dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011 http://dekois. com/dekois_orig. html BindingDB Li's decoy set (Diller 2011) 2003 -Literature and Li, 2003) Jain's decoy set (Jain 2006 http://www. jainlab. org/downloads. html PDBbind and Nicholls, 2008) GPCR Ligand 2012 http://cavasotto-lab. net/Databases/ GLIDA and PDB (GLL)/Decoys GDD/ structures and Directory of Useful Database (GDD) (Xia et al. , 2014) Decoys (DUD) (Huang 2006 http://dud. docking. org Literature and PDBbind Vilar et al. , 2010 et al. , 2006) Decoy Finder 2012 http://urvnutrigenomica-ctns. github. io/ User (Cereto-Massagué DecoyFinder/ et al. , 2012) DUD Clusters (Meyer, DUD Enhanced 2007) (DUD-E) (Mysinger 2008 http://dud. docking. org/clusters/ 2012 http://dud. docking. org/r2/ DUD CHEMBL WOMBAT Datasets et al. , 2012) 2007 http://dud. docking. org/wombat/ WOMBAT (Meyer, 2007) DEKOIS 2.
0 (Ibrahim 2013 http://www. dekois. com BindingDB et al. , 2015a) Maximum Unbiased 2009 -PubChem Validation (MUV) (Rohrer and Baumann, NRLiSt BDB (Lagarde 2009) et al. , 2014a) 2014 http://nrlist. drugdesign. fr CHEMBL DUD LIB MUBD-HDACs (Xia et al. , 2015) 2009 http://dud. docking. org/jahn/ 2015 -DUD-cluster CHEMBL and literature.
RESULTS Generation of Genome-Edited Cells for EndoA2-GFP, Dnm2-GFP, and CLC-RFP To verify the implication of endophilin in the constitutive uptake of IL-2R, we knocked down endophilinA1 (EndoA1) and/or EndoA2 by small interfering RNAs (siRNA) in Hep2b cells, an established epithelial cell line stably expressing only the b chain of IL-2R (IL-2Rb). We observed that the depletion of EndoA1 and/or A2 clearly inhibited IL-2Rb entry up to 50%, as quantified by Icy software  (Basquin et al. , 2015; de Chaumont et al. , 2012) , without affecting the internalization of transferrin (Tf, a bona fide CME marker) (Figures  S1A-S1C ). The inhibition of IL-2R was equivalent in either single-or double-knockdown cells for EndoA1 and EndoA2, hence indicating that endophilins do not have a compensatory effect.
We implemented this tracking method (plugin eTrack) in the open-source image analysis software Icy (http:// icy. bioimageanalysis. org/) (de Chaumont et al. , 2012). We compared eTrack to a state-of-the-art tracking algorithm (enhanced Multiple Hypothesis Tracking [eMHT] method;  Chenouard et al. , 2014) , and we observed that the shortest tracks (<40 s) were diminished by 30% with eTrack (42% of the tracks) compared to eMHT (54. 8% of tracks) for clathrin-coated structures and was similar (61%) for IL-2R. We also observed that the number of tracks with a large total displacement (>0. 5 mm), which shall correspond to tracks with false step-by-step associations leading to artifactual spot motion, was drastically diminished with eTrack (3. 0% for clathrin and 3. 5% for IL-2R) compared to eMHT (9.
EXPERIMENTAL PROCEDURES Statistical Analysis of CME and CIE Endocytic Tracks In the CLC datasets, we eliminated all the tracks with length smaller than 290 s and either starting at 1 s or ending at 300 s. Similarly, in the IL-2R datasets, we eliminated all the tracks with length smaller than 580 s and either starting at 1 s or ending at 600 s. Moreover, in all datasets of co-localized tracks, we also eliminated all the tracks with co-localization duration with the protein equal to 1 s. After these modifications, the number of data in each dataset is, on average, divided in half (from 20,000 to 10,000 for the IL-2R datasets and from 10,000 to 5,000 for the CLC datasets). In the following, we describe the statistical analysis for the CLC datasets. The same approach has been used for the IL-2R dataset.
