dataesr/scientific-paragraphs-categorization huggingface dataset:
The reads were then mapped onto Phatr3 using Bowtie with unique read mapping parameter  (Langmead and Salzberg, 2012). For peak calling, we used three different peak detection methods such as MUSIC  (Harmanci et al. , 2014) , BCP  (Xing et al. , 2012)  and MACS2  (Zhang et al. , 2008). We found a significant overlap between them and took consensus peaks from the three methods. Genome-wide analysis of tag density profile on TSS (transcriptional start sites) was performed using deepTools  (Ramirez et al. , 2014). Mean and standard deviations of the coverage depth were calculated and plotted using Qualimap and samtools. Analysis and visualization of the data were performed using Integrative genomics viewer from Broad Institute (IGV)  (Robinson et al. , 2011)  and R module ChIP peak annotation.
Co-occurrence and Correlation Analysis ChIP-seq peaks were annotated with gene annotation from Phatr3  (Rastogi et al. , 2018). The intersection of different sets of genes overlapping with individual ChIP-seq peaks was performed using UpSetR  (Conway et al. , 2017). All combinations of overlap are derived and ordered from most to least overlapping categories. Non-overlapping peak sets are removed from the plot. The aligned histone modification ChIP-seq dataset was binned for coverage with a window size of 1 kb. Replicate correlation was performed using multiBigwigSummary tools from deepTools. Pearson pairwise correlation coefficients were calculated from tag density. Scatter plot for correlation depicts the tag density for a bin of 1 kb each. Sequence data were deposited in NCBI Gene Expression Omnibus database (accession number GSE139676).
RESULTS Unlike in mammals and plants, Polycomb and Trithorax complexes are not well studied in unicellular species. Only a few recent studies of PcG proteins have been reported in 11 http://www. bioinformaticsbabraham. ac. uk/projects/fastqc/ microalgae  (Shaver et al. , 2010). To further investigate the diversity of Polycomb and Trithorax complexes in single-cell species, we first made an extensive scan of the MMETSP database using eDAF (see section "Materials and Methods") and reference sequences (Supplementary Table  S1 ). We retained only MMETSP sequences whose corresponding domain architectures were identical to at least one of the reference sequences (see methods for details). Next, we aligned both reference and MMETSP sequences with Clustal Omega  (Sievers et al. , 2011)  and built phylogenetic trees.
Phylogenetic Distribution of Polycomb Complexes in Marine Unicellular Species Three core components of PRC2 are discussed here, E(z), Esc and Su(z)12. Nurf55 was not considered in our study because it is ubiquitous and is related to a high number of different biological processes that do not involve PRC2. Phylogenies of E(z),  Esc and Suz(12)  show similarity to human and plant genes with very conserved domains displaying high support values (above 85%) (Figure  2 ). PRC2 components are in principle very conserved, however, the phylogenetic tree of E(z), Esc and Su(z)12 polypeptides all fail to reconstruct or represent the consensus tree of eukaryotic monophyletic tree, not only because of the normal weakness of individual gene phylogeny, but might also be due to insufficient information in the database.
GO Functional Categories of H3K4me3 Marked Genes To gain insights into the functional categories enriched in H3K4me3 marked genes, we performed a GO classification based on DAMA and CLADE and refined by eDAF results. We found an enrichment mostly in categories such as: (1) tetratricopeptide repeat containing proteins which act as a protein-protein interaction module involved in regulation of different cellular functions including cell cycle, hormone signaling, neurogenesis, protein folding and transport, and transcriptional control  (Schapire et al. , 2006) ; (2) mitochondrial carrier proteins the mediate the transport of ions, nucleotides and metabolites across mitochondrial membranes  (Palmieri et al. , 2011) ; (3) WD domain, G-beta repeat involved in a variety of functions ranging from signal transduction and transcription regulation to cell cycle control and apoptosis; (4) Aldo/Keto reductase families of proteins which are important intermediates in many metabolic pathways, including sugar metabolism, steroid biosynthesis, amino acid metabolism, and biosynthesis of secondary metabolites  (Ellis, 2002) ; and (5) Kelch motif involved in fundamental cellular activities in multiple cellular compartments such as actin-binding activity, organization of cytoskeletal, plasma membrane and organelle structures  (Adams et al.
