dataesr/scientific-paragraphs-categorization huggingface dataset:
2013 ) and HIRES on the Keck telescope  (Howard et al. 2013) , although stellar activity has been a serious limitation of these analyzes. Both spectroscopic studies, using different data sets and independent methods for the correction of the stellar jitter, resulted in very similar parameters for the planet mass (1. 86±0. 25 and 1. 69±0. 41 Earth mass, respectively, in circular orbits). Grunblatt et al (2015)  and  Hatzes (2014)  also determined planet masses from the same data set. Combined with the radius inferred by Kepler, the planet density is very similar to Earth's density, although Kepler-78 b evidently has had a very different orbital evolution and most probably has a different internal structure. Concerning the stellar properties, Kepler-78 has been identified as a young, late G dwarf, with an age of about 625±150 Myr and a stellar mass of 0.
76-0. 83 M (Sanchis-Ojeda et al 2013;  Pepe et al. 2013; Howard et al. 2013). Due to a relatively young age, the star is active as seen from its Kepler lightcurve. The mean rotation period of the star based on the lightcurve autocorrelation analysis has been found to be 12. 588±0. 03 days  (MacQuillan et al 2014). Despite the low error bar associated with this measurement, it is clear from the lightcurve that a single period cannot explain all the observed variability. We have computed the periodograms of Kepler data using the Exoplanet Archive online tool 1  for the 4 years of data (Figure  1 , left). The main peak covers the range 11 to 14. 5 days, and a secondary set of peaks at about half the main period, between 6 and 6. 5 days. Then we did the computation quarter by quarter, excluding the first slot of data which covers less than a stellar period (see Figure  A1  for each individual periodogram).
In his pre-whitening analysis,  Hatzes (2014)  finds maximum power at 6. 44 days in HIRES data and 3. 42 days in HARPS-N data. These frequencies are interpreted as P rot /2 and P rot /4, respectively, which points towards a rotational period in the range 12. 7 to 13. 6 days. The detection of the rotational period in the radial velocity data is, however, not as precise as with Kepler, because of the sparse sampling of these two data sets.
Since the polarized signal was detected in these spectra, we applied for more telescope time to observe Kepler-78 with ESPaDOnS over a full rotational cycle (about two weeks). These observations were performed between July 22 and August 28, 2015 3. We secured 13 spectropolarimetric sequences with a peak signal-to-noise ratio ranging from 250 to 285 per 2. 6 km/s pixel, at 780nm. The last three exposures are separated from the first ten by about a month. Since the last three sequences nicely fill the gap in the phase coverage of the rotational cycle, in the following, we analyze the data set of the all 13 sequences acquired in 2015. The log of the observations is in Table  1. Reference ephemeris for the phases are T 0 = 2,457,226. 9, P rot = 12. 588 days (MacQuillan et al 2014) for the stellar rotation and P orb = 0.
355074 day and T 0 = 2,454,953. 95995 (transit time) for the planet orbit (Sanchis-Ojeda et al 2013). The ESPaDOnS data were processed by the Libre-Esprit pipeline  (Donati et al 1997; Donati et al. 2007 ), which includes flatfield correction, wavelength calibration and optimal extraction of all (highly distorted) orders along the (tilted and wiggly) slit formed by the image slicer at the 2 Discretionary Time Program 14BD91 3 Using the programs 15AC21, 15AF09, and 15BD96 entrance of the spectrograph. The pipeline also provides a correction for the instrumental spectral drift using the telluric spectrum achieving an average rms radial-velocity precision of 20-30m/s  (Moutou et al. 2007 ). We then used the Least Square Deconvolution technique  (Donati et al 1997)  to take advantage of the very large number of stellar lines in the spectrum (6740 in average).
MAGNETIC IMAGING AND DIFFERENTIAL ROTATION 4. 1 Method To reconstruct the magnetic maps of Kepler-78, we use the tomographic Zeeman Doppler Imaging (ZDI) code. It is set up to invert the time series of Stokes V profiles into magnetic maps of the stellar surface, i. e. the distribution of magnetic fluxes and orientations  (Donati et al 1997). The magnetic map is iteratively derived from a comparison with the observed and modeled Stokes V profiles until we reach a predefined χ 2 r level. The ZDI code decomposes the stellar surface into multiple grid cells of similar projected areas and calculates the contribution of each grid to the spectral profile. The reconstructed profiles are obtained by summing the spectral contribution of all cells, for each rotation phase. Given that the problem is ill-posed, we use the maximum-entropy criterion to select the image with the least amount of information among all those fitting the data at the required level.
