dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion We propose a simple but efficient framework, AED-Net, based on self-supervised learning method. Raw data from surveillance video clips are used to calculate optical flow maps; then their high-level features are extracted by PCAnet, which are further used to determine the anomalism of the local abnormal events and global abnormal events. From the results in the experiments we can see that the framework performs well on detecting both global abnormal event and local abnormal event. Further, after the LRN layer is added to address overfitting problem, the performance of this framework becomes better. That the framework achieves results better than the state-of-the-art methods indicates that it can effectively extract motion pattern from raw videos and detect anomaly due to it.
Acknowledgment This work is partially supported by the  National Key R&D Program of China  ( 2016YFE0204200 ), the  National Natural Science Foundation of China  ( 61503017 ), the  Fundamental Research Funds for the Central Universities  ( YWF-18-BJ-J-221 ), the  Aeronautical Science Foundation of China  ( 2016ZC51022 ), and the  Platform CAPSEC  (capteurs pour la sécurité) funded by  Région Champagne-Ardenne  and  FEDER  (fonds européen de développement régional).
With the advent of fully automated sample preparation robots for Hydrogen-Deuterium eXchange coupled to Mass Spectrometry (HDX-MS), this method has become paramount for ligand binding or epitope mapping screening, both in academic research and biopharmaceutical industries. However, bridging the gap between commercial HDX-MS software (for raw data interpretation) and molecular viewers (to map experiment results onto a 3D structure for biological interpretation) remains laborious and requires simple but sometimes limiting coding skills. We solved this bottleneck by developing HDX-Viewer, an open-source web-based application that facilitates and quickens HDX-MS data analysis. This user-friendly application automatically incorporates HDX-MS data from a custom template or commercial HDX-MS software in PDB files, and uploads them to an online 3D molecular viewer, thereby facilitating their visualization and biological interpretation.
Introduction By following the isotopic labeling of amide protons, which largely depends on their solvent accessibility and dynamics, Hydrogen-Deuterium eXchange coupled to Mass Spectrometry (HDX-MS) is now an established method for investigating ligand binding, protein-protein interactions, protein dynamics, conformational changes and folding/unfolding. Although considered a low resolution technique compared to other biophysical approaches, HDX-MS has the main advantage of not being limited in size  (Sheff et al. , 2017) , as the proteins of interest are digested before analysis of the labeled peptides. Originating from the 90s, HDX-MS has suffered from its reputation of being poorly reproducible and hard to implement, mostly due to very time-consuming sample preparation and data processing/analysis.
Over the past 30 years, academic research successfully developed several strategies to increase the robustness and throughput of the method. Dedicated algorithms were developed to allow the automatic extraction of raw data  (Kan et al. , 2011; Lou et al. , 2010; Pascal et al. , 2012; Weis et al. , 2006) , their statistical analysis  (Hourdel et al. , 2016; Lau et al. , 2019) , or both  (Rey et al. , 2014). However, HDX-MS really gained momentum when MS manufacturers allowed its transfer to biopharma industry, thereby opening a hitherto limited market. From then, commercially available pipelines including automated sample preparation, online proteolytic digestion and dedicated analysis software, such as DynamX (Waters) and HDExaminer (Sierra Analytics), democratized HDX-MS from a handful of specialized teams to structural MS, proteomics laboratories and eventually to industry.
These developments coincided with the keen interest of biopharmaceutical companies toward epitope mapping  (Wei et al. , 2014) , monoclonal antibodies and antibody-drug-conjugates structural characterization for quality control assessment  (Beck et al. , 2015). These all-in-one platforms shifted the methodological bottleneck from sample preparation to data analysis, at least concerning the extraction of the raw data. The output of an HDX-MS experiment consists in relative deuterium uptakes throughout the amino-acid sequence. Their mapping to 3D structures or models (when available) significantly facilitates biological interpretation. However, in most cases the outputs from the extraction data algorithms are Python scripts that need to be run on a PyMOL console. To our knowledge, MSTools  (Kavan and Man, 2011)  and MEMHDX  (Hourdel et al.
, 2016)  are the only web applications that enable the 3D visualization of HDX-MS data. However, both require manual reformatting of the deuteration data. Furthermore, most recent software such as Deuteros  (Lau et al. , 2019)  or MEMHDX  (Hourdel et al. , 2016)  are restricted to differential analysis only (comparison of two states) and do not allow the visualization of all the measured peptides but only the ones passing a defined statistical threshold. We think that visualization of raw deuteration data for each single condition is a key process in HDX-MS data inspection and interpretation and should be performed alongside visualization of differential deuteration between experimental conditions. To provide a solution that can be readily accessible to most HDX-MS users or neophyte collaborators, we developed a standalone tool to visualize, inspect and compare either individual (single condition) or differential (subtraction between two conditions) data from any HDX-MS software.
