dataesr/scientific-paragraphs-categorization huggingface dataset:
This distributed design improves the flexibility and resilence of the camera: for instance, during data-taking the ARM computer memory (256 MB per module, for a total of 15. 6 GB) is used for buffering the data, preventing data loss during event bursts. The software was written from scratch; it has a distributed, multi-architecture nature, as required by the new camera design. Its main functions are slow control and event acquisition; it also includes text-based and web-based user interfaces, extensive unit tests, integration tests and validation routines needed for the mass production, and a full commissioning and calibration suite able to take runs, analyze them and adjust camera parameters independently of the main H. E. S. S. DAQ. The full codebase is around 100,000 lines of code long, composed by 82% C++, 11% ANSI-C and 7% python.
Its implementation was one of the major efforts of the upgrade, and required around 6 man-years by a team composed of two full-time coders and four part-time contributors. This paid off with a 10-to 1,000-fold improvement in speed and reliability over the previous system (see Sect. 5. 3 for some performance measurements). To maximize efficiency, extensibility and maintainability of the codebase, the development team made use of well-tested off-the-shelf open source solutions wherever possible. A single source tree was used for both ARM and x86 64 architectures; cross-compilation was handled by the CMake build system. The operating system running on the ARM computers is the Yocto embedded Linux  [17]. It runs a Linux kernel v3. 0 patched by the manufacturer, and a custom-built DMA-enabled driver for communicating with the FPGA.
The remote procedure call framework required to control the camera is implemented using the Apache Thrift library. The camera slow control software was interfaced to the already existing H. E. S. S. data acquisition software (DAQ,  [18] ) via the CORBA protocol. Data transfer is accomplished via the ØMQ  [7]  smart socket library. The raw data serialization protocol is custom, and optimized for speed; for monitoring and logging the generalpurpose Google Protocol Buffers library  [19]  is used instead.
The line offset DAC settings can thus be corrected to compensate this difference. By repeating this process several times, the default baseline offset is approached. In only 5 iterations, the RMS of the baseline decreases from ∼ 20 to ∼ 4 ADC counts on average, see Fig. 8. This procedure needs to be performed in principle only once, but in practice it is performed for the whole camera each time a drawer is exchanged, or the PMT gain is re-adjusted, so about twice per year on average. The values of the line DACs are then permanently stored in a MySQL database. This correction is not strictly necessary for regular observations, where the only measured quantity is the charge integrated over 16 samples, minus a time-averaged pedestal baseline. This is because the width of the ROI is a multiple of the line dispersion period, so any event-wise baseline shift due to it is cancelled out.
The corresponding average PMT gain is 2. 72 × 10 5. The PMT voltages range from ∼ 850 V up to ∼1,350 V, and are stored in a MySQL database. To achieve a precise gain flat-fielding (better than 4%), the procedure is iterated several times, each with a finer voltage step. The performance of the PMTs degrade with time, so their gains need to be flat-fielded every six months, adding on average 13 V to the PMT voltages. After gain flat-fielding, the position of the region of interest N d is readjusted, because the PMT transit time t p depends on the voltage V applied to it.
The FWHM of this interpolation is also shown with dashed lines. Right: The distribution of charges from a typical PMT gain calibration run, fitted to a linear combination of Gaussian functions as described in  [14] , section 6. 2, equation 6. In this particular case, γ ADC e ("Gain") is 79. 3 and σ P is 12. 9 ADC counts, corresponding to a gain of 2. 7 × 10 5 and a noise level of 0. 16 p. e. flat-fielding pulses arrive isochronally at their entrance window. The flatfielding is performed several times per observation period (one lunar month), and the obtained coefficients for the period are then averaged and stored in a MySQL database. As for the previous cameras, the distribution of the C i coefficients is a Gaussian with an RMS of 10%, there is no discernible gradient across the camera. Trials with a new flat-fielding unit are ongoing.
