dataesr/scientific-paragraphs-categorization huggingface dataset:
The overall intensity of light emerging from the waveguides was deduced using the MATLAB software. Assessments of waveguides with a width ranging from 10 to 50 µm indicated that the emerging light intensity lay in a rather narrow range of +/-15%. This observation firstly provides an indication of the reasonable reproducibility in our experimental conditions, even though the XYZ and angle positioning of the injection fiber has not been optimized. Besides, it enables to conclude that i/ the channel waveguide / diffraction grating coupling efficiency is not significantly influenced by the waveguide width, and ii/ the overall intensity of light propagating in the multi-mode waveguides remains identical regardless of the number of guided modes and their interferences. • Architecture assessment: discussion The previous data provide a proof of concept that channel waveguide / diffraction grating coupling can effectively been achieved in our sol-gel derived micro-structured architecture.
If there were no better variants than the parent (screening round 2), no secondary round was performed. To determine which variants to patch, data across all screening runs was evaluated. This way each construct was measured at least three times (from primary screens) or more (if included in the secondary screen). Primary cell culture. Hippocampal neurons were isolated from embryonic day 18 Sprague Dawley rat embryos by dissociation in RPMI medium containing 5 units/mL papain (Worthington Biochemical) and 0. 005% DNase I at 37 °C and 5% CO2 in air. Dissociated neurons were plated at a density of 3. 5 × 10 4 cells/cm 2 in 24-well plates on washed 12-mm No. 0 glass coverslips pre-coated overnight with > 300-kDa poly-D-lysine hydrobromide (Sigma-Aldrich). Cells were plated for several hours in Neurobasal media with 10% FBS, 2 mM GlutaMAX, and B27 supplement (Life Technologies), then media were replaced with Neurobasal with 1% FBS, 2 mM GlutaMAX, and B27.
Half of the media was replaced every 3-4 days with fresh media without FBS. 5-Fluoro-2′-deoxyuridine (Sigma-Aldrich) was typically added at a final concentration of 16 µM at 7-9 DIV to limit glia growth. Neurons were transfected at 9-11 DIV using a modified Lipofectamine 2000 (Life Technologies) transfection procedure in which media in one well of a 24-well plate was replaced for 60-90 min with 200 µL of DNA-lipid complexes (100 ng indicator DNA, 400 ng empty pNCS vector, 1 µL Lipofectamine 2000, 200 µL Neurobasal with 2 mM GlutaMAX). Procedures were carried out in compliance with the rules of the Stanford University Administrative Panel on Laboratory Animal Care. Whole cell patch clamping and imaging of HEK293A cells. Patch-clamp experiments were mainly done as previously described  11. Electrical signals were recorded in voltage-clamp mode with Multiclamp 700B amplifier and pClamp software (Molecular Devices).
Images were captured at 200 Hz, and fluorescence was analyzed from the perimeter of the cell. To characterize kinetics of ASAP indicators, images were acquired at 2. 5 kHz from an area cropped down to 64 × 64 pixels and further binned to comprise only 8×8 pixels. Fluorescence was quantified by summing signal from all 64 pixels. Command voltage steps were applied for 1 s, and an average of two identical steps was contributed by each cell for analysis. Double exponential fits were applied to a 500-ms interval after the voltage step using IgorPro sofware (WaveMetrics). For all experiments fluorescence traces were corrected for photobleaching by dividing the recorded signal by an exponential fit to the data. Data were fit using a double exponential or a single exponential (once the initial rapid photobleaching was excluded).
The fluorescence signal was acquired by a PMTSS photomultiplier tube (Thor Labs) low-pass filtered at 3 kHz and digitized at 50 kHz. Whole cell patch clamping and imaging of cultured hippocampal neurons. Cultured rat hippocampal neurons were used for patchclamping at 11-14 DIV, 2-4 days post-transfection. Patch-clamp recordings were performed as above with HEK293A cells. Bath and intracellular solutions were the same, except CNQX disodium salt hydrate (Sigma-Aldrich) was added to the extracellular solution in some cases to isolate current-evoked APs. Recordings in neurons were performed in current-clamp mode without holding current injection. APs were elicited by injecting 1-ms pulses of 700-1100 pA of current. pClamp (Molecular Devices) software was used to record electrode voltages, which were corrected for the liquid junction potential post hoc.
