dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analyses Choice behavior analyses Choice behavior was analyzed based on panel data analysis, using the statistical software package STATA (StataCorp, College Station, TX, USA). We ran mixed (panel data) logit regressions, which took each participant as the unit and the trial as time, and estimated both random and conditional fixed effects. Parameters were estimated by maximum likelihood. Given that Pr(g 1 ) = 1 À Pr(g 2 ), where Pr(g 1 ) and Pr(g 2 ) are the probabilities of choosing lottery 1 and lottery 2, respectively, we defined the probability of choosing g 1 in terms of three factors affecting the choice: expected value (dEV), anticipated regret (r), and anticipated disappointment (d). x 1 , y 1 and x 2 , y 2 are the two possible outcomes of the first (g 1 ) and second (g 2 ) lotteries, respectively, with x 1 > y 1 , and x 2 > y 2.
In the complete feedback condition, ON is the outcome of the non-chosen lottery and NRN the non-realized outcome of the non-chosen lottery. The statistical analyses were conducted with the statistical software package STATA (StataCorp; Release 9/SE). Nonparametric tests were applied to the datasets because the data violated several parametric assumptions, particularly distribution normality as the affective scale is truncated. The significance of the difference between behavioral variables, such as subjective evaluations, is estimated with the Wilcoxon 198 Journal of Behavioral Decision Making signed-rank test (WSRT, nonparametric test); the hypothesis tested is that the distribution of two random variables for matched pairs is the same. A mixed linear regression was run in the complete feedback condition (800 observations) to confirm results from comparison of mean affective ratings.
Eye-tracking recordings and analyses We used the video-based Tobii 1750 eye-tracker with a 1280 × 1024 pixel screen. The table-mounted camera and IR light system perform a 50-Hz sampling rate acquisition and computes position for both eyes. Participants were sitting approximately 50 cm from the screen, and their head was free of movement. A pre-analysis using CLEARVIEW software (ClearView 2. 6. 0 analysis software 2007, Tobii AB, Danderyd, Sweden) divided eye movements into fixations and saccades. Fixation points were defined as gaze pause when remaining within a 30 × 30 pixel square for at least 40 milliseconds. Fixation points accounted for 67. 9% of all data points. Additional analysis steps were performed with MATLAB MathWorks, Natick, MA, USA. Parametric tests were applied because the data did not violate parametric assumptions.
In the partial feedback condition, a loss of 5 euros was perceived as more unpleasant and a gain of the same value as less pleasant when the non-realized was not significant (β = À. 04, p =. 337). Note that the effect of ON on subjective ratings is almost three times higher than that of NRC. Given that NRC and ON show a relatively high correlation, it is difficult to draw any conclusion on the effect of NRC in the complete feedback condition or, put differently, on the role of disappointment in regret situations, based on the analysis of subjective ratings alone. We thus turned to eyetracking data to investigate whether participants will still attend the NRC outcome in the complete feedback condition.
Stage 3: temporal stability Temporal stability, or "test-retest", is an important measure of reliability for a psychometric instrument  [39]. The purpose of this stage was to assess the temporal accuracy of the HIVESS. An instrument is considered relevant when a reasonable level of temporal stability can be associated to the determined construct measures. For assessment of test-retest reliability at least 50 participants is required for the sample  [40]. An interval of 4 weeks was considered to be an acceptable compromise between an attempt to reduce recall bias and test reliability independent of significant clinical change  [41]. The 17-item questionnaire was hereby administered to participants twice over a 4-week period. Intraclass correlation coefficients (ICCs) and the 95 % confidence interval of the ICCs (ICC 95 % CI), as well as paired/dependent sample t-tests were conducted as data analyses to examine significant differences in subscale scores from time 1 to time 2.
Material and Methods A 29-question survey was designed, with most questions related to the location, nationality, type of research, research practices, the researcher's own perception of bats, as well as their views on the general public perception on bats. We also included a quiz to test the researcher's knowledge regarding bats and the role of bats as reservoir hosts of pathogens, as well as a series of questions related to the causes of the global decline in bat populations. These detailed questions and responses are available in the S1 File. The questionnaire was sent to the corresponding authors of one or more scientific papers published between 2010 and 2014 that were referenced in the database ISI Web of Science as related to Chiroptera. The choice was based on the occurrence of a series of combined keywords (see S2 File).
