dataesr/scientific-paragraphs-categorization huggingface dataset:
Position Estimation in 2D A calibration, which consists of adjusting the sensor outputs S ϕ and S ψ to the ratios X/Z and Y/Z, is processed using the Vicon system. minimize 1 n ∑ (X re f -X) 2 + (Y re f -Y) 2 subject to - X re f Z = a ϕ1. S 2 ϕ + a ϕ2. S ϕ + a ϕ3. S 2 ψ + a ϕ4. S ψ + b ϕ - Y re f Z = a ψ1. S 2 ϕ + a ψ2. S ϕ + a ψ3. S 2 ψ + a ψ4. S ψ + b ψ (1) The coefficients a ϕi and a ψi are determined using Matlab R. The optimization Criterion (1) is to minimize the mean square error between the reference values and the actual data values provided by the sensor. The Matlab R function fminunc is used to compute the coefficients. The localization was tested indoors for several lighting conditions. The optical sensing device was moving in X and Y at a fixed height as presented in Figure  5.
• The ground station connected to the Vicon system runs Matlab/Simulink R and QUARC R software programs. The nonlinear control law of the mobile robot presented in Section 5. 2. 2 is designed with Matlab/Simulink R and compiled. The program is transferred via WIFI radio link to the Gumstix microcontroller embedded on the mobile robot. The control algorithm runs onboard the robot. The mobile robot aims at tracking a desired trajectory using HyperCube in a feedback control loop. An Arduino 380 board controls in closed-loop the angular velocity of each wheel. An Arduino Mega 2560 board is connected to the custom-made demodulation board of HyperCube which provides analog signals. The analog processing of the photodiode's output signal is depicted in Figure  4. The Gumstix board uses the estimation of the robot position X = ( x, ŷ, φ) for control purpose.
The observed nucleotide and haplotype patterns of variation were then compared with those of several African, European, and Mediterranean human groups for which comparable data were publicly available. Admixture analysis on a 5 Mb genomic region surrounding the LCT/MCM6 loci was also performed by extracting genotypes from a previously generated genome-wide dataset in order to deepen the reconstruction of the evolutionary history of these loci. Results: We found that lactase non-persistence (LNP)-related alleles and haplotypes were predominantly present in the examined population. A clear differentiation between Tunisian, African, and North European/North Italian samples was found, while the Tunisian population showed more genetic affinity to Central and South Italian groups. Conclusions: Our study provided a first report of LP-associated alleles and haplotypes in the Tunisian population.
The Sequenom's MassARRAY Designer software (Sequenom, Inc. , San Diego, CA, USA) was used to design PCR and extension primers for the multiplex-PCR, with the total number of SNPs being divided in two multiplex. The first one was composed of 21 SNPs and the second one of 19 SNPs. Genotyping was performed using the iPlex Gold Genotyping Assay and Sequenom MassArray DNA analysis  [39]  with Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (Sequenom, Inc. , San Diego, CA, USA) at the Centre for Applied Biomedical Research (CRBA) of the Bologna S. Orsola University Hospital. The obtained data for the Tunisian population (TN, N = 117) were compared with published datasets including 453 healthy Italian subjects  [18]  from North-Western and Central-Western Italy (NCWI, N = 105), North-Eastern Italy (NEI, N = 139), Central-Eastern and Southern Italy (CESI, N = 159), and from Sardinia (SARD, N = 47).
Then, we merged these data with those for 10 populations of African and European ancestries from the 1000 Genomes Project  [40]. Namely, 99 Utah residents, with North and West European ancestry (CEU), 99 Esan in Nigeria (ESN), 99 Finnish in Finland (FIN), 91 British in England and Scotland (GBR), 113 Gambian in Western Division (GWD), 107 Iberian populations in Spain (IBS), 99 Luhya in Webuye, Kenya (LWK), 85 Mende in Sierra Leone (MSL), 107 Tuscany in Italy (TSI), 108 Yoruba in Ibadan, Nigeria (YRI). The final merged dataset contained 38 common SNPs (one SNP was not found in the 1000 genomes data). Further information about the studied populations were reported in Additional file 1.
