dataesr/scientific-paragraphs-categorization huggingface dataset:
27, P = 0. 05). Second, based on our previous results in CHO-KI showing a decrease of specificity from the N-to the C-terminal end of the array, we monitored the effect of mismatches on the last two positions (called N and N-1) of the TALEN array as a function of the array length. A subset (14 TALEN) of the previous TALEN collection was screened for activity on targets containing the four possible nucleotides at positions N and N-1. When the activity toward the mismatched targets was averaged and compared with the activity on the wild-type target, a statistically significant correlation between the TAL array length and the loss of activity due to mismatches at these positions was obtained (position N: r = 0. 68, P = 4eÀ6; position N-1: r = 0. 7, P = 7eÀ6, Figure  2B ). Based on the previous results and data from the literature, we further focused on the shorter 9.
This analysis of the data suggested that the first few N-terminal RVD/nucleotide pair may have the strongest impact on TALEN activity, consistent with our first experiments in CHO-KI and in other studies  (28). Furthermore, to assess possible context dependence at the RVD level, we systematically analyzed the impact, on a central RVD, of the neighboring two RVDs (positionsÀ 1 and +1). The recovered uniform activity levels strongly suggest independence of the central RVD from their nearest (right and left)-neighbors  (Supplementary Figure  S7A  and B ). These data represent an unambiguous indication that no short distance (1 base) strong context dependence between RVDs is present in the N-terminal region of TALEN array.
RVD specificity and prediction of effect of mismatches on TALEN activity Having observed an absence of context dependence between RVDs, we next performed an in-depth analysis of our entire dataset to further decipher the individual specificity of each RVD. For each position, from 1 to 7, we computed an experimental matrix describing the specificity of each RVD on the four DNA bases (Figure  4A  and Supplementary Table  S4 ). Interestingly, these matrices appear to be similar (mean standard deviation: 0. 1), indicating the absence of positioning effects on specificity. By taking advantage of the conserved specificity of RVDs along the TALEN array, we then computed a global matrix (or logo) (Figure  4B  and C and Supplementary Table  S2 ) representing the global specificity of each RVD of a TALEN.
The global matrix confirms that the currently used RVD/nucleotide pairing code represents the most appropriate solution to generate highly active TALEN. However, taken individually each RVD can tolerate to varying extents the three other bases, although at the expense of a reduction in activity (Figure  4B  and C ). We next wanted to determine whether a direct correlation between the score given by our matrix (based on in vivo activity measurements) and the sole protein/DNA binding parameter (based on in vitro measurements) could be found. Toward this goal, we designed a single TALE DNA binding array that targets an endogenous sequence of 17 bp (in line with predominant natural-size TALEs) within the human IL2RG gene. The corresponding protein (lacking the FokI catalytic domain, Supplementary Table  S5 ) was produced as soluble protein in E.
Although localization of the off-site sequence in the genome (e. g. essential genes) should also be carefully taken into consideration, the specificity data presented above indicated that most of the TALEN should only present low ratio of off-site/in-site activities. To confirm this hypothesis, we designed six TALEN that present at least one potential off-target sequence containing between one and four mismatches. For each of these TALEN, we measured by deep sequencing the frequency of indel events generated by the non-homologous end-joining (NHEJ) repair pathway at the possible DSB sites. The percent of indels induced by these TALEN at their respective target sites was monitored to range from 1 to 23. 8% (Table  1 ). We first determined whether such events could be detected at alternative endogenous off-target site containing four mismatches.
Substantial off-target processing frequencies (>0. 1%) were only detected at two loci (OS2-B, 0. 4%; and OS3-A, 0. 5%, Table  1 ). Noteworthy, as expected from our previous experiments, the two off-target sites presenting the highest processing contained most mismatches in the last third of the array (OS2-B, OS3-A, Table  1 ). Similar trends were obtained when considering three mismatches (OS1-A, OS4-A and OS6-B, Table  1 ). Worthwhile is also the observation that TALEN could have an unexpectedly low activity on off-site targets, even when mismatches were mainly positioned at the C-terminal end of the array when spacer length was unfavored (e. g. Locus2, OS1-A, OS2-A or OS2-C; Table  1  and Figure  5C ). Although a larger in vivo data set would be desirable to precisely quantify the trends we underlined, taken together our data indicate that TALEN can accommodate only a relatively small (<3-4) number of mismatches relative to the currently used code while retaining a significant nuclease activity.
