dataesr/scientific-paragraphs-categorization huggingface dataset:
Regarding the agent execution function mentioned above, it considered as the place where to specify the execution mechanism mapping. The last criterion focuses on the support of hierarchical systems. Two agent frameworks support explicitly such systems, i. e. GAMA and Janus. Only Janus fully supports the concept of holon. According to all these assessments, we have decided to adapt the Janus platform in order to create a run-time environment for the SARL applications. Indeed, Janus is the framework, which covers the largest amount of features that are expected by SARL developers. The key element that has been considered for taking this decision is the support of holonic multi-agent systems, which is the best within Janus, from our point of view. This decision is also taken according to the software development effort to be spent for creating a SRE: lower is the amount of code to be written, higher is our interest to the SRE candidate.
So far, the definition could be a definition of modular programming in the 1970s. 8 Google Guice: https://code. google. com/p/google-guice/ 9 In-Memory Data Grid like Hazelcast: http://www. hazelcast. com 10 Service-Oriented Architecture Standards: http://www. opengroup. org/standards/soa Service-oriented architecture is less about how to modularize an application. It is more about how to compose an application by integration of distributed, separately-maintained and deployed software components. SOA is enabled by technologies and standards that make it easier for components to communicate and cooperate. According to these principles, the Janus platform is redesigned in order to provide eight services. As illustrated by Figure  4 , these services constitute the basis of the built-in capacity implementation.
Events: • ContextCreated, ContextDestroyed when a context was created or destroyed. • SpaceCreated, SpaceDestroyed when a space was created or destroyed. Janus enables the distribution a SARL program over a network of (virtual) machines -(V)M's. From the SARL agent point of view, this distribution over (V)M's is hidden. Indeed, we argue that any SARL program runs on a big virtual machine that is covering all the connected (V)M's. These low-level machines are not visible to the SARL agent. Consequently, a context is seen as a single entity over the (V)M's by the agents. From the Janus point of view, each context instance is a shared object over the (V)M's. Thanks to the underlying Hazelcast in-memory data grid library, the different context instances are replicated and dynamically synchronized over the (V)M's.
Spaces are also shared upon the same infrastructure. A safety question arises when enabling (V)M communication: what happens when two (V)M's cannot communicate any more? Thanks to the Hazelcast library, no data is removed from the shared data structures (context list, space list, agent list, etc. ) It means that the agents can continue to send events to agents that are at the other side of the lost connection. But, Janus does not send the events to the remote agent because of the connection loss. According to the Janus specification, there is no warranty that an event is delivered to another agent into a remote (V)M. Consequently, the agent communication protocol should take care of any loss of event. When the (V)M's connection is back, Hazelcast library synchronizes the local instances of the data structures with the remote instances.
Conclusion and Perspectives SARL is a general-purpose agent-oriented programming language. This language aims at providing the fundamental abstractions for dealing with essential agent features: concurrency, distribution, interaction, decentralization, reactivity, autonomy and dynamic reconfiguration. Every programming language specifies an execution model, and many implement at least part of that model in a runtime system. In the case of SARL programs, the SARL run-time environment (SRE) provides the tools and the features that are mandatory for running such a program. In this paper, we present the adaptation of the Janus platform for becoming the official and default SRE. Janus adopts the best practices in current software development, such as Inversion of Control, and benefits from new technologies like Distributed Data Structures.
The human cytosolic low molecular weight isoform Tpm1. 8 is associated with stress fibres and lamellipodia, where it is involved in regulating the highly dynamic process of cell migration  (Bryce et al. 2003; Brayford et al. 2016 ). It exhibits one of the strongest affinities for actin  (Moraczewska, Nicholson-Flynn, and Hitchcock-DeGregori 1999)  , but its assembly and turn-over on actin and how these properties relate to the dynamics of Tpm1. 8-containing cellular actin structures remain unresolved. In this study, we used microfluidics and total internal reflection fluorescence (TIRF) microscopy to measure the dynamic interactions of Tpm1. 8 molecules with preformed actin filaments in vitro at the single filament and the single molecule level. Our data provide a complete experimentally parameterised model of Tpm1.
