dataesr/scientific-paragraphs-categorization huggingface dataset:
Camera setup and calibration The Kinect camera should be facing the floor. Its front edge should be at 63 centimeters from the floor (floor is bottom of the cage considering no sawdust) to get the best resolution for a 50x50 cm cage. The Kinect should be connected to an USB3 port. The Kinect should also be taped to enable close observation as detailed in supplementary blueprints. The 6 LiveMouseTrackerCalibration program (demonstrated in the supplementary movie Hardware blueprint and assembly instruction) helps by positioning the camera perpendicular to the floor (Supplementary Fig. S2 ). It provides a live matrix display of distance measurements to finetune the pan/tilt of the camera. It also warns if the sensor is not taped. The provided setup provides images at a resolution of 1 pixel = 0.
RFID calibration The reading frequency of the antennas should be at 134 kHz, or 125 kHz depending on the RFID probed used (the frequency used is region dependent). Frequency should be accurate at -1/1 kHz to get the best reading range. This study and the provided blueprint have been designed and performed with 134 kHz probes (Glass probe ISO 11784/11785 2x12mm). Antenna reader enables the self measurement of the antenna reading frequency in a process called "Measure Unit Operating Frequency" (MOF). Use the provided RFIDReaderTest program while soldering to tune its frequency by adjusting wire length. Extra capacitor should be soldered on the reading board to switch to 125 kHz reading.
Triggered-RFID closed-loop control To read a passive RFID probe carried by an animal, the antenna induces an electric current in the probe which powers it. Then the probe modulates the received signal to transfer its identification back to the antenna. This whole process is performed in 100 milliseconds by the RFID reader hardware (code at rfid. RFIDAntenna. run). If several probes are powered up at the same time (i. e. several animals in the range of an antenna), the signal is jammed and no ID is read. The signal is also jammed if several antennas closer to 70 centimeters are activated at the same time. The RFID identification is in closed-loop with the video tracking. The software data structure contains a list of antennas with their respective COM port number, position and range in image field.
code: rfid. RFIDManager. activateAntennas. If an antenna gets faulty during the process, the user can see it on the interface (the corresponding antenna is drawn red). An antenna can get faulty because of an inopportun USB unplug or loss of power. The program is communicating with each antenna within its own process to avoid any lock while transmitting data. If a timeout is reached, the antenna is set as faulty. To monitor the quality of each antenna, the interface displays the number of reading attempts and the number of effective probe readings for each antenna.
Database description All the data are stored locally on a simple standalone database, in a redundant manner to target different levels of query, which complies with the Big Data principles  26. This means that the database can be accessed by any third party software such as R, Matlab, Python or Java and that the user can query the database on simple or complex features. No server is needed to store or query databases as the data are shared between experimenters by simple file transfer (contrary to dedicated database servers that would lock-in the data into labs). The software creates the database for the current experiment automatically as the tracking starts. We choose the SQLite database format as it is server free. The whole database is a single file, and each database represents an experiment, making it easy to backup, share, query and transform for users.
Tables are Animal(Id, RFID, Genotype, Name), RfidEvent(Id, RFID, Time, X, Y), Frame(FrameNumber, TimeStamp, NumParticle, Paused), Event(Id, Name, Description, StartFrame, EndFrame, IdAnimalA, IdAnimalB), Detection(Id, FrameNumber, AnimalID, Mass_X, Mass_Y, Mass_Z, Front_X, Front_Y, Front_Z, Back_X, Back_Y, Back_Z, Rearing, Lool_Up, Look_Down, Data). The data field is an XML text containing the full mask of the animal in zip format. To perform fast SQL Queries, we advise to add indexes to the database (Provided in Supplementary SQL script). Database can be explored with the software DB Browser for SQLite (http://sqlitebrowser. org/). We also provide scripts and examples to create all the figures and query shown in this paper from the software R (https://www. r-project. org/). Code: experiment.