pombe and S. cerevisiae  (Shaver et al. , 2010; Margueron and Reinberg, 2011). In the present study, using eDAF, which is an ensemble of modules for gene prediction, ontology, architecture extended information and automation, we interrogated MMETSP, currently the largest transcriptome reference database for eukaryotic marine microbes, and found that although the coexistence of PcGs and TrxGs is not found frequently in unicellular species (39 out of 203; 19. 21%), there are several species that have both PcG catalytic enzymes and TrxGs, including Bigelowiella natans, Corethron hystrix, Attheya septentrionalis, Pelagomonas calceolate, and Proboscia alata (Supplementary Table  S2 ). This number of species might be limited by the nature of the MMETSP database, which contains only transcriptomics sequences, and sequencing more unicellular genomes might reveal additional species with PcG and TrxG complexes.
JeLLyFysh (JF) is a general-purpose Python application that implements ECMC for a wide range of physical systems, from point masses interacting with central potentials to composite point objects such as finite-size dipoles, water molecules, and eventually peptides and polymers. The application's architecture closely mirrors the mathematical formulation that was presented previously (see  [9,  Sect II]). The application can run on virtually any computer, but it also allows for multiprocessing and, in the future, for parallel implementations. It is being developed as an open-source project on GitHub. Source code may be forked, modified, and then merged back into the project (see Section 6 for access information and licence issues). Contributions to the application are encouraged. The present paper introduces the general architecture and the key features of JF.
It accompanies the first public release of the application, JeL-LyFysh-Version1. 0 (JF-V1. 0). JF-V1. 0 implements ECMC for homogeneous, translation-invariant N -body systems in a regularly shaped periodic simulation box and with interactions that can be long-ranged. In addition, the present paper presents a cookbook that illustrates the application for simplified core examples that can be run from configuration files and validated against published data  [9]. A full-scale simulation benchmark against the Lammps application is published elsewhere  [35]. The JF application presented in this paper is intended to grow into a basis code that will foster the development of irreversible Markov-chain algorithms and will apply to a wide range of computational problems, from statistical physics to field theory  [13].
State handler The state handler (an instance of a class that inherits from the abstract StateHandler class) is the sole separate element of JF to access the global state. In JF-V1. 0, the global physical state (all positions of point masses and composite point objects) is contained in an instance of the TreePhysicalState class represented as a tree consisting of nodes (each node corresponds to a Node object). Each node contains a particle (a Particle object) which holds a timesliced position. In JF-V1. 0, each leaf node may in addition have charges as a Python dictionary mapping the name of the charge onto its value. Each tree is specified through its root node. Root nodes can be iterated over (in JF-V1. 0, they are members of a list). Each node is connected to its parent and its children, which can also be iterated over.
In JF-V1. 0, the children are again members of a list. These lists imply unique identifiers of nodes and their particles as tuples. The first entry of the tuple gives a node's root node list index, followed by the indices on lower levels down to the node itself (see Fig. 3 ). The global lifting state is stored in JF-V1. 0 in a Python dictionary mapping the implicit particle identifier onto its time stamp and its velocity vector. This information is contained in an instance of the TreeLiftingState class. Both the physical and lifting states are combined in the TreeStateHandler which implements all methods of a state handler. To communicate with other elements of the JF application (such as the event handlers and the activator) via the mediator, the state handler combines the information of the global physical and the global lifting state into units (that is, temporary Unit objects, see Fig.