Fluorescence intensity traces were generated at the sites of initial binding and analysed with a step fitting algorithm that could detect the increase or decrease of the fluorescence intensities as discrete quantal steps (Figure  4C ). We extracted the mNeonGreen-Tpm1. 8 binding kinetics on surface-bound actin filaments by step fitting fluorescence traces recorded during the association phase. The distribution of dwell times between successive positive steps in the fluorescence intensity traces decayed exponentially (Supplementary Figure  9 ). The association rates obtained by fitting these exponentials for the respective concentrations are shown in Figure  4D  and represent the addition of mNeonGreen-Tpm1. 8 molecules at both edges of the Tpm1. 8 domain. The polymerisation rate constant estimated from this data (5.
16). Image analysis at the single molecule level. Image stacks were analysed using the JIM Immobilized Microscopy Suite to extract intensity traces at the sites corresponding to nucleation events ( https://github. com/lilbutsa/JIM-Immobilized-Microscopy-Suite ). Intensity traces were divided by the intensity of a dimeric mNeonGreen-Tpm1. 8 molecule (equal to twice the intensity measured for mNeonGreen). Stepfitting of traces was achieved using the findchangepts function in Matlab with a threshold of 0. 03. Histograms of the intensity step heights for nucleation (first step), elongation (subsequent positive steps) and dissociation (negative steps) of Tpm1. 8 were generated using Wolfram Mathematica. Cumulative probability functions of the time before a positive/negative step with fitted with single exponential decays to obtain elongation/dissociation rates, respectively.
Dividing the association rates by the concentration gave the association rate constant. Stochastic Model. The stochastic model was solved using a custom program written in Mathematica 10. 2 (Wolfram). The script used the Gillespie algorithm to generate statistically correct trajectories  (Gillespie 1976 (Gillespie , 1977) ). A list containing the ends of each tropomyosin domain was used to calculate the probability of each reaction. The code for this program is freely available at ( https://github. com/lilbutsa/Tropomyosin_Stochastic_Model ). Weigt, C. , A. Wegner, and M. H. Koch. 1991         Binding of an isolated Tpm to the actin filament 4. 55⋅10 11 m -1 ⋅M -1 ⋅s -1 * 2. 3⋅10 3 M -1 ⋅s -1 # Fig. 5C  R I Release of an isolated Tpm from the actin filament 4. 125⋅10 -8 m⋅s -1 ‡ 1. 25 s -1 Fig.
The intracellular solution contained in mM: K-Gluconate 150, NaCl 10, K-Hepes 10, Mg-ATP 3, Na-GTP 0. 3, EGTA 0. 05 (305 mOsm, pH 7. 3). A liquid junction potential of +13 mV was corrected for. All electrophysiological measurements were performed with a HEKA EPC10 amplifier (HEKA Elektronik, Lambrecht/ Pfalz, Germany) under control of the Patchmaster software. All measurements were performed at 34-37˚C.
Current clamp recordings in GCs Action potentials were evoked in current-clamp mode by current pulses (amplitude 20-400 pA, duration 300 ms). To determine the input resistance, subthreshold current pulses were applied from À20 to + 20 pA in 2 pA steps. The resistance of the solution-filled patch-pipettes was between 6 and 12 MW. Data were sampled at 200 kHz.
Outside-out recordings in GCs To reliably clamp potassium currents from the soma of GCs (Figure  2 ), potassium currents were measured in outside-out patches pulled from the soma of inner and outer GCs by applying 10-ms voltage steps from À90 to +60 mV with 10 mV increments at an intersweep interval of 1 s. The intersweep holding potential was À90 mV. Data were sampled at 100 kHz.
Excitatory postsynaptic potentials in PC Excitatory postsynaptic potentials (EPSPs) in PC were elicited by voltage stimulation of the PFs within the inner, middle or outer third of the molecular layer from horizontally cut cerebellar slices (Figure  7 ). 10 mM SR95531 was added to the external solution to block GABA A receptors. The stimulation pipette was filled with extracellular solution, and the voltage was adjusted between 6 to 25 V to elicit EPSPs with amplitudes between 1 and 2 mV. EPSPs were measured after a single 100 ms voltage stimulation or five stimulations (100 ms duration) at a frequency of 100 and 500 Hz. Averages of 30 trains per stimulation protocol were used for data analysis.