Automatic tracking quality control Live Mouse Tracker records events in the database of the experiment to monitor the tracking. Five types of events are stored: 1. If the machine learning dedicated to the identification of tracks performs an association, an event "MACHINE LEARNING ASSOCIATION" is logged. 2. For the RFID, we log three events. a) If an anonymous track is assigned thanks to RFID, then "RFID ASSIGN ANONYMOUS TRACK" is triggered. b) If a RFID read corrects the identity of a track, then the "RFID MISMATCH" is triggered (and the track is corrected). c) Finally when all animals are identified, the RFID are used to control the identity of animals. In that case, at each reading confirming current track identity, a "RFID MATCH" is stored in database. 3. We can also monitor if an animal is not detected at a given frame.
Software Development Kit This work represents two years of work dedicated to the development of this complete solution. A large part of the solution consists in connecting to driver, grabbing stream, ensuring that streams are synchronized, dealing with java real time consideration, creating code probe to help debugging, creating display overlay, creating streamed data structure, dealing with calibration, output of data as video stream, database or network stream for immediate control of third party devices. For developing purposes, a number of side tools have been created, that are only used by the developers, not by the users. They consist in small programs that aim at helping the developers to debug code and test new features. Those tools are also provided in open source to build on top of the existing tracking.
Among those tools, developers will find a raw data recorder and player, which mimics a live kinect device, but allows to play several times the exact same film. A large part of those developments is not to said "scientific", but it is mandatory to build a final working product. We documented all this trunk of code so that newcomer can test their own techniques directly in this framework, in order to focus their energy on the problem of enhancing the phenotyping instead of working on all the side engineering problems. Finally, to ease distribution of new version, Live Mouse Tracker is developed as a plugin of Icy, which makes easy fork and further distributions.
Mice ProSAP1/Shank2 mutant mice (hereafter named Shank2 mutant mice) were initially described in a previous study  8. They were bred on a C57Bl/6J background (>12 backcrosses), and maintained by crossing heterozygous parents. We tested adult female littermates aged between 3 and 12 months of age (the experiments were spread over several months). For the single object exploration, we used 10 Shank2 +/+ and 8 Shank2 -/- female mice aged of 4 months in the first cohort. These mice also underwent a previous behavioral characterization with classical methods (data not shown). The second cohort included 12 Shank2 +/+ and 7 Shank2 -/-female mice and 8 Shank2 +/+ and 8 Shank2 -/-male mice (aged of 2. 5-4 months at the time of testing). For the object exploration in pairs, we used 12 Shank2 +/+ and 12 Shank2 -/-mice aged of 9 months at the time of testing in the first cohort (only two Shank2 +/+ and four Shank2 -/-did not undergo the single object exploration test).
In the second cohort, we constituted 4 pairs of Shank2 +/+ & Shank2 +/+ , 4 pairs of Shank2 +/+ & Shank2 -/-and 4 pairs of Shank2 -/-& Shank2 -/-. These pairs were together for at least one week before the experiment. We used the software Python 3. 6 (Python Software Foundation. Python Language Reference, version 3. 6; available at http://www. python. org) to compute distances and stretched attend posture in the experiments.
Software and system general description We developed the methods as a plugin of Icy. The code is developed in Java 8 (189 classes. 19934 lines of code. 950 Methods. 32 Packages. ). We put specific effort to make the code readable and documented for further extension. Experiments were first conducted on intel core i5-6400/2. 4GHz/16GB and then on Ryzen 1800x. We recommend this last version.
Motivation and review of the existing tracking methods We present here an overview of the current tracking methods used to follow mice and to extract behavioral information. We selected here the most representative ones of each type of method used. We first present the current needs in phenotyping studies and then we detail to what extent the different methods answer these needs. Phenotyping mouse models usually involves one individual screened through various behavioral tests to measure anxiety, learning, memory, or locomotion. Social aspects are determined in dyadic interactions, but these aspects are the least examined and remain to be complemented in existing phenotyping databases  27, 28. With the aim of documenting in the most comprehensive way the phenotype of an individual, studying social aspects within a group of animals would allow to document both dyadic interactions and more complex ones involving more than one other individual.