Data analyses Summary statistics and allele frequencies analyses Summary statistics, such as nucleotide (π) and haplotype (H) diversity at the examined genomic regions and the number of haplotypes (k), were calculated for all the studied groups using the Arlequin package v. 3. 5. 2  [41]. Allele frequencies for the common genotyped SNPs were calculated and compared between the examined population samples by applying a Chi-square test with the PLINK software package beta release 1. 9  [42] , significance level was set at 5%. Bonferroni's correction was applied to the obtained asymptotic p values to account for the adopted multiple testing procedures.
Population structure and differentiation analyses To test the genetic structure of our samples, we computed principal component analysis (PCA) by using the adegenet and ade4 packages implemented in the R software [http://www. R-project. org/]. For this purpose, SNPs pairs that showed r 2 > 0. 2 calculated on sliding window of 5 SNPs were removed from the dataset with PLINK by maintaining only one SNP per pair. After such a LDpruning, 20 SNPs remained for the Tunisian dataset. The Arlequin software was also used to calculate genetic diversity and pairwise Fst genetic distances between Tunisian samples and the other studied populations. Generated genetic distances (Fst matrix) were plotted to visualize data in multidimensional scaling (MDS) using the statistical package for the social science (SPSS, version 20.
Admixture analyses To better understand the genetic background and population structure of the LP-related genomic region, we extracted SNPs data for 5 Mb encompassing the LCT and MCM6 loci and the surrounding genes from a dataset generated by means of the Genome-Wide Human SNP Array 6. 0 (Affymetrix, Santa Clara, CA, USA) on 135 Tunisian healthy individuals that were recruited from the middle coast of Tunisia as described previously  [44]. The study was conducted according to the declaration of Helsinki principles and approved by the Institutional review board (under the reference PV09/06, IRB#0000000044). These data were merged with the HapMap3  [45]  and other publicly available data  [46] [47] [48]  to produce a dataset composed of 1560 variants for 1677 individuals (Additional file 1).
The same quality control (QC) procedures used for filtering the Sequenom data (see Results section) were applied also to that dataset, which showed a mean genotyping rate of 77%. To avoid bias due to LD, variants that showed r 2 > 0. 8 were filtered by pruning one SNP per five using the sliding windows approach implemented in the PLINK package and as a result 347 SNPs were removed. Admixture proportions in the LCT/MCM6 region were inferred for each subject included in the pruned dataset by means of the algorithm implemented in the software ADMIXTURE  [49] , which provides a maximum likelihood estimation of population structure. Individual genotypes were clustered by running an unsupervised analysis, testing numbers of potential ancestral populations (K) from two to eight, and by performing five iterations for each K.
Linkage disequilibrium and haplotype analyses Patterns of LD at the genotyped loci were investigated using the software Haploview 4. 2  [50]. Haplotypes were statistically inferred by considering all SNPs located within high LD blocks and through the Bayesian algorithm implemented in the PHASE software v. 2. 1  [51]. Evolutionary relationships of the reconstructed haplotypes were finally visualized by means of a median joining network  [52].
Population structure analyses PCA was applied to the generated Tunisian genotypes and showed no clear genetic clustering of the different examined groups (Additional file 2: Figure  S1 ). In addition, we performed DAPC that confirmed the absence of welldefined population clusters (Additional file 2: Figure  S2 ). Observed patterns of Tunisian variation were thus compared with those specific to African and European populations using Fst estimates computed for the 15 SNPs spanning the longest identified LD block (see LD block analysis, Additional file 2: Figure  S3 ) and plotted using MDS (Fig. 1 ). Since PCA and DAPC applied to the Tunisian dataset showed no appreciable internal structure, the overall Tunisian population is considered as a single sample (TN). Accordingly, the first dimension differentiates mainly populations of African and European ancestry.
Admixture analysis We applied the unsupervised ancestry inference algorithm implemented in the ADMIXTURE software on the obtained pruned datasets. As the number of ancestral clusters increased, we observed the emergence of specific population clusters (Fig. 2 ). At K = 2, the ancestry assignment mainly differentiates an African-like (red) and a non-African-like (green) component. K = 3 further distinguishes an Asian-like (green) ancestry fraction from a European-like one (blue), while K = 4 identifies an additional component (yellow) that is mainly shared among East, Sub-Saharan, and North African clusters. Remarkably, the Tunisian population, and at a lower extent, the Algerian samples appeared to be clearly differentiated from the other African populations. They showed more than 95% contribution from different ancestral populations along different K values.