HDX- Viewer is an open-source web application leveraging modern web browser features such as the recent WebGL 3D rendering API. Written using HTML5 technologies, it relies on several Javascript libraries: NGL Viewer (v2. 0. 0-dev. 33) (Rose et al. , 2018)  for the 3D visualization of PDB files, MSAViewer (v1. 0. 3)  (Yachdav et al. , 2016)  for the visualization of the protein sequences and gif. js (v0. 2. 0) for the recording of animations in the GIF format.
The different time points can be visualized independently with the embedded 3D viewer. The user can rotate the structure, tune the color scale to enhance the rendering and define the undetected regions and background colors, as well as the mode of structural representation. Animation options are also available. Finally, the protein sequence(s) appear in the bottom of the window, with the same coloring scale as the 3D display. The resulting image/video can be exported with a simple click on the button 'Export as PNG' or 'Record GIF'. We believe HDX-Viewer bridges the gap between manual curation of raw HDX data and their biological interpretation based on structural knowledge. Together, it facilitates and significantly quickens protein analysis using HDX-MS.
Funding This project was supported by the  Region Occitanie  and the  French Ministry of Research :  Investissement d'Avenir Infrastructures Nationales en Biologie et Santeprogram (ProFI, Proteomics French Infrastructure project)  [ ANR-10-INBS-08 ]. Novo Nordisk Foundation Center for Protein Research  was supported financially by the  Novo Nordisk Foundation  [Grant Agreement NNF14CC0001].
-Algorithmic method for obtaining irreducible and non-redundant architectures. We aimed at generating a recombinase device database composed of irreducible and non-redundant architectures and without chiral pairs of architectures. An irreducible architecture is an architecture in which no part can be removed without changing the logic implemented (e. g. Fig  S3B ). A non-redundant architecture is an architecture which implement a Boolean function which is not simplifiable into a Boolean function with a reduced number of inputs. A chiral pair of architectures corresponds to two architectures which are reverse-complement from each other (e. g. of chiral structures Fig  S2C ). We performed the generation and sorting at the level of functionalized structures, and the obtained set of irreducible and non-redundant functionalized structures was then converted in architectures (Fig S1 ).
-Database analysis. We performed the database generation for up to 4 inputs. We obtained 18,163,227 architectures implementing 3,608 P-classes corresponding to 59,820 logic functions. We found that using our specifications, all 1-, 2-and 3-input functions and P-classes were implementable (Table  1 ). On the other hand, 92. 25% of the 4-input logic functions were implementable, corresponding to 90. 4% of the 4-input P-classes (the discrepancy between the number of functions and P-classes arise from the fact that P-classes contain a different number of functions). S3 ).
Influence of design specifications on P-classes implementability Previously, various strategies have been defined for the design of recombinase-based devices using either only excision  11, 12, 26  or mainly inversion and terminator based elements 10. In our database, we generated all possible designs using both excision and inversion, and a flexible use of the previously-defined biological parts. By sorting these architectures, we obtained the percentage of P-classes implementable with restrictive design criteria corresponding to the previous works implementing recombinase logic 10,11.
By filtering the database for architectures possessing a single gene in the 5' or 3' extremities, we found that all 3-input P-classes and 57% of 4-input implementable P-classes are implementable using this design. Additionally, we tested the effect of combining criteria two by two, and found that for stringent criteria such as the use of only excision, the combination with another criteria drastically reduced the number of implementable P-classes (Fig S7  -8 ).
-A web-interface to sort architectures according to user-defined constraints. Based on this large database, we created a user-friendly web-interface allowing the users to obtain all architectures implementing logic functions of their choice (Fig  6 ). For each architecture, the various specifications of the design are listed, such as the number of inputs, inversions, excisions, genes, terminators, and promoters, and the total number of parts. Additionally, the approximate length of the architecture is specified (calculated by considering as lengths: 20bp for a promoter, 40bp for a terminator and an integrase site, and 1 Kbp for a gene). We also specify if the architecture respects or not design criteria of interest, such as having the gene at the extremity of the construct (5' or 3'), the absence of asymmetric terminators, or the absence of promoters facing each other.
-A reduced set of 16 NP-equivalent architectures based on DNA inversion to implement all 3-input functions. For most logic functions, the database provides many different possible architectures (with a median of 252, and a maximum of 1. 4 million for the logic function in supplementary text 3. 6). An important gap remains between theoretical designs and well-behaving biological devices, and experimentally testing all proposed architectures is challenging. We therefore wanted to identify a method enabling us to reduce the number of biological devices to be tested and optimized, while maximizing the number of implementable logic functions. We already used permutation classes to reduce the number of generated architectures by not assignating integrase sites to inputs. Here we built a minimization strategy based of NP-classes, which corresponds to class of logic functions which are equivalent by permutation and negation of inputs 24.
