dataesr/scientific-paragraphs-categorization huggingface dataset:
Deployment and Installation Methodology Technically the use of a DBMS offers unrivaled operational flexibility. It also affords a more precise "life cycle" of every data point, in particular thanks to "empty values". The description information of every point can be provided progressively, and it is in no way required that the points of the same type be similarly filled. The definition of each point can be realized in 4 steps: 1) Identification of the points. 2) Placement of the points in an understandable space. 3) Detection of their "appearance". 4) Validation of proper function and expected behavior. The identification of the points consists in giving a symbolic name to the point, a comment describing it (for example "Switch 26, 4 th floor, zone 20"), and a code which gives its general type such as "switch", "light", "heat pump" without going into further detail.
Characteristic BMIoT "Digital Panel" BTM with PCVUE32  One should note that there exist software products such as the ETS program from KNX which are not properly supervision applications. The ETS software is used to configure the parameters of the fieldbus (KNS home automation standard) statically. There are telecommunications devices on the market whose role is to convert the KNX protocol into standard protocols such as MODBUS/TCP for example, and let KNX hardware interconnect with our software solution, just as the controllers perform the conversion between fieldbuses and IP protocols.
Business Model for our Solution The software solution which we have developed provides uniform supervision of both "typical" building equipment such as heating, lighting and energy consumption, as well as enduser IoT devices with new use-cases such as media consumption meters or health-related devices. Its goal is to facilitate the transition of companies for maintenance in good operation conditions of buildings or facility management to a business model of Internet Player  [10]. The main point of this transition lies in going from "small data" of traditional BTM to the "big data" which is the foundation of new and additional economic activities. A "big data" can be described by three major characteristics:  A huge amount of data,  The sources of the data are both human, but also machines (IoT).
This paper describes the development and application of a suite of tools, called PBxplore, to analyze the dynamics and deformability of protein structures using Protein Blocks (PBs). Proteins are highly dynamic macromolecules, and a classical way to analyze their inherent flexibility is to perform molecular dynamics simulations. The advantage of using small structural prototypes such as PBs is to give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, PBs allow analysis of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is able to process large amounts of data such as those produced by molecular dynamics simulations.
INTRODUCTION Proteins are highly dynamic macromolecules  (Frauenfelder, Sligar & Wolynes, 1991; Bu & Callaway, 2011). To analyze their inherent flexibility, computational biologists often use molecular dynamics (MD) simulations. The quantification of protein flexibility is based Figure  1  (A) The 16 protein blocks (PBs) represented in balls with carbon atoms in gray, oxygen atoms in red and nitrogen atoms in purple (hydrogen atoms are not represented). (B) The barstar protein (PDB ID 1AY7  (Sevcík et al. , 1998) ) represented in cartoon with alpha-helices in blue, beta-strands in red and coil in pink. These representations were generated using PyMOL software (DeLano, 2002) (C) PBs sequence obtained from PBs assignment. Z is a dummy PB, meaning that no PB can be assigned to this position. Full-size DOI: 10.
7717/peerj. 4013/fig-  1  on various methods such as Root Mean Square Fluctuations (RMSF) that rely on multiple MD snapshots or Normal Mode Analysis (NMA) that rely on a single structure and focus on quantifying large movements. Alternative in silico approaches assess protein motions through the protein residue network  (Atilgan, Turgut & Atilgan, 2007)  or dynamical correlations from MD simulations  (Ghosh & Vishveshwara, 2007; Dixit & Verkhivker, 2011). Another noticeable development is the MOdular NETwork Analysis (MONETA), which localizes the perturbations propagation throughout a protein structure  (Laine, Auclair & Tchertanov, 2012). Here we use an alternative yet powerful approach based on small prototypes or ''structural alphabets'' (SAs). SAs approximate conformations of protein backbones and code the local structures of proteins as one-dimensional sequences  (Offmann, Tyagi & De Brevern, 2007).
