dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Results were displayed as group means ± standard deviation of the mean, or, as quartiles in the box and whisker plots with the median, first, and third quartiles, as well as ranges, indicated. Statistical analysis was performed using the SPSS Software (IBM, Ehningen, Germany) and a P value of less than 0. 05 was chosen as the significance level for all analyses. The Kolmogorov-Smirnov test was used to establish normal distribution of the data and Levine's test was used to establish normality of variance. Cerebral blood flow and DTI data were compared using two-way repeated measures analysis of variance (ANOVA) with time and region as the two within-subject factors. Water maze data were compared using a mixed design two-way repeated measures ANOVA with surgery (sham versus microcoil) and drug treatment (vehicle versus C21) as the betweensubject factors, and time as the within-subject factor.
However, the main effect of treatment just reached significance (F(1,44) 4. 110, P = 0. 047). Post hoc comparisons revealed a treatment difference only in the microcoil animals. Those treated with C21 exhibiting slightly larger basilar arteries than those treated with vehicle (Figure  6C ). We also calculated ventricle-to-brain ratio from the T 2 -weighted images, which is generally used as an overall indicator of brain atrophy. Ventricle-to-brain ratio was reasonably comparable among the groups despite high variability (data not shown), suggesting no gross brain atrophy in any of the mice.
Compound 21 Treatment Promoted Lymphocyte Infiltration into the Brain We used flow cytometry to estimate the proportion of lymphocytes, and their subtypes, in the brain and blood from animals in each group at the conclusion of the experiments. Interestingly, large increases in T and B cells and macrophages were observed in the brains of all C21-treated animals (microcoil and sham) compared with the corresponding vehicle-treated groups (Table  1 ). This suggests that the microcoil model itself does not produce a systemic inflammatory response, but that treatment with C21 is capable of recruiting leukocytes to the brain. The same was true for CD4+ T cells and B cells in the blood samples (data not shown).
Analysis of grooming and other behaviours Prior to testing, mice were transported from their holding room to the testing room and allowed at least 1 h for acclimation. All observations were performed between 2:00 and 7:00 pm to ensure uniformity throughout trials. Animals were individually placed in a clear cylinder (13 cm in diameter, 16 cm height) for behavioural observation. To assess spontaneous grooming, the mice were video-recorded for 2-3 h using the HomeCageScan software (CleverSys, Inc. , Reston, VA) by side-view and top-view web cameras (Pacific Electro-optics model PA-290 DN). Some mice were also video-recorded for 3 h after L-DOPA injection. The observation cylinder was thoroughly cleaned using 30% ethanol (vol/vol) between subjects. The videos were manually analysed as described by Kalueff et al.
[6]. Single grooming duration was calculated as total duration divided by the number of bouts  [2]. The number of vertical rears and sleeping time ("resting time") during the observation session were also scored for each mouse. The number of clockwise and counter-clockwise rotations (360°) made by the animals were also counted during the sessions. Data were generated for the percentage of ipsilateral events for turns and grooming, compared to the total events, the percentage of complete vs. incomplete grooming bouts and for the percentage of correct vs. total transitions. A correct transition was defined following the typical cephalo-caudal progression (i. e. , paw lick-phase 1 > head wash-phase 2 > body/leg washphase 3 > tail/genital grooming-phase 4), see details in  [3].
Funding was provided by  Inserm  (http:// www. inserm. fr), the  Université Pierre et Marie Curie  ( UPMC, Paris 6 ) (UPMC, http://www. upmc. fr), the  Agence Nationale de la Recherche  (http://www. agence-nationale-recherche. fr)( ANR09-MNPS-014 ) and the  European Research Council  (ERC, http://erc. europa. eu)advanced investigator grant to  JA Girault. Equipment  at the IFM was also supported by  DIM NeRF from Région Ile-de-France  and by the  FRC/ Rotary  "Espoir en tête". The  Girault & Hervé group  is affiliated with the  Paris School of Neuroscience (ENP)  and the  Bio-Psy laboratory of excellence.