In addition, when looking at a naturally occurring pairing between TALE and a plant target promoter, most, if not all, contain mismatches relative to a perfect one-to-one code (NI:A, HD:C, NN:G and NG:T). For instance, AvrBs3 (the TALE scaffold used in this study) contains, when bound to its target, three mismatches (two HD:A and one NG:C at positions 1, 15 and 17. 5, respectively) indicating a certain degree of liberty relative to a perfect one-to-one association. In this work, we aimed at providing an extensive knowledge of the specificity of each TALE binding module (NI, HD, NN and NG) along TALEN/DNA binding arrays. Interestingly, although individual RVDs can tolerate mismatches, the cumulative effects of multiple mismatches within an array rapidly out-balances the overall TALE DNAbinding affinity.
Assume the camera makes a so-called fronto-parallel move, meaning that the focal plane is the same for all positions of the camera and that its motion is parallel to the x-axis of the camera CCD array. This is the case for instance in the Middlebury benchmark dataset  4. Then, the disparity is scalar and its absolute value is inversely proportional to the distance from the observer. More precisely, if the camera moves from the left to the right, any point has a negative disparity (see Figure  2 ). Hence, the lower the (signed) disparity, the lower the depth. In more general cases, the situation is slightly different. After stereo-rectification, some information is lost, such as the position of the camera principal points. The coordinates of pixels in the images are then not given with respect to the optical center, but to an arbitrary point (denoted O L and O R ).
Comparison with Original Algorithm The authors of the original algorithm  [8]  propose a Matlab implementation  6. It performs similarly to our implementation, the slight differences being attributed to: • Image color to gray conversion formulas. • Handling of cost for pixels outside the image, term N (i, i + d) of (  8 ): the Matlab code assumes a constant color R = G = B = b with b = 3 of I R outside its boundaries, while we assign the maximum possible cost. • Numerical inaccuracies. A comparison on the Middlebury stereo benchmark is given in Table  2. The authors' implementation is not their Matlab code. The latter uses d max = -1 instead of 0, but otherwise gives results Error threshold=1:   similar to our implementation. The CUDA code (not available) gives sensibly better results for the error threshold = 1 (only disparity errors strictly larger than 1 pixel are reported).
Comparing both error thresholds, we see that our implementation has significantly more errors of 1 pixel. After a personal communication with the CUDA implementation author  [5] , it appears that on the Tsukuba pair, the left-right consistency threshold is d LR = 1 instead of 0. Whereas taking such a threshold yields slightly better results on Tsukuba and Cones, it deteriorates Venus and Teddy, so does not improve the final rank in the benchmark. Moreover, we see in the green areas of Figure  21  some wrong disparity pixels that are not detected as occluded with the CUDA code but are still corrected in the final map. This is inconsistent with the described algorithm. No explanation could be found.
Results We now show some other results obtained by our implementation. The experiments were done on images without ground truth. Hence, the results can only be appreciated visually. If not stated otherwise, the results shown were obtained by the whole process (including occlusion detection, filling and post-processing). The algorithm was launched with the default parameters (see previous section). Globally, the results tend to show that this method gives very good results. The default parameters are fairly well chosen, which is sufficient most of the time. Hence, one can launch the code without tuning any parameter and yet get satisfactory results. Nevertheless, they can be enhanced by tuning the parameters, which are numerous. Thus, one has to tune and test each parameter several times to find the optimal parameters.
Chair. The code was tested on the Chair pair (Figure  22 ). Results are fairly good. However note that this pair is easy to deal with, since almost none of the stereovision difficulties are encountered (almost no reflection, textured regions). This result is compared with the result obtained before the left-right consistency check (Figure  23 ). Results are visually better. However, Figure  24  shows that this process degrades some parts of the disparity map, especially on some objects boundaries. Densification leads to better overall results, but some accuracy is lost. Umbrella. Results on the Umbrella pair are shown in Figure  25. This experiment discloses a strong fattening effect, that is, neighbor points of objects in the foreground are labelled as if they belonged to the objects. Hence, such objects appear fatter than they actually are.
