dataesr/scientific-paragraphs-categorization huggingface dataset:
3 ' 3 seq-RNA Profiling (3'SRP) 14 datasets were obtained from Kilens et al (2018) and Castel et al (2020). Concerning the plots made from 3'SRP datasets, the values lower than 0. 1 mRNA molecules per million of mRNA molecules have been arbitrarily represented as 0. 1 mRNA molecules per million of mRNA molecules for convenience.
ACCEPTED MANUSCRIPT / CLEAN COPY laboratory to further analyze hNPSCs and hTSCs protein extracts. As a matter of fact, other unique peptides corresponding to extracellular domains of OR5M8 could potentially be obtained through a controlled sample digestion by some other enzymes. Interestingly there is another long unique peptide reported by MassIVE: ESVEQGKMVAVFYTTVIPMLNLIIYSLRNKNVKEALIK (mzspec:PXD022531:j7912_PDIA6. mzXML:scan:9162:ESVEQGKMVAVFYTTVIPM LNLIIYSLRNKNVKEALIK/3). Of note is that the mRNA corresponding to TRBV18 and OR5M8 were not or barely detected in the list of 24,849 transcripts generated during our transcriptomic analysis; which eventually makes the identification of these unique peptides and calls for a cross-validation by the trans-Proteomics.
Retrieval of protein sequence A web server NCBI was employed to download protein sequence of F. tularensis with the accession number (CAG44634. 1). The Physical and Chemical properties of the protein were determined by EXPasy ProtParm  [14]. To determine antigenicity of protein VaxiJen server 2. 0 was employed  [15]. VaxiJen performs the antigenicity analysis of a protein using the information based on physicochemical properties.
Molecular Docking The designed protein was docked with TLR9 (PDB id 3WPF) with help of Cluspro to nd interaction. Docking provides multiple complexes and is further analyzed by the Pymol server. PDBePISA and PDBsum have used a representation of different interactions. The H-bond result revealed the interaction formed between the residues are ASP259-SER122, ARG346-ASP127, ARG377-ASP127, PHE402-LYS130, HIS260-SER136, ALA261-LYS152, PRO262-LYS152, ARG482-TYR179, ASP424-TYR179, ARG482-ASP183, ARG482-ASP183, ARG470-ASN189, ARG470-LYS190, GLN399-ARG193, THR395-ARG193, ASN473-ASP204, LYS472-ASP204 and ASN466-LYS219 showing the distance of 2. 83, 2. 87, 3.
Different servers PROCHECK, ERRAT and were used for validations. Docking analyses were carried out by the Cluspro server and the PDB sum was used to generate a full set of PDBsum structural analyses. MD simulations were performed via Amber20. To gain high protein expression in the E. Coli K12 strain, Jcat software was utilized for optimization of codon to increase translation and transcription e cacy. Protein expression linked with GC and CAI of reverse translated optimized sequence. Via SnapGene software, constructed vaccine cloned in E. coli K12 strain plasmid pET28a (+). As a result, a reliable immunoinformatics technique was applied to build a stable and safe vaccination in this work. Vaccines, on the other hand, require scienti c validation and consensus.
This sound synthesis framework is specifically designed such that it facilitates the trial and error process of piano manufacturing. It can also be used to investigate the vibroacoustic behavior of piano soundboards, including the relationship between the mechanics of the soundboards and the resulting sounds in playing situation, which is paramount when designing instruments. The software is capable of producing the sound corresponding to a single note or a complete piece of music, at the price of a longer computation duration. This open-source SCAD program is made available for any piano maker or academic researcher in the form of a software, called MAESSTRO, 2  which offers several functionalities to assist the piano maker in the design process. The software is open source and the source code of the software is freely available  [19].
