dataesr/scientific-paragraphs-categorization huggingface dataset:
Supporting comparative connectomics In the near future, VFB will ingest multiple large connectomics datasets with variable coverage and accuracy of neuron type annotation. BLAST-like algorithms, in the short-term NBLAST for morphology, but longer term supplemented by CBLAST  (Scheffer et al. , 2020)  for connectivity and potentially methods that use subcellular features  (Schubert et al. , 2019; Zinchenko et al. , 2022) , will be critical to help users to interpret this data by facilitating prediction and assignment of neuron types. For example, a user finding paths between untyped neurons from FlyWire using our circuit browsing tool will be able to use NBLAST to find predicted types for neurons in the circuit, where these exist in other reference data sets. We will also investigate adding precomputed predicted neuron types based on NBLAST scores, with appropriate caveats, as a way of making browsing more efficient.
Integrating connectomics with transcriptomics The ability to resolve neuronal cell types in Drosophila single cell RNAseq data to the same granularity as achieved when typing by morphology and connectomics is improving as larger numbers of cells are profiled  (Bates et al. , 2019)  as developmental data is integrated, and with the help of bulk scRNAseq data for cells marked with Split-GAL4 drivers and mappings from these to cell types  (Davis et al. , 2020; Kurmangaliyev et al. , 2020; Özel et al. , 2021). The fruits of these approaches are most apparent in the optic lobe where we now have transcriptomics profiles of 200 cell types and the first integrated analysis across transcriptomic and connectomic data is now available  (Kurmangaliyev et al. , 2020; Özel et al. , 2021). While VFB currently only has limited scRNAseq data available (see Figure  9 ), the number of annotated datasets is growing rapidly thanks to a collaboration with FlyBase and the EBI single cell expression atlas.
As the number of datasets and cell types covered by transcriptomics and connectomics data and mapped to split-GAL4 lines increases, mapping between datasets for combined analysis will become increasingly challenging. Providing uniform, standardized annotation of cell types and their classifications across all these data types and datasets puts VFB in a strong position to facilitate these combined analyses. The VFB web application provides mechanisms for browsing connections and finding paths in the connectomics data (Figure  8 ) and for rapidly navigating from this to transcriptomic profiles. More sophisticated analyses will be facilitated by accessing this data through the VFB_connect API.
Conclusions Virtual Fly Brain enables its users to search, browse, view, and download diverse, cross-integrated data relevant to developing and testing hypotheses about the circuit basis of complex behaviors in Drosophila. As the volume and diversity of both small and large Drosophila neurobiology datasets increases, and these are incorporated into VFB, the role of VFB as a data integrator will become increasingly important, especially for solving the problem of identifying neuron types in poorly annotated datasets and for finding reagents to target these neurons. Similar data integration issues are faced in large atlasing projects in other species, including major planned atlases of mouse, human and non-human primates  (Kaiser, 2022). The solutions developed by VFB are likely to prove useful in these cases too.
Acknowledgments Data providers: FlyCircuit: CC Lo, AS Chiang; FlyLight:  W Korf ,  G Meissner ,  R Svirskas ; FlyEM:  K Shinomiya ;  T Wolff ,  L Scheffer ,  S Takemura ;  H Otsuna ; , A Cardona ,  T Lee ,  Y Aso ,  S Hampel ,  S. Cachero ,  M-J Dolan ,  K Ito ,  J Kohl ,  CE McKellar ,  T Nojima ,  D Turner-Evans ,  M Ho ,  D Wood. Technical support: MetaCell:  D del Piano ,  G Idili ,  M Cantarelli. CATMAID:T Kazimiers ,  A Cardona Advisors :  K Ito ,  D Shepherd ;  M Landgraf ;  M Pankratz ;  G Tavosanis ;  I Horrocks ;  CJ Mungall ,  J. Balhoff ,  P Schlegel ,  V Hartenstein.