BRAIN SLICE RECORDING Whole-cell patch recordings were obtained using an Axon instruments Axoclamp 700B Amplifier (Molecular Devices), and pClamp Version 10. 2 software was used for data acquisition. Recording pipettes had tip resistances of 3-7 MΩ when filled with a solution containing (in mM): 135 K-gluconate, 5 KCl, 1 MgCl 2 , 0. 02 CaCl 2 , 0. 2 EGTA, 10 HEPES, 4 Na 2 -ATP, and 0. 3 Na-GTP. The pH and osmolarity of intracellular solution were adjusted to 7. 3 and 290 mOsm, respectively. Alexa 594 (50 µM) was included in the internal solution for visualization of dendrites. Recordings were done in "current-clamp" configuration and cells were held at -65 mV. For electrical stimulation experiment, synaptic responses were evoked with 15-60 µA, 0. 4 ms current pulses delivered through a concentric bipolar stimulating electrode  (FHC, 100 µm o.
DIFFERENCES BETWEEN SINGLE-AND MULTIBRANCH INTEGRATION Whole cell patch recordings were made on CA1 neurons in transverse hippocampal slices. Alexa594 was placed in the patch electrode and was allowed to dialyze into the dendritic arbor. Once the fluorescence signal of the oblique dendrites became visible, the 3D coordinates of the photolysis sites on the spines were identified. Individual spots first stimulated individual spines (lower panels Figure  2A ) and were then sequentially combined (upper panels). In the presence of TTX the supralinearity of single branch integration was decreased (red vs. black exponentially fitted lines in Figure  2A ). The average depolarization immediately before the onset of the sodium spike occurred was about 7. 8 mV at an expected response of 5. 3 mV (group data in Figure  2Aii ).
Combining all of this information yields an expected qTAG purity of 73 ± 4%. The purity is expected to decrease by a few percent with increasing jet energy due to an increase in the neutral-beauty-meson production fractions. The qTAG purity is measured directly using events where both bTAG jets also satisfy the qTAG requirement using the fraction of events where the two muons have opposite charges. This gives an integrated qTAG purity of 70. 3 ± 0. 3%, which agrees with the predicted value, and values of 71. 6 ± 0. 5%, 68. 8 ± 0. 8% and 66. agrees with expectations. The qTAG purity is found to be consistent in data for all ∆y. As a further consistency check, a separate study of the qTAG purity is performed using events with a jet and a fully reconstructed self-tagging B + → J/ψK + or B + → D 0 π + decay.
The same would not be true in Antarctica, where a strong discrepancy between firnification models and data is observed  (Landais et al. , 2006). For NGRIP, it has been shown that the δ 15 N profile is best reproduced when the ss09sea accumulation rate is decreased by ∼ 20 % over the period 20 to 60 ka  (Kindler et al. , 2014; Huber et al. , 2006). It thus appears that the way NGRIP was implemented in the Datice tool for the AICC2012 chronology is not optimal. In addition to GICC05 chronological uncertainties that were not taken into account by construction, imposing the DJ-GICC05 accumulation rate and thinning scenarios with artificially reduced variances most probably led to incorrect output scenarios for these glaciological parameters. In this paper, we propose an improvement of Datice to better implement the chronological uncertainties.
The new age scales are then deduced from the correction functions X a (Appendix Sect. A). They are hereafter called analysed chronologies following the unified notation for data assimilation  (Ide et al. , 1997). By propagating the errors stored in the B and R matrices, Datice estimates the error associated with the age solution (Appendix Sect. D2). We refer to this error as the analysed error. Both the analysed age scales and analysed errors are highly sensitive to the errors specified in the B and R error covariance matrices. In this article, we wish to design Datice experiments with duration constraints derived from the GICC05 counted layer chronology. In Sect. 2. 3, we especially investigate the setting of the observation error covariance matrix R ad associated with these markers: R ad ij = ρ ad ij σ ad i σ ad j , (6) where R ad ij accounts for the error covariance between the i th and j th pair of markers Y ad i and Y ad j.
Validation of Datice developments: twin experiments In this section, twin experiments are performed to test the incorporation of duration constraints within the Datice tool. A twin experiment enables one to test any data assimilation system. It consists in the construction of some synthetic data and background by applying random perturbations of known statistical distribution to a given model scenario. The unperturbed model scenario is referred to as the "truth". The aim of this validation method is to rebuild the truth by running the data assimilation system on the perturbed data and background. In our case, we designed a twin experiment based on 51 simulations where the Datice system is run with the NGRIP ice core alone. The duration constraints are the only type of observation included.