Images were captured at 1 kHz with 4 × 4 binning, and fluorescence was measured from all pixels of the cell body. Only neurons with resting membrane potential less than -50 mV, Ra less than 17 MΩ, and Rm at least 10-fold greater than Ra were analyzed. For those cells, only APs with a peak amplitude of at least 0 mV and width less than 5 ms at -20 mV were used for ASAP characterization. Three to 34 APs were captured per neuron, and their average (APs per cell) was used for statistics. Fluorescence traces were corrected for photobleaching and normalized to baseline by custom routines in MATLAB, then exported to Excel (Microsoft) for graphing. For Supplementary Movie 1 and Supplementary Fig. 8a , ASAP3-expressing hippocampal neurons were imaged while in voltage-clamp mode with an ORCA Flash4.
0 V2 C11440-22CA CMOS camera (Hamamatsu) at 200 Hz. Evaluation of brightness and soma-targeting in hippocampal neurons. GEVIs were transiently expressed in cultured hippocampal neurons and imaged 2 days plater at 12 DIV in extracellular solution. FusionRed with a C-terminal farnesylation motif for membrane targeting was co-expressed from the same plasmids via an internal ribosome entry site. Cells were imaged on an Axiovert 200M inverted microscope with a 20× 0. 75-NA (for brightness) or 40× 1. 2-NA (soma-targeting) objective (Zeiss) and an X-Cite 120 metal-halide lamp (Lumen Dynamics) as the excitation light source. The following excitation (ex) and emission (em) filters were used: ASAP, ex 490/10-nm, em 525/40-nm; FusionRed, ex 555/25-nm, em 620/60-nm. Images were captured unbinned on an Orca-ER CCD camera (Hamamatsu) with Micro-Manager software  50.
Image processing was done using a custom algorithm written in MATLAB that segmented neurons into soma and neurite regions and quantified fluorescence from the perimeter for both channels. ASAP fluorescence intensity was then divided by FusionRed intensity to obtain a measure of relative brightness per molecule or to determine depletion of ASAP3-Kv signal in neurites relative to the soma. Confocal imaging of cortical slices. Stereotaxic injections of ASAP3 and ASAP3-Kv AAVs were performed on C57BL/6 mice under ketamine/xylazine anesthesia. AAV2/1-EF1α-ASAP3 or AAV2/1-EF1α-ASAP3-Kv (obtained from IBENS core, both at 3. 5 × 10 12 genome copies (GC)/mL) were diluted 1:5, and co-injected with AAV2/1-hSYN-Cre (Penn vector core, 1. 9 × 10 13 GC/mL, 1:500 dilution). 500 nL of virus mixtures were injected at 200 nL/min into the same animal, with ASAP3 into the left hemisphere (coordinates AP 2.
A Dodt scanning gradient contrast was used to guide the pipette relative to the slice for patch-clamp recordings. ASAP-expressing neurons were identified under two-photon illumination. The pipette (resistance in the bath of 3-5 MΩ) was carefully approached to targeted neurons. The intracellular solution contained 120 mM K-gluconate, 20 mM KCl, 10 mM HEPES, 2 mM MgCl2, 2 mM Mg2ATP, 0. 3 mM NaGTP, 7 mM phosphocreatine, 0. 6 mM EGTA (pH = 7. 2, 295 mOsm). Slight negative pressure was applied to break the giga-seal to the whole-cell configuration. All experiments were performed in the current-clamp mode. The electrophysiological signal was amplified with a Multiclamp 700B (Molecular Devices) and low-pass filtered at 2 kHz. The signal was digitized at 10 kHz with a Digidata 1440A (Molecular Devices) and recorded with Clampex 11.
Photon counts were acquired on a personal computer and the microscope was controlled with a software written in LabVIEW. ), 400 nL of which was injected at a flow of 50 nL/min into the cerebellum, the CA1 area of the hippocampus and sensory cortices (CA1 coordinates from bregma : AP -2. 3 mm, ML -2 mm, DV -1. 3 mm from dura, n = 2 mice; S1FL : AP -0. 3 mm, ML -2. 5 mm, DV -0. 3, n = 1 mouse; V1 : AP -3 mm, ML -2. 5 mm, DV -0. 3 mm). A preoperative analgesic was used (buprenorphine, 0. 1 mg/kg). Hippocampal surgery was performed 7 days later as previously described  26, 54  , but without water restriction. Briefly, a glass-bottomed cannula was inserted on top of the dorsal hippocampus after aspiration of the cortex, and secured with Kwik-Sil at the tissue interface and Superbond at the skull level.
