dataesr/scientific-paragraphs-categorization huggingface dataset:
These were successfully tagged without any screening of gRNAs or ssODN; those that failed could be due to the gRNA not working effectively or the ssODN donor being sub-optimal. These could be re-tested with replacements -particularly for the ssODN, other tag can be used that works effectively in some instances (data not shown). Alternatively, the protein maybe expressed at levels too low to detect by ICC, but the tag insertion might still be detected by PCR genotyping. This is likely the case for 10-20% of the genes we explored. The pipeline that emerges is fully scalable and does not require sophisticated tools, expertise, or know-how. There are no significant bottlenecks in either the design, acquisition/production of reagents or delivery into cells. It was possible to generate such data with relatively little labour investment, and the whole pipeline from obtaining the reagents to imaging data was accomplished within three weeks.
crRNA/ssODN design tool The implementation of our crRNA/ssODN design tool was completed in four stages: extraction of a target genomic sequence from GRCh38. p5 or GRCm38. p4 genome builds, retrieval of crRNA sequences matching the pattern N 20 NGG, scoring and ranking of each crRNA using the 'Rule Set 2'  (Doench et al. , 2016)  and 'MIT' scoring models  (Hsu et al. , 2013) , and design of each corresponding ssODN sequence. To accommodate genomic sequence extraction, an SQL database of genomic coordinates was built using the Genomic Features package in R. This SQL database was used to retrieve coding DNA sequence (CDS) ranges upon user query with a desired Ensembl transcript Id. Given a CDS range, a genomic sequence was then extracted from a corresponding GRCh38. p5 or GRCm38. p4 Fasta file.
For each target genomic sequence, crRNAs were extracted limited to the pattern N 20 NGG. crRNAs were then ranked using two scoring models, 'Rule Set 2' for assessing crRNA efficiency, and the MIT scoring system for crRNA specificity  (Hsu et al. , 2013; Doench et al. , 2016). The former was utilised as a standalone script retrieved from the 'sgRNA Designer' website (https://portals. broadinstitute. org/gpp/public/analysis-tools/sgrna-design) and the latter was implemented as documented on the 'CRISPR Design' website (http://crispr. mit. edu/about). Off targets for each crRNA were found using the short read aligner tool Bowtie, searching up to three mismatches  (Langmead et al. , 2009). Off targets that then match the PAM pattern NAG and NGG were extracted from the Bowtie output. In addition, crRNAs that cut close to the stop codon (8-15 bp in the 3'UTR), and within the UTR region, were prioritised.
Trypsin activity was inhibited by acidification of samples to a concentration of 1% TFA. Samples were desalted on a C18 Stage tip and eluates were analysed by HPLC coupled to a Q-Exactive mass spectrometer as described previously  (Turriziani et al. , 2014). Peptides and proteins were identified and quantified with the MaxQuant software package (1. 5. 3. 8), and label-free quantification was performed by MaxLFQ  (Cox et al. , 2014). The search included variable modifications for oxidation of methionine, protein N-terminal acetylation, and carbamidomethylation as fixed modification. The FDR, determined by searching a reverse database, was set at 0. 01 for both peptides and proteins.
Acknowledgements Vectors were provided by  Addgene  for Cas9 recombinant protein production (Addgene Plasmid 53261). ESCs were provided by  Ian Chambers. Vivien  for tissue culture, flow-cytometry facility,  Bertrand Vernay  provided support for microscopy and image analysis. We thank  Donal O'Carroll ,  Ian Chambers , and  Abdenour Soufi  for helpful comments on the manuscript. The project was also supported by the  BBSRC/EPSRC/MRC Synthetic Biological Research Centre  ( BB/M018040/1 ), part of the  UK Research Councils' investment in 'Synthetic Biology  for Growth'. SMP is a  Cancer research UK Senior Research Fellow  ( A17368 ).
