dataesr/scientific-paragraphs-categorization huggingface dataset:
ABL1 The SOMs obtained on ABL1 with ADvina are shown in Figure  4. U-matrices revealed two high neuron consensus areas. The first one is the AS of ABL1, containing the cocrystallized ligand (PDB entry: 2HZI; Figure  4 ; label (1')). The second area matches a big pocket labeled (6'). It is close to the AS and involves the activation loop of ABL1  [67]  and will be referred to as the BS2 of ABL1. Fragments from the EGF library yielded a more compact map than the DUD-E molecules (Figure  4(c) ). The highest neuron consensus appeared at the AS. The docking scores at the AS were lower than at the BS2 with the three libraries (Figure  4 (a) to (c)). DUD-E active molecules and EGF fragments had better scores at the AS than the decoys. The SOM analysis of Dock outputs are reported in Figure  5. Although the spheres defining the docking area cover the AS, BS2 and other pockets, molecules only docked in the AS.
ABL1 Cavities corresponding to ABL1's AS (1') and BS2 (6') have volumes of 257. 1Å 3 and 615. 5Å 3 , respectively. Neuron densities at the AS presented the same trend as that observed for HIV-RT. The EGF fragments had the highest densities (3. 940 and 2. 677 neuron/Å 3 with ADvina and Dock, respectively), followed by the DUD-E active molecules (1. 851 and 1. 254 neuron/Å 3 with ADvina and Dock, respectively), and finally the DUD-E decoys (1. 081 and 0. 590 neuron/Å 3 with ADvina and Dock, respectively; Table  2 ). For the BS2, the EGF fragments yielded the highest densities (2. 600 neuron/Å 3 with ADvina). The DUD-E decoy showed the second highest neuron density at the BS2, followed closely by the active molecules, (2. 587 and 2. 244 neuron/Å 3 , respectively; Table  2 ). Overall, the EGF fragments yielded the highest neuron densities at the active sites regardless of the target and the docking software.
Then, we compared SOM-BSfinder performances to other energy-based/probemapping methods (FTSite  [19] , Q-SiteFinder  [21, 22]  and SiteHound  [27] ), based on their success rates (SRs). For that, we had to adapt the precision and radius cutoffs to match those used by the authors of the concerned programs. FTSite  [19]  and Q-SiteFinder  [21, 22]  consider that the AS was successfully identified if the fraction of ligand atoms within 1. 6 Å of SOM CC points was superior to 0. 25. In contrast, SiteHound uses a cutoff distance of 2. 0 Å, a fraction superior to 0. 15 and only consider heavy atoms. The results for the different methods and SOM-BSfinder with the respective parameters are shown in Table  3. In this specific context and with regards to the fact that different datasets were used by the described methods, SOM-BSfinder outperformed all three methods (see Additional file 1: Table  S4 ).
Chemical descriptors We tested if we could use information on the ligand chemical composition to provide information on relevant chemical groups, in addition to the fact that they refined the acuity of the active site identification. We made this analysis on HIV-RT and ABL1, the benchmarks used to setup the method. We used the Morgan fingerprints as chemical descriptors (see Methods). They describe the molecule chemistry through an inventory of each atom environment, which can be viewed as local chemical groups or moieties. Their application gave higher neuron density and consensus for both HIV-RT and ABL1 (Figure  9 ), thus refining the BS geometrical definition. To evaluate the insight provided with this approach, we calculated the enrichment of known "active features". The latter term denotes chemical substructures observed in the active ligands provided by the DUD-E database.
One should note that these results were obtained on different datasets, except for FTSite and Q-SiteFinder. A major difference between SOM-BSfinder and the other methods is the probe library: SiteHound used a carbon atom probe, Q-SiteFinder used a methyl probe and FTSite used 16 organic probes with an average size of 4. 3 heavy atoms. FTSite achieved the best SR among these three methods. In contrast, SOM-BSfinder used 1500 fragment molecules as probes, and achieved an SR equal or superior to FTSite. This may be a direct result of the diversity of the probe library used. Moreover, SOM-BSfinder takes into account the size and shape of the fragments during docking, which is less meaningful when the probe accounts for less than 8 heavy atoms (FTSite). The ABL1 target was among the 9 cases out of 102 where the AS was identified at the second CC (labeled (6') in Figure  4 ) by SOM-BSfinder.