Dynamic Clamp of MF conductance in GCs In order to analyze the response of GCs on in vivo-like MF inputs, we used a Dynamic Clamp implemented with the microcontroller Teensy 3. 6 (https://www. pjrc. com) as described by  Desai et al. (2017). The Teensy was programmed using the Arduino integrated development environment with the code provided by  Desai et al. (2017)  and modified for our need as described in the following. The time course of MF conductance was G EPSC t ð Þ ¼ G max A norm Àe À t tr þ X 3 i¼1 a i e À t t i ! (1) where the exponential rise time (t r ) was 0. 1 ms, the decay time constants (t 1 , t 2 , and t 3 ) were 0. 3, 8, and 40 ms, respectively, and the relative amplitude of the decay components (a 1 , a 2 , and a 3 ) were 0. 7, 0. 26, and 0. 04, respectively. The peak conductance (G max ) was 1 nS  (Hallermann et al.
Data analysis Current-clamp data were analyzed using custom-written procedures in Igor Pro software (WaveMetrics, Oregon, USA) as previously described  (Eshra et al. , 2019). Intrinsic properties of GCs were determined from the injected currents that elicited the largest number of action potentials. The action potential threshold was defined as the membrane voltage at which the first derivative exceeded 100 V s À1 , the minimal action potential peak was set at À20 mV and the minimal amplitude at 20 mV. All action potentials with a half-width shorter than 50 ms and longer than 500 ms were excluded. Action potential voltage threshold and half-width were calculated from the average of the first five action potentials. If a trace contained less than five action potentials, only the first action potential was considered.
The action potential frequency was determined by dividing the number of action potentials during the 300-ms-lasting current injection by 300 ms. Membrane capacitance, resting membrane potential and series resistance were read from the amplifier software (HEKA) after achieving the whole-cell configuration. Input resistance (R in ) was analyzed from alternating subthreshold current injections from À20 to 20 pA (2 pA steps). The resulting voltage was plotted against the injected current and a spline interpolation was performed to obtain the slope at the holding membrane potential (0 pA current injection). The peak-current from outside-out patches was determined from voltage steps (À90 to +60 mV) with Fitmaster software (HEKA). Steady-state inactivation was determined from the last 2 ms of the respective sweep.
Cells were only included if 50 pA <I max < 1 nA to exclude potential whole-cell measurements and membrane-vesicles. EPSP measurements from PCs and EPSC measurements from GCs were analyzed with the Fitmaster software (HEKA). For PC EPSPs, 20-80% rise time and time to peak were determined from the average of 30 individual single EPSPs. GCs EPSCs were averaged from 25 traces. To obtain the decay kinetics, single EPSPs/EPSCs were fitted with either one or two exponentials. The weighted time constant was calculated as: t w ¼ A slow t slow þ A fast t fast A slow þ A fast (4) Paired-pulse ratio was determined between the first and the 5 th EPSP after stimulation with 100 Hz trains. Single EPSCs from inner-and outer-zone GCs were averaged and fitted with two exponentials. The decay kinetics and amplitude of the grand-average was used to implement the MF EPSCs for the Dynamic Clamp.
Target sequence and van Rossum error Based on in vivo firing patterns  (Witter and De Zeeuw, 2015a) , an arbitrary target firing sequence of 80, 40, and 120 Hz for 300, 100, and 100 ms, respectively, was chosen. The distance between the PC and the target spiking sequence was quantified with the van Rossum error  (van Rossum, 2001). Both spiking sequences were convolved with an exponential kernel with a decay time constant of 30 ms (or values ranging from 2 to 300 ms in (Figure  8 -figure supplement 1A,B). The van Rossum error was defined as the integral of the square of the difference between these two convolved traces. We also tested another algorithm to calculate the van Rossum error  (Houghton and Kreuz, 2012) , C++ code taken from http://pymuvr. readthedocs. io/ and incorporated into Matlab via the MEX function and results were comparable.
The fluorescence values were normalized by the controls (DMSO treated cells) and expressed as percent viability. The IC 50 values of berberine and berberine's derivatives on cell viability (i. e. , the concentration of derivative causing a reduction of 50% of the cell viability) were calculated using GraphPad ® Prism 7 software (San Diego, CA, USA). t-Test and two ways ANOVA analyses were used to address the significant differences between mean values with significance set at p < 0. 05.
Docking simulations of reference ligand PC190723 into its FtsZ structure (PDB code: 4DXD) were beforehand performed to validate the docking protocol. This control study is used to determine the best docking parameters for the target  [26]. The PC190723 ligand was successfully redocked into its binding site with a RMSD value of less than 1 Å between crystallized conformation and predicted pose (Supplementary Material Figure  S1 ). All generated poses were subjected to visual analysis using Pymol (http://pymol. org) and the binding mode of interest was selected accordingly. The latter should highlight shared binding mode for the berberine core (5 fused-rings substructure) for all considered compounds (15, 16, 24, 25) and be reasonable in terms of docking score and explicit interactions with the binding site.