References were then checked to avoid the selection of unrelated publications. Once the duplicates were removed, the questionnaire was sent out to valid email addresses. When the same email address was found for several publications in different years, the most recent one was retained and the others were discarded. The questionnaire was sent through the online survey platform QuestionPro (http://www. questionpro. com/) to 2,180 potential participants in May 2015 and was closed in June 2015 after three reminders had been sent. Statistical analyses were performed using R software  [7]  and model selection was performed with the use of the package glmulti  [8]. A total of 610 completed responses were received, accounting for an average response rate of 27. 98%. We obtained the following increasing response rates from 2010 to 2014: 17.
Acknowledgments We are really grateful to  Ben Holt  for allowing us to use the map with the zoogeographic regions that was presented in Holt et al. 2012. An  Update of Wallace 's  Zoogeographic Regions of the World Science 339  ( 6115 ),  p74-78  ( 10. 1126/science. 1228282 ). We wish to thank  Julien Cappelle ,  Boripat Siriaroonrat ,  Kevin Olival  for helpful comments on the questionnaire. We are extremely grateful to  Neil Furey  for his many useful suggestions on the design of the survey and insightful comments on a previous version of this manuscript. This study is supported by the  French ANR CP&ES   BiodivHealthSEA  project (grant  ANR   11 CPEL 002 ; Local impacts and perceptions of global changes: Biodiversity, health and zoonoses in Southeast Asia).
Cross-Language VA Span Comparison As shown on   1  for the Arabic and Spanish languages. The French data were lost due to technical problems and the French norms taken from young adults of the same age in the EVADYS test  (Valdois et al. , 2014b)  are provided in Table  1 , suggesting very similar abilities in French and Spanish. Comparison of Spanish and Arabic single letter processing skills showed that isolated letters were identified faster by the Spanish than the Arabic participants [F (1,82) = 62. 07, MSE = 138. 89, p < 0. 0001, η 2 = 0. 43]. To assess whether VA span performance was related to single letter processing skills in the Arabic group, a composite score of single letter processing was computed as the weighted sum of letter identification performance at each of the five display durations (score at 33 ms * 5 + score at 50 ms * 4 + score at 67 ms * 3 + score at 84 ms * 2 + score at 101 ms).
Last, we examined the relationship between text reading speed and VA span abilities in all three languages. A significant relationship was found for French but not for Spanish or Arabic, which could reflect differences in orthographic transparency. These findings differ from those previously reported in children. Indeed, VA span skills are tightly related to reading speed in children from both shallow and deep orthographies, including Spanish  (Lallier et al. , 2014)  and French  (Lobier et al. , 2013). Similar evidence is lacking for Arabic children. Overall, the available data suggests that VA span abilities relate to text reading performance in French and Spanish (and potentially Arabic) children but that adult French readers alone show a significant relationship. Longitudinal or cross-sectional studies are needed to confirm a potential modulation of VA span influence on reading speed performance depending on the participant's age and language transparency.
FUNDING FA was supported by a fellowship from the  Iraqi Ministry and Campus-France , TP by a  Ph. D grant  from the  "Fondation de France " and AA by a  Basque Government  PhD grant. The study was supported by the  ANR  ( ANR-12-BSH2-0013-01 ) grants to SV, the  European Research Council  ( ERC  advanced grant, Proposal No. 295362 , BILITERACY awarded to MC) and the  Spanish Government (Plan Nacional -PSI2012-32128  awarded to ML, and Plan Nacional-PSI2012-31448 awarded to MC).
Neuronal recordings and stimulus A detailed explanation of the experimental procedures was given by  Mesgarani et al. (2009). Four adult, female, awake, and head-restrained ferrets were used in the neurophysiological recordings. The protocol for all surgical and experimental procedures was approved by the Institutional Animal Care and Use Committee at the University of Maryland and was consistent with National Institutes of Health Guidelines. Speech stimuli were taken from the Texas Instruments/Massachusetts Institute of Technology (TIMIT) database  (Garofolo et al. , 1993). Thirty different sentences (3 s, 16 kHz sampling) spoken by different speakers (15 men and 15 women) were used to sample a variety of speakers and contexts. Each sentence was presented five times during recordings. Speech spectrograms were binned at 10 ms in time and in 30 logarithmically spaced spectral bins between 125 and 8000 Hz.
