dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Functionality The software aims at comparing ocean model output, which may be both physical or biogeochemical, to oceanographic observations. The user selects • a data set containing a pre-defined list of oceanographic stations, i. e. , all stations of a particular measurement network, • an oceanographic station from this dataset, either via a drop-down list or via a map, • a variable to compare between model results and measurements, e. g. , temperature, • between one and four models whose results will be compared to the observed data, and • a plot type of their choice. Five different kinds of plots can be selected, see Figure  1:  • Time series • Model data are averaged vertically over a chosen depth interval and drawn as curves over time. Measurements from within the depth range are drawn as points.
Implementation overview The software is implemented as an R Shiny app  [3] , making it easy to use via an interactive web-based front-end. The back-end can run both on a private computer or on a server. It requires RStudio or Shiny Server, respectively, and a Linux operating system, possibly in a virtual machine. A Dockerfile is supplied such that the software can be used with example data without the need to install the software itself. The use of the Dockerfile and installation instructions are explained in the user's guide. The software can load model data from a predefined list of models given in a text file. The same applies for measurement datasets. The app requires read access to the data over the (serverside) file system, an online upload of data is not intended. Model data have to be provided in the NetCDF format  [4]  and measurement data as text files or via an SQL database.
Alternatives This software is, to our best knowledge, the first example of a web-based open-source solution aimed at validating ocean model output. There are, however, alternative software packages which could be used to obtain similar validation results. The first alternative is the use of visualisation software for geodata. This includes the programs NOAA Ferret [5] and GrADS [6], which are specialised software solutions for geodata visualisation. The diversity of statistics and plots which can be produced by these programs strongly exceeds the possibilities in our software. This, however, comes at the expense of a dedicated script language which needs to be learned in order to perform the required tasks. A second alternative is the use of more general script languages like R  [7]  or MATLAB  [8] , for which packages for model data access or validation routines exist.
An example for accessing model results from the R programming language is the package rNOMADS  [9]. An example for a library of scripts for doing a numerous set of validation plots is the open-source Python package VACUMM by Ifremer  [10]. A third alternative to our Validator is the use of software which aims at assigning model scores as a measure of the goodness of the fit, rather than providing graphical evaluation. Software of this kind can be used for model intercomparison and assessing which model performs best compared to a predefined set of measurements. Examples for these tools are the NOAA model skill assessment software  [11]  or the ESMValTool  [12] , which is designed for obtaining performance metrics of Earth system models from the Coupled Model Intercomparison Project (CMIP6).
Implementation and architecture The software provides a user interface generated by the R Shiny framework. In this framework, a Shiny server acts as a web server which creates an Asynchronous JavaScript and XML (AJAX) based user interface from R code. This user interface contains widgets, in our case drop-down boxes and radio buttons which allow the user to select • a measurement dataset containing a pre-defined list of oceanographic stations, i. e. , all stations of a particular measurement network, • an oceanographic station from this dataset, either via a drop-down list or via a map, • one to four models whose results will be compared to the observed data, • a variable to compare between model and measurements, e. g. , temperature, • a plot type of their choice, and • range settings (time, depth, season) depending on the type of plot.
When the user selects one option, the AJAX widgets communicate this choice to the Shiny server, where the chosen values are stored in so-called "reactive expressions". These reactive expressions are passed as arguments to the plotting function, which will be explained in detail later. The result of the plotting function is the desired diagram, which is shown as output in the validator application. The concept of the "reactive expressions" is that an update of their values, e. g. , when the user selects a new oceanographic station, causes all functions depending on them to be re-evaluated. In this case, this means drawing a new plot. The plotting function launches a separate process using the "mcparallel" function in R (system package "parallel"). This keeps the user interface responsive during the plotting process.
Loading measurement data Measurement data can be either loaded from an ASCII file or from a database. Both methods are illustrated in the example setup we provide. We use the open-source data base MariaDB, but any database which supports SQL queries should work. R connects to the data base via the package RMariaDB. Two tables need to be provided in the data base: The "measurements" table contains the observation values and the time and depth in which they were taken, while the "variables" table contains details about the observed variable, e. g. , the name and unit. Details can be found in the user's guide. After the data are loaded from a data base, they are stored in a cache directory to minimise data base requests. In summary, this takes the following steps: 1. If data for the selected station and temporal range do not exist in the cache directory, perform a corresponding data base request and save the acquired data in ASCII format.