Molecular Dynamics Simulations The selected binding modes from molecular docking were subsequently subjected to molecular dynamics simulations as final refinement stage. All MD simulations were performed in explicit solvent with periodic conditions with the AMBER package version 18. The initial protein coordinates were taken from the ligand-free crystal structure of FtsZ protein from S. aureus (PDB code: 4DXD). MD simulations were performed for each ligand of interest: compounds 15, 16, 24, 25 and the native ligand from the 4DXD structure considered as positive control. In contrast to native ligand, the initial coordinates for compounds 15, 16, 24, 25 were taken from previous docking simulations. The CHARMM-GUI web services (http://www. charmm-gui. org) were used to read the PDB structures and generate inputs and scripts to run the MD simulations  [27, 28].
The therapeutic index of each derivative was calculated by dividing its IC 50 value on a specific human cell type by its MIC value on S. aureus. TI were found dependent on both the specific compound and human cells considered. Thus, for human intestinal epithelial cells (Caco-2 cells), TI ranged from 3. 37 to 20. 24 with the following order of safety: compound 26 > 23 > 8 > 16 > 17 > 25. For human airway epithelial cells (BEAS cells), TI ranged from 2. 83 to 41. 52 with the following order of safety: compound 26 > 16 > 23 > 17 > 8 > 25. Finally, for human liver epithelial cells (HepG2 cells), TI ranged from 39. 39 to 342. 00 with the following order of safety: compound 17 > 8 > 23 > 26 > 16 > 25. Overall, these data showed that although antimicrobial berberine's derivatives gave good TI values, suggesting that they could potentially be used to treat systemic infections without taking the risk of toxicity for the patients.
Molecular Docking FtsZ protein from S. aureus (FtsZ, PDB code: 4DXD) has been previously shown, using in vitro biological assays, to be the target of antibacterial berberine's derivatives  [30]. Since FtsZ protein is involved in septum formation and bacterial cell division, the interaction of berberines with this bacterial protein was proposed to explain their antibacterial activity  [30]. For this reason, molecular docking studies were conducted on the FtsZ protein from S. aureus. The berberine core (5 fused-rings substructure) and compounds 15, 16, 24, 25 were investigated. Based on docking experiments (Figure  5 ), the core berberine substructure was predicted to be deeply buried in a hydrophobic pocket from the binding site, with an overlap with the known PC190723 inhibitor from reference 4DXD structure.
((J-L ). Graphs (A,D,G,J) show the dose-dependent effect of 60 min incubation at 37 °C with increasing concentrations of derivatives. Graphs (B,E,H,K) show the time-dependent effect at 37 °C of derivative at 4-times their MIC. Graphs (C,F,I,L) show the time-dependent effect at 37 °C of derivative at 100 µ M. In all cases, results were expressed as percentage of membrane permeabilization using CTAB at 300 µ M as positive control giving 100% permeabilization. Results corresponding to means ± SD (n = 3) were fitted using the Graph Pad Prism software.
Molecular Docking FtsZ protein from S. aureus (FtsZ, PDB code: 4DXD) has been previously shown, using in vitro biological assays, to be the target of antibacterial berberine's derivatives  [30]. Since FtsZ protein is In all cases, results were expressed as percentage of membrane permeabilization using CTAB at 300 µM as positive control giving 100% permeabilization. Results corresponding to means ± SD (n = 3) were fitted using the Graph Pad Prism software. More precisely, the ABC rings (from the dioxole end) of the berberine core were superimposed on the chlorothiazolopyridine moiety of PC190723. Interestingly, the variable sidechain was also directed toward the exit of the binding site, allowing large modifications as highlighted by the diversity of the sidechains from reported active compounds in this study.
Acknowledgments: We would like to thank  Aurélie Tasiemski  ( Institut Pasteur de Lille ,  U1019-UMR 8204 -CIIL -Center for Infection and Immunity of Lille, F-59000 Lille, France)  for providing us the V. diabolicus strain used in this study. We would also like to thank  Gilles Iacazio  from the AVB platform (iSm2, Marseille) were antifungal activities were tested. Finally, we also thank the Datacenter IT and Scientific Computing (DISC) and  Molecular Modeling  ( Int3D ) platforms of the CRCM.