7 ). A given physical-state and lifting-state information for a node in the state handler is mirrored (that is copied) to a unit containing its implicit identifier, position, charge, velocity and time stamp. All other elements can access, modify, and return units. This provides a common packaging format across JF. The explicit identifier of a unit allows the program to integrate changed units into the state handler's global state. In the tree state handler of JF-V1. 0, the local tree structure of nodes can be extracted into a branch of cnodes, that is, nodes containing units. 3  Each event handler only requires the global state reduced to a single factor in order to determine candidate event times and out-states. As a design principle in JF-V1. 0, the event handlers keep the time-slicing of composite point objects and its point masses consistent.
12 ). The content of the configuration file is parsed by the configparser module and passed to the JF factory (located in the base. factory module) in run. py. Standard Python naming conventions are respected in the classes built by the JF factory, which implies the naming conventions in the configuration file (see Section 6. 3 for details). Within the configuration file, sections can be written in any order, but their explicit nesting is not allowed. The nestedness is however implicit in the structure of the configuration file. The JF application returns all output via files under the control of output handlers. Run-time information is logged (the Python logging module is used). Logged information can range from identification of CPUs to the initialization information of classes, run-time information, etc.
Globally used modules JF-V1. 0 requires that all trees representing composite point objects are identical and of height at most two. Furthermore, in the N V T physical ensemble, the particle number, system size and temperature remain unchanged throughout each run. After initialization, as specified in the configuration file, these parameters are stored in the JF setting package and the modules therein, which may be imported by all other modules, which can then autonomously construct identifiers. Helper functions for periodic boundary conditions (if available) and for the sampling of random positions are also accessible. JF-V1. 0 implements hypercubic and hypercuboid setting modules. Both settings define the inverse temperature and also the attributes of all possible particle identifiers, which are broadcast directly by the setting package.
Similarly, a function to create a random position is broadcast by the setting package. All the configuration files in Section 5 are for a three-dimensional cubic simulation box, that is, use the hypercubic setting with dimension = 3. Additional useful modules are located in the JF base package. The abstract Initializer class located in the initializer module enforces the implementation of an initialize method. This method must be called before other public methods of the inheriting class. The strings module provides functions to translate strings from snake to camel case and vice versa, as well as to translate a package path into a directory path. Helper functions for vectors, such as calculating the norm or the dot product, are located in the vectors module.
JF Cookbook The configuration files 8  in JF-V1. 0 introduce to the key features of the application by constructing runs for two charged point masses, for two interacting dipoles of charges, and for two interacting water molecules (using the SPC/Fw model). All configuration files are for a three-dimensional cubic simulation box with periodic boundary conditions, and they reproduce published data  [9]. As specified in their [Run] sections, the configuration files use a singleprocess mediator (an instance of the SingleProcessMediator class), and the setting package is initialized by an instance of the HypercubicSetting class (see for example Fig. 12 ). All configuration files in the directory use a heap scheduler (an instance of the HeapScheduler class), a tree state handler (instance of the TreeStateHandler class), as well as an tag activator (an instance of the TagActivator class) in order to activate event handlers, trash candidate events and prepare in-states.
Interacting atoms The configuration files in the coulomb atoms directory of JF-V1. 0 implement the ECMC sampling of the Boltzmann distribution for two identical charged point masses. They interact with the merged-image Coulomb pair potential and are described by a Coulomb pair factor. One of the two point masses is active, and it moves either in +x, in +y, or in +z direction. Statistically equivalent output is obtained for the merged-image Coulomb pair potential (the factor potential) associated with the inverse-power bounding potential (Section 5. 1. 1), or else with a cell-based bounding potential, either realized directly (Section 5. 1. 2), or through a cell-veto event handler (Section 5. 1. 3). Although the configuration files use the language of Section 1. 2 for the representation of particles, all trees and branches are trivial, and each root node is also a leaf node.