The CLC-siRNA Control-non-co-localized dataset follows an exponential distribution, Expðx; lÞ = le Àlx : To define different CLC populations, we fit the dataset with the following mixture model: Fðx; qÞ = p 0 Expðx; lÞ + X N i = 1 p i N ðx; m i ; s i Þ; X N i = 0 p i = 1; where N ðx; m i ; s i Þ = ð1=s i ffiffiffiffiffiffi 2p p Þe ÀððxÀm i Þ 2 =2s 2 i Þ represents the Gaussian distribution corresponding to the i-th population and q = À l; p 0 ; m 1 ; s 1 ; p 1;. ; m N ; s N ; p N Á denotes the set of parameters of the model. The (3N + 1) parameters are estimated by the maximizing the likelihood function via the expectation-maximization algorithm. The number of Gaussian distributions is chosen by performing the BIC (Figure  S4 ), which selects the model that maximizes the likelihood by minimizing the number of parameters involved in the model.
ACKNOWLEDGMENTS The authors would like to thank  P. H. Commere  from the flow cytometry facility of  Institut Pasteur  for the help and support with single-cell sorting. We also gratefully acknowledge  S. Dallongeville  and  V. Meas-Yedid  ( Institut Pasteur ) for debugging and  A. Saffarian  and  V. Malarde ( Institut Pasteur ) for technical support. We thank  D. Drubin  for generously sharing reagents. We also thank  M. Anderson ,  M. Ferrari , and  P. Sansonetti  for comments on the manuscript and support. T. L. and A. G. were supported by a  Roux Fellowship  from  Institut Pasteur  and T. L. partially supported by  LABEX IBEID  ( ANR-10-LABX-62-IBEID ). This is in part supported by an  ANR  grant to  J. -C. O. -M. ( ANR-10-INBS-04-06   France-BioImaging ). L. B. was funded by the  University Paris Sud/Saclay , ED SDSV.
This method can be a solution to the sizing problem. In this way, it allows the planning of FiWi communication networks considering certain restrictions. The speed of transmission of data does not intervene in the model as a restriction, but it can be estimated knowing the packet rate, transmission rates and data length generated by the SMs. In this research, these values are taken from the literature. Another calculated parameter that depends directly on the distance is the FSPL. These parameters are referents to determine the importance of the topology and how it affects the network for the minimization of the end to end delay and the losses in the free space. The model minimizes the number of SMs that use cellular technology through the incorporation of WiFi technology. In summary, in this work, we intended to deploy a WiFi communication network optimizing resources through clustering techniques.
Once the data are merged in the BSs, they will be transmitted through optical fiber to the central office with a cost C 3 (see Figure  1 ). The C 1 , C 2 and C 3 variables are identified as unit costs for each type of technology: cellular, WiFi and optical fiber, respectively. In addition, it should be noted that  In Equations (  1 )-(  3 ), the total costs of each technology are expressed: WiFi, cellular and optical fiber: C 3 >> C 1 >> C 2. C w f = C 2 * k ∑ j=1 (s j -1) (1) C cell = C 1 * k (2) C f op = C 3 * d f op (3) where s j represents the length of each cluster, k is the maximum number of clusters to be deployed in the network and d f op is the required distance of optical fiber to be used in the FiWi network. In this way, the optimization problem can be expressed as follows: min C w f + C cell + C f op (4) subject to: C i ∈ + , ∀ i = 1, 2, 3.
Therefore, the higher the density of SMs, the better the results obtained in terms of optimization due to the closeness of the SMs. Therefore, when varying the capacities of a UDAP, the following is modified: the topology, the average number of traversed links by the package to reach its destination, the length of the cluster, the end to end delay, the link capacity and the coverage distance. In Figure  5b , significant variations in the global delay are depicted for the data aggregation as the traffic generated by each SM increases. Therefore, the higher the traffic generated, the greater the FiWi network delay. This is because the increase in delay is directly proportional to the increase in capacity. If the capacity of the UDAP increases, the greater will be the length of the cluster, and therefore, the greater will be the traffic generated in each cluster; resulting in an increase in the global end to end delays.
The model has been shown to be scalable in time and space and has the following characteristics: presents finite solutions and optimizes the resources required by the FiWi network using an efficient clustering method (different to the traditional). Moreover, with the N-NST algorithm, balanced clusters can be built, which are subject to real restrictions, such as capacity and coverage. The heuristics works with georeferenced scenarios, reducing to the maximum the aggregation delays of data of each cluster using the ODB algorithm. Furthermore, it minimizes FSPL and is a planning model of NP-complete complexity. The complexity of the problem lies in the population density of SMs, since, in a graph with n SMs, there are n n-2 possible trees; thus, the proposed model is combinatorial in nature.