It is anyway not a problem because at the chosen gain, the single-electron signal is distinguishable in any case, being always at least 3 times higher than the noise. The linearity and cross-talk of the readout were measured by recording a pre-calibrated, PMT-like pulse of variable intensity. The results, which can be seen in the left panel of Fig. 13 , show that non-linearities in both high gain and low gain amount to less than 2%. The linear range of the high gain is 0. 3-200 p. e. and that of the low gain is 30-4,200 p. e. : the total readout dynamic range is greater than 80 dB. The ratio between high and low gain is ∼ 22 between 30 and 200 p. e. (see Fig. 13 , left, bottom panel). The data mentioned above was also used to characterize the cross-talk between two channels on the same analogue board.
In order to keep up with the usual data acquisition rates (up to 700 Hz) when using this acquisition mode, the additional waveform data must be stored on the camera server hard disks, and is transmitted to the H. E. S. S. DAQ off-line on the following day. This mode is only used for selected targets, due to the much greater amount of data created when it is active. Initial results on the performance of this readout mode are reported in  [30]. Regarding the slow control software performance, stress tests performed on the Apache Thrift RPC framework operating in the busy DESY lab network showed that it is capable of sustaining rates of 10,000 single point-topoint request/replies per second for more than 12 hours with no failures. One-to-many requests, such as distributing a command or collecting information from all drawers, are handled on the camera server by spawning one thread per connection.
About 2 hours of data were collected during this observation. The preliminary processing of the data using two independent analysis pipelines revealed a clear detection with a significance of 16σ. This was the first detection of a TeV gamma-ray source using the NECTAr chip technology  [32]  (see Fig. 14 , for a significance sky map of this detection and an example event). The upgraded cameras have been employed in routine observations since January 2017, and since then have achieved an average weather-corrected data taking efficiency of 98. 5%.
Acknowledgements The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  French Ministry for Research , the  CNRS-IN2P3  and the  Astroparticle Interdisciplinary Programme of the CNRS , the  U. K. Science and Technology Facilities Council (STFC) , the  IPNP of the Charles University , the  Czech Science Foundation , the  Polish Ministry of Science and Higher Education , the  South African Department of Science and Technology and National Research Foundation , and by the  University of Namibia. We appreciate the excellent work of the technical support staff in  Zeuthen, Durham, Hamburg, Heidelberg, Palaiseau, Paris, Saclay , and in Namibia in the construction and operation of the equipment.
Roquet et al. then generated a database of registers containing different combinations and permutations of gene-expression elements, and simulated their behavior. Then, a few registers implementing specific two and three-input history-dependent programs were built and validated experimentally. However, compared to other genetic design workflow  32, 33  , this method remains a trial and error process, and the ultimate functionality of the circuit is hardly predictable. Each program is executed using a different architecture, and context effects arising from unexpected parts interactions can alter recombination efficiency and gene expression. We previously reported such context effects for recombinase devices, as some attB and attP sites have cryptic promoter or terminator activities  28, 33, 34.
S3 ), see "Methods" (https:// github. com/synthetic-biology-group-cbs-montpellier/calin). The biological implementation consists of a graphical representation of the genetic circuit and the device DNA sequence of each strain (Fig. 1f ). To enable broad access to our design framework, we provide a website called composable asynchronous logic using integrase networks (CALIN) (http://synbio. cbs. cnrs. fr/ calin/sequential_input. php) that supports automated design of history-dependent programs. In the CALIN web interface, the user fills in the number of inputs to process and the desired lineage tree. The interface provides as an output the DNA architectures of the computational devices, the connection map between inputs and integrases along with the corresponding DNA sequences optimized for E.
2a ). Because each state corresponds to a physically different DNA molecule in which sequence can be fully predicted, OSIRiS enables the different recombination intermediate sequences to be generated in silico, synthesized, and characterized (Fig. 2a ). OSIRiS therefore decouples scaffold optimization from integrase-mediated switching, simplifies and shortens the characterization process, and allows for rapid iteration cycles. The OSIRiS script, written in python, automatically generates the intermediate sequences of any integrasebased scaffold, and is available on GitHub (https://github. com/ synthetic-biology-group-cbs-montpellier/OSIRiS) and Codeocean  43. We first used OSIRiS to generate all recombination intermediates for a 2-input, 3-output, single-lineage program (Fig. 2a ). All recombination intermediates behaved as expected (Fig.