Data availability statement Publicly available datasets were analyzed in this study. All data is available, following FAIR data sharing standards, through the VFB website (https://virtualflybrain. org) and API (https://pypi. org/project/ vfb-connect/). The VFB site can be accessed through any computer + browswer with standard WebGL support, which can be tested at https://get. webgl. org/. The Drosophila Anatomy Ontology is available under a CC-BY-4. 0 license from http://purl. obolibrary. org/obo/fbbt. owl. All code is available under open licenses at https://github. com/ VirtualFlyBrain. Jupyter notebooks showing anaylsing the success of NBLAST and CDMIP scoresqueries in finding known split combinationsare available at https://github. com/VirtualFlyBrain/ VFB_similarity_import/tree/vFrontiers/stats.
The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.
Introduction The BumpHunter algorithm was originally proposed by  [1]. Its aim is to search for a localized deviation in a histogram with respect to a reference, and to provide its statistical significance after having accounted for the Look Elsewhere Effect  [2]. One of the advantages of this method is that there is no need to assume any signal model, making the search for deviations model agnostic. Such algorithms can have a lot of applications, and in particular in High Energy Physics (HEP) when searching for new particles. Indeed, one of the most used strategies for such searches is to look for a localized excess in the distribution of a variable of interest. For this reason, several examples of analysis using this algorithm can be found in the literature  [3]   [4]. In recent years, the Python programming language has gained a lot of importance in HEP analysis, especially with the emergence of advanced libraries dedicated to data analysis and Machine Learning.
For this reason, we decided to propose the first public implementation of the BumpHunter algorithm in Python to be included in Scikit-HEP  [5]. The objective we aim to reach is to propose an accessible tool that enables the use of the BumpHunter algorithm in a Python workflow, and that also provides several new features extending the original algorithm. The source code of this package called pyBumpHunter can be freely accessed  [6]. Examples of use-cases are available in the main repository. In this paper we present the basics of the BumpHunter algorithm as well as all the extensions provided, including a signal injection based sensitivity test, an extension of the algorithm to 2D histograms and a background normalization procedure. The multi-channel combination method proposed in this implementation is similar to the one originally proposed in  [1].
BumpHunting in 2D 4. 1 Principle The pyBumpHunter package gives the possibility to look for localized deviations in 2D distributions. Here, a 2D distribution corresponds to the distributions of 2 different observables that may be correlated. A 2D histogram is built by counting the events falling in regions of the 2D plane delimited by 4 bin edges. This extension of the original BumpHunter algorithm applies the same procedure described previously using the 2D histograms. In this case, the interval width is defined according to the two axes of the histogram, resulting in rectangular windows. For each width of the scan window, all the positions in the plane are tested, similarly to what is done with 1D histograms.
Conclusion BumpHunter is a useful algorithm that can have several applications in HEP. By evaluating both the local and global p-value of a localized deviation in a binned distribution, it allows for model independent searches for new physics signatures. The pyBumpHunter package was developed for anyone using an analysis workflow based on Python language. This tool is public, easy to handle and has been accepted in Scikit-HEP. In addition to the basic functionalities presented in  [1] , pyBumpHunter also provides several extensions of the algorithm, including signal injection based sensitivity check, side-band normalization, 2D scans and multi-channel combination. All of these features have been tested in order to evaluate their behaviour and performance. The results show the capacity of the tool to find the potential presence of a signal with good accuracy and efficiency.
In fact, research regarding M&S has recently expanded to the use of products that were not initially intended for SE. Software like commercial game engines has already been assessed by actors such as the Department of Defense, NATO, Audi, and the maritime sector  (Hjelseth et al. , 2015). This use of entertainment technology has started to be studied as a support to SE processes under the name of gamification and serious games  (Uskov and Sekar, 2014). Madni (2015)  states that game engine environments enable the creation of virtual worlds which aim to improve comprehensibility and transparency between system stakeholders. This is especially relevant to the goals of any HSI endeavor. Finally, it should be noted that there are commonalities between the HiTLS approach described above and research on Digital Twinss (DTs) design  (VanDerHorn and Mahadevan, 2021).