Similarly, the function f might be included in the (partial) functional decryption keys. The correctness property states that, given (mpk, (sk i ) i , (ek i ) i ) ← SetUp(λ), for any label , any function f : M n → R, and any vector x = (x i ) i ∈ M n , if C ,i ← Encrypt(ek i , x i , ), for i ∈ {1,. , n}, and dk f ← DKeyComb((DKeyGenShare(sk i , f )) i , f ), then we have Decrypt(dk f , , C = (C ,i ) i ) = f (x = (x i ) i ). The security model is quite similar to the one defined above for MCFE, except that for the DKeyGen protocol, the adversary has access to transcripts of the communications and can make some senders play maliciously. Corrupt-queries additionally reveal the secret keys sk i. Definition 5 (IND-Security Game for DMCFE). Let us consider a DMCFE scheme between a set of n senders.
Materials and methods Author contributions: the manual tracing was done using a custom MATLAB  [53]  script by SRM; the Bezier curve model was designed and implemented in Python 2  [54]  by RJ; the statistical analyses and plots reported here were conducted in R  [55]  by DD. The Python 2  [54]  source code for fitting a Bezier curve to existing data and for generating Bezier curves for given parameter values, and the R  [55]  code used for the analyses presented here, as well as the anonymized hard palate traces, are freely available as Supplementary Information and on our GitHub repository (https://github. com/ddediu/bezier-hard-palate) under a GPL v2 license (https://www. gnu. org/licenses/old-licenses/gpl-2. 0. en. html).
We then discretized this curve at 100 equidistant positions on the x-axis between 0. 0 and 1. 0, resulting in 100 points β 0 = (0, 0), β 1 = (x 1 , y 1 ),. β 100 = (1. 0, 0. 311). The minimum and maximum y-coordinates of these points are used to define the x y ratio r = (max i = 1‥100 (y i ) -min i = 1‥100 (y i )) -1 used to rescale the y-coordinate values. These 6,765,201 discretized and normalized Bezier curves can be found in Zenodo at https://doi. org/ 10. 5281/zenodo. 1154779. To interactively explore the structure of these systematically generated Bezier curves, we wrote an R  [55]  script designed for Rstudio  [59]  using the library manipulate  [60] , which allows the real-time manipulation of the values of the four parameters and displays the corresponding Bezier curve (for details see S1 Script).
Participants and hard palate tracing Our data is composed of two datasets. The first comprises 22 MRI scans reported in  [31]  from native speakers of American English for which the gender and age are given in the paper, together with sufficiently high resolution midsagittal (and smaller coronal) MRI images acquired during the production of American English /r/. The second contains 85 MRI structural scans from the ArtiVarK sample, covered by amendment 45659. 091. 14 (1 June, 2015) "ArtiVarK: articulatory variation in speech and language" to the ethics approval "Imaging Human Cognition", Donders Center for Brain, Cognition and Behaviour, Nijmegen, approved by CMO Regio Arnhem-Nijmegen, The Netherlands. The MRI scans were acquired at the Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands, using a 1.
5T MAGNETOM 1 Avanto Siemens (http://www. healthcare. siemens. com/magneticresonance-imaging/0-35-to-1-5t-mri-scanner/magnetom-avanto); these are high-resolution structural T1 scans (T1 MPR NS PH8, TE = 2. 98ms, TR = 2250ms, flip angle 15˚, slice thickness 1mm, pixel spacing 1mm x 1mm, FOV 256 x 256), but we used here a JPEG image of the midsagittal slice to ensure comparability with the other 22 scans. For each of the 107 MRI scans we worked with one midsagittal slice image that captured the full hard palate (an example is given in  Fig 6). Slices were oriented so that the teeth appear on the right of the image while the pharynx/posterior portion of the hard palate appears on the left. In each such image, the hard palate was then manually traced by SRM using a customwritten MATLAB 1  [53]  script, resulting in a sequence of 2D points (ranging between 8 and 25 across tracings) such that the contour connecting them best approximates (as judged visually) the shape of the hard palate shown in the slice.
This resulted in a total of 107 × 3 = 321 tracings. Each tracing is uniquely denoted using a "T" for the data from Tiede and colleagues  [31]  and an "A" for the ArtiVarK participants, followed by the numeric participant id, and, if needed, the tracing (1 to 3) preceded by a dot ". "; for example "A01. 1" represents the first tracing for participant ID 01 from the ArtiVarK dataset.
