyses of the dataset included both living and fossil taxa and incorporated both morphological and molecular data using a total-evidence approach  (Ronquist et al. 2012a). Swallowtail fossils are scarce, but two unambiguously belong to Parnassiinae  (Nazari et al. 2007 ). The first is †Thaites ruminiana  (Scudder 1875)  To assess the phylogenetic positions of fossils, our morphological dataset comprised 236 characters  (Hiura 1980; Nazari et al. 2007 ) including adult external (160) and internal (54) morphology, egg (2) and larval (10) morphology, pupal morphology (5), and characters for pigment color and chromosome number (5).
Morphological characters were coded for all extant species in each genus except Parnassius (only eight species: one per subgenus). Yet, Parnassius had little impact on our analyses since there are no fossils related to this genus  (Nazari et al. 2007 ). Fossils were only coded for adult external morphology. Morphological and molecular data were combined to construct a total-evidence phylogeny using MrBayes 3. 2. 6  (Ronquist et al. 2012b ). Morphological data were modeled using the Mk model  (Lewis 2001). The best-fitting partitioning scheme for the molecular data was selected with PartitionFinder 1. 1. 1 (Lanfear et al. 2012) , using the greedy search algorithm and the Bayesian Information Criterion (BIC). Rather than using a single substitution model per molecular partition, we sampled across the entire substitution rate model space  (Huelsenbeck et al.
Phylogeny and estimates of divergence times We examined whether the rate of molecular evolution evolved in a clock-like pattern using PATHd8  (Britton et al. 2007 ). Since the hypothesis of a strict molecular clock was rejected for 68 of the 92 nodes (P<0. 05), we estimated divergence times using Bayesian relaxed-clock methods accounting for rate variation across lineages  (Drummond et al. 2006). MCMC analyses implemented in BEAST 1. 8. 2  (Drummond et al. 2012b ) were employed to approximate the posterior distribution of rates and divergences times and infer their credibility intervals. We set the following settings and priors: a partitioned dataset (after the best-fitting PartitionFinder scheme) was analyzed using the uncorrelated lognormal distribution (UCLD) clock model, with the mean set to a uniform prior between 0 and 1, and an exponential prior (lambda=0.
All analyses were performed on the computer cluster CIPRES Science Gateway  (Miller et al. 2015) , using BEAGLE  (Ayres et al. 2012). Convergence and performance of MCMC runs were evaluated using Tracer 1. 6  (Rambaut and Drummond 2009)  and the effective sample size (ESS) criterion for each parameter. A maximum-clade credibility (MCC) tree was reconstructed, with median age and 95% height posterior density (HPD). Bayes factor (BF) comparisons using stepping-stone sampling  (Xie et al. 2011) , which allows unbiased approximation of the marginal likelihood of Bayesian analyses  (Baele et al. 2013) , was used to select among competing models. We considered 2lnBF values >10 to significantly favor one model over another  (Kass and Raftery 1995). The dataset and BEAST xml files generated for this study are available on Dryad (Appendix 4).
Historical biogeography Definition of biogeographic units was based on the present-day distribution of species but also informed by plate tectonics  (Sanmartín et al. 2001 ) and alpine orogeny reconstructions  (Bouilhol et al. 2013)  AF=North Africa and Arabia. Species ranges were defined by presence-absence in each region (Appendix 5). Biogeographic analyses were performed on the MCC tree (outgroups removed), using the Dispersal-Extinction-Cladogenesis model of range evolution (DEC;  Ree and Smith 2008)  implemented in the R-package BioGeoBEARS 0. 2. 1  (Matzke 2014). Because of concerns with statistical validity and model choice in BioGeoBEARS (Ree and Sanmartín in review), we did not use the DEC+J model  (Matzke 2014). We also preferred DEC to DEC+J because the latter often infers null or extremely low extinction rates  (Sanmartín and Meseguer 2016) , an effect of the model favoring direct dispersal over widespread ranges, which might not be adequate for reconstructing the history of ancient groups.