In the case of Kepler-78, we truncated the spherical harmonics expansion to the 5 lowest terms ( <5), which is adequate for slow rotators (e. g. Fares et al 2010). Fig. 2  shows our fit of the data using the ZDI modeling. We adopted the vsini value of 3±1 km/s, from literature values  (Howard et al. 2013) , with little impact on the ZDI analysis. For the inclination, we used i=80 • , assuming that the orbit of the planet is more or less aligned with the rotation of the star. The star inclination is known to have little impact on the output of the reconstruction, within typically 10-15 •. The fits we obtain correspond to a χ 2 r equal to 1. 0 starting for a χ 2 r of 2. 1. A map in brightness cannot be obtained for Kepler-78 which has a too small rotation rate and too low profile distortions.
g. ,  Boisse et al. 2011). It was not necessary here to add a third signal with a P rot /3 frequency, as was done in previous activity studies of Kepler-78  (Pepe et al. 2013; Howard et al. 2013) , because our data set contains very few points. The improvement of the fit due to the second period is mostly seen on the Hα residual signatures; for CaII residuals, the addition of the first harmonic does not change the fit significantly. When the period is let free to vary, we find an optimal value of 11. 9±0. 4 days with all activity proxies included. It means that the main active regions are located at a latitude of 20±16 •. These values are in good agreement with both the values derived from the magnetic map (section 4. 2) and the values expected from the extrapolation of the spot cycle found in the Kepler lightcurve (section 2).
Since the Kepler lightcurve covers a 1560 day period, it is yet a bit short to confirm any periodic behaviour at such scale; further photometric monitoring could confirm, or refute this interpretation. Also, ultimately, it is only the reversal of the large-scale magnetic field which would demonstrate the existence of an activity cycle similar to the solar cycle, for which further spectropolarimetric campaigns would be required on a few years timescale. From this analysis, we find a differential rotation larger than the Sun, of at least ∼0. 08 rad. d -1. This is confirmed by the differential rotation rate of 0. 105±0. 039 rad. d -1 found by analyzing the spectropolarimetric observations with Zeeman Doppler Imaging, although this detection of differential rotation is just a hint and more spectropolarimetric data would be necessary to confirm it.
The rotation properties and main active latitudes found in our ESPaDOnS data (magnetic analysis and chromospheric proxies) are in agreement with the expectation from extrapolating the Kepler lightcurve activity cycle. Such complex and high-quality lightcurve would benefit, however, from a more complete spot modeling as done for other Kepler active planet hosts like Kepler-17  (Bonomo & Lanza 2012). The similarity between Kepler-78 and Kepler-17 is important to note. Both stars are young GK dwarfs (Kepler-17 being both a bit more massive and older than Kepler-78) and rotate in about 12 days. Their Kepler lightcurves behave similarly, although Kepler-78's amplitude is ten times lower than Kepler-17's; the differential rotation derived from spot modelling by  Bonomo & Lanza (2012)  lies in the range 0.
Cyclic activity is important for star-planet interactions. Previous studies have invoked intrinsic variations in the stellar extended magnetic envelopes at the origin of intermittent phenomena, as the enhancement of CaII emission modulated by the orbital period  (Shkolnik et al 2008)  or changes in the upper atmosphere of a planet (Lecavelier des Etangs et al 2012). It is then important to get a more global understanding of the stellar activity timescales, and of the amplitude of this variation. This task is, however, enormous due to the large quantity of data necessary to reconstruct the magnetic topology at a given epoch (typically 22 hours of observations on a 3. 6m telescope for a rather faint star like Kepler-78). Ideally, each search for a specific star-planet interactions signature should be joined by a contemporaneous campaign in spectropolarimetry to constrain the input stellar conditions.
This constraint is consistent with that inferred from the upper limit of the orbital decay rate for Kepler-78 b  (Laine and Lin, in prep. ). Non detection of stellar spots which co-rotate with the planet's orbit at intensity level substantially below the stellar irradiation on the planet would indicate that unipolar induction between Kepler-78 b and its host star may be interrupted by field reconnection or there is insufficient time for the Alfven waves to complete the circuit. These theoretical implications will be presented elsewhere. APPENDIX A: QUARTERLY PERIODOGRAMS FROM THE KEPLER LIGHTCURVES Figure  A1  shows the periodograms obtained on Kepler data of Kepler-78 for each quarter. These periodograms were used to measure the apparent rotational period plotted in Figure  1  and its variation in time.