Conclusion In this paper, a methodology based on genetic algorithm is proposed to solve IPPS problem by considering various assembly sequences for a same product and a set of machines for a same operation. We intent to enrich our model by introducing many other criteria as transportation times for all pairs of machines, set-up times between operations, ergonomy and so on. Subsequently we will incorporate specific knowledge of the IPPS in the GA, so which generally improves its efficiency. A software "Orasse Process", proposing users the whole method, is under development.
Conclusion This paper introduced Formal Modelling and Simulation of the impact of Information on Individuals in Social Networks. We have presented how DEVS and Cell-DEVS can be very useful techniques for modelling and simulating of Social influence. At the individual level, the DEVS model proposed is very simple keeping raw Maslow parameters as state variables and being simply influenced by arriving messages. In its turn the individual model can transmit the message after having potentially transformed its strength according to several criteria. The Cell DEVS structure then use these models of human extremely simplified and start building on top of its connected modules to form Cell-DEVS network. In addition, the separation between the model simulator followed by DEVS and CD++, has enabled the modeller to concentrate on building the behavioural model on one side and preparing the spreading logic on the other side using the CD++ toolkit.
This is the first of a series of papers analyzing the relation between SF activity and galaxy environment defined as the membership of a galaxy to a massive dark matter halo. In this paper we carefully describe the catalog and present a calibration of all the relevant quantities involved in our analysis. We use this unprecedented dataset to study the evolution of the relation between the total SFR in galaxy groups at 0. 15 < z < 1. 1 with the group global properties, mainly the total halo mass, and to the stellar mass content of the groups and Halo Occupation Distribution (HOD) to understand how the group galaxy population evolves though cosmic times. The paper is organized as follows. In Sect. 2 we describe our dataset. In Sect. 3 we describe how all relevant quantities are estimated. In Sect.
THE DATASET The aim of this work is to analyse the evolution of the star formation activity in galaxy groups. For this purpose, we build a dataset which combines wide area surveys with good X-ray coverage, deep photometry, and high spectroscopic coverage. Galaxy clusters and groups are permeated by a hot intracluster medium radiating optically diffuse thermal emission in the X-ray band. Under the condition of hydrostatic equilibrium, the gas temperature and density are directly related to halo mass. A tight relation (rms∼0. 15 dex) exists also between the cluster dynamical mass and the Xray luminosity (LX,  Pratt et al. 2007; Rykoff et al. 2008). A similar scaling relation, though with a larger scatter, holds also in the galaxy group mass region  (Sun 2012, rms∼0. 3 dex). Thus, the X-ray selection is the best way to select galaxy groups and clusters and to avoid incorrect galaxy group identifications due to projection effects associated with optical selection techniques.
In addition, deep and accurate multi-wavelength catalogues are necessary in order to identify the group membership and to study the properties of the group galaxy population. Thus, we combine X-ray selected group catalogues and photometric and spectroscopic galaxy catalogues of four major blank field surveys: AEGIS, COSMOS, ECDFS and CDFN. Throughout our analysis we use spectroscopic redshifts to define the group membership and the multiwavelength photometric information for studying the galaxy properties. For calibration purposes we will also make use of photometric redshifts. In the following section we describe the multiwavelength dataset of each field.
AEGIS The All-Wavelength Extended Groth Strip International Survey (AEGIS) brings together deep imaging data from X-ray to radio wavelengths and optical spectroscopy over a large area (0. 5-1 deg 2 ;  Davis et al. 2007 ). This survey includes: Chandra/ACIS X-ray (0. 5-10 keV;  Laird et al. 2009) , GALEX ultraviolet (1200-2500 Å), CFHT/MegaCam Legacy Survey optical (3600-9000 Å), CFHT/CFH12K optical (4500-9000 Å;  Coil et al. 2004 ), Hubble Space Telescope/ACS optical (4400-8500 Å;  Lotz et al. 2008) , Palomar/WIRC near-infrared (1. 2-2. 2 µm;  Bundy et al. 2006), Spitzer/IRAC mid-infrared (3. 6, 4. 5, 5. 8, 8 µm; Barmby et al. 2008) , Herschel far-infrared ( 100, 160 µm), VLA radio continuum  (6-20cm; Willner et al. 2012 ) and a large spectroscopic dataset. In particular, the X-ray data come from sensitive Chandra and XM M -N ewton observations of this field which lead to one of the largest X-ray selected samples of galaxy groups catalog to date  (Erfanianfar et al.