We added a third integrase, Integrase 5 (Int5)  41  to the controller plasmid, under the control of the pBEN promoter responding to benzoate (input c) (Fig. 5b ). We confirmed the functionality and quantified the recombination efficiency of each integrase in the triple controller plasmid by using OSIRiS intermediate DNA states as recombination targets (Fig. S9 ). Then, we cotransformed the triple controller with the corresponding target plasmid for each program, and performed sequential overnight inductions with aTc (Bxb1), arabinose (Tp901-1), and benzoate (Int5) for 4 days (see "Methods"). Measurements of fluorescence intensity by flow cytometry in each of the 16 input states were consistent with the expected program (Fig. 5c , Figs. S10  and S11 ). In addition, the fluorescence observed for each input state were similar to those observed during OSIRiS characterization (Fig.
We found that the results from scaffold characterization using OSIRiS closely matched device behavior when operating in real conditions, i. e. , responding to recombinase mediated inversions or excisions. The recombination intermediate DNA states provided by OSIRiS can be used to optimize the scaffold and characterize the recombinase efficiencies of the integrases in the genetic context and experimental condition in which the program will be used (Fig. S9 ). G B R While we do operate our system in fundamental mode, some inputs could arrive in a mixed manner. The time resolution of the system (i. e. , the minimum time between two inputs so that different outcomes can be observed) and the proportion between the resulting populations having entered different lineages would depend on the dynamics of the recombination reactions, which is governed by integrase expression, stability, and catalytic rate, among others.
Plasmid extraction and DNA purification were performed using kits from Biosentec (Toulouse, France). Sequencing was realized by GATC Biotech (Cologne, Germany). To build the triple controller plasmid, we constructed an inducible cassette containing the benR gene regulator, constitutively expressed by promoter J23106, the P ben promoter (both benR and P ben from metabolic and sensing modules, respectively  61  ), and Int5 41 controlled by P ben promoter (see vector map). The cassette was inserted upstream of dual controller integrase plasmid 26. After assembly the triple controller vector was transformed and cloned in E. coli strain DH5alphaZ1. The design of DNA scaffold for target plasmid was done using a python script to automatically generate a library of DNA sequences (https://github. com/ synthetic-biology-group-cbs-montpellier/Generate_DNAseq), which minimizes the number of errors.
For plate reader measurement an aliquot of cells from each day were diluted four times in phosphate buffered saline PBS before read. For microscopy analysis, aliquots of cells from each day were mixed with glycerol at final concentration 15% v/v and kept at 80 °C until its analysis. Every experiment was repeated two or three times. OSIRiS design and characterization. To automate the design of intermediate recombination state, we developed a python script giving from any integrase-based scaffold and a list of integrase of interest the recombination intermediate DNA sequences. OSIRiS code is widely available on GitHub (https://github. com/ synthetic-biology-group-cbs-montpellier/OSIRiS), requires Python 2. 7 and Biopython installation. To use OSIRiS script, a csv file containing integrase sites of interest is required, we provided a csv file containing widely used integrase sites but any specific sequences can be added.
Of note, this script is only considering irreversible DNA excision and inversion. As output, GenBank file with integrase site annotations is obtained for all intermediate recombination states. In our scaffold design, we selected the fluorescent proteins sfGFP, mKate2, and BFP, as their excitation and emission spectrums do not overlap. We used P6 as the promoter and B0034 as the ribosome binding site  62. To insulate the translation from the genetic context, we placed a ribozyme in the 5′ end of each output gene, catalyzing the cleavage of the mRNA at this position  63. We used different ribozymes for each output gene (RiboJ, BydvJ, and AraJ) to avoid multiple repetitions of sequences in the construct. Based on this design, we generated intermediate recombination input states using our OSIRiS python script.