The approach used by  Alavandar and Nigam (2008)  in solving the IK problem is based on an adaptive neurofuzzy interface system, which was used to predict and estimate the problem. The said researchers collected training data from the forward kinematics of the 2-Dof and 3-Dof robot manipulator to show the effectiveness of this approach. The obtained results are inconsistent with the analytical IK function. The disadvantage of this method is its limited convergence because collecting data from forward kinematics covers only a part of the IK function domain. Karlik and Aydin (2000)  implemented a robot manipulator with six Dof through the best ANN configuration from two ANNs: a three-layer back-propagation (BP) with six outputs and six four-layer BPs with one output. The error obtained from the second ANN is smaller than that from the first configuration.
3 numerical aperture, NA) objective lens, excitation at 488 nm by scanning laser, and emission light filtered through a HQ528/50 splitter/filter. Quantification of images was performed using Image J software (NIH). Vesicle recycling. Recycling synaptic vesicles in DRG axons were labelled with FM 4-64 (Life Technologies). To stain the total pool of recycling vesicles, axons were loaded by electrical stimulation (10 Hz, 30 V, 5 ms, biphasic square wave) for 20 s in media containing 2. 5 mM FM 4-64 dye. The dye was allowed to remain on the cells for 60 s after cessation of the stimulus to permit complete compensatory endocytosis, and was subsequently removed during a 7-min period with eight complete solution changes. Pharmacology. Drugs were added directly to the culture dish before experiments.
2a ). Recordings were made without series resistance compensation. Series resistances were monitored during recordings and cells showing a change of 430% were discarded. Whole-cell recordings were obtained using Multiclamp 200B amplifier (Molecular Devices), filtered at 5 kHz and digitized at 10 kHz. Digitized data were analysed off-line using pClamp 10. 1 software (Molecular Devices). Current densities for steady-state I-V relationships of OPCs were obtained by dividing the measured current amplitudes by cell capacitance. The amplitudes of inward sodium currents were measured at different voltage steps from þ 20 to À 110 mV after leak subtraction using a MATLAB custom routine (MATLAB, MathWorks, Inc). The ohmic leak current for each potential was estimated by scaling the responses at most hyperpolarizing pulses.
According to the data structure (non-normally or normally distributed), the Kruskal-Wallis or twoway analysis of variance test was used for comparisons. Multiple Kolmogorov-Smirnov test was used to compare distributions of data. All statistical analysis and plotting were performed with Minitab version 12 (State College, PA, USA), Sig-maPlot and GraphPad Prism 5. 00 software (GraphPad Software Inc. , USA).
Acknowledgements We thank  Andrea Helo  for her help on building the MATLAB custom routine used for leak subtraction. This work was supported by  NICHD  funds for intramural research. M. C. A. is part of the  ENP-Ile-de-France  network and was supported by grants from  Agence Nationale de la Recherche  ( ANR ,  R14193KK ), Fondation pour l'aide ala recherche sur la Sclerose en Plaques (ARSEP) and  IDEX-CiteParis Sorbonne. FCO is supported by a post-doc fellowship from  Fondation pour la Recherche Medicale (FRM).
Compared to existing theoretical and computational proposals, our work elaborates on the concept of programmability of neural networks, which entails two novel proposals: a novel way to encode multiple motor primitives in multi-purpose and reusable neural networks, and a novel control scheme for exerting cognitive control. We test our approach in a Robotic scenario: a multiple T -maze with eight possible different goals. Firstly, our experimental scenario starts with a comparison of the learning capability of standard nonprogrammable approach versus the HPNNA in an idealization of eight different sub-tasks. Then we test the overall HPNNA, implementing on the lower layer an interpreter of motor primitives, receiving commands from a higher level interpreter layer capable of sequencing the low-level primitives in order to achieve the proper task.
The proposed computational scheme is compared with a non-organized architecture (NOA), i. e. a neural network without a structure explicitly subsuming neither program nor a hierarchical organization, and results in a robust, scalable and flexible scheme able to successfully decompose the desired task. Because of these features, ours results in an appealing proposal to explain brain function and hierarchical control organization. Furthermore, this computational scheme provides many advantages from a learning perspective, including the possibility to learn novel primitives incrementally without disrupting the existing functionalities, to flexibly reassemble and off-line learning novel behavioral sequences using feedback signals generated by the existing motor primitives and to build modular networks (interpreters) splitting the task space into more manageable (learnable) parts.