LD block analysis The analysis of LD patterns that was carried out on the Tunisian dataset showed the existence of a long block of relatively high linkage disequilibrium (266 kb), which includes the two functional variants -13,910 C/T and -22,810 G/A (Additional file 2: Figure  S3a ). Indeed, the NT group presented a slightly shorter block (157 kb) that contains only six SNPs of the 15 variants observed when the total population was considered (Additional file 2: Figure  S3b ). However, in CT, the same long haplotype (266 kb) was observed and includes a different SNP (rs1438307) (Additional file 2: Figure  S3c ). Interestingly, this region of high LD appeared to be split into two different blocks spanning respectively (12 kb) and (185 kb) in the ST group (Additional file 2: Figure  S3d ).
10 and 0. 10, respectively). When we compared the Tunisian population to the other populations, we found that it presented several private haplotypes (e. g. , H33, H34, and H35) that carry the reference -13,910 C and -22,080 G alleles. The most frequent haplotype (H1) in the merged dataset (Additional file 2: Table  S4 ) carried the -13,910 C and the -22,080 G alleles and was represented in all populations. It reaches the highest frequencies in YRI, TN, TSI, MSL, GWD, LWK, CESI, SARD, NEI, ESN, and NCWI, (0. 66, 0. 58, 0. 57, 0. 56, 0. 56, 0. 53, 0. 53, 0. 51, 0. 44, 0. 49, and 0. 41, respectively). In contrast, the second most common haplotype (H19)which carries the adaptive alleles -13,910 T and -22,080 A was highly represented in GBR, CEU, and FIN (0. 71,0. 70, and 0. 55, respectively) and absent in ESN, LWK, MSL, and YRI.
g. , from the ELM database of eukaryotic linear motifs, elm. eu. org,  16  or from the literature), we extract fragments from the PDB based on this sequence motif, and map these fragments using the PIPER 17 rigid body docking protocol (instead of mapping solvent molecules, as e. g. in  PeptiMap 15 ). The pooled results are then clustered, and an approximate model (within 4. 0 A ˚RMSD) can usually be found among the top-ranking clusters (to be published). Since then, a range of other original and very successful tools for modeling peptide-protein complexes have been proposed, 18-26 and a book about modeling these interactions is about to appear (Modeling Peptide-Protein Interactions, to appear in the Methods in Molecular Biology Series, Ed. Springer). This progress has been recently spurred also thanks to the addition of this type of interactions to the CAPRI pool of challenges (see other manuscripts in this editions of Proteins).
Modeling of peptide-protein complexes in CAPRI rounds 28-29 using Rosetta FlexPepDock We used our Rosetta FlexPepDock program to generate models of peptide-protein complexes, starting from an approximate model of the interaction (see below). Docking was performed as previously described, 13,14 using Rosetta version 3. 4 with the scoring function score12 (with modifications to the electrostatic potential, as specified in  Alam et al. 47 ) , and selecting models using interface score, as well as reweighted score. 47,48 In short, the structure was first prepacked to remove internal clashes in the receptor (and the peptide), by separating the two partners, repacking each, and putting them back together. Then, the complex was either refined using the FlexPepDock refinement option, 13 or the peptide was modeled using fragments with the ab initio FlexPepDock protocol.
Selection of receptor template structure For each target, we collected all solved homolog structures of the receptor, and focused in particular on bound conformations (see Results). For T60-64, we used the structure of importin bound to a major-groove specific peptide, SV40Tag (PDB id 1ejl 39 ), since the peptide was found to bind both the minor and the major site in the crystal. For T67, we proceeded with the solved structure of a homolog receptor bound to a template (PDB id 1eg4 50 ), rather than with the structure of the free receptor provided by CAPRI (see Results). For T65, many different structures had been solved of the unbound RNAse, showing a range Native contact between peptide and receptor residues are defined as by CAPRI: at least one atom pair across the interface within 4. 0 A ˚distance.