2013). The total X-ray exposure time with Chandra in this field is about 3. 4 Ms with a nominal exposure of 800 ks in three central fields. The XM M -N ewton observations in the southern part of this field have an exposure of 100 ks. The spectroscopic information is taken from different spectroscopic surveys performed in this field. The AEGIS field, as part of the Extended Groth Strip (EGS) field, has been targeted with the DEEP2 galaxy redshift survey  (Davis et al. 2003; Newman et al. 2012 ) and it is the only field that has been a subject of extensive spectroscopic follow-up data in DEEP3  (Cooper et al. 2011 (Cooper et al. , 2012)). In the DEEP2 fields EGS is the only field which is not color selected, so it gives us a nearly complete sample with redshift. In addition to DEEP2 and DEEP3, EGS is located in Sloan Digital Sky Survey coverage so we have additional spectra for low-redshift galaxies.
We also used redshifts of spectroscopic galaxies obtained in follow-up observations of the DEEP2 sample with the Hectospec spectrograph on the Multiple Mirror Telescope (MMT;  Coil et al. 2009). Furthermore, the EGS field is located at the center of the third wide field of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS-Wide3, W3) which is imaged in u * , g ′ , r ′ , i ′ and z ′ filters down to i ′ =24. 5 with photometric data for 366,190 galaxies  (Brimioulle et al. 2008). The EGS field also contains the CFHTLS Deep 3 field, which covers 1 deg 2 with ugriz imaging to depths ranging from 25. 0 in z to 27 in g. For this work, we have used the T0006 release 1 of the CHTLS Deep data. The CFHTLS Deep field also contains near-infrared coverage in the JHK bands via the WIRCam Deep Survey (WIRDS -  Bielby et al.
Group Identification To associate the respective galaxy population to any X-ray extended emission and to define properly the group redshift we follow the same procedure described in  Erfanianfar et al. (2013)  and performed on the AEGIS X-ray dataset. We extend here this procedure to all the other fields described in the previous section. In brief, we estimate the galaxy over-density along the line of sight in the region of each Xray extended emission following the red sequence technique  (Finn et al. 2010 ). Additionally we screen for the existence of an over-density of red galaxies in the 3rd dimension using the spectroscopic redshift distribution of the X-ray extended source. As described in  Erfanianfar et al. (2013) , we assigned to each X-ray extended source a flag that describes the quality of the identification.
Infrared data For all considered fields we use the deepest available Spitzer MIPS 24 µm and PACS 100 and 160 µm datasets. For COSMOS, these are coming from the public Spitzer 24 µm (Le  Floc'h et al. 2009; Sanders et al. 2007 ) and PEP PACS 100 and 160 µm data  (Lutz et al. 2011). Both Spitzer MIPS 24 µm and PEP source catalogues are obtained by extracting sources using NIR priors as described in  Magnelli et al. (2009). In short, IRAC and MIPS 24 µm source positions are used to detect and extract MIPS and PACS sources, respectively, at 24, 100 and 160 µm. This is feasible since extremely deep IRAC and MIPS 24 µm observations are available for the COSMOS field  (Scoville et al. 2007). The source extraction is based on a PSF-fitting technique, presented in detail in  Magnelli et al. (2009).
This is probably due to two aspects. First, any blank field is characterized by a different dataset in terms of multiwavelength coverage (number and type of broad band filters) and, thus, by a different sampling of the galaxy SED. Second, different recipes, thus different star formation histories, and different fitting techniques are used for estimating the stellar masses and the SFRSED. This also explains why there is such a large scatter in the SFRSED derived with different methods. The result of this exercise shows that we can not use the SFRSED-SFRIR relation observed in one of the fields to calibrate the SFRSED of the other fields or obtained with a different method. Thus, we use the following approach. In order to correct a posteriori for the stellar mass bias in the SFRSED, we fit the plane SFRIR-SFRSED-Mass, separately for each field.