The different functionalities of the sound synthesis framework MAESSTRO are the following: (i) entering the geometry and the materials of the soundboard via a Graphical User Interface (GUI), (ii) feeding MAESSTRO with MIDI files to be synthesized, (iii) simulating numerically the physical phenomena involved in the production of piano tones, (iv) post-processing the software outputs, and (v) creating audio files of synthesized piano tones. Figure  1  represents the global functioning of the software. It consists in a sequence of software operations that yields output synthesized piano tones from inputs specified by the users, including the geometry and the materials of the virtual soundboards, the string set parameters, and the tones to be played. The normalized data about the geometry and the materials of the virtual soundboard can be defined with the help of a specifically designed GUI.
The modal basis of the virtual soundboard is computed with either a semi-analytical model  [11] , or a finite-element method  [12]. Both models use a light fluid approximation. To synthesize specific tunes, the user may directly give a MIDI file from which MAESSTRO extracts the tone information, namely the note index, the key activation and release instants, as well as the hammer initial velocity. A nonlinear finite-element model of string dynamics  [12]  is then used to compute the bridge excitation force applied by the struck strings. Using the elements of the modal basis of the virtual soundboard, we can then compute the soundboard dynamics and the acoustic radiation at any listening point specified by the user. This paper intends to demonstrate the possibilities of the software at characterizing the mechanical and acoustical behavior of the piano soundboards, and also to investigate the impact of structural modifications of soundboards.
In the last sections, we present examples of structural modifications that may be considered as naive. However, even if one admits that any maker is capable of predicting the sounding effect of such modifications, this prediction capability is itself the result of a rather long process of building plates, tables and sounding them in realistic conditions (excitation with strings under tension, for example): a lot of practical work. This tool simply shortens this process. 2 Functionalities of the MAESSTRO software 2. 1 Using the software Since the physical models of the MAESSTRO software may require long computations using large amounts of memory, we chose to propose to standard users to run their simulation on external servers that possess large memory capabilities. This solution also presents the advantage to remove the software installation process on the personal computer of each users, which may result in various OS compatibility issues.
For standard users, the software is accessible in the numerical platform Simulagora,  3  developed by the company Logilab. The user will have to ask for opening an account,  4  which comes with free hours of simulations. Then, the user just has to upload the data online and to run the simulation. The data include the geometry of the virtual soundboard, the string set parameters, the note or the piece of music to be played (stored in the MIDI file), and a material dictionary containing the mechanical properties of the materials used to build the soundboard.
Computer-Aided-Design of the virtual soundboard The software needs data about the geometry of the virtual soundboard and about the mechanical properties of its materials, which are gathered into a geometry file in a normalized format (JSON). In order to assist the user to build this geometry file, a specifically designed Graphical User Interface 5 has been developed in TypeScript with React language with the help of Logilab. The choice of developing our own GUI has been motivated by the fact that adapting geometric data from standard threedimensional CAD commercial software to our modeling would have added too much complexity, including naming convention of the soundboard components (panel, bridge, ribs, etc. ) to extract the corresponding volumes. The computation of the soundboard dynamics considers three main classes of structural components of the soundboard geometry, namely the main panel, the bridges and the ribs.
Computation of the modal basis Currently, the software proposes two different methods to compute the modal basis of the soundboard: a semianalytical method derived from Trevisan et al. [11] , which requires strong simplifications of the soundboard geometry, and a finite-element method, derived from the models proposed by Chabassier et al. [12]. For this paper, we chose to use the finite-element method, associated with the library Montjoie. 6 The mesh is generated automatically by the software GMSH  [20]  according to the geometry file created in the graphical user interface. The method to compute the modal basis of the soundboard is based on a Reissner-Mindlin model of a clamped soundboard at its boundaries. This choice has been motivated by the fact that the superstructures, i. e. the ribs and the bridges, are modeled by the thickness variation of the soundboard, assuming they are centered with respect to the neutral fiber, hence the choice of a thick plate model over a thin plate model.