Acknowledgements MN acknowledges funding from the  Investissement d'avenir bioinformatics programme  ( Grant Bip:Bip ), the  European Research Commission  (Advanced Grant  ERC-2011-StG 294809  BayCellS). IG and MN acknowledge funding from the  Institut Pasteur PTR programme  (grant  PTR426 ) and IG from the  Ministry of Higher Education and Research in Tunisia  ( LR00SP04 & LR11IPT04 ). EHS is a recipient of a  UNESCO-l'Oreal international fellowship , and recieved support from the  Institut Pasteur International Network. ICC is a fellow of the  Paris-Pasteur International PhD Programme. ND is recipient of an  AXA Research Fund PhD  fellowship. The SOM software is available on GitHub (https://github. com/bougui505/SOM/tree/SOM3D_dev).
In a model describing the kinetics of the restriction point, if the G1 cell cycle phase cyclin, Cyclin D1 (CycD in the model), is initially active (corresponding to presence of growth factors), the cell enters the cycle, otherwise, it stays stuck in G1 arrest  14, 29. In this manuscript, we suggest a quantitative methodology to convert a logical model of a regulatory network into a genetic interaction network, defined with respect to a chosen model phenotype (which can be any phenotype and not only survivalrelated as it is often the case). The methodology is based on using the formalism of continuous time Markov chains implemented in MaBoSS software  37. Using published models, we applied our method to derive several genetic interaction networks for the genes that compose these models. We analyze genetic network properties and show that they possess many features of experimentally-measured genetic networks.
Computing phenotype probabilities For each model, we computed the probability of reaching model phenotypes for all possible single and double mutants (resulting either from gain of function -modelled as fixing the corresponding node value to 1, and referred to as "overexpression" or "oe", or from loss of function -modelled as fixing the node value to 0 and referred to as "deletion" or "ko"). For these computations, we used both MaBoSS software and a set of scripts for processing the MaBoSS configuration and output files, implemented into BiNoM Cytoscape plugin  2, 3, 43. MaBoSS is a C++ software designed for simulating continuous/discrete time Markov processes, defined on the state transition graph representing the dynamics of a Boolean network. MaBoSS allows the modeller to associate different rates up and rates down to each variable of the model when the dynamics is known, enabling to account for different time scales of the processes described by the model.
Results and discussion The three Boolean models were downloaded either from The Cell Collective database  22  or from GINsim database  26. The stable state analysis was done in GINsim software. The models were then exported in MaBoSS for simulations. Finally, we used some scripts embedded into BiNoM cytoscape plugin to automatically compute probabilities for all single and double mutants (including both gain of function and loss of function mutants for all components of each model) and vi- sualize the results of paired interactions as genetic interaction networks. A thorough description of each model is given in supplementary materials (SuppMat description models) along with the Cytoscape sessions for each model, each phenotype and different initial conditions for one of the models (Supp-Mat GINs).
Comparison with experimentally derived genetic interactions We performed two types of comparisons: first, we compared the genetic interactions from our method to available experimental results, and second, we compared the genetic interactions between models. We have compared the results from each of the examples we have chosen in this analysis with genetic interactions listed in BioGRID database  34. In the database, we queried the genes that appeared as participating in pairs of genetic interactions in a significant manner in our three models. We found that in the MAPK model, TP53 and MDM2 interactions came out in both BioGRID and our study: TP53 and MDM2 were identified in a phenotypic suppression type of genetic interaction in BioGRID and we showed that overexpression of both TP53 and of MDM2 led to a suppressive genetic interaction with respect to the apoptosis phenotype.