The configuration file's [InputOutputHandler] section specifies the inputoutput handler. It consists of the separation-output handler (an instance of the SeparationOutputHandler class), which is connected to the sampling event handler. In the present example, it samples the nearest-image separation (under periodic boundary conditions) of any two point masses. The initial global physical state is created randomly by the random-input handler (an instance of the RandomInputHandler class). The configuration file coulomb atoms/power bounded. ini reproduces published data (see Fig. 14, 2  ). The configuration file coulomb atoms/power bounded. ini can be modified for N point masses. In the [RandomInputHandler] section, the number of root nodes must then equal N. In the [Coulomb] section, the number of event handlers must be set to at least N -1 (this instructs the Coulomb tagger to deepcopy the required number of event handlers).
In the setting package, the input handler specifies that there are two root nodes (number of root nodes = 2). Each of them contains two nodes (which is coded as number of nodes per root node = 2) and the number of node levels is two (number of node levels = 2). As this numbers are set in the setting package, all the JF modules can autonomously construct all possible particle identifiers. Statistically equivalent output is obtained for pair factors for all interactions (Section 5. 2. 1), for dipole-dipole Coulomb factors and their factor potential associated with a cell-based bounding potential (Section 5. 2. 2), for dipole-dipole Coulomb factors with the cell-veto algorithm (Section 5. 2. 3), and by alternating between concurrent moves of the entire dipoles with moves of the individual point masses (Section 5.
Conclusions, outlook As presented in this paper, JF is a computer application for ECMC simulations that is hoped to become useful for researchers in different fields of computational science. The JF-V1. 0 constitutes its first development milestone: built on the mediator design pattern, it systematically formulates the entire ECMC time evolution in terms of events, from the start-of-run to the end-of-run, including sampling, restarts (that is, end-of-chain), and the factor events. A number of configuration files validate JF-V1. 0 against published test cases for long-range interacting systems  [9]. For JF-V1. 0, consistency has been the main concern, and code has not yet been optimized. Also, the handling of exceptions remains rudimentary, although this is not a problem for the cookbook examples of Section 5.
All the methods are written in Python. Considerable speed-up can certainly be obtained by rewriting time-consuming parts of the application in compiled languages, in particular of the potential package. One of the principal limitations of JF-V1. 0 is that pseudo-factor-related and unconfirmed events are timesliced, leading to superfluous trashing and re-activation of candidate events. Optimized bounding potentials for many-particle factor potentials appear also as a priority. The consistent implementation of an arbitrary number n ac of simultaneously active particles is straightforward, although it has also not been implemented fully in JF-V1. 0. (As mentioned, this is planned for JF (Version 2. 0)). This will enable full parallel implementations on multiprocessor machines. Simplified parallel implementations for one-dimensional systems and for hard-disk models in two dimensions are currently being prototyped.
Acknowledgements P. H. acknowledges support from the  Bonn-Cologne Graduate School of Physics  and Astronomy honors branch and from  Institut Philippe Meyer. L. Q. and M. F. F. acknowledge hospitality at the  Max-Planck-Institut für Physik komplexer Systeme, Dresden, Germany. M. F. F. acknowledges financial support from  EPSRC  fellowship  EP/P033830/1  and hospitality at Ecole normale supérieure. W. K. acknowledges support from the  Alexander von Humboldt Foundation.
In the remainder of this paper, the authors propose the HealthSHIELD tool, a software prototype based on the Azure Kinect camera. The prototype has been developed for monitoring, in real-time, people's tendency to touch their face, increasing their awareness with real-time warnings, and providing statistical reports to limit, as far as possible, any sources of contagion from pathogens such as Sars-CoV-2. The authors describe the approach followed to define the system requirements, the functionalities, and a preliminary validation study assessing the system's accuracy in laboratory conditions. Finally, the authors draw their conclusions and discuss the pros and cons of the proposed solution. To the best of the authors' knowledge, there are no other applications of RGB-D-based technologies for monitoring operator compliance with BPPs counteracting virus contagion in the workplace.