RFR qrow n orgniztionEwide ulture The HiTLS-based methodology we mentioned earlier is highly iterative and builds upon agile principles. However, some participants expressed their reservations about agile methods as they are traditionally applied and sometimes even imposed by customers. They deem agile methods like SCRUM too theoretical as they actually are intensive for developers and too punitive as the team feels like work is never finished between two successive sprints, making motivation decline. They also reported to us that some projects mix highly detailed initial requirements with agile system acquisition processes, which makes it hard to be clear about which baseline ultimately serves as the reference for system verification and validation. Finally, participants were concerned about which hierarchical management model was best suited to agile development, and how agile methods could apply to non software-intensive systems containing hardware components that are hard to redesign or rethink in an iterative manner.
DISCUSSION AND FUTURE RESEARCH Some of the outlined challenges mentioned above do not depend solely on the engineering team: they may stem from organizational complexity, regulatory constraints, and even the nature of relationships between stakeholders that may be tainted by conflicts of interest for financial and political reasons. Nonetheless, our results provide scope for further development towards a more human-related SE effort, especially regarding gaining better system knowledge during the acquisition phase. We believe that a framework that combines scenario-based design  (Rosson and Carroll, 2002)  with software-intensive HiTLS can improve the understanding of the human characteristics of the people involved. The following example gives some guidelines as to how such a framework could support the design of a virtual air traffic control center.
Moreover, designers could not tell which solution to adopt, nor could they analyze user activity when users get confronted with the adopted solution. An HiTLS-based framework can circumvent the limitations described above. At this stage, designers must decide on an allocation compatible with remote center conditions. They need an early tool which users can interact with. HiTLS are not new in the field of remote ATC centers  (Schier, 2016) , but they are often carried out within heavy camera-based simulation environments and with costly physical devices. This is where a more software-based simulation environment supported by tools like game engines could be a significant asset to a more flexible acquisition of system knowledge. In our landing gear check example, we could quite rapidly set up a remote environment in which visual verification of the landing gear state is carried out by a camera well positioned on the airfield.
It may be hard to determine whether the state of the gear should eventually be determined by the ATCo seeing the video signal sent by the camera, or by an image processing algorithm that determines this state automatically. Maybe the ATCo wants to have the freedom to set the automation level at run-time by themselves. If the system carries out the check, how does it deliver the signal to the ATCo? Should the ATCo get a video signal in any case? What happens if the landing gear is still up? And who is in charge of warning the pilot? These questions should be carefully discussed with domain experts. Then, the established scenarios should feed the simulation software to enable designers to assess human and system performance through HiTLS. There are many benefits to using an entirely game-based virtual simulation framework.
First, virtual worlds enable flexibility in scenario creation, as the simulated environment can provide a common unambiguous language to users, designers and even non expert stakeholders. They can exploit visual cues given by the simulated environment to communicate more efficiently and agree on scenario staging and conduct characteristics. The simulation configuration can also be easily changed after the HiTLS has been carried out. Since all simulation assets are virtual, no commitment to any particular device is necessary. As such, if the simulation shows that the camera-based landing gear check is unsuitable to user needs, the camera can be ditched immediately. Stakeholders can therefore explore and define many scenarios in one session. Game-based simulations are easy to set up, as off-the-shelf game engines tend to have many layers of abstraction, including graphical scripting languages that enhance their access to non-developers.
In this work, we proposed a variant of the osmostat implementation that we implemented in RASPA,  26  a software dedicated to simulation of adsorption in porous materials. Our ultimate goal would be to employ in the future our osmostat version for the study of various electrolyte insertion in porous materials in equilibrium with a saline reservoir. The paper is organized as follows. First, we proposed another version of chemical potential calibration in the semigrand canonical ensemble (∆µ, N, V, T ) by using the Widom insertion technique with NCMC move to transform alchemically water into an electrolyte unit. Then, in order to assess the accuracy of our chemical potential calibration, NCMC move is used to study thermodynamics of electrolyte solvation at infinite dilution by calculating the free energy of water solvation with our modified Widom method.