Motor forces The main variable of interest in this paper is the force generated by molecules, molecular assemblies, muscle fibres and muscles. Our dataset includes 13 motor types aggregated in five motor classes depending on the nature of the generated force. (ii) forces produced by large molecular assemblies (denoted M2): F 0 F 1 -ATPase, bacterial flagella, pili, spasmonemes and myofibrils. These motors can be also classified as non-locomotory (ATPase) and locomotory (the others) or as rotary (ATPase, bacterial flagella) and linear (the others); (iii) forces produced by single muscle fibres (i. e. muscle cells) or bundles of a few muscle fibres (both denoted FI), frequently demembranated (skinned), while maximally stimulated and clamped at constant length (isometric contraction), with electrical or chemical stimulations; (iv) maximum force produced by dissected large bundles of fibres or isolated whole muscles stimulated isometrically with electrical stimulation of the nerve or the muscle (denoted MU); and (v) forces measured in behaving animals engaged in a wide range of activities including running, jumping, swimming and biting (denoted MV).
For each type, potentially relevant papers were searched using the Google Scholar database using as keywords the motor type plus 'force', 'torque' or 'pN'. For non-molecular motors, we proceeded in two steps. First, relevant papers were identified from previous review papers  [1, 2, 4, 18] ; all their cited references were included, except the rare cases for which the full text was not available or the paper could not be feasibly translated into English. Second, other potentially relevant papers were searched without restriction on language or date in the Google Scholar database using keywords ('specific tension', 'muscle stress', 'fibre', 'fiber', 'N/m 2 ', 'N m -2 ', 'N/cm 2 ', 'N cm -2 ', 'N mm -2 ', 'pascal', 'kPa', 'physiological cross-sectional area', 'PCSA', 'CSA', etc. ). Bibliographic searches were discontinued in April 2015.
The papers in this preliminary list were screened based on their title and abstract to exclude those unrelated to biological motors, then collected. The useful information was extracted from each of them (see below) with independent checks by the two authors for most of them. Papers without original measurements were excluded. Data published more than once by the same author(s) or reproduced by other authors were identified and only the paper with the original measurement was kept in the reference list. Measurements not fulfilling our criteria (stall force of single molecular motor, maximum isometric tension of non-molecular motors) were not considered. No relevant papers were excluded.
Statistics Statistical distributions were compared with the Kolmogorov-Smirnov test  [194]. Multiple distributions were compared with the one-way analysis of variance (ANOVA) and corresponding multiple comparison of means using Tukey-Kramer adjustment. Slopes of least-square regressions of log 10 (f ) versus log 10 (M) were compared with 0 using the F test. Details of statistical analyses are given as the electronic supplementary material, tables S1-S6 for ANOVA and multiple comparison of means and tables S7-S12 for regressions. All tests were performed with the MATLAB STATISTICAL TOOLBOX (The Mathworks, Natick, USA).
3. 3. There is no large-scale variation with cell or body mass Log-log plots of the 329 pairs of (M, f ) values are shown in figure  4. Overall, values of cell and body mass M range from 2 × 10 -16 kg (bacterium) to 2500 kg (elephant), whereas values of specific tension f range from 3. 6 to 1944 kPa. Hence, whereas M varies by more than 19 orders of magnitude, f only varies by a factor of 500. For easier reading, polygons enclosing all points of the same category are shown: types of motors (figure  4b ) and taxonomic groups (figure  4c ). Overall, there is no large-scale variation with cell or body mass. Indeed, the power law regression calculated for the entire dataset is f = 159 M α with α = -0. 5 × 10 -3 ± 7. 7 × 10 -3 (95% confidence limits -8. 2 × 10 -3 , 7. 2 × 10 -3 ), this slope is not significantly different from zero (p = 0.
Their cross-sectional area has sometimes been corrected for the area occupied by mitochondria (dragonfly,  [116] ), sometimes not (beetle,  [115] ) and never for the sarcoplasmic reticulum (e. g. [206] ). The pennation angle has not always been taken into account. Temperature during the experiments has been noted and is usually close to the working temperature of the muscle. Although data are not fully homogeneous, the similarity of the distributions of specific tensions measured in vivo and in vitro suggests that uncorrected factors do not introduce important bias. In principle, corrections for these factors should lead to less variable data. However, no corrections have been attempted for two reasons. First, the information needed is not always provided, so corrections cannot be done systematically.