Protein Blocks (PBs)  (De Brevern, Etchebest & Hazout, 2000)  are one of these SAs  (De Brevern, 2005; Etchebest et al. , 2005; Joseph et al. , 2010). PBs are composed of 16 blocks designed through an unsupervised training performed on a representative non-redundant databank of protein structures  (De Brevern, Etchebest & Hazout, 2000). PBs are defined from a set of dihedral angles describing the protein backbone. This property makes PBs interesting conformational prototypes of the local protein structure. PBs are labeled from a to p (see Fig. 1A ). PBs m and d are prototypes for central α-helix and central β-strand, respectively. PBs a to c primarily represent β-strand N-caps and PBs e and f, β-strand C-caps; PBs g to j are specific to coils, PBs k and l are specific to α-helix N-caps, and PBs n to p to α-helix C-caps  (De Brevern, 2005).
, 2015). To go further, we recently used PBs to analyze long-range allosteric interactions in the Calf-1 domain of αIIb integrin  (Goguet et al. , 2017). To our knowledge, the only other related approach based on SA to assess local deformation is GSATools  (Pandini et al. , 2013) ; it is specialized in the analysis of functional correlations between local and global motions, and the mechanisms of allosteric communication. Despite the versatility of PBs and the large spectrum of their applications, PBs lack a uniform and easy-to-use toolkit to assign PB sequences from 3D structures, and to analyze these sequences. The only known implementation is a an old C program not publicly available and not maintained anymore. Such a tool not being available limits the usability of the PBs for studies where they would be meaningful.
We thus propose PBxplore, a tool to analyze local protein structure and deformability using PBs. It is available at https://github. com/pierrepo/PBxplore. PBxplore can read PDB structure files  (Bernstein et al. , 1977) , PDBx/mmCIF structure files  (Bourne et al. , 1997) , and MD trajectory formats from most MD engines, including Gromacs MD topology and trajectory files  (Lindahl, Hess & Van der Spoel, 2001; Van der Spoel et al. , 2005). Starting from 3D protein structures, PBxplore assigns PBs sequences; it computes a local measurement of entropy, a density map of PBs along the protein sequence and a WebLogo-like representation of PBs. In this paper, we first present the principle of PBxplore, then its different tools, and finally a step-by-step user-case with the β3 subunit of the human platelet integrin αIIb β3.
DESIGN AND IMPLEMENTATION PBxplore is written in Python  (Van Rossum, 1995; Python Software Foundation, 2010; Bassi, 2007). It is compatible with Python 2. 7, and with Python 3. 4 or greater. It requires the Numpy Python library for array manipulation  (Ascher et al. , 1999) , the matplotlib library for graphical representations, and the MDAnalysis library for molecular dynamics simulation files input  (Michaud-Agrawal et al. , 2011; Gowers et al. , 2016). Optionally, PBxplore functionalities can be enhanced by the installation and the use of WebLogo  (Crooks et al. , 2004)  to create sequence logos. PBxplore is available as a set of command-line tools and as a Python module. The command-line tools allow easy integration of PBxplore in existing analysis pipelines. These programs can be linked up together to carry out the most common analyses on PB sequences to provide insights on protein flexibility.
In addition, the PBxplore Python library provides an API to access its core functionalities which allows the integration of PBxplore in Python programs and workflows, and the extension of the method to suit new needs. PBxplore is released under the open-source MIT license (https://opensource. org/licenses/MIT). It is available on the software development platform GitHub at https://github. com/pierrepo/PBxplore. The package contains unit and regression tests and is continuously tested using Travis CI (https:// travis-ci. org/). An extensive documentation is available on Read the Docs  (Holscher, Leifer & Grace, 2010)  at https://pbxplore. readthedocs. io.