Calling the actual plot function, depending on the plot type The actual plot function will create a plot showing both the model data (of up to four models) and observations. The "ggplot2" package is used. The first three plot types (time series, vertical profile, scatter plot) require no further explanation. Choosing the option "Taylor diagram" will plot a Taylor diagram  [1]  displaying three measures of the model fit to the measurements at the same time: Pearson´s correlation coefficient, root mean squared error and the standard deviation. Choosing the option "seasonal/long term trend" will fit each one Generalised Additive Mixed Model (GAMM)  [2]  through the observations and every model's data. The data are modelled by fitting two smooth splines: One of them is periodic with a one-year period describing the seasonality of the measured variable, the second one describes the long-term behaviour.
Operating system The software is written in R, but still requires a Linux environment due to the use of forking (function mcparallel) in the parallel package. However, it can be run both locally or as a web server application. When running as a web server, e. g. , on a virtual machine, clients can use the software from any operating system with a modern web browser (works at least with Mozilla Firefox 65. 0 and Microsoft Edge 42. 17134. 1. 0).
Acknowledgements We would like to thank  Steffen Bock  from the IT department at our institute for setting up and maintaining the R Shiny server. We are grateful to the  R Core team  [7] and the developers of the R Shiny framework [14] as well as the other packages stated above [ 15][16][17][18][19][20 ] for providing their contributions as free software. The development of the Validator supports the  Earth System Science for the Baltic Sea region of the Baltic Earth program  (Earth System Science for the Baltic Sea region, see http://www. baltic. earth).
Acknowledgements We thank  Stefano Allesina  and  Matthew J. Michalska-Smith  for their generous assistance with the genetic algorithm. We also thank  Christopher M. Baker  and  Mengyuan Jia  for their advice on data visualization. HX is supported by  ARC  future fellowship (to EMM), a  CSIRO top-up scholarship (Julius Career Award  to IC), and a  University of Queensland Graduate School International Travel Award. EMM is supported by  ARC  future fellowship grant.
Synergistic neuronal activity promotes emergence from anesthesia. Our data indicate that selective activation of aNGCglutamatergic cells or inhibition of aNGC-GABAergic neurons induces arousal from a deep coma. Since we can modulate these populations to facilitate arousal suggests that similar patterns in aNGC may occur when animals spontaneously emerge from anesthesia. We hypothesized that activating HA neurons and inhibiting LA neurons would generate a synergistic effect that promotes arousal when animals spontaneously emerge from anesthesia. To examine states of neuronal activity during emergence from anesthesia, we recorded local field potentials in the cingulate cortex while gradually reducing isoflurane concentration. We assessed single-cell activity in aNGC in conjunction with these recordings (Fig.
The scale was adapted in the following way: Level 1-no effect indicated no visible movements or signs of behavioral arousal. Level 2partial arousal indicated signs of behavioral arousal, including apparent purposeful movement of tail or feet. Level 3-generalized purposeful body movementsattempt to right itself. Level 4-organized movements-fully right up. The data are the result of averaging the score of two trained researchers that were unaware to the experimental conditions and exposed to videos showing the motor behavior. Their scores were averaged by the authors. The nonparametric Mann-Whitney test was used to establish differences between saline and bicuculline groups in rats and mice. Grooming. We conducted grooming analysis using videos obtained from animals that reached level 3 from the arousal scale (purposeful attempt to right itself) after pharmacologic stimulation during exposure to isoflurane or hypoglycemic coma.
Continuous field potential in the cortex and single-unit spiking events were recorded using the Plexon Omniplex System with Plexcontrol software (Plexon Inc. , TX) or the 3600-Channel extracellular amplifier and the Power 1401-3 A data acquisition interface with Spike2 software. To record single units, the Plexon multichannel acquisition processor was used to amplify and band-pass filter the neuronal signals (150 Hz-8 KHz). Signals were digitalized at 40 kHz. To obtain the cortical field potential from wideband (0. 2 Hz-40 KHz), we used a causal 4th order butterworth filter to minimize phase distortion. Signals were downsampled to 1 kHz. Electrolytic lesions (60 µA, 30 s current) were made in rats for histological electrode localization. Using standard methodology, the terminally anesthetized animal was intracardially perfused with paraformaldehyde (4%), followed by brain extraction, postfixation, microtome sectioning, and staining to confirm electrode/fiber placement/scar or for immunostainings to amplify ChR2-mCherry or ArchT-GFP signal.
While using rats, we did not discard animals. After each microinjection of drug/saline and recorded activity, we performed electrolytic lesions. After perfusion and tissue preparation, we examined the lesion location, and grouped experiments based on the location of the lesion within the brainstem. Then, we analyzed the cortical spectrograms. We reported each of the samples in Supplementary Figs  2  and 3. Spike sorting. For spike sorting, we used the Offline Sorter Software (Plexon Inc, Dallas, TX, USA) or the offline sorter, Spike2 (Cambridge Electronic Design). Waveforms from individual neurons were detected using raw waveform amplitude of the signal with a threshold that optimizes the ratio between signal and background noise. We sorted considering the waveform energy and the first three principal components of a spike waveform to reveal clusters that classify different spike shapes.