The agent exhibits two different behaviors in the environment according to two different codes P r and P l. When L 1   is fed with the programming input P r or P l the robot follows the wall to its right or its left, respectively. In Subsection 3. 1 we show the first learning step, preparing a synthetic dataset, in which the stimuli and the programs are simplified in order to study the different learning properties of the proposed architecture versus a non-programmable one. The second step is presented in Subsection 3. 2 where the primitives are actually learned in a simulated robotic environment and then the overall architecture is tested in the multiple T-maze simulated robotic scenario.
Learning motor primitives composition -HPNNA versus NOA The task of this section is the learning phase of the L 2 PNN module. The aim of the learning is to endow L 2 with the capability of driving the agent in a multiple T-maze environment by sequencing specific motor primitives (see Figure  5 ). Following from  Yamauchi and Beer (1994) , we sought a network capable of changing its state when an external trigger is given. Let us suppose we have a network that selects the two programs P r and P l by means of the output of one of its neurons. A high value of this neuron selects the program P r while a low value selects the program P l. In order to test how able the proposed architecture is to learn different programs, in this section we prepare a synthetic dataset in order to capture the stylized different tasks in a multiple T-maze code.
We compare our HPNNA versus a traditional non-organized architecture (NOA, see below) showing how learning multiple behaviors is computationally more difficult with respect to learning multiple behavior codes. In this experimental scenario we imagined an agent exploring a multiple T-maze (see Figure  6 ). In the considered mazes each corridor has the same length d. This d parameter is an environment variable we varied during the experiments. Accordingly with our strategy, the control module of HPNNA, sequencing primitives, L 2 has two kinds of input lines: the data input line is fed with the external trigger; the programming input line encodes the different sequences that constitute our high level program. Thus, given the fixed structure interpreter L 2 , we learn the structure of a neural network memorizing the input codes to be sent to L 2 , testing our HPNNA approach.
As a comparison a similar learning is performed on a CTRNN layer that it is not structured as in equation (  5 ) but follows the ordinary CTRNN equation (1); we refer to this module as a non-organized architecture (NOA). By means of layer L 2 , the agent is supposed to control two different low-level primitives: Left-Wall Follower denoted with P l , i. e. the behavior ''follow the wall on the left''; Right-Wall Follower denoted with P r , i. e. the behavior ''follow the wall on the right''. We assume to learn a control module of an agent, with two inputs, a task-input T and a trigger-input I D , plus a motor-output U calling the two primitives, P l or P r. The agent is supposed to move inside the maze perceiving it with sensors able to detect walls. When it reaches a T-cross, the trigger-input is activated and the agent consequently moves in order to regain the wall performing respectively a left-turn or a right-turn depending on the input task the agent receives.
Given these conditions, it is possible to define a parameter l l = DT low DT high = d v A Á DT high ) l } d ð8Þ which corresponds to the size of a chosen labyrinth, with respect to the trigger inputs to the controlling unit. We build two different synthetic datasets, D 1 and D 2 :  By means of the parameter l this information is implicitly stored in the trigger-input signal. The corresponding Target output O(t) is consequently created in order to create datasets of input-output couples. The aim of learning is to replicate this target on the network output U(t) at each time step. A number of ten target sequences for programs has been created for each maze. A reference sequence is about 40 time steps t = (1/5) Át, where t is the time constant unit used for the neural network modules. This means that dataset D 1 has 80 sample input-target sequences, while D 2 has 240 sequences.
In all of these strategies DE generates new individuals by adding to an individual a number of weighted difference vectors between couples of population individuals. In this experiment the HPNNA learning is assigned an architecture based on equation (  5 ), which is a fixed structure neural network (no synaptic connections are learned for this module), and an input module, that is a neural network that has to ''memorize'' the different codes allowing the different task. The aim of the learning is to find the programming inputs able to let HPNNA solve the task. The NOA architecture is a full-connected CTRNN of equation (  1 ), without any particular internal structure. In this case, the aim of the learning is to find suitable CTRNN weights able to let NOA solve the presented task. To keep the comparison fair, we keep a similar number of parameters during the experiments.