We then synthesized and constructed these sequences. We characterized all the constructs by flow cytometry. The fold change over the negative control was determined from mean value over that of the negative control. The mean fold change was represented in the figure corresponding to the mean of the fold change of the three different experiments. Flow-cytometry and plate reader analysis. Flow cytometry was performed on Attune NxT flow cytometer (Thermo Fisher) equipped with an autosampler and Attune NxT™ Version 2. 7 Software and BD LSR Fortessa (Becton Dickinson), with FACSDiVa software. Experiment on Attune NxT were performed in 96-well plates with setting; FSC: 200 V, SSC: 380 V, green intensity BL1: 460 V (488 nm laser and a 510/10 nm filter), and red intensity YL2: 460 V (561 nm laser and a 615/25 nm filter).
Setting for experiments on Fortessa were FSC: 400 V, SSC: 300 V, green intensity GFP: 580 V (488 nm laser and a 530/30 filter), red intensity mCherry: 565 V (600 nm laser and a 610/20 nm filter), and blue intensity V1: 460 V (405 nm laser and a 450/50 nm filter). All events were collected with a cutoff of 20,000 events. Every experiment included a positive control expressing GFP, RFP, or BFP and a negative control harboring the plasmid but without reporter gene, to generate the gates. The cells were gated based on forward and side scatter graphs and events on single-cell gates were selected and analyzed, to remove debris from the analysis (Fig. S21 ), by Flow-Jo (Treestar, Inc) software. Original FCS files for each program are available on Flow repository at the links provided in the "Data availability" section.
Plate reader measurements were done on Cytation 3 microplate reader (Biotek Instruments, Inc) and data were collected using Biotek Gen 5 software. After induction time, cultures were diluted four times in PBS and measured with the following parameters: GFP: excitation 485 nm, emission 528 nm, gain 80, BFP: excitation 402 nm, emission 457 nm, gain 70, RFP: excitation 555 nm, emission 584 nm, gain 100, absorbance: 600 nm. For each sample, GFP, BFP, and RFP fluorescence intensities were normalized to absorbance at 600 nm. The arbitrary units of fluorescence were used to graph bars in each figure. Plots for all data analysis were obtained using python plotting codes available on GitHub  64. Plate reader data are available as a Supplementary Excel file. Microscopy and microfluidic analysis. Cell samples from sequential induction experiments were analyzed by confocal microscopy.
Images were acquired using a Zeiss Axioimager Z2 apotome, Andor's Zyla 4. 2 sCMOS camera (MRI platform, Montpellier). Two microliters of cells were spotted on Azure/glycerol 2% agarose pads. Images were taken from phase contrast, GFP, RFP, and BFP fluorescence images at ×100 magnification. Images were analyzed using OMERO software. Source images are provided as a Source Data file. For microfluidic analysis, experiments were performed using a mother machine microfluidic device consisting in arrays of parallel chambers (1 µm × 1 µm × 25 µm) connected to a large channel. Chambers were fabricated using electron-beam lithography on SU-8 photoresist (MicroChem), while the channel was fabricated using soft-lithography. From the subsequent master wafer, microfluidic chips were molded in polydimethylsiloxane (PDMS) and bonded to a glass slide using plasma activation.
S3 ) (https://github. com/synthetic-biology-group-cbs-montpellier/calin; http://synbio. cbs. cnrs. fr/calin/sequential_input. php). The algorithm takes as input a lineage tree (equivalent to a sequential truth table). The output corresponds to the biological implementation, such as a graphical representation of the genetic circuit and its associated DNA sequences for each strain. The lineage tree is decomposed into subtrees consisting of a single-lineage containing one or multiple ON states. This decomposition is done by iteratively subtracting the lineages containing ON states. To obtain the lowest number of subprograms, we prioritize among the lineages with ON states the ones for which the highest number of inputs occurred (from the right to the left of the lineage tree). After decomposition, for each selected lineage, two pieces of information are extracted.