LFP spectral analysis. To examine changes in spectral content evolving over time, we computed spectrograms using Thomson multitaper method implemented in Chronux toolbox  68, 69  in Matlab (Mathworks). The parameters for spectral analysis for the bicuculline experiments in mice were the following: moving window = 5 s with 0. 1 s overlap, time bandwidth product TW = 3, number of tapers K = 5. Power spectral density was computed using total power and then subtracting the mean. In anesthetized mice, we calculated average spectrograms within a time window that includes 60 s prior to injection and an interval of 15-120 s after injection. In hypoglycemic mice, average spectrogram was concatenated by 50 s prior to injection and an interval of 10-60 s after injection. The parameters for spectral analysis for optogenetic experiments were the following: moving window = 2 s with 0.
1 s overlap, time bandwidth product TW = 3, number of tapers K = 5. The power spectral density at each point in time/frequency was computed using fractional power and the spectrograms show the deviation from the mean. Similar parameters were used to compute spectrograms from rats in which bicuculline/saline was injected. To calculate the average spectrograms, we averaged over spectrograms of each trial at the following time points: before (60 s before injection), during (15-120 s after injection) and washout (1500-1600 s after injection). To detect a high cortical state during emergence from isoflurane in rats, we implemented a function custom-written in Matlab to define a stable high cortical state (h). To achieve this purpose, we first calculated the spectrogram of the form S t; i; j ½ ð Þ.
A video camera was synchronized to LFP recordings to observe an animal's behavior. Mice were head restrained at all times using a head restrainer approved by animal protocol 2016-0055. We implemented a function custom-written in Matlab to detect movements from vibration sensor signal v(t). To achieve this, we first subtracted v(t) from its mean every 10 s. Then we selected time instants (denoted as v1), when v(t) is above a certain threshold. We computed this threshold by looping from 0 to 100 (step size = 5) until less than 0. 05% of time instants in v(t) was above this threshold. No voltage fluctuations occurred if the threshold was less than 30. We scanned time instants in v1 in ascending order and include a time instant to v2 if this time instant is at least 5 s after the latest time instant in v2.
three using saline (vehicle). For quantification, we used the opensource ClearMap software published at www. idisco. info. An investigator not involved in the experimental procedure blindly ran the algorithm for quantification. As the data were not normally distributed, we did two-tailed Mann-Whitney U test per brain area. The statistical tests were performed using Matlab. In addition, data were compared with a group of intact animals active in a home cage. This comparison showed similar trend for increasing c-Fos in active brain areas and reduction of c-Fos in an area like VLPO when compared with control (saline) mice. Lilliefors test was applied to the firing rate data to determine if data were normally distributed. Normalized firing rates were analyzed using a one or two-way ANOVA with the Bonferroni post hoc test to examine changes in the mean firing across groups.
As the Lilliefors test rejected the null hypothesis that data are normally distributed, we applied the Kolmogorov-Smirnov test for each of the stimulations. To determine the sample sizes of the experimental groups, we performed pilot experiments with three mice for the pharmacologic and optogenetic experiments. We considered the strength of the effect and the variance across the groups to determine the sample size (number of units). For experiments assessing arousal, grooming or breathing frequency, we estimated samples sizes using data previously published  36, 50, 65. In iDISCO experiments, we estimated sample size considering previous data published by one of the authors  29. We estimated ideal samples by conducting power analysis. All experiments met or exceeded ideal sample size. In iDISCO experiments as well as those in which LFP and firing rates were analyzed, individuals completely blind to the experiment performed the data analysis.
Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. The source data underlying Fig. 1c, e , j, l, Fig. 2d, e, f, k, l, m, Fig 3b, Fig. 4c and Fig b, c are provided as a Source Data file located at https://doi. org/10. 6084/m9. figshare. c. 4504691.
Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host taxon predictor (Htp) software written in python which is freely available at https://github. com/ wojciech-galan/viruses_classifier. Htp's performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture-and alignment-free approach for distinction between phages and eukaryotic viruses.
Host Taxon Predictor (HTP) is a versatile tool in sense of target group of viruses and is able to predict whether a virus infects eukaryotes or Bacteria/Archaea with very high accuracy. The source code of HTP is available on https://github. com/wojciech-galan/viruses_classifier. In the course of the study, viral nucleotide sequences were represented by simple sequence features (mono-, dinucleotide absolute frequencies and di-, trinucleotide relative frequencies, see Methods section) and nucleic acid type. Four supervised machine learning methods were used to predict taxon of viral hosts. These methods, when trained with [object, class] pairs, allow for assigning a class to a previously unseen object. Logistic regression (LR) looks for the best fitting model to describe the relationship between the class and features of an object, and transforms the model's output using the sigmoid function to return class probabilities  29  , k-Nearest Neighbors (kNN) is an algorithm that classifies an object to a class by a majority vote of its k nearest neighbors  30.