DISCUSSION Here we took a systematic approach to explore the design space of recombinase logic devices. We devised a formal language to express and interpret the output state of genetic devices, along with several strategies allowing us to minimize the number of generated constructs to a non-redundant and irreducible set. We obtained a total of ~19 million architectures, to date the largest database of biological logic designs, implementing a total of 59,820 Boolean functions (corresponding to 3,610 P-classes). We proved that all 3-input and 92% of 4-input Boolean functions are implementable in single cell using integrase-based devices within the constraints of using one integrase and one pair of recombination sites pair per input. Most P-classes can theoretically be implemented by hundreds of architectures.
To navigate within the millions of architectures, we constructed a web-interface allowing users to obtain all architectures implementing a logic function of interest with the possibility of sorting them according to specific design and biological constraints. This web interface will help biological logic designers to obtain minimized integrase-based logic circuits operating in single-cell. Yet, for each function, myriad architectures with highly-divergent designs are possible. Many will not be functional, and not all possible designs can be tested, making the obtention of working logic devices from the database challenging. To circumvent this issue, we reasoned that characterizing and optimizing a single inversion-based device per NP-class would approximate the characterization of all logic devices from this NP-class.
The semantic we created, along with our semantic concatenation rules, support unambiguous interpretation of genetic construct functionality. The reduced set of semantics proposed here could be extended to allow interpretation of larger set of genetic constructs, and support numerous synthetic biology applications. While our minimization strategies allowed us to reduce the generation time to 1h30 for 4-input logic, this systematic generation method is unlikely to be applied for highest number of inputs due to the enormous computation time required (more than one year for 6 inputs). To obtain logic devices responding to a higher number of inputs, future work might focus on extracting from the database systematic design rules generalizable to N-inputs. Additionally, several characterization and optimization cycles could help determine rules for designing architectures responding to an increasing number of inputs.
-Database creation and Recombinator web-interface. The database construction is decoupled from the generation as the used high performance computer does not have any database management system. From the text files containing the functionalized structures, we created a database satisfying the two normalization form  29  which permit to not have redundancy in the database. We decomposed the database in five tables: Boolean functions, P-classes, semantics, structures and functionalized structures (Fig  S10 ). Each table contains in addition to its primary key the filtering criteria depending on this key, such as for the structure table, the number of excisions and number of inversions. We implemented this database generation algorithm in C++ 14 with Boost and libpq++ libraries. The database management system is PostgreSQL (code available in git directory: https://bitbucket.
Acknowledgments: We thank the synthetic biology group and members of the  CBS  and LIRMM for fruitful discussions. Funding: Support was provided by an  ERC   Starting Grant  "Compucell", the  INSERM Atip-Avenir program  and the  Bettencourt-Schueller Foundation. S. G. was supported by a Ph. D. fellowship from the  French Ministry of Research  and the  French Foundation for Medical Research (FRM)   FDT20170437282. The CBS acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01.
-Algorithm for architecture generation. The generation algorithm is summarized in Fig S3. To generate the architecture database, we first generated all the structures. Structures correspond to well-balanced sequences of two sets of parentheses ([ ] and ( ) ) corresponding therefore to all the Dyck word of size 2N (with N the number of inputs) with the two sets of parentheses. For non symmetric structures, we removed one of the two structures of a chiral pair. The next step of the algorithm consists of assigning a semantic between parentheses. We defined variables as the space between the parentheses, each structure is composed of 2N+1 variables. We defined a semantic domain for each variable, corresponding to a list of semantic which can be assigned to this variable. Domains are composed of either all possible 26 semantics or a reduced set defined to reduce the generation of reducible functionalized structures (see supplementary text 3.
2), as an example, fGP is replaced by fG, fP and fN. If the obtained structures implement the same logical function than previously, the initial functionalized structure is reducible. The truth tables of irreducible functionalized structures are computed by generating all derived structures and computing the semantic of each structure and derived structure. By arbitrarily assigning each parenthesis to an input, one possible Boolean function is obtained and its redundancy can be tested. If the Boolean function cannot be simplified into a function with a reduced number of inputs, the functionalized structure as being non-redundant is saved with its associated Boolean function corresponding to one of the Boolean functions of the implemented P-class. We implemented the generation algorithm in C++ 17 with Boost library (code available in git directory: https://bitbucket.
Low distance P-G Boolean Architecture with distance P-G lower than 1000 From this file, python scripts were used to extract the percentage table (Fig5) and generated P-class cluster and distribution plots (Fig4, FigS4-S5-S6). For distribution plots, histograms were generated with 20 bins for P-class clusters and 40 bins for histograms corresponding to all P-classes. Additionally for the database, a file containing the full list of architectures and architecture characteristics was generated. This file was used to generate the two-by-two constraint tables (FigS7-S8-S9) using an automated python script.