Hence, the first two N-terminal and the last two C-terminal residues are always assigned to PB Z. The program PBassign reads one or several protein 3D structures and performs PBs assignment as one PBs sequence per input structure. PBassign can process multiple structures at once, either provided as individual structure files or as a directory containing many structure files or as topology and trajectory files obtained from MD simulations. Note that PBxplore is able to read any trajectory file format handled by the MDAnalysis library, yet our tests focused on Gromacs trajectories. Output PBs sequences are bundled in a single file in FASTA format.
PBcount During the course of a MD simulation, the local protein conformations can change. It is then interesting to analyze them through PB description. Indeed, as each PB describes a local conformation, the variability of the PB assigned to a given residue throughout the trajectory indicates some local deformation of the protein structure. Thus, once PBs are assigned, PBs frequencies per residue can be computed. The program PBcount reads PBs sequences for different conformations of the same protein from a file in FASTA format (as outputted by PBassign). Many input files can be provided at once. The output data is a 2D matrix of x rows by y columns, where x is the length of the protein sequence and y is the 16 distinct PBs. A matrix element is the count of a given PB at a given position in the protein sequence.
PBstat The number of possible conformational states covered by PBs is higher than the classical secondary structure description (16 states instead of 3). As a consequence, the amount of information produced by PBcount can be complex to handle. Hence, we propose three simple ways to visualize the variation of PBs which occur during a MD simulation. The program PBstat reads PBs frequencies as computed by PBcount. It can produce three types of outputs based on the input argument(s). The first two use the matplotlib library and the last one requires the installation of the third-party tool Weblogo  (Crooks et al. , 2004). PBstat also offers two options (--residue-min and --residue-max) to define a residue frame allowing the user to quickly look at segments of interest. The three graphical representations proposed are: • Equivalent number of PBs (N eq ).
Python module PBxplore is also a Python module that more advanced users can embed in their own Python script. Here is a Python 3 example that assigns PBs from the structure of the barstar ribonuclease inhibitor  (Lubienski et al. , 1994)  The documentation contains complete and executable Jupyter notebooks explaining how to use the module. It goes from the PBs assignments to the visualization of the protein deformability using the analysis functions. This allows the user to quickly understand the architecture of the module.
RESULTS This section aims at giving the reader a quick tour of PBxplore features on a real-life example. We will focus on the β3 subunit of the human platelet integrin αIIbβ3 that plays a central role in hemostasis and thrombosis. The β3 subunit has also been reported in cases of alloimmune thrombocytopenia  (Kaplan, 2006; Kaplan & Freedman, 2007). We studied this protein by MD simulations (for more details, see references  (Jallu et al. , 2012; Jallu et al. , 2013; Jallu et al. , 2014) ). The β3 integrin subunit structure  (Poulain & De Brevern, 2012)  comes from the structure of the integrin complex (PDB 3FCS  (Zhu et al. , 2008) ). Final structure has 690 residues and was used for MD simulations. All files mentioned below are available in the demo_paper directory from the GitHub repository (https://github.
Each mutated structure barely showed any modifications at the mutation sites while distant conformational changes were detected by PBxplore. These results highlight the relevance of MD simulations in the study of both short and long range effects on protein structures, and demonstrate how PBs can bring insight from such simulations. In this context, we propose PBxplore, freely available at https://github. com/pierrepo/PBxplore. It is written in a modular fashion that allows embedding in any PBs related Python application.
SOFTWARE AVAILABILITY PBxplore is released under the open-source MIT license (https://opensource. org/licenses/ MIT). Its source code can be freely downloaded from the GitHub repository of the project: https://github. com/pierrepo/PBxplore. In addition, the present version of PBxplore (1. 3. 8) is also archived in the digital repository Zenodo  (Barnoud et al. , 2017). • Pierre Poulain conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Funding This work was supported by grants from the  National Institute for Blood Transfusion (INTS, France)  and the  Lab of Excellence GR-Ex  to  Jonathan Barnoud ,  Hubert Santuz ,  Pierrick Craveur ,  Agnel Praveen Joseph ,  Vincent Jallu ,  Alexandre G. de Brevern  and  Pierre Poulain ; and from the  Ministry of Research (France) ,  University Paris Diderot ,  Sorbonne Paris Cité  (France),  National Institute for Health and Medical Research (INSERM, France)  to  Jonathan Barnoud ,  Hubert Santuz ,  Pierrick Craveur ,  Agnel Praveen Joseph ,  Alexandre G. de Brevern  and  Pierre Poulain. The labex  GR-Ex , reference  ANR-11-LABX-0051  is funded by the  program ''Investissements d'avenir' ' of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. Alexandre G. de Brevern  was also supported by the  Indo-French Centre for the Promotion of Advanced Research/CEFIPRA  grant (number  5302-2 ).