Extracted PFs from decoding filters. Similar to the procedure with encoding filters, we can also extract simplified decoding filters that resemble the theoretically defined PFs. For each neuron n, we restricted the above analysis to a single time delay to recover frequency-only decoding filters. We used fivefold cross-validation in each training set to optimize the regularization parameters. To find the right delay between real stimulus and its model reconstruction (see Fig. 5A ), we shifted the stimulus by 10 ms from 0 to 200 ms and reported the maximum correlation coefficient. This is done for all competing models. Selection of neurons for analysis. The dataset consists of 128 individual recordings from four animals. We combined the data from all animals into a single model assuming that similar stimulus selectivities would exist across animals.
Predicting neural activity To use the model to get predictions for the neural activity (see Fig. 4 ), we obtained frequency-only decoding filters from the neural data as described in the previous section. We used these decoding filters as the PFs in the model. There were three free parameters that were adjusted globally to optimize the fit to neural data (r on ϭ 1 and r off ϭ 20 for each neuron and a global gain parameter for the output of the spectrogram). In all models (i. e. , intact model) without inhibition and SRF, we found an optimum delay for each model prediction that maximizes the correlation coefficient to the neural responses on the training set. These delays were not changed during predictions over the test set. The dataset was divided into 10 equal portions over time, and all model prediction accuracies were averaged over 10-fold cross-validation process.
Transient and sustained responses To identify responses after a sudden change of input to a neuron (see Fig. 6 ), we first identified the best frequency of each neuron from the maximum of its one-dimensional, frequency-only encoding filter. Then for each neuron, we searched for sudden increases in the spectrogram of the stimulus restricted to Ϯ10 frequency bands around the best frequency and found the timings of the largest 150 local maxima in the derivative of the stimulus averaged over frequencies. For local maxima, we used findpeaks. m function in MATLAB (The MathWorks) with the condition that two consecutive local maxima are at least 200 ms apart. This gave us 150 events for each neuron. Transient responses are defined as the first 100 ms after such an event. Sustained responses are the following 100 ms after a transient response.
3B , left). Our Bayesian normative model suggests that context-dependent encoding filters could be replaced by the static decoding filters approximated from neuronal data, and then be used to predict the neuronal responses. To test this idea, we combined neuronal recordings from the primary auditory cortices of 4 ferrets listening to a 90 s speech stimulus from the TIMIT database  (Garofolo et al. , 1993)  (see Materials and Methods). For each neuron, we computed a one-dimensional (frequencyonly) approximation of the decoding filter that summarizes the full decoding filter (Fig. 3B , right). We call this filter the PF of the corresponding auditory neuron as it represents the contribution of the neuron in predicting the stimulus. Then, we plugged these PFs into the model as q ij s (no learning is involved).
The model predictions are better at capturing the transient dynamics of neuronal responses than SRF and model without inhibition. Temporal precision of the model is higher in the entire dataset, as illustrated by the mean cross-covariance of the model versus real responses (Fig. 4C , blue) and SRF versus real responses (Fig. 4C, red ). We also compared how well the stimulus can be reconstructed from the model neural responses using the full decoding filters (as in Fig. 3B , left) obtained from the real neural data. We took the responses of the Bayesian decoding model, SRF model, and model without inhibition and convolved them with the full decoding filters of the real neurons to reconstruct the stimulus. As summarized in Figure  5 , we can observe that the Bayesian decoding model massively outperforms the encoding model.
Figure2. Changes in the RF of a neuron as neurons are removed from the network. The RF of a feature detector neuron (red) is computed from its responses in a small network of 5 neurons (left) and a larger network of 36 neurons (right). The stimuli for both cases were the same speech sentences from the TIMIT database. The responses of this neuron are shaped by the activity of all other neurons in the network that resulted in significant changes in its RF.