The main objective of the software is to be able to reproduce sound variations due to structural modifications. Therefore, as an illustration of the different functionalities of the MAESSTRO software, we present in this paper a preliminary study that compares the mechanical and acoustical properties of different virtual soundboards only. The virtual soundboards consist in a reference piano, derived from a real piano, which is subject to several modifications to evaluate their mechanical and acoustical impacts. The chosen modifications include the increase of the panel thickness, the removal of half the ribs, and the removal of all the ribs. This results in four cases, namely the reference piano (RP) and the three modified pianos (MP1, MP2, and MP3). These modifications correspond to change in stiffness, namely a global increase for MP1, a local decrease for MP2, and both a global and local stiffness decreases for MP3.
Data availability statement The source code of the open-source software MAES-STRO  [19]  is freely available in the GitLab maesstro repository (https://gitlab. com/benjamin. elie/maesstro). The maesstro repository contains audio examples of synthesized piano tones (https://gitlab. com/benjamin. elie/maesstro/-/tree/master/examples/audio). Additional data are available on request from authors.
In fact, for small DM masses around 100 GeV, cτ 0 ( ψ0 ) > 100 sec at leading order even for λ ≲ 10 -5. However, this region is excluded by LHC constraints, so we do not consider BBN bounds in our analysis. In Fig. 5 , the colour code shows ∆ Ω 1s as defined in Eq. (  27 ). This indicates the importance of solving two Boltzmann equations instead of just one. We see that, for m χ around 100-200 GeV, the splitting into two dark sectors (1 = χ and 2 = ψ± , ψ0 ) is relevant already at λ ∼ (a few) × 10 -4. With increasing mass, the importance of the two-dark-sectors Figure  9 : Illustration of mass difference ∆m needed to obtain Ωh 2 = 0. 12. Points marked as crosses are excluded by SMODELS v2. 2. 0. (dark red points), a finely adjusted mass difference in the range of about 10-30 GeV is needed.
A precise calculation of the mass splitting among the triplet-like states as well as the inclusion of ψ± → χπ ± decays are important to that end. The new version of MICROMEGAS, v5. 3. 35, is publicly available at https://lapth. cnrs. fr/ micromegas/. It includes the STFM implementation together with a README explaining its usage, and a demo program (demo. c) illustrating some of the new functionalities presented in this paper. It also includes an updated interface to SMODELS v2. 2  [46]. The new MICROMEGAS routines relevant for co-scattering are described in detail in the appendix. An important caveat is that, so far, MICROMEGAS employs momentum-integrated Boltzmann equations only. For a precise calculation of the DM relic density including the effects of early kinetic decoupling, the full momentum-dependent Boltzmann equations should be solved, as advertised in  [30].
For the initialisation of the MICROMEGAS settings, one has to call the • YdmNEq(T,α) calculates the thermal equilibrium abundance at temperature T for particles of sector α, where α has to be presented by a text label. For instance, YdmNeq(T,"1"). • vSigmaN(T,channel) calculates the thermally averaged cross-section 〈vσ〉 in [pb•c] units. Here channel is a text code specifying the reaction, e. g. , vSigmaN(T,"1100") for 1, 1 ↔ 0, 0 processes. Note that to calculate 〈vσ〉 for processes with incoming bath particles, MICROMEGAS uses a short cut: to avoid calculating n0 for bath particles, it substitutes n0 = s in 〈vσ 2010 〉 in Eq. (20). To compensate for this factor, the rate of co-scattering processes (expressed in GeV).
ABSTRACT BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets.
While automatic reconstruction of neuron tree structures based on 3D microscopy imaging datasets was expected to be a feasible, if not easy, task for computers, experience during the last decades has underlined the difficulty of this challenge. Diversity among animal species, developmental stages, brain location, and image quality of microscopy datasets implies that algorithms with an impressive performance in small sets of images do not generalize well when applied to image volumes obtained by different research teams. Recent technological advances in labeling  (Cai et al. 2013; Nern, Pfeiffer, and Rubin 2015; Daigle et al. 2018) , tissue preparation  (Chung et al. 2013; Hama et al. 2011) , and imaging techniques  (Huisken et al. 2004; Verveer et al. 2007 ; A. Li et al. 2010 ) allow both small neuroscience laboratories and especially-large-scale brain science projects  (Chiang et al.