Samples related to programs that have been correctly learned can be excluded from learning. However, this option can be selected only for HPNNA architecture, because for NOA architecture, if this option is selected, the change in the synaptic weights would cause the well-known effect of catastrophic forgetting of previous learned behaviors, so that the learned module would at last collapse to learn only the last program selected. The results of the tests are evaluated by comparing 20 learning-runs for each architecture, HPNNA and NOA. It is possible to see that HPNNA is able to achieve solutions that correctly perform eight out of eight programs. We show: 3. 1. 2 Learning D 1 dataset -single maze size. Table  2  and Table  3  detail the final fitness value for NOA and HPNNA. For dataset D 1 HPNNA is able to learn all programs in the 40% of learning-runs.
In the remaining 60% of learning-runs, HPNNA learns at least seven out of eight programs. On the other hand NOA architecture is able to learn only a maximum of five out of eight programs (see Table  5 ). Notice that a NOA with the selectSamples catastrophically forgets previous programs and is able to learn only the last seen program (see Table  5 ). On the other hand, learning HPNNA is more computationally efficient if counting the number of total steps required while it is able to learn all programs with a smaller number of iterations (see Table  4 ). In Figures  8  and 9    by means of the parameter l. The parameter values are l = 2, l = 3 and l = 4. HPNNA was able to learn all eight programs (see Table  9 ), while NOA was not able to learn more than five programs. 3. 1. 4 Testing on unknown maze sizes.
We test all the learned instances of the previous subsections in sequences subsuming mazes of never seen size. This is to verify the generalization capabilities of the architectures. We choose test mazes with l = 2. 4 and l = 3. 6 (see equation (  8 )). For both the architectures we test best and worst cases learned in datasets D 1 and D 2. Though, as expected, modules learned in dataset D 1 perform worse (HPNNA could not execute all programs (see Table  10 )), modules learned in dataset D 2 generalize very well on new unseen dimensions (HPNNA can successfully execute all eight programs (see Table  10 )).
HPNNA in a simulated robotic environment In the previous section we made the hypothesis of having ideal motor primitives, in order to build a control module L 2 with suitable inputs to guide the agent towards the desidered goal. In this section we actually implement a lower level interpreter L 1 of motor primitives (see Figure  16 ) in order to complete the HPNNA architecture and show its performance in a simulated robotic environment. Robot simulations were carried out using the open source software project Player-Stage  (Gerkey, Vaughan, & Howard, 2003)  to simulate a Pioneer 3DX robot (see Figure  17 ). The robot is equipped with ten sonars placed on the frontal and the lateral parts of the robot (s1,. ,s10 in Figure  17b. ) Note that L 1 governs the robot by setting its angular and linear velocity corresponding to the output of two neurons belonging to L 1.
The interpreter L 1 fed with the best evolved code programs was tested placing the robot in ten different positions in the maze and observing the robot behavior while driving through the crossroads three times. The positions were chosen in such a way that the robot starts its test in the middle of a corridor, oriented with its lateral part parallel to the wall. We tested one code at a time for each execution without dynamically changing the values. In these conditions the interpreter L 1 fed with P r and P l was successful in all the trials, showing the appropriate behavior in each of the corridors: L 1 was able to control the robot without crashing and preserving the right motor primitive. Finally, we show the results of the whole HPNNA control framework in mazes of the kind of Figure  6 , for all possible programs learned in the exploring behavior experiments.
Conclusions We have proposed a hierarchical programmable neural network architecture, HPNNA, composed of a hierarchy of modules where each module can be viewed both as an interpreter network capable of running different programs without modifying its synaptic connections and as programmer network capable of controlling the behavior of the lower modules. This implies that the same neuronal substrate can encode multiple motor primitives. Furthermore, the motor primitives can be learned incrementally by increasingly adding more programs to the interpreter network. The learning of primitives in a lower level is transferred to the higher level; new primitives can be added in a fixed lower level by searching for the corresponding programming inputs that the higher level should send. We explored the parameter space resulting from this modelization by means of an evolutionary-based learning approach.