We performed three separate binary classification tasks to identify viruses infecting seed plants, vertebrates, and arthropods. Our approach allowed to obtain predictors that exhibited good classification properties (AUC > 0. 8) for viruses infecting seed plants and vertebrates (Fig. 2 , Table  2 ). Unfortunately, we obtained only poor (AUC < 0. 7) classification efficacy when we tried to identify arthropod-specific viruses. Host taxon predictor software performance. We utilized four of our classifiers designed to distinguish between phages and non-phages (LR, kNN, QDA, and SVC trained with features selected with Lasso, bottom-up, genetic algorithms, and RFE approaches, respectively) to create a Python console application that we named Host Taxon Predictor (HTP). HTP's performance was then evaluated on complete viral genomic sequences that were published in NCBI Nucleotide after October 11, 2017 (thus after we had downloaded the initial collection of sequences that the classifiers were trained on).
Discussion To the authors' best knowledge, our study is the first that attempts to distinguish between phages' and eukaryotic viruses' sequences in an alignment-independent manner. Our solution does not require prior knowledge of a host's sequence. In our study, we aimed to create an application neither limited to any group of viruses nor any group of hosts. First, we created classifiers capable of such distinction. The classifiers use only simple sequence features (nucleic acid type, mono-, di-, trinucleotide abundances). Feature subsets and classifier hyperparameters, resulting in the best distinction, were selected using cross-validation set and final classifiers were later evaluated on a separate test set. The test set provided an unbiased evaluation of a final model fit on the training dataset.
Obtained results indicate excellent prediction capabilities of our classifiers. Along with feature extraction code, the classifiers were wrapped in one Python console application that we named Host Taxon Predictor. We chose those classifiers' hyperparameters and feature sets that performed best on cross-validation data. We later evaluated HTP's performance on a collection of complete viral genomes that were published in the NCBI Nucleotide database after we had created our cross-validation and test sets. The additional inspection confirmed that HTP is able to properly classify newly sequenced viruses. We also wanted to investigate, whether HTP can make correct predictions on short sequence fragments of different lengths that resemble contigs or reads obtained during metagenomic studies. We received good classification results for at least 3000 nt-long, fair for at least 500 nt-long, and poor for shorter subsequences.
Inclusion of hexanucleotides would result in a greater number of features than a number of elements in our dataset, thus increase in the risk of overfitting. Nucleic acid type is the most important sequence feature for distinction between phages and eukaryotic viruses as it appeared in all of the feature subsets. Having genetic information stored in the form of DNA strongly increases the probability of a virus to be classified as a phage. It is not surprising, because most of the known phages are actually DNA viruses (only 18 of 975 and 5 of 244 phages in our cross-validation and test set, respectively, are RNA viruses). The underrepresentation of RNA phages is most probably an effect of undersampling of viral diversity, rather than a general tendency among Viridae. For example, most of metagenomic studies focused on DNA only and could not evaluate novel RNA bacteriophages  43.
We did not discover any coded amino acid pattern between the most abundant features. Although measuring computational performance of various feature selection approaches was out of scope of our study, we observed that QuiPT was much faster than other methods. Albeit binarization of the features required by QuiPT leads to loss of information, we obtained features similarly useful for classification as with other methods. Likewise, our bottom-up feature selection approach resulted in the same (kNN) or similarly performing (QDA) feature sets in addition to being quicker. Similarly to the authors of VirSorter and VirFinder, we investigated the performance of our software on viral genomes published after construction of our original dataset. Since the analysis included about 10 times more eukaryotic viruses than phages, MCC is an appropriate metric for classifier evaluation.
Some viruses are not easily culturable, so they could not be discovered in the pre-metagenomic era  55  , and many of the sequences discovered using metagenomic approaches could not be identified as viral, because of being distant and unalignable to the already known viruses  27. Thus, databases of viral sequences may be biased and so our cross-validation and test sets as well as additional collection of newly sequenced viruses. Despite being alignment-independent, machine learning approaches to classify viral sequences assume that viruses infecting distinct host taxa share certain (oligo-)nucleotide usage pattern concealed at first glance. As new taxons of viruses are being discovered it may turn out that the patterns utilized by our classifiers are not shared among large groups of newly identified viruses and, therefore, the classifiers would need update.