Generation of starting models For T60-64 and T67, we generated starting models based on homologous interactions. For T65 and T66, we generated starting models by arbitrarily positioning an initial peptide conformation (taken from a structure of SSB bound to another receptor, e. g. PDB id 3sxu 52 ) into a given binding site. Ab initio FlexPepDock was then applied to generate a final model of the interaction. For the new PeptiDock mapping, a starting structure was already provided for further refinement.
Assessment of model quality and contribution to our understanding of an interaction New measures for CAPRI assessment The current CAPRI criteria are listed in Table  I. In addition, we use the Interface side-chain RMSD (S-RMSD), routinely calculated in CAPRI in the same way as Interface backbone RMSD (I-RMSD), but for side chains instead of the main chain atoms. We also include the fraction of native interface hydrogen bonds (fnat hb ): Interface hydrogen bonds (including short range salt bridges) were detected using the program HBplus, 53 and fnat hb was calculated analogously to fnat, by counting the fraction of identified native hydrogen bonds in the models.
Alanine scanning for hotspot identification The following computational alanine scanning protocols were applied to identify hot-spots at the interface: (1) Robetta alanine scanning, 32 as implemented in http:://robetta. org (a local executable was provided to us by Tanja Kortemme) and a newer implementation in Rosettascripts. 54  Both protocols remove the side-chain of the tested residue, but do not change the surrounding residues at the interface. This approach was found to work best in several large-scale tests 32,55 ; (2) FoldX versions 2. 5. 2 and 3 30,56 ; and (3) mCSM 33 (using a script provided by David Ascher to run the protocol on many models). All these different protocols were applied to the target crystal structures, and the Robetta, FoldX2. 5 and mCSM protocols were selected to scan for hotspots in models.
For T67, the free structure of the Nedd4 WW3 domain was provided, but crystal structures of homolog WW domains bound to peptides were also available (PDB ids 1eg4 50 and 4lcd 57 ). To assess the importance of using the correct protein versus using a bound conformation, we first performed docking simulations on a WW-peptide interaction where both bound and free receptor structures were available. For the bound run we kept the homolog bound receptor structure, as well as the peptide backbone (PDB id 1eg4), and only changed the peptide sequence by replacing side chains. For the unbound run, we copied that peptide onto the unbound structure provided by the CAPRI organizers. These starting structures were subjected to FlexPepDock local refinement runs. Assuming that the homologous interactions are structurally similar, we expected that the optimization would converge in a narrow funnel centered on a near-native conformation.
The better convergence on the bound receptor conformation (Supporting Information Fig. S1 ) indicates the presence of conformational changes upon binding that are critical for accurate modeling of the peptide. Furthermore, analysis of conformational variation of all solved WW domain structures revealed, despite overall sequence variation, a distinct conformation in the bound structures, mainly of the conserved tryptophan W449 of the WW domain that interacts with the P 0' proline of the peptide binding motif PP. Y. We therefore chose as a template for the receptor the bound conformation of the dystrophin WW domain (PDB id 1eg4 50 ), onto which we threaded the sequence of the nedd4 WW3 domain (using fixed backbone design, see Methods). We added the unaligned part of the peptide in an extended starting conformation and optimized the structure using FlexPepDock ab initio and then refinement, under constraints that maintain the bound motif [i.
e. , constraining the distances between the peptide motif residue P 0' and W449 (the conserved tryptophan of the domain), motif residue P P'1 and F438, and between motif residue Y P 4' and receptor H442, see Tables (II-IV)]. Our best model reproduces the PPXY motif conformation at high resolution. It also positions the specificity determining valine at P 1 3' accurately, but not at atomic resolution [Fig. 1 (A) and Table  V ]. T60-64: sometimes, simplest is best-Use the homolog template as is and change the sequence to that of the target peptide. Importins contain two distinct sites to bind NLS, and often use both to bind so-called bi-partite motifs that contain two repeats of the characteristic basic-residue-rich motif. The structural data available prior to the publication of these targets included the complex of such a bi-partite motif bound to both sites (PDB id 1pjn 58 ), as well as the complex of an NLS peptide from SV40TAg that binds specifically to the major site, as evaluated by mutagenesis studies that showed abolishment of binding only when the major site was disrupted (PDB id 1ejl 49 ).