This work was supported by  European Research Council "Predispike "  ERC-2012-StG_312227 ,  James MacDonnell Foundation Award , and  Agence Nationale de la Recherche   ANR-10-LABX-0087 IEC. N. M. was funded by a grant from the  National Institutes of Health ,  NIDCD ,  DC014279. The authors declare no competing financial interests. This article is freely available online through the  J Neurosci Author Open Choice option.
Noise characterization The sample was placed in a custom setup in a Faraday cage. A current-voltage converter based on an operational amplifier feedback loop transformed the transistors currents into voltages; it further amplified and low pass filtered them at 16 kHz. A second amplification stage removed the DC component of the signal and amplified it by a factor of 100. A NI DAQCard acquired the output voltages and a LabVIEW program calculated the PSD.
Cell measurements A home-built system was used for the measurement of cell action potentials. The transistors were operated at a constant source-drain and gate voltage. Operational amplifier feedback loops transformed I DS into a voltage and amplified it. The signal was also band-pass filtered between 0. 5 Hz and 10 kHz. A NI DAQCard acquired the voltage at a sampling frequency of f s = 30 kHz. Using MATLAB, the data were also band-pass filtered from 4 Hz to 3 kHz after digitalization.
Acknowledgements We acknowledge  M. Seifert ,  L. H. Hess ,  M. Sachsenhauser , and  M. Prexl  for fruitful discussions. JAG, BMB, ML and SD acknowledge support by the  German Research Foundation (DFG)  in the framework of the  Priority Program 1459 Graphene , the  Nanosystems Initiative Munich (NIM) , the  European Union  under the  NeuroCare FP7  project  (Grant Agreement 280433 ) and the  Graphene Flagship  (Contract No. 604391 ).
To analyze cell type markers, BrdU-positive cells in the granule cell layer of each mouse were examined by confocal microscopy (LSM 510 META; Zeiss). Densitometric analyses of GFP expression was performed by measuring mean pixel intensity with the software Image-ProPlus 7 (Media Cybernetics). DAB staining was performed as previously reported  (Wang et al. , 2008; David et al. , 2009). A goat polyclonal antibody against DCX (1:500) was obtained from Santa Cruz Biotechnology. The secondary antibody was biotinylated donkey anti-goat IgG (1:500, Jackson ImmunoResearch Laboratories). Sections were developed using avidinbiotin complex (Vector Laboratories) and DAB kit. Bright-field images were taken with a bright-field microscope (Nikon). DCX ϩ cells were subcategorized according to their dendritic morphology: DCX ϩ cells with no tertiary dendritic processes and DCX ϩ cells with complex, tertiary dendrites.
We confirmed the expression of histone H3, but not synaptophysin, in nuclear fraction used in this study (data not shown). Protein concentration was measured by BCA protein assay (Thermo Scientific). Western blotting. The DG was isolated as previously reported  (Hagihara et al. , 2009; Uchida et al. , 2014). Western blotting was performed as previously reported  (Uchida et al. , 2011a (Uchida et al. , , 2014)). Equal amount of proteins were separated on 7% or 12% Bis-Tris gels (Invitrogen) and transblotted onto polyvinylidene difluoride membranes (GE Healthcare Bio-Sciences). After blocking the membrane with 5% skim milk or 5% BSA, the membranes were incubated with antibodies directed against ␤-actin (1:5000; Sigma), GAPDH (1:5000; Millipore), pS16-stathmin (1: 1000, Cell Signaling), pS25-stathmin (1:1000, Abcam), pS38-stathmin (1:1000, Cell Signaling), total stathmin (1:5000, Millipore), histone H3 (1:4000; Cell Signaling), GluA1 (1:500; Millipore), GluA2 (1:500; Millipore), GluN1 (1:500; Millipore), GluN2A (1:500 Millipore), GluN2B (1:500; Millipore), CREB (1:500, Millipore), or pCREB (1:500, Millipore).
After incubation with appropriate HRP-conjugated secondary antibodies (HRP-linked anti-mouse IgG or HRP-linked anti-rabbit IgG antibody, Cell Signaling), the blots were developed using the ECL-Plus Detection Kit (GE Healthcare Bio-Sciences or Thermo Scientific). When the phospho-specific antibodies were used, all buffers included sodium fluoride (50 mM). Densitometric analysis was performed by ImageQuant software (GE Healthcare) after scanning (Typhoon, GE Healthcare). Real-time PCR. Quantitative real-time PCR was performed as reported previously  (Uchida et al. , 2010 (Uchida et al. , , 2011a)). The total RNA from dissected tissues was extracted using RNeasy Mini Kit (QIAGEN) or TRIzol Reagent (Invitrogen) and treated with DNase (DNA-free kit, Ambion). The quality of the RNA (A 260 /A 280 ) was 1.