Study design This mixed-method research combines a large quantitative study alongside in-depth qualitative case studies  [41]  (See Fig. 2 ). The case study design is used because we seek to understand the contextual conditions which we think will influence the phenomena studied. By combining multiple data sets, it should provide complete and in-depth picture of the phenomena  [42]. The sample This research will based on city members of the French WHO Healthy Cities Network, set up on the initiative of the WHO Regional Office for Europe. The French Healthy Cities Network, which is one of the most developed and structured networks in Europe, has been chosen as a sample for this study as it covers health issues across various fields, policies and programs conducted by the city members. The members of the French Healthy Cities network take a keen interest in health and health inequities.
Work package A (QUANT): Baseline study: Healthy Cities network characteristics (N = 80 cities) A large quantitative study (see Table  1 ) will be done across the 80 city members of the French WHO Healthy Cities Network. A database will be developed. This database will be used to select 6 city cases (first step in Work package B). Furthermore, drawing on the data from the Table  1  Goals, dimensions, methods and tools for work package A Goal: • constitute a baseline database of each healthy city according to its socio-demographic, geographic, green spaces characteristics and characterize the HiAP approaches in the 80 cities • build city profiles according to its socio-demographic, geographic, green spaces characteristics and characterize the HiAP approaches in the 80 cities.
Dimensions analyzed Methods and tools Elements of description of each city that will be used as context factors concerning the demographic situation and dynamics, socio-economic heterogeneity, urban segregation (a dissimilarity index will be constructed by an aggregation of different indicator: unemployment rate, worker's rate, higher education rate and median income at infra-communal scales) Web-based data collection using the national socio-demographic base (INSEE. Data will be analyzed with Excel software Information about urban green spaces (quantity, surface area, etc. ) to appreciate the coverage of urban green spaces in each city (relevance of urban green space in the city, share of urban green space per inhabitant, etc. ) Web-based data collection using the national geographic and topographic base (IGN) and the participative Open Street Map data set will be used.
Data will be analyzed with Excel software and mapped with Philcarto and ArcGis programs HiAP approaches will be studied according to the degree of maturity drawing on the works of Storm et al. [51]  and to the extent to which they address health inequities and green spaces in policy making Two different online surveys: one for the elected officials and the practitioners responsible for the cities' heath issues, and the other for the officials in charge of green space management 80 cities, city profiles will be identified and will contribute to the transferability of results (Work package D). Work package B (QUAL-quant): Characterization of the HiAP approach and governance and green space policies (N = 6) In order to develop the case studies, the aim is to select 6 city profiles based on socio-economic and urban green spaces characterization and a typology regarding the HIAP process in the cities and the inclusion of health into green spaces policy.
The Advisory Board will determine the number and nature of the criteria to put into the preference model. Once the 6 cities are identified, the qualitative indepth study for each city will be developed as presented in Table  2. Work package C (QUAL -QUANT) -Geographical characterization of green spaces and green spaces use (N = 6) In order to characterize the green spaces and their use in each one of the cities, the in-depth study of the cities will be completed with this workpackage. The aims, dimensions and methods are presented in Tables  3  and 4.