Here we present an innovative solution for the precise tracking of eye and hand movements in a single reference frame. Importantly, our solution allows behavioral explorations under normal and perturbed conditions in both humans and monkeys. It is based on the integration of two noninvasive commercially available systems to achieve online control and synchronous recording of eye (EyeLink) and hand (KINARM) positions during interactive visuomotor tasks. We also present an eye calibration method compatible with different eye trackers that compensates for nonlinearities caused by the system's geometry. Our setup monitors the two effectors in real time with high spatial and temporal resolution and simultaneously outputs behavioral and neuronal data to an external data acquisition system using a common data format.
Special care was taken to ensure the precise synchronization of the behavioral and electrophysiological recordings. For the purpose of cross-species comparison, we built identical setups for use in monkey and human experiments. In our monkey setup, the system was connected to a device that simultaneously records the activity of multiple single neurons and local field potentials (LFPs), whereas in our human setup it was connected to an electroencephalography (EEG) device. The setups employ similar hardware architectures to accommodate technical demands and run principally identical software relevant for task development, control of behavior, eye calibration, and online data processing. Asaad et al. (2013)  underlined the need to develop tools that are accessible by a large part of the scientific community and compatible with most of the classically used hardware.
Indeed, laboratories may develop local solutions to fulfill the needs of a specific project, but these solutions often rely on internal knowledge or skill (e. g. , a specific programming language or custom-made pieces of equipment). As a consequence, it could sometimes be difficult to apply them on other projects despite their descriptions in the methods sections of experimental papers. In this report, we propose a solution that can be adapted to multiple hardware configurations. A comparison with existing solutions is provided in DISCUSSION. For both monkey and human setups we selected the KINARM exoskeleton robot because of its ability to record continuous and precise arm/hand movements in the horizontal plane, with the possibility to perturb the movement and its visual feedback (KINARM Exoskeleton Laboratory, BKIN Technologies).
The KINARM Exoskeleton Laboratory offers eye tracking as an option in its human version, but this is not available in its nonhuman primate version. For measuring eye movements, the EyeLink system was selected for its infrared noninvasive eye movement recordings (SR Research; EyeLink 1000 for monkeys, EyeLink II for humans). However, these systems come with their own proprietary software and specific hardware features and were designed as independent platforms. To develop an integrated experimental setup, we had to establish a clear hierarchy between the participating systems. We selected the hand tracking system as the master component of the setup, which controls task behavior and timing, motor perturbations and load changes, effector calibration and feedback, visual stimuli, and output of behavioral data.
Moreover, experiments in the KINARM system are programmed in MATLAB language (Simulink environment), which is broadly used by experimentalists. The eye tracking system and the data acquisition (DAQ) system (Cerebus, Blackrock Microsystems for monkeys; Biosemi EEG and ADwin Keithley EMG for humans) were integrated as slave components in the setups, meaning that they can be replaced or upgraded without loss of setup functionality. As a consequence of this architecture using a unique master component, both eye and hand movements can be processed synchronously in real time. Finally, we developed a computationally lightweight eye calibration method for both setups to express the position of the gaze in the same (horizontal) coordinate system as the hand positions. In this report we present in detail the complete integration of the eye tracking system into the hand tracking system for each setup in parallel.
MATERIALS AND METHODS Hand/eye movement control systems. Both human and monkey setup configurations were built around the KINARM Exoskeleton Laboratory  (BKIN Technologies; www. bkintechnologies. com). This recording solution for arm/hand movements in a two-dimensional (2D) horizontal plane was composed of a motorized exoskeleton arm and a virtual reality (VR) display. The system was controlled and operated by two computers (see Fig. 1A ): the KINARM interface computer (Intel Core i5 760 2. 80 GHz, 4 GB RAM), where the operator can program a Simulink model (Simulink Real Time, The MathWorks) and gets control and feedback on the task with the provided software (Dexterit-E, BKIN Technologies), and the KINARM real-time computer (Intel Core 2 duo E8400 3 GHz, 1 GB RAM, DOS-based operating system: xPC-Target, The Math-Works) that runs the compiled Simulink models.