-Selection of architectures for the 16 3-input NP-classes. To select one architecture based on inversion per NP-class, the web interface was used. We first generated the list of NP-classes and associated P-classes with one Boolean function per P-classes. From this list, we randomly selected a single Boolean function per NP-class and searched in the database for architectures implementing this Boolean function. We sorted this list of architectures according to the number of excision in decreasing order to select architectures based on inversion only. We then sorted architectures according to the following two boolean constraints: no cross promotion and the low distance between the promoter and the gene. We selected one or several architectures following as much as possible these two criteria and having the lowest number of parts.
Lemma 10 (see  [4] ). After O(n)-time and space preprocessing the first position where two strings u i,r (S j ) and u i,r (S j ′ ) differ can be found in O(n log n/m) time correctly with error probability at most 1/n 3. Proof. For m = O(log n) the conclusion is trivial. Assume otherwise. We start by building the suffix tree for the string T which takes O(n) time and space  [36, 17]. Furthermore, we augment the suffix tree with an LCA data structure in O(n) time  [12, 19]. Let ℓ = ⌈3n ln n/m⌉. We can find the first ℓ positions q 1 < q 2 < • • • < q ℓ where S j and S j ′ differ in O(ℓ) = O(n log n/m) time using the kangaroo method  [28, 15]. We set q r = ∞ if a given position does not exist. The idea of the kangaroo method is as follows. We can find q 1 by one query to the LCA data structure in O(1) time.
Introduction Exploring physics beyond the standard model (BSM) is one of the key scientific goals of the LHC. Simplified models have turned out to provide useful benchmarks for interpreting LHC results and investigating their implications for the open questions of today's fundamental physics. The public code SModelS  [1, 2]  provides a very efficient framework for this reinterpretation by decomposing the signal of an arbitrary new physics model (respecting a Z 2 symmetry or a larger symmetry with a Z 2 subgroup) into simplified-model topologies. This then allows us to apply the simplified-model cross-section upper limits or efficiency maps provided by the experimental collaborations (typically as a function of the BSM masses involved) in the context of the new model. 1  So far only BSM searches in final states with missing transverse momentum (MET) could be taken into account within SModelS.
Topologies terminating in neutral LLPs (including stable neutral BSM particles) lead to the conventional MET signatures, which constitute the bulk of supersymmetry (SUSY) searches already included in the SModelS database  [1, 2, 12]. In order to be able to test also topologies terminating in electrically or color-charged BSM states, we have added the CMS searches for lepton-like HSCPs, color-octet (gluino-like) R-hadrons and color-triplet (squark-like) R-hadrons at 8 TeV  [13]  and 13 TeV  [11]  centre-of-mass energies. For the 8 TeV HSCP results, we make use of the recasting provided in Ref. [10] , while a dedicated recasting was performed for the  13  TeV results (see Appendix A for details). This allowed us to compute efficiency maps for the eight simplifiedmodel topologies introduced in Ref.
[8] , which are included in the SModelS v1. 2 database. For the R-hadron searches we consider only the direct production topology 4  and make use of the crosssection upper limits from Refs. [11, 13]  for the cloud hadronization model (assuming a 50% probability for g-gluon bound state formation). These limits are also included in the SModelS v1. 2 database. The relevant topologies and their notation in SModelS v1. 2 are summarized in Fig. 1.
In a similar way, points with light squarks and heavy gauginos and higgsinos lead to long-lived squarks which can also be constrained by the R-hadron searches, as shown by the orange histogram. In order to illustrate the constraining power of combining results for multiple simplified-model topologies, we also display (dark blue histogram) the distribution of excluded points obtained using only the CMS limits for pair production of long-lived staus, gluinos and squarks. As can be seen, the number of excluded points in this case (∼ 200) is drastically reduced when compared to the one obtained with all the topologies included in SModelS. We point out, however, that the constraining power of SModelS is still limited by the number of simplified-model results contained in its database. The points from the pMSSM scan performed here display a large variety of topologies and many of them do not fall within the eight HSCP or the two R-hadron topologies included in the database.
Conclusions Long-lived particles have received increasing attention from both the experimental and theoretical communities, due to their novel collider signatures and possible connection to dark matter. In this Letter we discussed how some of the LHC searches for LLPs, interpreted in the context of simplified models, can provide a powerful means for constraining interesting dark matter scenarios. For this purpose, we presented an implementation of (lepton-like) HSCP and (gluino-and squark-like) R-hadron signatures into SMod-elS. The SModelS database was extended by official CMS simplified-model results from the 8 and 13 TeV runs of the LHC, as well as additional efficiency maps obtained from a recast of the CMS analyses. We then used the new version of SModelS, v1. 2, to investigate how these searches impact two concrete physics scenarios: the Inert Doublet Model and a gravitino dark matter model containing long-lived staus.