SIM imaging Single and dual-color SIM imaging was performed on an 3D NSIM Nikon inverted fluorescence microscope (Eclipse Ti; Nikon) equipped with an electron charge coupled device camera (iXon3 897; Andor Technologies). The microscope was equipped with a 100x 1. 49 NA oil immersion objective (CFI Apochromat TIRF 100XC Oil; Nikon). Live-cell imaging was performed at 37°C using a 488 and 561 nm laser. Acquisition setting were adapted to yield the best image quality with minimal photobleaching (laser power 0. 5-15%, Exposure time 30-100 ms). Images were captured using NIS elements software (Nikon) at temporal resolution of 1 s for single-color and 6-8 s for dual-color imaging. Imaging was performed at 37˚C in pre-warmed Leibovitz medium. Each sample was imaged for a maximum of 90 minutes.
Instant SIM (iSIM), Single and dual color instant SIM imaging was performed on a custom-built microscope setup as previously described {York et al 2013; Curd et al 2015}. Fluorescence was collected with a 1. 49 NA oil immersion objective (APONXOTIRF; Olympus), with 488 nm and 561 nm excitation lasers and an sCMOS camera (PrimeBSI, 01-PRIME-BSI-R-M-16-C, Photometric). Images were captured at 0. 5-5 s temporal resolution for both channels. All imaging was performed at 37˚C in Leibovitz media. Raw iSIM images were subsequently deconvolved using the Lucy-Richardson deconvolution algorithm  11  provided by Hari Shroff implemented in MATLAB, and were run for 40 iterations.
Confocal imaging Ratio-metric imaging of Grx1-rGFP was performed on an inverted microscope (DMI 6000; Leica) equipped with hybrid photon counting detectors (HyD; Leica). The sample was excited sequentially frame by frame with 408 nm and 488 nm with the detection set to 500-535 nm. Fluorescence was collected through a 63x 1. 40 NA oil immersion objective (HC PL APO 63x/1. 40 Oil CS2; Leica). Images were captured using the LAS X software (Leica). All imaging was performed at 37˚C in pre-warmed Leibovitz medium for maximum 90 min per sample. For confocal microscopy of fixed BrU samples the imaging was performed using a Leica TCS SP8.
Foci that occurred in both channels were then counted as relevant mitochondrial RNA-transciption granules, whereas nuclear transcription for instance, was excluded from the analysis. Multiple thresholds were tried, where 150 a. u. provided the most sensible results in control cells and are represented in Extended Data Fig. 3g , though all other data are provided in the source-data files, and followed the same trend. The same pipeline was applied to both Control and UV-treated samples. The relevant Fiji-macro scripts & metadata allow for 100% traceability of the results, and will be available at github. com/TimoHenry or upon reasonable request.
Statistics Sample sizes were determined on the basis of prior experience in previous experiments. All statistics were performed using OriginPro software. First, datasets were tested for normal distribution using D'Agostino-Pearson normality test (significance value of 0. 05). If a dataset failed this test, a non-parametric test was chosen to compare significance of means between groups (Mann-Whitney test for two samples and Kruskal-Wallis test for more than two samples). For normally distributed datasets, a t-test was chosen to compare two samples and ANOVA was used for more than two samples. Critical comparative datasets (Fis1 and Mff datasets, Drp1 intensity analysis, ER analysis) were partially re-analyzed or analyzed with an automated analysis pipeline to exclude observer bias. p values < 0.
ACKNOWLEDGMENTS We thank  Cécilia Cottiny  and  Mariona Colomer  for experimental and technical assistance,  Mary-Claude Croisier  and  Graham Knott  from the  BioEM (EPFL)  for carrying out the electron microscopy and  Thierry Laroche  and the  BIOP (EPFL)  for imaging support. We thank  Thomas Misgeld ,  Jean-Claude Martinou  and  Pavan Ramdya  for feedback on the manuscript. Research in S. M. 's lab is supported by the  National Centre of Competence in Research Chemical Biology , the  European Research Council  ( CoG 819823 , Piko) and the  Swiss National Science Foundation  ( 182429 ). T. K. received funding from  European Molecular Biology Organization  ( ALTF-739-2016 ) and the  Munich Cluster for Systems Neurology. This work is supported by grants from the  Swiss National Science Foundation  to T.