At the same time, it goes beyond theoretical proposals on executive functions and suggests a plausible neural mechanism (programmability) for exerting cognitive control, which is based on the idea of ''reusable'' or ''recycled'' neuronal networks  (Anderson, 2010;  Table  10. Generalization capabilities of HPNNA and NOA when trained with dataset Dehaene, 2005) (and is therefore alternative to the idea of ''gates'' and of strongly modular networks). Further studies are of course necessary to evaluate the merits of this proposal, but it has to be noted that its computational parsimony, robustness and scalability (compared to alternative proposals) could offer advantages from an evolutionary viewpoint.
Control Module Learning D, M, opt, b, I max ð Þ. Require: Dataset D, Architecture Model M, optimum fitness threshold opt, DE parameters b, Maximum number of total iterations I max. Ensure: Model Parameters u 1: initialize Model Parameters u(M) 2: set Fitness Values F k = +N for each Task T k 2 TaskSet 3: set iteration i = 0 4: while F k. opt for all Tasks P k 2TaskSet and i \ I max do 5: select samples to learn selectSamples D T = D ð Þ 6: execute Differential Evolution step DE (D T , b) 7: update Architecture Model best parameters u 8: update Fitness Values F k 9: update iteration number i 10: end while results in learning D 1 dataset, with samples from a maze with single d length (see Paragraph 3. 1. 2); results in learning D 2 dataset, with samples from three mazes with three different d lengths (see Paragraph 3.
D 1 Dataset results for HPNNA on 20 learning-runs. Fitness value F k (mean, standard deviation, maximum and minimum) and success rate are shown for each task T k. The best HPNNA was able to execute eight out of eight programs. A high standard deviation in some cases underlines that the respective programs are more difficult to be learned than others (T 3 and T 7 ); this can also be noticed from the corresponding success rate values.
D 2 Dataset results for HPNNA on 20 learning-runs. Fitness value F k (mean, standard deviation, maximum and minimum) and success rate are shown for each Task T k. The best HPNNA was able to execute eight out of eight programs. A high standard deviation in some cases underlines that the respective programs are more difficult to be learned than others (T 2 , T 3 and T 7 ); this can also be noticed from the corresponding success rate values.
Algorithm 1 1 Control Module Learning D, M, opt, b, I max ð Þ. Require: Dataset D, Architecture Model M, optimum fitness threshold opt, DE parameters b, Maximum number of total iterations I max. Ensure: Model Parameters u 1: initialize Model Parameters u(M) 2: set Fitness Values F k = +N for each Task T k 2 TaskSet 3: set iteration i = 0 4: while F k. opt for all Tasks P k 2TaskSet and i \ I max do 5: select samples to learn selectSamples D T = D ð Þ 6: execute Differential Evolution step DE (D T , b) 7: update Architecture Model best parameters u 8: update Fitness Values F k 9: update iteration number i 10: end while results in learning D 1 dataset, with samples from a maze with single d length (see Paragraph 3. 1. 2); results in learning D 2 dataset, with samples from three mazes with three different d lengths (see Paragraph 3.
Data were automatically processed and analysed by Bio-Plex Manager Software 4. 1 using the standard curve produced from recombinant cytokine standards. Levels below the detection limit of the assays were set to zero for statistical purposes. Due to technical problems, plasma cytokine concentrations are from n = 7 animals in the "CS" groups, while lung tissue levels are from n = 6 and n = 7 of the airventilated "non-CS" and "CS" mice, respectively.
The membranes were subjected to chemo-luminescence using SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific). Exposed films were scanned, and intensity of immune-reactivity was measured using NIH Image J software (http://rsb. info. nih. gov/nih-image). Actin and vinculin (Santa Cruz Biotechnology, Inc. , Santa Cruz, CA) served as the loading controls. All immune-blots were repeated twice. Activation of the nuclear transcription factor κB (NF-κB) was determined using electrophoretic mobility shift assay (EMSA) as described recently  [8, 33, 34] : cell extracts were incubated with Poly-doxy-inosinic-deoxy-cytidylic acid (poly-dI-dC) and 32 P -labeled double stranded oligonucleotide containing the NF-кB (HIVкB-site) (5'-AGT TGA GGG GAC TTT CCC AGG C-3', Biomers, Ulm).