For speed/theta correlation analysis, the animal's speed was binned every 0. 5 cm/s. For optical spikes/theta oscillation phase-locking, we looked for the distribution of optical spike times on the oscillation phase at the peak frequency with 2π/16 radians bin size. We normalized the distribution by the number of deflections per cell. For display purposes only, two cycles are shown. Statistical analysis. For differences between ASAP2s and ASAP3 in responses to APs in cultured neurons, a power calculation was performed in the G*Power 3 program  55  to determine the number of neurons required for detecting a 25% difference in response amplitudes at an alpha level of 0. 05, based on measured variability in ASAP2s response amplitudes  11. For differences between ASAP variants in brightness in cultured neurons, variability of brightness was measured in a pilot experiment and a power calculation was performed in G*Power 3 to determine the number of neurons required for detecting a 25% increase in brightness between any two indicators among five indicator variants at an alpha level of 0.
Acknowledgements. We thank  Lin Ning  ( Lin laboratory ),  Luke Kaplan , and  Bianxiao Cui  ( Stanford University ) for assistance with neuronal cultures,  Chaleen Salesse  and  Paul De Koninck  ( CERVO Brain Research Centre, Université Laval ) for assistance with organotypic slice cultures,  Patrick Houlihan  and  David Clapham  for advice on CHO cell culture and transfection,  Stephane Supplisson  for assistance with electrophysiology on CHO cells,  Annick Ayon  for assistance with ASAP subcloning and viral vector constructions and  Walther Akemann ,  Astou Tangara  and  Yvon Cabirou  ( ENS facilities, Paris ) for assistance with in vivo imaging. We also thank  Gui-Rong Li  ( University of Hong Kong ) for the gift of the  HEK293-Kir2. 1 cell line and  Karl Diesseroth  ( Stanford University ) for the gift of pAAV-CAG-FLEX-WPRE plasmid.
This work was supported by the  Marc A. Cohen Graduate Fellowship Research Fund , the  Stanford Bioengineering PhD Program and American Epilepsy Society predoctoral fellowship  (M. C. ); the  China Scholarship Council   Joint PhD Training Program  (D. S. );  National Natural Science Foundation of China  grant  31630030  (G. B. );  Stanford Neuroscience PhD Program  training grant  5T32MH020016  and the Post-9/11  GI Bill  (S. W. E. ); NIH BRAIN Initiative  grant  1U01NS090600  (M. J. S. and M. Z. L. );  Canadian Institutes of Health Research  grant  MOP-81142  and  Natural Sciences and Engineering Research Council of Canada  grant  RGPIN-2015-06266  (K. T. ); a graduate fellowship from the  Natural Sciences and Engineering Research Council of Canada  (S. C. ); a postdoctoral fellowship from  Agence Nationale de la Recherche  grant  ANR-10-LABX-54   MEMO LIFE  (V.
V. );  La Fondation pour la Recherche Médicale  grant Equipes  FRM DEQ20140329498  and  Agence Nationale de la Recherche  grant  ALPINS ANR-15-CE19-0011  (S. D. ); and additional funding from  INSERM ,  CNRS , and  ENS  (J. B. , B. M. , V. V. , and S. D. ). We also acknowledge the assistance of the  Stanford Neuroscience Gene Vector and Virus Core facility , which was supported by  NIH  grant  P30NS069375 , and the  IBENS Imaging Facility , which was supported by grants  NERF 2009-44  and  NERF 2011-45  from the  Région Ile-de-France ; grant  DGE 20111123023  from the  Fondation pour la Recherche Médicale ; a 2011 grant from the  Fédération pour la Recherche sur le Cerveau and Rotary International France ; and grants  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02   PSL* Research University , and  ANR-10-INSB-04-01   France-BioImaging Infrastructure  from the  Agence Nationale de la   Recherche Investissements d'Avenir program.