Amplification of a single PCR product was confirmed by monitoring the dissociation curve and by electrophoresis on 1. 5% agarose gels stained with ethidium bromide. All measurements were performed in triplicate. Glyceraldehyde-3-phosphate dehydrogenase (Gapdh) mRNA levels were used to normalize the relative expression levels of target mRNAs. When a significant difference was observed between experimental groups, the data were further confirmed by normalizing to the other internal control, ␤-actin. Chromatin immunoprecipitation (ChIP) assay. Dissected DG tissues were minced into 1 mm pieces that were immediately frozen on dry ice and stored at Ϫ80°C until further use. To crosslink the protein-DNA complexes, the tissues were placed in 1% formaldehyde for 15 min at room temperature. Fixation was quenched by adding glycine at a final concentration of 0.
75 mA, 2 s). Mice were removed from the conditioning chamber 30 s after shock. Freezing was measured each day during the first 2 min baseline period by the FreezeView software (Coulbourn Instruments). After 10 sessions of conditioning in context A, mice underwent two sessions of generalization test. In the first session, mice were placed into the context A or context C for 3 min; and 2 h later, mice were placed in the opposite contexts for 3 min, with the order counterbalanced such that half of the animals were placed in context A first and half were placed in context C first. Context C had the same dimension as context A, but the chambers had a curved plastic wall and the flat plastic floor (no stainless steel rods). The chambers were cleaned with a lemon-scented solution. The room was lit with a red overhead light.
The time spent freezing was measured by the FreezeView software (Coulbourn Instruments). Five-pairing paradigm, paired versus unpaired protocol. On the training day, the mouse was placed in the conditioning chamber (Med Associates) for 700 s during which the paired group received five shocks (0. 7 mA, 50 Hz, 2 s) paired with the tone (2800 Hz, 30 s, 85 dB) with the intertrial interval ϳ70 s. The unpaired group stayed also 700 s in the conditioning chamber during which they received five pseudorandom tones (2800 Hz, 30 s, 85 dB) that were not contingent on delivery to the five shocks (0. 7 mA, 50 Hz, 2 s). For both groups after the last tone, mice stayed in the box 1 min before going back to the home cage. Testing occurred 24 h later, and the procedure was identical for the paired and unpaired groups.
, 2010). Mice were trained in two distinct environments: contexts A and B. Context A was cleaned with 1% acetic acid diluted in deionized water. In contrast, context B cleaned with 70% ethanol. On day 1, animals were trained in context A. After an initial 118 s baseline period, animals received three unsignaled footshocks (0. 5 mA, 2 s) spaced 58 s apart and were removed 60 s after the last shock. On day 2, animals were trained in context B using the same protocol as day 1. The animals received 3 min tests on days 3 and 4 (no footshock) to assess fear in contexts B and A, respectively. The time spent freezing was measured as described above. Open field test. This test was performed as previously reported  (Martel et al. , 2010). The open-field consisted of a white arena (43. 2 cm ϫ 43. 2 cm ϫ 40 cm) coupled to an automated video tracking system (Open Field Activity Software, Med Associates).
Pain sensitivity. Response to the electric shock was assessed with naive mice as described previously  (Shumyatsky et al. , 2002; Martel et al. , 2011). The intensity of the shock required for running, vocalization, and jump was determined for each mouse by delivering a 1-s-long shock every 30 s starting at 0. 08 mA and increasing the shock 0. 02 mA each time. Testing was stopped after all behaviors were noted. Statistical analysis. Analyses of the data were performed using an appropriate ANOVA. Significant effects were followed up with Fisher's LSD, Tukey's post hoc test, or Bonferroni's correction. Student's t tests were used for two-group comparisons. In all cases, p values were twotailed, and the comparisons were considered statistically significant when p Ͻ 0. 05. All data are presented as mean Ϯ SEM.