2011; Meissner et al. 2020; Markram 2006; Ecker et al. 2017; Stockton and Santamaria 2017)  to generate increasingly large fluorescence microscopy datasets for the reconstruction of single neurons. To this end, several automatic reconstruction algorithms have been proposed in the last decades  (Wang et al. 2011; Xiao and Peng 2013; Santamaría-Pang et al. 2015; Peng et al. 2017) , and individual groups have tackled the challenge of applying automatic neuron reconstruction by mainly focusing on their own datasets  (Winnubst et al. 2019; Peng et al. 2021). Even though further improving labeling and imaging quality is key for simplifying the task of automatic reconstruction  (Zhong et al. 2021) , manual correction and fine-tuning of the automatic results by experts are still needed, being a highly demanding bottleneck for the throughput of those projects.
A faithful annotation of the fine details of neuron morphology is particularly relevant for estimating potential connectivity, given that artifacts introduce biases in the estimation of connections between brain regions. This is even more relevant when whole brain modeling techniques rely on synthetic generation of neuronal populations based on available annotation data  (Shillcock et al. 2016). Similarly, fine-scale neuromorphological properties such as the radius of the neurite segments have a crucial impact on the electrical modeling of neurons, and seemingly small artifacts in the reconstruction process can lead to fully altered tree topology leading to unreliable simulation of signal integration and transmission. Thus, leveraging our understanding of the performance of the available algorithms and how they match with specific characteristics of different imaging datasets is an essential step toward reaching fully automatic neuron reconstruction  (Peng et al.
2015). Two common problems identified by neuroscientists interested in using reconstruction algorithms are that (1) imaging quality varies considerably among labeling and imaging techniques in different laboratories, and (2) the growing list of available algorithms complicates testing their suitability for specific tasks in a systematic and fast manner. Similarly, algorithm developers lack a standard set of images for benchmarking. The DIADEM challenge (https://diadem. janelia. org/history. html, Gillette, Brown, and Ascoli 2011) is an example of a successful initiative toward standardized benchmarking. However, the diversity of datasets tested in the challenge was relatively limited for studying the relevance of image quality features, and, since this challenge, several novel algorithms have been developed  (Peng, et al, 2015).
Thus, an interactive extendable framework for benchmarking existing and novel tools in diverse datasets was missing. The BigNeuron project was devised to address these challenges and advance toward a consensus on how to use and improve automatic neuron reconstruction tools. The results presented here summarize the goals reached through its completion, including (1) gathering and sharing a community-contributed, diverse and extensive set of 3D neuron imaging datasets, (2) providing gold standard annotations for a selected subset of images to be used as reference for bench testing, (3) organizing collaborative events for the development of novel automatic reconstruction algorithms, (4) providing a platform for benchmarking existing and novel algorithms against gold standard reconstructions, (5) integrating the obtained knowledge to improve the accessibility, accuracy, and efficiency of automatic reconstruction methods, and (6) providing a tool to suggest the best automatic reconstruction algorithm in external datasets based on our results.
An open bench-testing platform for neuron reconstruction We aimed to bench-test on a common open platform open-source, automated neuron reconstruction algorithms using large-scale, publicly available single 3D neuron image datasets acquired by diverse light microscopy systems. A total of 11 community hackathons and events were held in the first phase. The BigNeuron project utilizes neuron image stacks from different species (including fruitfly and other insects, fish, turtle, chicken, mouse, rat, and human) and nervous system regions such as cortical and subcortical areas, retina, and peripheral nervous system. The data includes multiple light microscopy modalities, especially laser scanning microscopy (confocal/2-photon) and brightfield or epi-fluorescent imaging. The neurons are labeled using different methods, such as genetic labeling and virus/dye/biocytin injection and span a broad range of types (e.