These two aspects of sociality can therefore be dissociated and will enrich phenotyping data  19, [29] [30] [31] [32]. Such approaches will increase the translational values of the phenotyping studies  24, 33. In clinical examination of patients with neuropsychiatric disorders affecting social relationships, both the initiation and the maintenance of these relationships are evaluated (ADI-ADOS for autism spectrum disorders). Therefore in mouse models for these disorders, we should also take into account these aspects by following the freely moving animals over longer durations  24. Indeed, short experiments have been shown to be misleading in some cases  34. New experiments should be built to gather precise data on individual mice over the long term to answer the more and more precise needs of phenotyping with ethologicallyrelevant measures  27, 28.
e. , 10-15 min). The way such data is collected does not allow users to investigate more individuals simultaneously or over a longer duration. de Chaumont and colleagues (2012) published a semi-automatic software to describe precisely the types of interactions and the sequences of social events occurring between two visually indistinguishable mice, from a top-view video camera  36  , but this solution is limited as it need to be supervised and corrected by an expert. Unger and colleagues (2017) built on MiceProfiler and used another segmentation method to improve detection and avoid manual corrections  37  (manual interventions remained necessary at the beginning of the tracking). Hong and colleagues (2013) developed a system with two video cameras and one depth sensor to follow two mice  38  , but they needed to be of different coat colors to track the identities.
The data extracted were detailed (attack, mounting, distances head to body and body to body), but they were calculated based on annotated video frames and training of a classifier. Finally, the highest level of details was provided by Matsumoto and colleagues  (2013). They developed a system using four depth sensors to follow in details the different steps of mating in rats in full 3D over ten minutes  39. It only requires manual intervention to define the initial position of the models. Giancardo and colleagues (2013) have developed a system able to track unmarked mice over one hour  40. The behavioral repertoire measured was large (types of contacts, above, following, non-social behaviors such as walking or standing alone), but the system needed an average of 1 manual intervention to correct identities every 34 s (in a 3-mice experiment).
ACKNOWLEDGMENTS Yannick Archambeau ,  Patrick Ollivon  at the workshop of the  Institut Pasteur  for building the 12 first setups and advising on hardware. William Meiniel  for mathematical proof in head/tail probability decision. Microsoft France for their technical support. Piernicola Spinicelli  for optical engineering and critical reading of the paper. Raphaël Marée  for machine learning support. Barbara König  for advices and critical reading of biological experiments. Jacqueline N. Crawley  for careful reading and comments on the manuscript. Angelos Barmpoutis  for providing us the early kinect 2 driver and his very helpful support. Nicolas Chenouard  that drove use to a Machine Learning solution. Pascale Dugast  for drawing the mice in the different behavioral events. Aaron Engelberg  for careful checking of English spelling.
Sébastien Wagner  and  Robert Accardi  for RFID advices. Marcio Marim  for the website development. Xavier Montagutelli and  Marion Bérard  for animal facility support. This work was partially funded by the  Institut Pasteur , the  Bettencourt-Schueller Foundation , the  Agence Nationale de la Recherche  through grants  ANR-10-LABX-62-IBEID  and  France-BioImaging infrastructure   ANR-10-INBS-04 , the  Centre National de la Recherche Scientifique , the  University Paris Diderot , the  BioPsy Labex , the  Institut National du Cancer , the  Foundation for Medical Research  (FRM, Equipe  DEQ20130326488 ), the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115300 , resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution.
Code: LiveMouseTracker. compensateZIntensityError. The Kinect firmware is originally designed to watch player's large movements standing in a room (like dancing), and is therefore not designed to observe close objects. Its original minimum firmware working range is set to 50 centimeters, but practical observation below 80 centimeters is not possible. To obtain a closer range, and a better resolution, we mask with a tape one of the infrared blaster of the Kinect to reduce scene illumination. (See Supplementary movie and file -Blueprint and assembly instructions). This could also be obtained with optical density placed in front of the blasters. Each histogram is built on either infrared data or depth data values corresponding to the mask of the animal. We set 16 bins starting from the min value to the max value of the dataset.