Work package D: Case study synthesis and transferability The collection of cases will be cross-analyzed (WP D1). Each case study will deliver: 1. a typology of cities regarding their local political system and HiAP governance concerning green spaces and health inequities (results of the socio-political analysis) which will be crossed with the distribution of green spaces across the city (results of the spatial analysis). This typology will help to identify different city profiles 2. a typology of green spaces for each city regarding their locations, representations, uses, amenities (i. e. defined criteria during the study) which will be crossed with the level of use of green spaces. In order to answer our main research question, those two types of typologies will be crossed to identify: for similar "green space" types and urban context but different "modes of governance", which mode of governance seems to favor wellbeing and tackle the health inequities  • Characterize the cities according to the HiAP process of each case: levels of governance implemented (internal, external) and the strategies related to health, policy making processes related to green spaces and urban planning (44,45) (WP B1).
WP C1 analyze the spatial distribution of green spaces in the city, the amenities, and the appropriateness with the social and demographic characteristics of cities and districts, their accessibility, configurations, nature and surface: -Size, topography (steeps slopes for example) and type (child recreation parks. ) -availability (walkable travel times, signposts, number of entrances, number of bus stops existing within 200 m of a green space, etc. ) -Location (spatial concentration vs. equitable spatial distribution of GS in towns for example) and neighborhood types (e. g. are they located in poor or rich districts, young or old populations?) -Broader context (are cities and their GS located near large national parks, the sea, mountains … or in a very dense and mineral area?) Urban green spaces will be chosen in each of the six cities in order to ensure data representativity and comparability.
These spaces will be identified by green space managers and municipal health departments as representing some form of strategy to reduce health inequities. Once these spaces have been identified, quantitative and qualitative data will be used for mapping and analysis: -IGN (BD-Topo base) and Open Street Map data to appraise the coverage of urban green space in each city (relevance of urban green space in the city, share of urban green space per inhabitant …). We will also measure the potential availability of urban green spaces potential availability using road network data and urban public network data -IGN (BD-Topo base) and Open Street Map data to appraise the location of each city and its green spaces in relation to important, or significant, natural places such as mountains, sea, and preserved national parks -Qualitative study results from observation in situ.
Field data will be necessary for analyzing the urban green space type and topography, accessibility (number of entrances), etc. Finally, collected geographical data will be integrated into and processed by a geographical information system (SIG). The data will be used according to traditional spatial analysis methods at different scales (city, districts, and neighborhoods). for similar "modes of governance" but different "green space" types and urban context, which configuration of green spaces seems to favor wellbeing and tackle health inequities. The aim is to set out contextualized recommendations for all the network cities, based on the lessons from the 6 cases (WP D2). We will first establish the profiles of the 80 cities according to 4 dimensions: -Contextual factors: socio-demographic situation and dynamics, position of the city in the French Healthy Cities Network, location of the city in relation to important or significant natural places such as mountains, sea, and preserved national parks, socio-economic characteristics: the socio-economic heterogeneity and urban segregation, the HiAP approach's degree of maturity, green space characteristics: size, nature, context and accessibility.
Lastly, the conditional a posteriori laws for hyperparameters are Gamma distribution p(γ x | x, b) ∝ γ - (N -1) 2 x exp -γ x Dx -b 2 2 (29) and p(γ b | b) ∝ γ -2N b exp -γ b 2N c=1 |b c | 2. ( 30 ) These scalar distribution are simulated with standard toolbox available in Matlab or Numpy for instance. The SEAP and EAP are approximated by the empirical mean of K samples, after discarding samples of the burn-in period, x EAP ≈ 1 K K k=1 x (k) , b EAP ≈ 1 K K k=1 b (k). (31) The pixel variance, diagonal of the posterior covariance, is also approximated by σ 2 EAP = diag(Σ x,EAP ) ≈ 1 K -1 K k=1 x (k) -x EAP 2. (32) IV. RESULTS The proposed method is tested on the "cameraman" image, a N = 256×256 pixels image with strong discontinuities. The observation operator has a squared impulsionnal response (IR) of size 5 × 5 pixels.