More details can be found in supplementary materials, SuppMat Analysis BioGRID. In conclusion, the comparison showed that some interactions predicted by our method were indeed confirmed in BioGRID database. This type of comparison can serve to validate Boolean models developed for various molecular mechanisms with respect to known genetic interactions and provide additional constraints on the choice of model network topology, logical rules and rate parameters. Of course, in this analysis one should take into account incompleteness of our knowledge on genetic interactions. We also compared more in detail the results of the genetic interactions among the three examples. Unfortunately, there was no overlap between the three models since the only common gene was BCL2 between the cell fate and the MAPK models.
The last point deserves further development. We aim at extending our methodology using existing databases containing genetic interactions (similar to what we did with BioGRID  34  ) for matching the model predictions with genetic interactions or single mutation phenotypes known from the literature or from screenings. Moreover, similar to the methodology of parameter fitting in constructing chemical kinetic models, one can fit the kinetic rates defined in our continuous-time discrete approach  37  in order to optimize the set of model predictions. Another set of experimental data that could be used with this approach is high-throughput cancer data, such as large-scale mutation landscapes that are collected for series of tumours. Patterns of co-occurrence or mutual exclusivity of mutations can reflect action of genetic interactions in cancer cells.
With this artificial data set we are able to establish a lower-bound on the number of sequences that would make our predictions on the PPI scale completely accurate if the generating model was the same model we use for inference. Given the growth-rate of current protein sequence databases (notably UniProt  [8] ), we expect that such a lower bound could be met in few years. In a second step, we apply the method to the proteins of the bacterial ribosome and to the proteins of the trp operon, and show that the results obtained for simulated data translate well to the biological sequences of this test-set.
Given that in this artificial setting we use the same model to generate the data as to analyze it, the approach is guaranteed to infer the model correctly for a large number of analyzed sequences and therefore to assign a higher interaction score to any interacting protein pair than to any non-interacting pair. To assess the coupling strength between two proteins, we average the four strongest residue coupling strengths between them. This leads to a score oriented toward the strongest signal while also reducing noise by averaging. In panel B of Fig. 3   The five strongest links are colored in green when they correspond to actual PPI according to panel A, and in red when they correspond to non-interacting pairs. For increasing sample size the predictions become more consistent and for M = 24, 000 any interacting protein-pair has a higher interaction score than any non-interacting pair.
The strong performance of the same algorithm on different and dissimilar systems naturally prompts us to expect that the approach can be used to detect interactions experimentally unknown so far. In fact, if we trust our results on the trp operon we can already draw some speculative biological inferences. While there are many high-resolution structures of the ribosome available, one might have expected that in the trp operon there could be more transient previously unreported interactions in the tryptophan biosynthesis pathway beyond the two interactions that have been structurally characterized. As mentioned, various enzyme fusions can be observed in the databases, suggesting that there is an evolutionary benefit to co-localizing the enzymes of the pathway in the cell. An obvious benefit of such co-localization would be that the pathway intermediates do not have to diffuse throughout the cell from one enzyme component to the next.
Given that the representations are mathematically equivalent, we will here in supplemental information treat the sequence as one simple sequence The inference proceeds by assuming as a working hypothesis that the dataset D is composed by configuration sampled uniformly from the equilibrium Boltzmann-Gibbs distribution P ( x) = exp(-H)/Z (as an inference process, we are free to consider T = β = 1). We are now ready to use D to infer the topology of the network. To do so -as discussed in the main text -in the last years different maximum-likelihood techniques have been proposed  [1, 12, 23, 29, 31, 39]. So far the most promising in terms of accuracy seems to be the pseudo-likelihood maximization introduced in  [12]  where from the previously defined Boltzmann-Gibbs measure we consider the following conditional probability distribution: P i (x i |x \i ) = exp j =i J ij (x i , x j ) + h i (x i ) 21 a=1 exp j =i J ij (x i , a) + h i (a) (8) Given a data set D we can thus maximize the conditional likelihood by maximizing L i (J i,\i , h i ) = 1 M M α=1 log P i (x α i |x α \i ) , (9) as a function of J i,\i , h i.