This change is known as rs200826424 in dbSNP and has an overall allele frequency of 0. 0091% in the Exome Aggregation Consortium (ExAC) Browser (no homozygotes were observed). In order to identify additional iRD-affected families with mutations in RCBTB1, we performed targeted next-generation sequencing on the coding region of RCBTB1 as previously described  6  (Table  S2 ) in a Belgian cohort of 281 probands with autosomal-recessive or sporadic iRD. This did not reveal any mutations. Inspection of WES data (Table  S3 ) in ~450 unsolved iRD cases from four cohorts from the European Retinal Disease Consortium revealed homozygous mutations in the probands of five additional families; these individuals display isolated or syndromic iRD with thyroid involvement or sensorineural hearing loss (Table  1 ).
9,10 ] [10] [11] [12]  On the basis of the initial models, the alignments were edited for model improvement, as judged by the DOPE score in MODELER, visual inspection, and Verify_3D 3D profile analysis of the models. 11, 13 The models are based on template structures of RLD (PDB: 4O2W) and the BTB domain complex (PDB: 4J8Z and 4AP2). The effects of the variants were analyzed in YASARA Structure. 9, 10 Figures were generated with UCSF Chimera. 14 -catenin signaling. A clinical overlap with RP was excluded in one of the probands given the absence of night blindness, a typical fundus aspect of FEVR without bony spicules or narrow vessels, and a preserved electroretinogram in one eye. 24 In the families included here, no FEVR signs could be observed, sustaining the hypothesis that distinct molecular consequences of RCBTB1 mutations and zygosity cause different clinical entities.
Ser401 is a surface-exposed residue that is either close to or at the edge of the BTB-CUL3 interface, depending on the template that was used. It is unclear whether p. Ser401Leu (c. 1202C>T) can disrupt the interaction with CUL3. Homology models were built on the basis of different structure templates with YASARA Structure. 9,10 Additional models were built in MODELER and YASARA Structure with alignments based on HHPRED and Phyre2. [8] [9] [10] [11] [12] On the basis of the initial models, the alignments were edited for model improvement, as judged by the DOPE score in MODELER, visual inspection, and Verify_3D 3D profile analysis of the models. 11, 13 The models are based on template structures of RLD (PDB: 4O2W) and the BTB domain complex (PDB: 4J8Z and 4AP2). The effects of the variants were analyzed in YASARA Structure.
Acknowledgments We are most grateful to the family members who participated in this study. This work was supported by grants from the  Funds for Research in Ophthalmology  (to G. A. ), the  Research Foundation Flanders (FWO)  ( FWO 3G079711  to  E. D. B. and  FWO 1520913N  and  1515615N  to F. C. ), the  Ghent University Special Research Fund  ( BOF15/GOA/011 ),  Belspo IAP  project  P7/43  ( Belgian Medical Genomics Initiative ), the  FP7-PEOPLE-446 2012-ITN programme EyeTN  ( 317472  to E. D. B. , F. C. , B. P. L. , and C. P. H. ), the  Hercules Foundation  ( AUGE/13/023  to E. D. B. ), the  NIH  ( 1S10RR026550  to R. C. ), the  National Eye Institute (NEI ,  R01EY022356  and  R01EY018571  to R. C. ), the  Foundation Fighting Blindness  ( BR-GE-0613-0618-BCM  to R. C. ), the  NEI-NIH   Core Grant for Vision Research  ( EY-002520 ), the  National Natural Science Foundation of China  ( 81470669  to R.
Real time B-mode imaging was initially used to control the placement of the probe on the field of view. The software-based architecture of the scanner enabled Matlab (MathWorks; Natick, Massachusetts, USA) programming of custom transmit/receive ultrasound sequences. Each session began with an anterior-posterior Doppler scan, used to visualize the shape of the blood vessels, and identify the stereotaxic coordinates. fUS acquisitions were achieved at the level of the choroid plexus of the lateral ventricle, corresponding to the anteroposterior coordinate Bregma -1 mm. Body temperature was maintained constant using a heating pad (Gaymar Industries; New York, NY, USA). A 1 cm incision was made in the skin and muscle of both hindpaws, above the femur in order to expose and isolate the sciatic nerve.