General simulation methodology We ran molecular dynamics (MD) and Monte Carlo (MC) simulations using the RASPA software. 26 We used a combination of Lennard-Jones and electrostatic potentials. Water molecules are represented with the rigid TIP4P model  27  and the electrolytes are modeled as charged particles that differs by their Lennard-Jones parameters and charges. We considered the following monovalent electrolytes which are: LiCl, NaCl, KCl, RbCl, CsCl, KF, KBr and MgCl 2 , CaCl 2 , SrCl 2 and BaCl 2. The details of the force field are given in the SI. 28, 29  used Lorentz-Berthelot mixing rules for cross-terms in Lennard-Jones potential, and Ewald summation was used to account efficiently for electrostatic interactions. We used a cutoff of 8. 0 Å for both the Lennard-Jones potential and the separation between real space and Fourier space in the Ewald summation.
We chose to develop and test our own version of the osmostat in RASPA to have access of functionalities already implemented in the software for studying adsorption of electrolytes solutions in nanoporous materials. We plan to use our osmostat to simulate adsorption of various electrolyte solutions in zeolithes or MOFs that requires calibration of osmostat of different electrolytes solution over a wide range of concentration, which was the main goal of the current work. This opens the route for probing thermodynamics of electrolyte intrusion, pression intrusion and structure of intruded electrolyte solution in nanoporous material which will be the object of the next study. However, because of the choice of our empirical force field that is not optimal for divalent cations, we do not focus on reproducing precisely the structure of divalent cation electrolytes although we expect to grasp the thermodynamics and thus concentration of divalent cation electrolyte in porous materials.
In addition, a version of RASPA with implementation of the osmostat will be provided therein. As a last remark, the osmostat was tested on relatively small boxes of monovalent or divalent-cation aqueous electrolytes as a proof-of-concept because RASPA is a sequential program and it would be time consuming to consider larger systems. In this regards, zeolites/MOFs are in most cases periodic so that only a supercell of several repeating units is required for future MC simulations where only few hundreds of free particles are adsorbed which is fortunately feasible with RASPA. material.
In a first filtering step, we eliminate GO terms with the lowest frequency bias (<1. 8 fold change) and corresponding to very generic components or processes, which typically include a very high number of proteins. Our algorithm then retains the GO terms encompassing the largest number of proteins and maximizing the coverage of the original dataset (called BestN GO terms, see Methods). When possible, it complements this set of bestN GO terms with one or more smaller GO terms displaying the highest position 2 frequency biases (called BestF GO terms, see Methods). Each BestF is ultimately linked to the nearest BestN based on the overlap of their respective set of proteins. Fig  1B2  and 1B3 illustrates the results of this filtering for the GO category "components" and represents the amino acid usage biases observed in the 17 GO terms that were finally retained after processing the initial list of 72 GO terms.
Amino acids for which the absolute value of the HGT score is greater than 3 (p-value<10 -3 ) and the aspecificity score is lower than 5% are highlighted. Such values indicate a significant and specific bias of usage at position 2 revealing a possible selection pressure. (B) Several protein groups defined by their annotation in the Gene Ontology database have a specific Nterminal signature. (B1) GO term selection procedure illustrated on the particular case of N-terminal preferences for leucine. Twenty-eight GO terms describing components were extracted on the basis of their significant overrepresentation of leucine at position 2 assessed by hypergeometric test (HGT score > 4). After several filters, 7 of them were finally retained, including a single GO BestN term and 6 GO BestF terms (see more details in S1 Supplementary Methods and S2 Fig).