The contact map is therefore a binary, symmetric matrix of size N all × N all with N all = N 1 + N 2 + N 3 + N 4 + N 5 where N i is the number of residues in the i th protein. We decided to use the Kunitz domain (PF00014) as a model for the proteins and set all N i = 53. The 53 × 53 submatrices that define the contacts within each protein were defined by extracting the contacts of the PDB structure 5pti of the Kunitz domain. This implies that the internal structure of every protein is the same. We defined as contacting proteins the protein pairs 1 -2, 2 -3, 3 -4, 4 -5 and 1 -5. For the 53 × 53 submatrices that define the contacts between contacting protein pairs we used random binary matrices with 10% of the number of internal contacts. This was done individually for each contacting protein pair such that no two contact matrices between two proteins were the same.
For non-contacting protein pairs all entries of the contact matrices were set to 0. The resulting contact map can be seen in Fig. 12. 2) Couplings for every contact in the contact map were defined. As a basis for this, couplings and fields inferred from the PF00014 PFAM alignment (Kunitz Domain) were used. This inference was done using a masking with the PDB structure, such that only couplings corresponding to PDB-contacts were allowed to differ from zero. Given that the same PDB-contacts were used to define the contacts within one protein in the artificial complex, we could use the couplings thus inferred without change for the couplings within the artificial proteins. Then we defined the couplings for residue contacts between two proteins. For every such a resiue contact we chose randomly a coupling of an internal contact as inferred from the Kunitz domain alignment and assigned it to the residue contact.
This technique has also been applied in Noels et al  [6, 7]  to metal forming simulation and in Noels et al  [8]  for a blade/casing interaction simulation. These explicit/implicit and implicit/explicit switches have been recently implemented in the industrial finite element code Code_Aster based on the research work of Mahjoubi et al  [9]  when implicit and explicit domains are not located in the same area, and a domain decomposition-like method is used to couple them. For phenomena that are restricted in space, i. e. to a small part of the computational domain, a wide range of methods has been developed. These approaches are always in progress and can be divided into exact (or direct) methods and iterative ones. The most popular exact approaches are the static condensation techniques and the exact structural reanalysis methods, such as used in Hirai  [10] , Hirai et al  [11].
2 and is computationally expensive. Note that in the following, we present the beam modeling as well as the 3D modeling of an unbalanced rotor. The finite element software that we are using in the scope of this research paper is Code_Aster developed by the Research and Development section of EDF  [39]. Code_Aster proposes a beam modeling of an unbalanced rotor in the Galilean frame, whereas the 3D modeling of the unbalanced 3D rotor, taking into account the gyroscopic effects of the 3D elements recently developed in the scope of the PhD thesis of Ghanem  [40] , takes place in the rotating frame. In this article, we therefore use for rotor dynamics modeling, the rotating frame for the 3D model, and the Galilean frame for the beam model. The gyroscopic matrix of the 3D elements is dependent on the rotation velocity.
A hybrid beam-3D model can be obtained as done in Tannous et al  [36]. This switch method can be applied to more diverse applications, such as local plasticity or thermoplasticity, cracking, and several other kinds of local linear or non-linear effects as long as the switch is performed in the linear stage preceding any non-linearity. The switch is a non-intrusive approach that was developed on Code_Aster, and can be easily implemented on a commercial finite element software.
The safflower collection device is an important service part of safflower harvesting device, the number of the air outlets and external structure of air collecting hood are the main factors influencing the effect of the flower's petals harvesting and collection. However, the design of pneumatic safflower harvest device is based on experience, resulting in long the design cycle, thus increasing the cost of design. Combined with the advantages of computational fluid dynamics software FLUENT, and the internal flow field of safflower closed style cover flowers as the research object, this paper designed gas collecting hood structures with different number of outlets through the analysis of the airflow distribution and motion law. The result of numerical simulation of gas-collecting hood with trapezoidal cone shows higher air flow speed with no low velocity zone, and the distribution of flow field of safflower collected cover with trapezoid cone type is better than that of parabolic type structure.