Normalizing and resampling the tracings The tracings are in the slice image's own coordinate system and, in order to ensure comparability, we normalized them as follows: (i) we first rotated around the tracing's midpoint such that the right-most point (i. e. , the beginning of the alveolar ridge) has the same height as the left-most point, followed by (ii) a translation so that the left-most point has an x-coordinate of 0 and the lowest point of the tracing a y-coordinate of 0, ending with (iii) the independent scaling on the two axes such that the horizontal and vertical lengths of the tracing are 1. 0 (i. e. , the x-coordinate of the right-most point is 1 and the y-coordinates of the lowest and highest points and 0. 0 and 1. 0 respectively). These tracings can be found in Zenodo at https://doi.
org/10. 5281/zenodo. 1154779, where we give the coordinates of the leftmost and rightmost points of the tracing (in the original image coordinates in pixels), the rotation (in radians), and the normalized x and y coordinates of their points. However, for Bezier curve generation and some of the statistical analyses, we needed to make sure that all normalized tracings have the same number of sample points at the same x-coordinates by resampling at m (in most such cases m = 100) equidistant positions on the x-axis between 0. 0 and 1. 0. More precisely, given a tracing described by the 0 < n + 1 m 2D points β 0 = (0, 0), β 1 = (x 1 , y 1 ),. β n -1 = (x n -1 , y n -1 ), β n = (1, 0), we computed the intersection between the verticals at each of the m equidistant positions on the x-axis x 0 i ¼ i nÀ 1 , i 2 {0, 1,.
In our runs we used a population size of 100 genomes for 1000 generations (or less), and for each tracing we performed 100 independent replications of the algorithm in order to explore the fitness landscape and prevent being captured by local optima. For each replication we used only the best fitting genome (i. e. , the parameter values that minimized the MSE between the corresponding Bezier curve and the actual tracing) for analysis; these data are available in the S2 Dataset. The actual parameters used are given in Table  1.
Acknowledgments We wish to thank the ArtiVarK participants,  David Norris  and  Paul Gaalman  ( Donders Institute for Brain, Cognition and Behaviour, Nijmegen ) for access to the Avanto 1. 5T MRI scanner and help with piloting,  Carly Jaques  for participant recruitment and management, and  Sabine Kooijman  for assistance with obtaining the ethical approval. This work is part of the G [3]bils (Genetic biases in language and speech) project funded by the  Netherlands Organisation for Scientific Research (NWO) VIDI  grant  276-70-022  to DD.
Using a reference molecular atlas to ensure self-consistency of wavelength calibration is widespread practice. Boesch & Reiners (Astronomy & Astrophysics 582 A43 (  2015 )) reported a line list from a discharge of molecular nitrogen from 4500 to 11000 cm -1 for this purpose. With a hollow-cathode discharge source, we have extended the experimental spectrum up to 15700 cm -1 , to include the range of Ti:sapphire lasers, since the density of N 2 lines is greater than atomic atlases in common use. Recognizing that experimental conditions can vary, we have also analysed the spectra (comprising several B ³Π g -A ³Σ u + , B' ³Σ u --B ³Π g , and W ³Δ u -B ³Π g N 2 bands) with standard Hamiltonians, so that any part of the discharge spectrum 4500 -15700 cm -1 can be simulated. Parameters are given to do this with the spectral simulation and analysis package PGOPHER.
The discharge ran at 150 mA, 600 V for about 2½ hours to record a rotationally-resolved spectrum at an instrumental resolution of 0. 023 cm -1 , averaging 80 scans. The spectrum was recorded using a Si-avalanche photodiode, which has a sharp peak in response around 960 nm. Raw intensities were therefore corrected using an instrumental intensity response function (deduced from the spectrum of a standard 100 W halogen bulb). Typical full-width half-maximum linewidths were 0. 04-0. 05 cm -1 , and we estimate peak position uncertainties ~ 0. 005 cm -1 for unblended lines of N 2. As noted by Reiners and coworkers  [7]  systematic calibration errors in the wavenumber scale can arise if the apertures of the FT instrument are not correctly set, or if the optical injection is imperfect. Below 11000 cm -1 , we compared peaks in our spectrum with the carefully recalibrated data from Boesch & Reiners.