Such tracking functionalities are ready to use. Furthermore, the k4a is easy to set up (e. g. , plug and play) thanks to the availability of pre-built drivers. Regarding the last three requirements, thanks to the availability of the Azure Kinect Sensor Software Development Kit (SDK)  [51] , it is possible to easily implement a software tool that fulfills them. Regarding the last three requirements, thanks to the availability of the Azure Kinect Sensor Software Development Kit (SDK)  [51] , it is possible to easily implement a software tool that fulfills them.
The HealthSHIELD Tool The HealthSHIELD tool has been implemented with the C# programming language by using the Azure Kinect Sensor SDK v1. 4. 0  [51] , the Azure Kinect Body Tracking SDK v1. 0. 1  [52] , the C# API wrappers for the previous SDKs v1. 4. 0  [53] , the Windows Presentation Foundation toolkit v3. 5. 50211  [54] , and the OpenGL. Net libraries v 0. 8. 4  [55]. For the main application, the tool should be in a fixed place (e. g. , office desktop, industry or laboratory workstation, store checkouts, school desk, and so on) where people accomplish their daily life activities. People under observation are expected to face the sensor, at a distance of about two meters, without turning their face more than 45 degrees with respect to the sensor, which is positioned at a height of about 170 cm.
Validation Procedure As previously described, the detection module together with the attitude monitoring module act as event classifiers by detecting and classifying the contact events into six classes-that is the mouthnose, the right-eye, the left-eye, the right-ear, the left-ear, and the low-risk contact events (Figure  5 ). Regardless, during daily life activities, it is possible to raise the hand and cause face-occlusion events without actual face contacts (i. e. , gestures in which one hand is raised at the face level and occludes it to the camera field of view without actually touching the face). It is worth noticing that the HealthSHIELD classifier does not detect the occlusion events and only considers the corresponding frames as no-contact state frames. To answer the research question, an experiment was carried out to evaluate the HealthSHIELD classifier's capability to distinguish between the aforementioned contact event classes.
Validation Procedure As previously described, the detection module together with the attitude monitoring module act as event classifiers by detecting and classifying the contact events into six classes-that is the mouth-nose, the right-eye, the left-eye, the right-ear, the left-ear, and the low-risk contact events (Figure  5 ). Regardless, during daily life activities, it is possible to raise the hand and cause face-occlusion events without actual face contacts (i. e. , gestures in which one hand is raised at the face level and occludes it to the camera field of view without actually touching the face). It is worth noticing that the HealthSHIELD classifier does not detect the occlusion events and only considers the corresponding frames as no-contact state frames. To answer the research question, an experiment was carried out to evaluate the HealthSHIELD classifier's capability to distinguish between the aforementioned contact event classes.
At this end, the detection and classification capabilities of a human observer were used as a baseline. After this evaluation, the collected data were further processed. The contact event classes were clustered into two main classes. The first one, named risk contacts, contains the mouth-nose, the right-eye, and the left-eye contact events. The second one, named no-risk contacts, contains the right-ear, the left-ear, and the low-risk contact events. After clustering, it was possible to evaluate the overall capability to distinguish between risk and no-risk contact events. Two hypotheses were formulated: Hypothesis 1 (H1). The HealthSHIELD classifier classifies the events into the six contact areas in agreement with the human observer. Hypothesis 2 (H2). The HealthSHIELD classifier classifies the events into risk and no-risk contact areas in agreement with the human observer.
Conclusions By analyzing the current practice for the containment of the Sars-CoV2 outbreak and searching for the available technological solutions, the authors designed and developed the HealthSHIELD, a software tool based on the Microsoft Kinect Azure D-RGB camera aimed at monitoring compliance with the BPPs. The preliminary study assessing the system detection effectiveness was encouraging and returned a 90. 96% accuracy. The results evidence that low-cost RGB-D technologies can be effectively exploited for monitoring the compliance with some of the BPPs for virus contagion containment. The capability to break the chain of contagion by improving people's compliance with hygiene best practices and avoiding risk conditions (e. g. , meeting people and being closer to them than the recommended security distance) will turn useful in the post-lockdown phase.