Transcranial ultrafast Doppler imaging sequence Functional ultrasound (fUS) imaging was performed using Ultrafast Doppler  (Bercoff et al. , 2011)  based on compounded plane-wave ultrasound imaging  (Tanter et al. , 2002; Montaldo et al. , 2009). This technique has sufficiently high sensitivity to detect subtle hemodynamic changes due to the neurovascular coupling with a high spatiotemporal resolution (1 ms, 100 μm). First, the vascularization map of the brain was obtained by using a dedicated MATLAB script to move the US probe, fixed on a three-axes microstep motor, 0. 5 mm after each ultrafast Doppler acquisition. The scanning sequence consisted in 15 tiled planar ultrasonic waves (-14°t o 14°angles, with a step of two angles 2°) with a pulse repetition frequency (PRF) of 7500 Hz. Once the choroid plexus of the lateral ventricle was identified, the ultrasound probe was fixed in coronal orientation over the hind limb primary sensory cortex (S1HL) at the anterior-posterior coordinate of Bregma -1 mm.
Treatment of fUS data The acquisition and reconstruction of both the vascularization and activation maps were carried out on the programmable US scanner with dedicated MATLAB scripts. Backscattered signals from the brain were filtered to distinguish the moving red blood cells and microbubbles from the static tissue. Singular value decomposition (SVD) was implemented on the stack of the fUS images and the largest Eigenvalues were eliminated to filter the slowest variations in the Power Doppler signal related to the tissue signal  (Demene et al. , 2014). This multidimensional (space and time) filter is able to discriminate very low speed blood flows (up to 1 mm/s) from tissue displacements by exploiting the difference of spatiotemporal coherence between tissue and blood flow signals. Then, the backscattered signals were filtered with a 4th order Butterworth high-pass filter with a cut-off frequency of 75 Hz to further remove tissue and motion artifact coming from the skull.
Acknowledgments The authors thank  Fondation Pierre-Gilles de Gennes  for funding of  C. Errico ,  Bracco Research SA  for providing the contrast agent and  Agence Nationale de la Recherche (MUSLI)  for financial support. This work was supported by  LABEX WIFI (Laboratory of Excellence   ANR-10-LABX-24 ) within the  French Program  " Investments for the Future"  under reference  ANR-10-IDEX-0001-02 PSL*. The research leading to these results has also received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 339244-FUSIMAGINE.
Selecting Behavioral Indicators As mood might affect behavior, it is necessary to record users' behaviors for further analysis. Users' behavioral data is represented by a multi-dimension vector. Each dimension stands for a type of behavior indicator defined by the researchers. In the computer-assisted experiments, keystroke and mouse activities are commonly used by researchers as behavioral indicators  (Zimmermann et al. , 2003; Salmeron-Majadas et al. , 2014). Keystroke behavior indicators are associated with frequency, speed, and strength and idle time. Frequency indicators measures how often a key is used. Speed indicators measures how fast users type on the keyboard. They measure the duration of a keystroke, the duration between two keystrokes, or the average number of keystrokes in a given time interval.
Recognizing Mood Various data classification methods such as statistical analysis  (Maehr, 2005) , k-nearest neighbours algorithms  (Lv et al. , 2008) , discriminant analysis  (Vizer et al. , 2009) , support vector machine  (Vizer et al. , 2009) , decision tree model  (Epp et al. , 2011) , Bayesian network  (Lee et al. , 2012) , and artificial neural networks model  (Khanna et al. , 2010)  can be used to recognize users' mood state. Researchers selected methods based on the size of samples, the type of behavioral indicator (i. e. raw data or normalized data) and the number of moods to by detected, so as to achieve a higher accuracy.
Research Method The authors partnered with a French e-commerce website (ECWP) to conduct the experiment, which consists of two stages: an offline stage to develop a mood recognition tool, and an online stage to test the tool with real users to find out how different mood states can affect users' reaction to online product recommendations (Figure  1 ). Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware? In order to recognize the mood state of numerous website users unobtrusively and dynamically, it is necessary to develop an automatic mood recognition tool. Hence, an offline experiment was conducted in the first stage to allow the authors to develop a mood recognition model and train it with behavioral data with a group of users. Several conditioning technics were used in this stage.