First, RCWA is chosen for its extremely efficient monochromatic simulations, which is very well suited for this type of 1D optimization calculations. For broadband calculations, the Discontinuous Galerkin Time-Domain (DGTD) method  [49]  gives accurate broadband results and serves as a reference. Here, we have used the RCWA implementation reticolo within the optimizer. The use of RCWA can be justified by a numerical cross-comparison with the DGTD solver from the DIOGENeS software suite  [50] , as depicted in Fig. 1(d ). The comparison is carried out over a wide spectrum and shows excellent agreement. Generally, if not indicated otherwise, all simulations have been performed in the transverse magnetic (TM) polarization, i. e. for an electric field oscillating perpendicular to the ridges axis.
Optimization results For the optimization we apply a gradient-free pattern search algorithm, implemented in Matlab's optimization toolbox. Pattern search is well suited for this kind of setups due to its simplicity and robustness. The optimizer seeks to maximize the transmission efficiency of the first diffracted order. In a first step, we optimize the one-dimensional grating structure at a fixed design angle and investigate the broadband and geometric robustness of the transmission efficiency η -1 of the first order diffracted mode. The height of each grating element, the grating period and the minimum feature size are given by h = 1000 nm, Γ = 1322 nm and d = 90 nm, respectively. The grating period Γ implies a deflection angle of θ = 27°, i. e. we seek for an optimal geometry resulting in maximum light intensities at θ = 27°.
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. CDLIB: a python library to extract, compare and evaluate communities from complex networks.
Network analysis programming libraries are nowadays available to computer scientists, physicists as well as mathematicians; moreover, graphical tools were developed for social scientists, biologists as well as for educational purposes. One of the hottest topics in network science is the Community Discovery, the task of clustering network entities belonging to topological dense regions of a graph. Although many methods and algorithms have been proposed to cope with this problem, and related issues such as their evaluation and comparison, few of them are integrated into a common software framework, making hard and time-consuming to use, study and compare them. Only a handful of the most famous methods are available in generic libraries such as NetworkX and Igraph, and running any other method requires to: 1 Find a reliable implementation 2 Learn how to use it 3 Transform the graph to study in the requested format 4 Export and transform the resulting clustering in a format suitable to the user needs.
This laborious process is probably the cause of two strong weaknesses of the Community Discovery field: • Despite the large number of algorithms published every year, most of the newly proposed ones are compared only to a few classic methods. • Practitioners barely ever try different methods on their data, while it is well known in the field that different methods often provide widely different solutions. To cope with these issues -as previously done also in the network diffusion context with NDLIB  (Rossetti et al. 2018 ) -we introduce a novel library designed to easily select/apply community discovery methods on network datasets, evaluate/compare the obtained clustering and visualize the results. CDLIB represents a comprehensive, easy to use solution for network clustering. This paper aims to introduce CDLIB, describing its main features, and placing it among other tools already available to social network analysis practitioners.
CDlib: community discovery library We designed CDLIB -"(C)ommunity (D)iscovery Library" -to simplify the definition/execution/evaluation of community discovery analysis. CDLIB is a Python package built upon the network facilities offered by NetworkX 1 and Igraph 2. The library, available for Python 3. x, is currently hosted on GitHub 3 , on pypi 4 and has its online documentation on ReadTheDocs 5. Moreover, CDLIB is also made available through the SoBigData. eu catalogue 6. 1 NetworkX: https://goo. gl/PHXdnL 2 Igraph: https://goo. gl/Hi5Srf 3 CDLIB GitHub: https://goo. gl/Gu3VSV 4 CDLIB pypi: https://goo. gl/FPtHHU 5 CDLIB docs: https://goo. gl/ggGbUz 6 SoBigData: http://www. sobigdata. eu A complete list of the diffusion models implemented in CDLIB v0. 1. 0, along with their short descriptions, is reported in the Appendix.
Such abstract type exposes several facilities that will be discussed further on. The standardization of clustering representation -and the decoupling of input/output w. r. t. algorithmic implementations -makes easy to extend CDLIB with novel algorithms. Any CD method written in python can be included in our library just wrapping it into an ad-hoc input/output harmonization process that: • ensures the conversion, if needed, of a NetworkX/Igraph object into the graph representation used by the algorithm, and • reshapes the algorithm results in a concrete class derived by Clustering. The following code shows an example of experiment definition, configuration and execution. Listing 1 Example of the execution of a CD algorithm on a NetworkX Graph object. In lines 1-2 are imported all the required modules; in line 5 the Zachary's Karate Club  (Zachary 1977)  g is built using NetworkX; in line 8 the Demon  (Coscia et al.