The communication between the two computers used a UDP protocol through a direct Ethernet connection. The motorized KINARM exoskeleton was fixed to a chair, with the upper arm and forearm of the participant placed in arm supports at shoulder height, enabling the arm to move in a horizontal space (Fig. 1A ). The positions of the joints (shoulder and elbow) were recorded continuously in real time, with two torque motors capable of applying mechanical loads at each joint independently. The hand position was calculated from the joint angles in real time with a trigonometrical reconstruction (KINARM Simulink Library). The chair/exoskeleton module was then fixed to a VR environment, which displayed the visual stimuli and the hand feedback to the participant. The VR display consisted of a horizontal computer screen (Benq VW2420H, resolution 1,920 ϫ 1,080 at 60 Hz) facing downward, with its image reflected on a horizontal semitransparent mirror.
The signals recorded by these photoresistors were sampled at 1 kHz and stored in the same data file as the behavior. Precise target onset and offset times could therefore be determined off-line according to the space-time function defined above (Fig. 1B ). By adjusting the height of the screen and the mirror appropriately, the reflection of the screen images, i. e. , the stimuli (targets) and the hand feedback representation, fell into the plane of the hand position  (Scott 1999). With internal calibration, the target coordinates and the hand position were expressed in a single reference frame, enabling the setup to react according to the behavior of the participant. The hand underneath the semitransparent mirror could be illuminated, which allowed direct vision of the hand, or obscured and replaced by a projected hand feedback from the task.
The advantage of such an architecture was that the data flow was centralized in the KINARM real-time computer (xPC Target, The MathWorks; https:// www. mathworks. com). An operator controlled the experimental sequence of events and the data collection process in a unique real-time program developed on the KINARM interface computer (Simulink Real-Time, The MathWorks). These programs then ran on the realtime computer, while the operator monitored eye and hand move- ments and visual stimuli on the screen of the interface computer (Dexterit-E, BKIN Technologies; Fig. 1A ). The voltages corresponding to the positions of the eye in the eye tracker image (hereafter referred to as eye position) were collected from two analog outputs of the EyeLink Host PC and sampled in the real-time computer at 1 kHz with an A/D input card (National instruments, PCI-6221).
In this reference frame, the x-axis described the position of the gaze (or the target) along the width of the screen. The y-axis described the position of the gaze in depth. A single real-time program controlled the interactive components of the task for participants by directly reacting to inputs from the eye tracker and the KINARM exoskeleton. In parallel, the program sent behavioral data continuously to the interface computer for storage in C3D format, together with task parameters. Additionally, the gaze and hand positions were outputted at 1 kHz with a D/A output card (National Instruments, PD2-AO-16x16) to the electrophysiology DAQ system. Although our system was designed with the intention of being compatible with any DAQ system equipped with enough analog and digital channels, here we used DAQ systems that were already in use in our laboratory for monkeys (Cerebus, Blackrock Microsystems; http://blackrockmicro.
2A ). The main nonlinearity came from the depth of the work area. Indeed, the use of a horizontal display means that the eye rotation angles (␣ and ␤ in Fig. 2A ) were different for two equivalent gaze shifts on the screen (A and B in Fig. 2A ) when they were located at different depths. To a lesser extent, this difference also existed between the center and the side of the screen. As we used the raw signal, no correction was made by the EyeLink software and an unknown supplementary nonlinearity may come from the eye tracker itself (see EyeLink 1000 user manual, section 4. 4. 2. 1). These nonlinearities were highly dependent on the head and eye positions with respect to the screen and the eye tracker camera. This was particularly true in the human setup, where the camera position had to be adapted more often because of the larger group of participants.
A graphical user interface (available via https://github. com/INM-6/eye-calibration-GUI) was designed in MATLAB (The MathWorks) to provide a user-friendly interface for calibration of the eye position signal that included trial selection, compensatory model generation, and export of model parameters to a Dexterit-E task file. After trial selection (e. g. , after removing blinks and saccades), we computed the average voltages recorded during the different trials for each of the 25 reference points. One typical example of eye position recordings is presented in Fig. 3A , scaled to the range of the targets to facilitate the visualization. It clearly shows that the X v channel amplitude varies as a function of target position along X cm and Y cm. This dependence on the X cm and Y cm couple is even more pronounced for the Y v amplitude.