First, based on which states are ON, we directly design the corresponding scaffold by specifically inserting genes at the adequate GOI positions. Second, the order of occurrence of inputs corresponding to the lineage is used to identify which sensor modules are needed among the different connection possibilities between control signals and integrases. Then, by combining the design of the different lineages, we obtain the global design for biological implementation of the desired history-dependent gene-expression program. To simplify the construction process of logic circuits, DNA sequence of computation devices is generated by our Python code (available also at Code Ocean platform). In CALIN, sequences are adapted for E. coli. But sequence generation can be adapted to other organisms (databases are available for Bacillus subtilis and Saccharomyces cerevisiae) or customly designed using the source Python code available on GitHub (https://github.
Acknowledgements We thank the synthetic-biology group and members of the  CBS  for fruitful discussions. We thank  Montpellier Ressources Imagerie  ( MRI ) for microscope and cytometry support, especially  Marie-Pierre Blanchard  and  Myriam Boyer. 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. Z. B. M. and P. H. were supported by an  ERC   Consolidator grant  " Smartcells. "The CBS acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01.
Data availability DNA sequences for all constructs are available as a Supplementary File. All raw data are available in Supplementary Files. Original FCS files are available on Flow repository 65 ; 2SP1; 2SP2; 2SP3; 2SP4; 2SP5; 2SP6; 2SP7; 2SP8; 2SP9; 3SP1; 3SP2; 3SP3; 3SP4; 3SP5; 2MP1; 2MP2; 3MP1; 3MP3. The webpage for CALIN is available at: http://synbio. cbs. cnrs. fr/calin/sequential_input. php. All other raw data are available from the corresponding author on reasonable request. Plasmids are available from Addgene: 2SP1 ID:126526; 2SP2 ID: 126527; 2SP3: 126528; 2SP4 ID 126529; 2SP5 ID: 126530; 2SP6 ID: 126531; 2SP7 ID: 126532; 2SP8 ID: 126533; 2SP9 ID: 126534; 3SP1 ID: 126535; 3SP2 ID: 126536; 3SP3 ID: 126537; 3SP4 ID: 126538; 3SP5 ID: 126539; and pITC (Integrase triple controller) ID: 126540.
Purpose: Geant4 is a multi-purpose Monte Carlo simulation tool for modeling particle transport in matter. It provides a wide range of settings, which the user may optimize for their specific application. This study investigates GATE/Geant4 parameter settings for proton pencil beam scanning therapy. Methods: GATE8. 1/Geant4. 10. 3. p03 (matching the versions used in GATE-RTion1. 0) simulations were performed with a set of prebuilt Geant4 physics lists (QGSP_BIC, QGSP_BIC_EMY, QGSP_BIC_EMZ, QGSP_BIC_HP_EMZ), using 0. 1mm-10mm as production cuts on secondary particles (electrons, photons, positrons) and varying the maximum step size of protons (0. 1mm, 1mm, none). The results of the simulations were compared to measurement data taken during clinical patient specific quality assurance at The Christie NHS Foundation Trust pencil beam scanning proton therapy facility.
measured beam sizes in air, but yields up to 20% longer execution times compared to the EMY electromagnetic constructor (maximum beam size difference 0. 79 mm). The impact of this on patientspecific quality assurance simulations is clinically negligible, with a 97% average 2%/2 mm gamma pass rate for both physics lists. However, when considering the CT-based patient model, dose deviations up to 2. 4% are observed. Production cuts do not substantially influence dosimetric results in solid water, but lead to dose differences of up to 4. 1% in the patient CT. Small (compared to voxel size) production cuts increase execution times by factors of 5 (solid water) and 2 (patient CT). Conclusions: Taking both efficiency and dose accuracy into account and considering voxel sizes with 2 mm linear size, the authors recommend the following Geant4 settings to simulate patient specific quality assurance measurements: No step limiter on proton tracks; production cuts of 1 mm for electrons, photons and positrons (in the phantom and range-shifter) and 10 mm (world); best agreement to measurement data was found for QGSP_BIC_EMZ reference physics list at the cost of 20% increased execution times compared to QGSP_BIC_EMY.