Biogeographic ranges larger than four areas in size were disallowed as valid biogeographic states if they were not subsets of the terminal species ranges; widespread ranges comprising areas that have never been geographically connected  (Sanmartín et al. 2001)  were also removed. We constructed a timestratified model (Appendix 6 on Dryad) with four time slices that specified constraints on area connections and spanned the Parnassiinae evolutionary history; each interval represents a geological period bounded by major changes in tectonic and climatic conditions thought to have affected the distribution of these butterflies.
Diversification analyses in a Red Queen model. -We first tested the hypothesis that diversity is bounded or at equilibrium, meaning that diversity expanded rapidly in early diversification, filled most niches and saturated toward the present. We explored the effect of diversity-dependence on speciation and extinction rates using the method of Etienne et al. (2012) implemented in the R-package DDD 2. 7. We applied five different models: (i) speciation depends linearly on diversity without extinction, (ii) speciation depends linearly on diversity with extinction, (iii) speciation depends exponentially on diversity with extinction, (iv) speciation does not depend on diversity and extinction depends linearly on diversity, and (v) speciation does not depend on diversity and extinction depends exponentially on diversity.
For each model, the initial carrying capacity was set to the current number of described species. We also tested the effect of host-plant association on diversification rates, since previous study has pointed to a potential correlation between shifts in diversification and hostplant switches  (Ehrlich and Raven 1964). We used a diversification model of the statedependent speciation and extinction (SSE) family of models, in which extinction and speciation rates are associated with phenotypic evolution of a trait along a phylogeny  (Maddison et al. 2007). In particular, we used the Multiple State Speciation Extinction model  (MuSSE; FitzJohn et al. 2009 ) implemented in the R-package diversitree 0. 9-7 (FitzJohn 2012), which allows for multiple character states. Larval host plant data were taken from previous work  (Ehrlich and Raven 1964; Scriber 1984; Collins and Morris 1985; Tyler et al.
There have been concerns about the power of SSE models to infer diversification dynamics from a distribution of species traits  (Davis et al. 2013; Maddison and FitzJohn 2015; Rabosky and Goldberg 2015). First, SSE models tend to have a high type I error bias related to the shape (topology) of the inferred tree  (Rabosky and Goldberg 2015). To test whether our diversification results were potentially biased, we estimated the difference of fit (ΔAIC) between the best model from MuSSE and a null model (corresponding to the model with no difference in speciation, extinction and transition rates between the states of a character) and compared this with the difference between the same models as estimated from simulated datasets. 1000 sets of traits were generated on the Parnassiinae phylogeny using the sim.
2016a ). To provide an independent assessment of the relationship between diversification rates and host specificity, we used models that allow diversification rates to vary among clades. BAMM 2. 5  (Rabosky et al. 2013 (Rabosky et al. , 2014a) )  was used to explore for differential diversification dynamic regimes among clades differing in their host-plant feeding. BAMM analyses were run for 10 million generations, sampling every 10,000 th and with four different values of the compound Poisson prior (CPP) to ensure the posterior is independent of the prior  (Moore et al. 2016). Mixing and convergence among runs (ESS>200 after 15% burn-in) were assessed with the R-package BAMMtools 2. 1  (Rabosky et al. 2014b ). BAMM has been criticized for incorrectly modeling rate-shifts on extinct lineages, i.
2011). Geographic characters require a third widespread state, because, unlike morphological traits, ancestors can be present in more than one state (area). This requires the modeling of cladogenetic state change, the probability associated with the division of an ancestral range between the two descendants, alongside anagenetic state change (i. e. the probability of character change along branches). Species were coded by their elevation zone, using data from literature, museum records, and field observations. We evaluated 12 models of increasing complexity to test the relationship between elevational distributions and diversification. As in MuSSE above, we evaluated the performance of GeoSSE using simulation tests. Trait simulation was more complex here because there is no direct transition between states A and B.