The fits below demonstrate the relative accuracy is much better than this, though not quite as good as the estimates based on fitting individual line profiles. As discussed below, an important part of the assignment process involves comparison of calculated and observed intensities. The fluorescence intensities were calculated from the Einstein A coefficients for the transition (derived from the ab initio dipole moment and potential energy curves for the purpose of initial assignment), weighted by the Boltzmann factor for the upper state. The calculated area is then proportional to the number of photons emitted per second on the transition, requiring a correction factor proportional to frequency for the experimental areas, as these are proportional to emitted power. The Boesch and Reiners data gives peak width as well as intensity at the line centre, so the product of these two is taken to give the peak area.
It is clear from these plots that all the constants show a smooth trend with v, and an alternative fit was tried, where constants were expressed as polynomials in v + ½, and the polynomial coefficients were fitted rather than constants for individual states. This approach worked well for the smaller constants, such as the spin-spin constant, λ, but required high order polynomials for origin, B and D making them of little value, so this was not pursued, particularly as it is not likely to give a good extrapolation to higher vibrational levels. Instead, the ab initio potential energy curves described above were used as a basis for a global fit. For this the DUO program  [37]  was used, which allows coefficients in the potential energy expressions above to be fitted to observations. While DUO can fit directly to observed transitions, we chose to fit to rovibrational energy levels calculated by PGOPHER, as it was more convenient to fit the electronic states separately, and we also did not have line lists from some of the earlier work, but could calculate energy levels from published constants.
Supplementary Material: Selected supplementary material for this paper has been deposited with the Journal, and a full set is available from the University of Bristol Research Data Repository, doi:10. 5523/bris. 1u7h3szdcpk5r2dfa1hao9o2u1. The files deposited with the Journal include a PGOPHER input file, the calibrated Fourier transform spectrum taken as part of this work, a sample calculated line list for the origin band of the B-X transition and supplementary tables S1 -S13, containing ab initio results and constants not given elsewhere. The additional files in the data repository include a complete line list, ab initio output files and input files to the fitting process.
Finding the λ that guarantees SoC tg is equivalent to finding the zero of the function SoC(T ) -SoC tg = f (λ) -SoC tg = g(λ). Thanks to its monotonicity we can use simple, fast, and efficient ways to find its zero. The method used here is a simple bisection algorithm. A shooting algorithm is designed under Matlab R as in Figure  3. The algorithm uses the shooting function previously designed to perform simulations using a value of λ as an entry. The search of the appropriate λ is done using bisection. As an output of the shooting algorithm, the value of λ that reaches the targeted final SoC is returned. The embedded shooting algorithm is ran at the beginning of a trip. It takes the predictions that connectivity provides and returns the optimal λ which will be used in real-time Hamiltonian minimization during the trip.
SIMULATIONS ON FORD FOCUS 48V P2 MODEL: COMPARISON OF PERFORMANCES The model of Ford Focus P2 48V hybrid vehicle, furnished by the CostBased method developers, is used for the next validation. It is representative of all the energetic flows on the vehicle which are not taken into account in the optimization model developed in the first section (such as thermal dynamics, mechanical transmission losses,. ). Note that P 12V is not constant in this reference model. The optimal control applied here uses a constant value of P 12V = 280W synthesized as mean value from NEDC driving cycle simulations. The reference vehicle model was developed under the modeling software AMESim R. The control model using predictive and adaptive approaches is developed under Matlab/Simulink R. Therefore, a cosimulation is performed, a real-time exchange of data between AMESim and Simulink is implemented.
We illustrate our framework with data from three quantitative networks of plant and frugivorous birds, varying in size from 16 to 115 species, and 17 to 2,745 interactions. These data sets illustrate that, for the same dataset, sampling sufficiency may be reached at different sample sizes depending on the metric of interest. The bootstrap confidence limits reached stability for the two largest networks, but were wide and unstable with increasing sample size for all three metrics estimated based on the smallest network. The bootstrap method is useful to empirical ecologists to indicate the minimum number of interactions necessary to reach sampling sufficiency for a specific network metric. It is also useful to compare sampling techniques of networks in their capacity to reach sampling sufficiency.