g. , unipolar, multipolar, releasing different neurotransmitters, and with a wide variety of electrophysiological properties). Many of these image volumes were generated by large-scale neuroinformatics projects such as the Allen Mouse and Human Cell Types projects (http://celltypes. brain-map. org/), Taiwan FlyCircuits (15,921 image volumes; http://www. flycircuit. tw/;  Chiang et al. 2011), and Janelia Fly Light (13,449  image volumes; https://www. janelia. org/project-team/flylight), but several data sets are also contributed directly by neuroscientists worldwide (Dataset gathering). In total, about 30,000 single-neuron 3D image volumes were gathered. To generate a representative dataset of various organisms, cell types, and imaging conditions, we randomly selected a small subset from the datasets of the large-scale projects.
Thus, 166 neurons were selected for the generation of a diverse set of manually curated "gold standard" reconstructions in annotation workshops and for posterior bench-testing of automatic reconstruction algorithms, defining the Gold166 dataset. As a result of the hackathons, 16 novel automatic reconstruction algorithms were developed and included in the largescale bench-testing phase. Some of the novel algorithms have been shown to be suitable for specific practical situations, and 13 of those were published in peer-reviewed scientific journals (Supplementary Materials Table  2 ). In the benchtesting phase, all ported algorithms were tested on the Gold166 dataset primarily using TITAN at Oak Ridge National Laboratory (USA), as well as supercomputers at Lawrence Berkeley National Laboratory (USA) and Human Brain Project (Europe).
The community effort (Fig. 1 ) of multiple institutions and single research labs involved in the project yielded several notable outcomes: • In a series of hackathons worldwide, developers learned (from each other) the relative pros and cons of various methods and how to leverage existing resources to refine or develop new algorithms. • The project has served as a practical guideline for neurobiologists in determining the suitability of specific reconstruction methods for a variety of image datasets and providing feedback regarding the utility of various sample preparation and imaging protocols. • Bringing neuron reconstruction methods and results together also encouraged method developers to collaborate, share, and reuse each other's software modules. • We developed one of the largest community-derived phenotype databases for single neurons, cataloging neuron shape and projection patterns from different species and different brain regions, and offering an opportunity to mine and query the patterns of neurons with distinct shapes.
In neuroscience labs worldwide, neuronal morphology image stacks are acquired through multiple microscopy modalities from a diverse array of experimental preparations. At the same time, a broad range of reconstruction algorithms is continuously developed using various programming languages, computational platforms, and custom testbeds. Harnessing powerful supercomputers and the open-source community, BigNeuron brings together tens of thousands of state-of-the-art 3D neuronal images with all major classes of automated tracing systems to generate an unprecedented number of digital morphological reconstructions. Expert crossanalysis of the initial bench testing dataset against a manually curated gold standard allowed us to generate a unique toolbox of morphometric measurements. Using this knowledge, we developed (1) an interactive web app that allows any user to find similarities between their data and the BigNeuron dataset, (2) a method for obtaining consensus reconstructions, and (3) a tool for neuroscientists to predict the best automatic reconstruction algorithm in any single neuron imaging dataset.
A web app to allow interactive navigation of heterogeneous bench-testing results The data generated through the bench-testing phase has produced an extensive collection of heterogeneous results. To allow user-defined interactive exploration of the data, we organized gold standard annotations, automatic reconstructions, their imaging datasets, and associated metadata in R data frames. For bench-testing, we measured the distance between all the automatic reconstructions and the respective gold standard annotations, and computed image quality metrics for each dataset. All the data were pooled in an interactive web-app (Shiny: https://linusmg. shinyapps. io/BigNeuron_Gold166 and https://neuroxiv. net/bigneuron/, Fig. 2 ) that allows users to perform principal component analysis, t-distributed stochastic neighbor embedding (t-SNE), correlation and clustering, visualization of imaging data and reconstruction in 2D projections, and benchmarking of automatic reconstruction algorithms in user-defined data subsets.