The consequence of the axis change was to transform regularly distributed data points (in the cm space) into sparsely distributed data points (in the voltage space). To reconstruct a regularly distributed sample, we adjusted a grid to our data with B-spline interpolation (MATLAB function griddata, using V4 method; Sandwell 1987). The results are shown in Fig. 3, B  and C. Finally, to generalize the transformation to the whole work area and to make it easily transferable into the Simulink models to be used during experiments, we fit mathematical functions to these grids. We used 2D polynomials because this family of functions can fit to multiple combinations of distortions (see Fig. 1C , Fig. 3 , Fig. 5 , and Fig. 7 ) and therefore adapt to a large variety of grid shapes. Parameters of the functions are stored in variables in the Simulink task program to adjust its online position conversion module on a daily basis.
Software environment. Our system was designed around the KINARM system, integrating the signal of an eye tracker as an input to the Simulink model that was in charge of the real-time control over the experiment. Multiple third-party solutions exist to manage experimental setups, such as PsychToolbox  (Brainard 1997) , MonkeyLogic  (Asaad et al. 2013 ) based on MATLAB language, or PsychoPy  (Peirce 2007 ) based on Python. They all provide complete toolsets for eye movement collection but no direct integration of the KINARM exoskeleton. The use of one of these solutions would have required an extra computer and would have increased the complexity of the communication. As the KINARM system already provided an open programming environment, we had the possibility to integrate the EyeLink signal directly into it.
GRANTS Funding was provided by  Deutsche Forschungsgemeinschaft  Grant  DE2175/2-1   Priority Program  ( SPP 1665 ),  Helmholtz Portfolio "Supercomputing  and  Modeling for the Human Brain", European Union 's  Horizon 2020 Framework Program for Research and Innovation  under Grant Agreement  N°7 20270  ( Human Brain  Project  SGA1 ,  SGA2 ), and  Associated International Laboratory "Vision-for-Action " between  Centre National de la Recherche Scientifique  and  Forschungszentrum Jülich.
Wikipedia's accuracy for its scientific entries has been proved by comparing it to Encyclopedia Britannica and to PDQ -NCI's Comprehensive Database in  [3, 4]. To sum up, Wikipedia has become the largest accurate reliable free online open source of knowledge. Wikipedia is an interesting target domain for network analysts due to the hyperlinked structure that provides a direct relationship between web pages and topics. Research on such networks has derived content-independent effective metrics to rank nodes and edges of the graph based on their relevance to a given criteria (clustering, importance ranking, etc. ). In this study we concentrate on one of the most popular network analysis algorithms: the PageRank algorithm  [5, 6]. PageRank algorithm could be seen as a Markov chain process relying on the definition of the so-called Google Matrix which describes the network interconnection.
For various language editions of Wikipedia it has been shown that the PageRank vector produces a reliable ranking of historical figures over 35 centuries of human history  [7] [8] [9] [10] [11]  and a solid Wikipedia ranking of world universities (WRWU)  [7, 12]. It has been shown as well that the Wikipedia ranking of historical figures is in a good agreement with the well-known Hart ranking  [13] , while the WRWU is in a good agreement with the Shanghai Academic ranking of world universities  [14]. This paper analyses the networks extracted from 5 language editions of Wikipedia to study the influence of countries on each other. We proceed with this analysis for two sets of countries: i) the 27 member states of the European Union and ii) the top 40 countries according to English Wikipedia PageRank.
This is done in practice by increasing GR (i, j) and then normalizing the column again to unity as it required by the definition of the Google matrix. From our sensitivity analysis on both sets of countries, we extract reasonable and really interesting geopolitical influences. Indeed, for instance in the set of 27 EU countries, our data shows clearly that the Nordic group of nations (Sweden, Denmark, Finland) have strong relationships together. If one of them increases its ties to another EU country alone, the remaining ones see their importance drop. The same observation is made for the group created by Austria, Hungary and Slovenia nations. These observations have been made by geopolitical specialists as well in  [27]  and  [34] , respectively. Another striking result is the impact of the exit of Great Britain from EU on the other European countries.