g. , PDB id 3sxu 52 ). Because conformations of the SSB terminal peptide were known, we decided to proceed in parallel with an alternative approach, in which we docked these peptide conformations instead of solvent molecules (see Methods). With this approach we identified a region adjacent to the correct binding site on 2rn2. Model 10 of our submission to CAPRI is based on this site, but was not accurate enough to pass CAPRI criteria [Fig. 1(G)]. (E-G ) RNAse bound to SSB-terminal peptide (T65). 45 (E) Superposition of the bound conformation and a representative free conformation shows significant movement of an adjacent loop (bound conformation: white and magenta, 4z0u 45 ; free conformation: green, 2rn2). (F) Peptimap misses the binding site on the free conformation that we used as template (2rn2), but mapping of SSB-peptide fragments identifies the site (submission 10; peptide in spheres).
Definition of critical features of the peptide-protein docking targets of CAPRI As a first step, we need to define the characteristic features at the peptide-protein interfaces assessed in this study. Tables (II-IV) list, for the different targets, all interface residues (see Methods for definition) and characteristic features such as hydrogen bonds and salt bridges across the interface. We highlight residues critical for the interaction-peptide motif residues and receptor residues for which experiments have demonstrated considerable decrease in binding upon mutation to alanine or other amino acids. In the following we will term this set of residues "hotspots," even though for some the observed effect on binding is mild and does not necessarily abolish binding. This constitutes the dataset of interactions and hot spots that we would like to identify in a structure, or in a structural model.
DISCUSSION AND CONCLUSIONS Peptide-protein interactions are now recognized as crucial players in many biological processes, and the modeling of peptide-protein complex structures has come of age. The last CAPRI rounds have included for the first time targets of peptide-motif-receptor interactions, and spurred the development of a range of new approaches to address this particular challenge of the structural modeling of interactions (see also other manuscripts in this issue). Overall, the peptide docking results from these rounds look very promising and indicate a rosy future for the modeling of the many peptidemediated interactions of biological relevance. Here we have summarized successes and challenges of the Furman group submissions that we generated using the Rosetta FlexPepDock protocol, among the first dedicated peptide docking protocols developed.
Additional Supporting Information may be found in the online version of this article. Grant sponsor:  European Research Council  ( ERC Grant Agreement ); Grant number:  310873 ; Grant sponsor:  USA-Israel Binational Science Foundation ; Grant number: 2009418. version of this article. Grant sponsor:  European Research Council  ( ERC Grant Agreement ); Grant number:  310873 ; Grant sponsor:  USA-Israel Binational Science Foundation ; Grant number: 2009418.
The function Z (which depends on φ or equivalently on t ∈ [0, T ]) is the solution of the adjoint variational equation dZ(t) dt + A(t) T Z(t) = 0 (2) which satisfies the normalization condition Z(t) dX 0 (t) dt = 1, (3) where A(t) = D X F (X)| γ is the linearization of system (1) around the limit cycle γ. The adjoint equation should be integrated backwards in time to eliminate all the transient components except the periodic one, which gives the solution. An algorithm to compute solutions to adjoint equations, based on backward integration, is embedded in software package xppaut  [58] , or can be coded in matlab  [59] , in addition to a continuation-based approach in MatCont  [60]. Canard explosions occur in slow-fast systems in a very narrow parameter range which is exponentially small in the timescale separation parameter 0 < ε 1.
Naturally, this parameter range gets narrower as ε tends to 0, and limits the usage of classical tools to compute family of canard orbits and their adjoints. This limit has been acknowledged by Govaerts and Sautois who have introduced a direct numerical approach in the continuation package MatCont  [60]. In addition to existing methods, we propose an alternative and simpler continuation-based strategy using the software package auto  [61]. We formulate a periodic continuation problem which allows us to compute rapidly and reliably a family of cycles with associated non-trivial periodic solution of the adjoint equation. Note that a boundary-value problem (BVP) approach has been proposed in  [62] , outside of a continuation setup given that the system was with reset. Here, for simplicity, we avoid dealing specifically with boundary conditions and opt for the most natural periodic setting of this numerical problem.