Antibodies The following antibodies were used for western blotting or immunoprecipitation: rabbit IgG-HRP polyclonal antibody (PAP; code Z0113; Dako), rabbit anti-Hsp60p and mousse anti-Atp2p polyclonal antibody (gift from J. P. Di Rago), anti-Pgk1p mouse monoclonal antibody (ab113687, abcam) and mousse moclonal anti-myc antibody (11667149001, Roche). sequences and their distribution vary depending on yeast strains  [74] , the 91 proteins annotated as transposons according to the Saccharomyces Genome Database (https://www. yeastgenome. org/) were removed from the proteome. Gene Ontology annotations were downloaded from the GO consortium database  [75, 76]  (http://current. geneontology. org/annotations/sgd. gaf. gz). For extensive analysis of mitochondrial proteins, the 1,190 proteins belonging to the mitochondrion Gene Ontology term (GO:0005739) were manually curated: proteins that were associated to mitochondria based on only one high-throughput experiment were removed, leaving 726 proteins for which association with mitochondria was strongly evidenced.
Curated mitochondrial proteins were then sorted depending on whether they carry an N-terminal mitochondrial targeting sequence, according to sequence annotations provided by the UniProtKB database  [73]. 361 over 726 mitochondrial proteins were annotated as carrying a MTS (S2 Table ). For the comparative analysis, 17 species were selected from the Saccharomycotina yeast lineage  [26]  (see Table E in S1 Supplementary Methods for the list of selected species). Ortholog groups recently established for 322 budding yeast species  [27]  were used to retrieve the 311 groups containing a mitochondrial protein from S. cerevisiae, among which 161 had an N-terminal MTS. More than 95% of these groups (298) contained orthologs for at least 13 of the 17 selected species, and all recovered orthologs for at least 9 of these species.
Analysis of the amino acid usage bias at position 2 in subsets of proteins of the Gene Ontology We have implemented a set of functions in R to detect gene ontology terms showing, among their associated proteins, a significant and specific amino acid usage bias at position 2. The gene annotations were downloaded from the GO consortium and stored in MGSA objects (R package MGSA  [77] ). The second residue associated with each protein was extracted from the downloaded proteome (UniProtKB database). For each GO domain (cellular component, molecular function, biological process), contingency tables were generated that count, for all GO terms, the number of proteins with a given amino acid at position 2. To avoid detecting amino acid usage bias due to proteins with the same N-terminal sequence, proteins sharing the same first 10 residues were grouped under the same identifier, which was counted only once.
Mass spectrometry analysis of sel-TRAP experiment Mass spectrometry analysis procedures, performed at Proteomics Core Facility of Institut Jacques Monod, are described in S1 Supplementary Methods, and mass spectrometry proteomic data were deposited on the ProteomeXchange Consortium  [78]  via the PRIDE partner repository with dataset identifier PXD034922. Mascot score distributions of immunoprecipitations from control (unlabeled BY4741) or protein A-labeled strains were compared to distinguish specifically interacting proteins from background. Based on the distribution of Mascot scores in the control, a cut-off score of 250 was chosen to retain less than 5% false positives. Only proteins with a Mascot score greater than 250 in the immunoprecipitation data of protein A-labeled strains were considered for further analysis.
Microrray analysis of sel-TRAP experiment The microarray data and the related protocols are available at ArrayExpress website (https:// www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-11772/) with the dataset identifiers E-MTAB-11772. After sel-TRAP experiment, input and immunoprecipitated (IP) RNAs were purified using the RNeasy Mini Kit (Qiagen). One μg of RNA from each sample was then reverse-transcribed and labeled with Cy3 or Cy5 dye using the indirect labeling procedure. cDNAs of input and IP samples were then hybridized on Agilent's ab 8×60K S. cerevisiae custom DNA chip (AMA-DID: 027945). Arrays were read using an Agilent scanner at 2 μm resolution and the signal segmentation was done using the feature extraction software (Agilent). The data was normalized without background subtraction using the global Lowess method  [79].