But there is rarely reported on pneumatic study of this small and lightweight materials like safflower which are separated from fruit ball by the air flow. Combined with the biological characteristics of safflower, negative pressure collecting device collects and transports safflower petals to the storage device timely and effectively, therefore it is very important to research on the rules of the internal air flow in collection device for collecting safflowers effectively. Therefore, this paper takes the safflower negative pressure collecting device as the research object, and makes the numerical simulation of the internal flow field of safflower pneumatic collection device by Fluent software and analyzes local, then establishes energy loss equation, finally optimizes and designs the structure parameters of safflower negative pressure collecting device R E T R A C T E D A R T I C L E.
(1) According to fluid simulation of collecting device structure for different number of exports. The distribution of flow field with design of single outlet at the center of collecting device is more suited to collect safflower by analysis. (2) The distribution of flow field of safflower collected cover with trapezoid cone type is better than that of parabolic type structure. (3) The result of numerical simulation of internal structure with trapezoidal cone-type by FLUENT software shows the area of internal high-speed flow is larger, and the original three streams of longitudinal section are converged into one share to reduce the gap between the pipe blowing flowers, form no low velocity zone, save energy, and manufacture easily.
5F. Nevertheless, the true values are rather close to the final estimates, lying within the 1-standard deviation confidence region (Fig. 6C ). Importantly, only a reasonable amount of data is required to produce these estimates. Network activity was monitored on 15 independent runs, each run consisting of 180 repetitions for each of the 3 stimuli. On each run, a different set of N = 170 random neurons were simultaneously recorded-out of a total population of N tot = 5000. As a result, (i) individual neuron statistics such as C ij (t, u) or d ? i ðtÞ display an important amount of measurement noise, (ii) population statistics such as indicator V are computed from relatively few neurons i. Numerically, this noisiness introduces a number of biases in the above indicators, such as overfitting, which require counteracting with specific corrections (see Methods and supplementary material for details).
Naturally, the width of the confidence intervals in Fig. 6  is directly related to the amount of data available. In conclusion, if the data conform to a number of hypotheses (optimal linear readout from a neural ensemble typical of the full population, and smaller than the recording pool size), then it is possible to estimate the underlying readout's parameters, from a plausible amount of experimental samples. Case 3: less than K ? cells recorded By construction, the method presented in Case 2 can only test ensemble sizes K smaller than N, the number of neurons recorded simultaneously by the experimenter. If N is smaller than the true size K ? , the method will provide biased estimates. In current-day experiments, N can range from a few tens to a few hundred neurons. While it is not excluded that typical readout sizes K ? be of that magnitude in real neural populations (as suggested, e.
Experimental statistics of neural activity and choice Classic measures in decision-making experiments can be interpreted as estimates of the firstand second-order statistics of choice c and recorded spike trains r i (t), across all trials with a fixed stimulus value s: cðsÞ ≔ E½cjs; ð23Þ m i ðt ; sÞ ≔ E½r i ðtÞjs; ð24Þ C ij ðt; u ; sÞ ≔ Cov½r i ðtÞ; r j ðuÞjs; ð25Þ d i ðt ; sÞ ≔ Cov½r i ðtÞ; cjs: ð26Þ Here, ψ(s) is the psychometric curve, m i (t; s) is known as the PSTH, and C ij (t, u; s) as the JPSTH. The choice covariance (CC) curve d i (t; s) is our proposal for measuring each neuron's "choice signal". Theoretically, the temporal signals in eq. 24-26 are well-defined quantities in the framework of continuous-time point processes  [38]. In practice, they are estimated by binning spike trains r i (t) with a finite temporal precision, depending on the amount of data available.
Autonomous mobile robots are constrained in their long-term functionality due to a limited on-board power supply. Recharging the batteries of a mobile robot in an autonomous way is a difficult and disruptive event. The homing action is commonly only achievable when the robot is within line of sight of a recharge station's beacon. The robot can either move towards the beacon using its navigation system, or wonder around within a described region while searching for the beacon signal. A mechanism is finally needed to secure the robot to the charger. Automated docking with This work has been partially supported by a grant from the CPER Nord Pas de Calais CIA and the EquipEx FIT project. Kobuki is already implemented  [6]. The objective of this work is to enable Wifibot robots to achieve long-term autonomy and provide a similar functionality as for  Kobuki.