2003). We used NODF (nested overlap and decreasing fill) algorithm proposed by  Almeida-Neto et al. (2008) , which corrects biases resulting from matrix fill and matrix dimensions. Similar to connectance, information about interaction frequencies is lost. NODF ranges from 0 (non-nestedness) to 100 (perfect nesting). 3) Modularity is characterized by the degree to which there are groups of nodes (species) that interact more among each other than with other groups (modules) in a network  (Guimera and Amaral 2005). We assessed modules using the QuaBiMo algorithm that computes modules in quantitative bipartite networks, based on a hierarchical representation of species link weights and optimal allocation to modules  (Dormann and Strauss 2013). It ranges between 0 (random network with no modules) to 1 (maximum modularity).
Examples from Mutualistic Networks We illustrate our framework with data from three quantitative networks of plant and frugivorous birds (Table  1 ). Network size in each dataset varied from 16 to 115 species and 17 to 2,745 interactions events. Despite the fact that the original datasets contained quantitative data, we used two unweighted metrics to assess sampling sufficiency (connectance and NODF) that only distinguish whether links are present or absent (binary links), and one weighted (quantitative) metric (QuaBiMo algorithm). However, it is not possible to use unweighted (binary) data with the bootstrap method because the frequency of interactions is necessary for the resampling procedure. In two networks, collected by ourselves (named CCS and SS; additional details for methods in Supplementary material Appendix 1), the birds were captured with mist-nets, and then placed into fabric bags to collect their faeces from bags.
The seeds found in faecal samples were identified to the species level, when possible, to build an interaction matrix between birds and the plant that they consumed, with the number of interaction events (the number of times a specific bird species was captured with seeds of a specific plant species found in the faeces). For another test case we used the plant-frugivorous birds network described by  Schleuning et al. (2011)  (data available from the Interaction Web Database), which was built based on the observation of focal plants, comprising primary and secondary forests and various vegetation strata (Table  1 ). We used this network as an example of a large quantitative network. To record bird species feeding on each focal plant species, frugivorous bird visits were recorded at each focal plant individual.
The interaction frequency was defined as the number of fruit-eating individuals on a plant species independent of fruit handling. We also used, as a separated network, only the data collected in secondary forest areas in Schleuning et al. (2011) (Table  1 ). The aim was test if the stability for these metrics is reached with a lower sampling effort compared to the entire network of  Schleuning et al. (2011) , that probably has a high interaction diversity for comprising primary and secondary forests. To obtain the bootstrap sample (algorithm step 1), for SS, we started with nk = 10 interaction events with replacement, and we repeated the resampling procedure 1,000 times (algorithm step 3). We then increased sample size by five interaction events (δ), and the process was repeated with nk + δ up to the maximum number of n events.
For the smallest network CCS (with only 17 interactions events), we started with nk = 7 and used δ = 1. For the largest network of  Schleuning et al. (2011)  (2,745 interactions events) we used nk = 30 and δ = 50, and for the data collected in secondary forest areas in  Schleuning et al. (2011)  (with 568 interaction events) we used nk = 10 and δ = 10. In a second analysis, we used as sample size the actual number of birds captured in our own datasets (CCS and SS), with the same nk and δ than the interaction events. For all test case, we used 95% confidence intervals based on 1000 resampling interactions at each sample size.
A1 ). The results were similar when we considered captured bird individuals as sampling units (Fig. 2 ). Again, the smallest network (CCS) did not present sufficiency for any of the analyzed metrics, since the confidence limit values were wide and unstable with increasing sample size up to 14 captured individuals. The SS network was considered sufficient for all analyzed metrics, as the bootstrap confidence limits reached stability (Fig. 2 ; detailed results in Supplementary material Appendix Table  A1 ). Similar results were expected in these cases, for the number of individual consumers were very similar to the number of interaction events because we captured most bird individuals with only one plant species in its faeces. Consequently, the matrix using the number of events and the number of individuals captured in our data were very similar (see Supplementary material Appendix 1 Table  A2  for CCS matrix with number of events as sample size, and Table  A3  for CCS with number of birds captured as sample size).
Despite the similarity in these results, we show and discuss them because the data available for most networks in literature and online databanks unfortunately only allow the extraction of interaction events. Figure  1. Observed value (star), median and confidence limits of Connectance, Nestedness (NODF) and Modularity metrics obtained by resampling with replacement method using three quantitative mutualistic networks (plants and frugivore birds) and number of interaction events as sample size. The 95% confidence intervals were set based on 1000 resampling interactions at each sample size. See Table  1  for detailed information of networks.