MP4 video recording To be able to go back to visual data for control, we record the infrared stream in MPEG. We add overlay information to display tracking information, RFID state, time of acquisition, memory load, CPU time computation for the last frame and real time/date. MPEG is recorded in full infrared resolution (512 x 424) at half the input rate by default (15 fps), customizable by user. MPEG is recorded in its own thread in low priority, meaning that in weak configuration, frames will be dropped from record. For storage and browsing convenience, each MPEG is 10 minutes long. Code: package livemousetracker. MPEGRecorder.
Database player The database player allows to read database by replaying the data extracted from the software. Its GUI displays color-coded animal segmentation and the event corresponding to the current time points. Events can be displayed, no matter if they were computed from the original tracker, or subsequently in R, matlab, python or any software. If events are related to only one animal, the segmentation carries those descriptions. Otherwise, a column showing all interaction is color coded to better understand which mice are involved. Events can be filtered to help studying specific events. Users can seek forward or backward in the database with different time-steps, or change the frame rate. code: package livemousetracker. dataplayer. Supplementary movie : LMT -Method overview.
Given an example dataset we show how NeoPredPipe is able to rapidly provide insights into neoantigen heterogeneity, burden, and immune stimulation potential. Conclusions: Through the integration of widely adopted tools for neoantigen discovery NeoPredPipe offers a contiguous means of processing single and multi-region sequence data. NeoPredPipe is user-friendly and adaptable for high-throughput performance. NeoPredPipe is freely available at https://github. com/MathOnco/NeoPredPipe.
Their input varies from raw sequencing files (e. g. fastq)  [7, 8, 10]  to highly annotated vcf files  [9] ; some provide HLA-typing as part of their pipeline [7, 10], but require further dependencies for HLAtyping software. Most rely on a version of netMHC or netMHCpan for binding prediction, but  [9]  offers a choice of additional software. For an in-depth comparison of available pipelines for neoantigen calling, we refer the reader to the recent review of Lancaster et al. [11]. Despite the increasing number and diversity of neoantigen-prediction tools, none of them possess the capability of providing predicitions on multi-region sequence data and assessing ITH of the antigenic landscape of tumours. Here, we present NeoPredPipe, a pipeline connecting commonly used bioinformatic software via custom python scripts to allow for the processing of single and multi-region variant call format (VCF) files, variant annotations, neoantigen predictions, cross-referencing with known epitopes, and performing in silico TCR recognition potential predictions in a single, clear, and proficient workflow (Fig.
From this step, mutations that give rise to a single amino acid change, and mutations that mutate a larger peptide segment (e. g. indels and stoplosses) are handled separately and reported in separate files to help further assessment. Once the VCF files have been annotated and partitioned with ANNOVAR, the program determines if HLA haplotypes have been provided by the user containing the HLA-A, -B, and -C haplotypes. NeoPredPipe does not include HLA allele identification as this step in the pipeline is highly dependent upon the source of the data (WES, WGS, targeted gene panels, transcriptome data, or Fig. 1  NeoPredPipe workflow differentiating between user steps (green) and execution processes (purple). NeoPredPipe provides low level details and high level summary statistics as output for downstream analysis (red) conducted via experimental methods), but the pipeline's github page provides detailed advice on haplotyping from WES/WGS data using the popular tool POLYSOLVER  [13] , and the output of POLYSOLVER is automatically processed in NeoPredPipe.
In order to predict the recognition potential we employ the algorithms and process utilized by  [16]. The recognition potential is defined as the product of A and R, where A is the amplitude of the ratio of the relative probabilities of binding for the wild-type and mutant epitopes to the MHCclass I molecules; and R is a measure of similarity to pathogenic peptides, meant to represent the probability that the neoantigen in question is recognised by a TCR clone already present in the tissue/blood. To define A it is necessary to perform neoantigen predictions for the wildtype and mutant epitope: this is not performed by default by NeoPredPipe, but is supplied as an option to employ as a contiguous pipeline. To define R, NeoPredPipe utilizes the multistate thermodynamic model employed by  [16] , which requires alignment scores for each epitope to a curated Immune Epitope Database list of known epitopes (can be refined and updated by the user, but is provided).