This differentiation among clustering types allows to fulfill two main goals: (i) enable clustering specific evaluation methods/representation for each specific concrete class; (ii) make the CDLIB framework easily extendible to support, in future releases, even more specific clustering types (e. g. , multiplex node/edge clustering, dynamic node/edge clustering). For the sake of simplicity, let us consider as an example the structure of the object instance produced by a generic node clustering algorithm of our library. The NodeClustering object contains the following information: (i) the list of communities obtained; (ii) a reference to the original graph object; (iii) all the metadata regarding the community extraction process (i. e. , community discovery algorithm name and parameter configuration); (iv) a flag that specifies if the clustering is overlapping or not; (v) the percentage of nodes that are involved into the clustering.
e. , pooling different algorithms with the aim of comparing their best partitions w. r. t. a given quality score). Listing 3 Example of multiple execution of two CD algorithm. In Example 3 is shown how to perform both pooling and optimal partition search using two facilities offered by cdlib. ensemble. As a first step, lines 9-18 defines the parameter ranges of the two algorithms selected to partition the NetworkX graph (Louvain  (Blondel et al. 2008 ) and Demon). Parameters and BoolParameters are named tuples defined in cdlib. ensemble that allow the submodule functions to generate parameter ranges for CD methods. Line 21 the function ensemble. pool executes the specified methods (leveraging all the parameter combinations previously expressed) on the input graph g. In some scenarios, one's objective could be to search the (potentially complex) parameter space of an algorithm in order to discover automatically the clustering optimizing a given quality function (parameter-tuning).
Visualization Facilities To allow the final user visualising clustering results, CDLIB exposes a set of predefined visual facilities using Matplotlib 7. These facilities are exposed through the visualization submodule cdlib. viz. Such submodule offers two different classes of visualization: Network Visualization, that allows plotting a graph with node color coding for communities (Fig. 1 ). Listing 5 Example of community network visualization (Results shown in Fig. 1 ) 1 from c d l i b import a l g o r i t h m s , v i z 2 import networkx a s nx 3 4 g = nx. k a r a t e _ c l u b _ g r a p h ( ) 5 coms = a l g o r i t h m s. l o u v a i n ( g ) 6 pos = nx. s p r i n g _ l a y o u t ( g ) 7 v i z. p l o t _ n e t w o r k _ c l u s t e r s ( g , coms , pos ) 8 v i z. plot_community_graph ( g , coms ) Analytics plots, where community evaluation outputs can be easily used to generate a visual representation of the main partition characteristics (Fig.
CDlib competitors To the best of our knowledge, until now, no existing library includes large numbers of CD algorithms and corresponding measures to compare their results. Users that want to compare CD algorithms encounter several difficulties: (i) different programming languages, (ii) different input format for the graph file, (iii) different output format of the results. Two widespread libraries, NetworkX and Igraph, have implemented a handful of the most famous CD algorithms and performance measures, but they represent only a minor fraction of existing algorithms and measures. CDLIB is the first library to provide a standardized input/output for a wide range of CD algorithms, encompassing not only those available through those two libraries, but also a large spectrum of methods they do not provide.
Collections of implementations The most simple collections of implementations are repositories, listing of methods that can be implemented in different languages, with different input and outputs. While those collections are helpful to discover existing implementation, the problem we stated in the introduction is still present: one needs to satisfy all the required implementation dependencies, learn how to instantiate each method, and format inputs and outputs to suit its needs. Among such repositories, we can cite: • RapidsAtHKUST 10  : a repository collecting some overlapping CD algorithms written in C++, Java and Python. In other projects, wrappers are built over existing methods, so that they can be run more easily, notably in CoDACom 11. The result is not a standalone library, but a collection of scripts allowing to run methods more conveniently.
Standalone libraries We already cited NetworX and Igraph, well-known network science libraries. Both of them contains several algorithms (5 for NetworkX, 9 for Igraph) and some quality functions to evaluate them. A few other resources exist: • CommunityDetection 12  : A python library, with no English documentation, this git-hub repository includes implementations for 7 well-known methods. It has not been maintained in the last three years. • Circulo 13  : a Community Detection Evaluation Framework, is probably the closest competitor to CDLIB; it is a project started in 2014. The framework is written in Python, based on Igraph, and includes functionalities of data extract transform load (ETL) and performance metrics. The metrics include internal statistical measures of a community (i. e. density), external measurements (i.