Results The output of the pipeline depends largely on the options set by the user, but at the very least, NeoPredPipe provides two tables of putative neoantigens and their predicted binding affinities, one for single nucleotide/amino acid, and one for indel(-type) variants. With additional options selected it is possible to include, within a single output, whether an epitope matches a reference proteome, its expression on the RNA level and the neoantigen's recognition potential. In additon, for rapid assessment, NeoPredPipe yields summary statistics on the neoantigen burden for each sample, a rapidly executed webbased visualization, as well as information to assess ITH by reporting neoantigen burdens for clonal, subclonal, and shared variants for multi-region samples. A detailed description of NeoPredPipe's output tables and each field in these can be found at https://github.
Use Case While a small, two sample, multi-region example dataset is provided with the source code for users, we demonstrate the usefulness of NeoPredPipe by applying it to a previously published dataset examining the evolutionary landscape of colorectal tumors  [17]. We select two exemplary patient samples (Adenoma 3 and Carcinoma 7 in the original paper) from the dataset, and apply our pipeline using default parameters to evaluate neoantigens in each sample. Figure  2  illustrates the information included in the standard output of NeoPredPipe and potential analysis that can be performed if NeoPredPipe is combined with the output of other standard bioinformatic methods. Figure  2a  provides a summary of the complex interactions between different regions of Adenoma 3, and highlights both Region 4, which harbours the highest amount of subclonal (only present in a single region) neoantigens, and the overall clonality of the sample, with 72 neoantigens detected in all regions.
Conclusions We present NeoPredPipe, an efficient, high-throughput, and user-friendly pipeline for neoantigen prediction and interrogation for single and multi-region tumor VCF files. By tying together commonly utilized bioinformatics toolsets and integrating recent advances in neoantigen assessment, NeoPredPipe yields concise information typically required by researchers and clinicians. Through user options, based on the individuals own computational limitations, the pipeline is scalable for a high performance computing (HPC) cluster environment and customizable for individual research questions. Furthermore, unlike existing methods[7-10], NeoPredPipe can process a directory containing numerous samples in a single command; therefore provides a user-friendly way for not computer-proficient users to analyse the output of large studies or compare against reference datasets.
6 The exploration of the fusion landscape in these pathologies could, therefore, help to unravel the particularity of these malignancies and provide new therapeutic targets. Given the limited data in solid pediatric cancer patients at relapse, we conducted our study using previously published RNA-seq data of 48 pediatric patients with different malignancies, generated as part of the Molecular Screening for Cancer Treatment Optimization (MOSCATO-01, ClinicalTrials. gov: NCT01566019) trial conducted at Gustave Roussy. 14 Our aim was to identify new fusion transcripts in recurrent pediatric cancers and to predict their probable oncogenic potential and targetability. For this, we established a reliable in silico detection method named ChimComp that integrates information from three different fusion detection algorithms, along with their corresponding oncogenic effects and targetability.
Patient Characteristics Clinical characteristics of patients are provided in Table  S1. Briefly, the transcriptome of 48 patients included in the MOSCATO-01 clinical trial with 22 different tumor entities at relapse or resistance was previously sequenced using RNA-seq. 14 The median age was 10. 9 years (range, 0. 8-23. 3 years) and the sex ratio was equal to 0. 71 (28 males). Patients were classified into 3 categories based on their tumor types: sarcomas (40%), brain tumors (35%), and other types (25%). All patients had undergone antineoplastic treatment (median number of lines, 2; range, 1-8). The median number of tumor cells in the analyzed specimen was high (around 80%). ChimComp Is a Powerful Pipeline to Detect Fusions from RNA-Seq Data Pediatric cancers are known to harbor a low mutational rate, which suggests that other genomic alterations, including, among others, fusion transcripts and structural variants, may be causative.