dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, when applying the clustering step to a dataset showing an important multimodality, at least for some genes, the sigmoidal function may be not adapted for modeling the behaviour of the underlying process. The clustering step of CARDAMOM, described in Section 4. 2. 1 could therefore be seen as a preliminary to the choice of the "right" bursts rate function that should be used for minimizing the risk  (17)  (see Section 4. 2. 2): this function should be chosen in accordance with the number of modes detected for each gene. The neural network framework introduced above is interesting, as it suggests that the choice of the bursts rate function could correspond to the choice of the number of layers in the neural network of Figure  13. In that point of view, the work achieved in this paper treats the basic case of one layer, which could be extended, although the interpretability of the nodes for more than one layer remains an open question.
The method is implemented as a Python package called CARDAMOM, which is available on open-access. This algorithm seems to be very accurate for small but complex networks, and its computational speed allows to make it suitable for realistic number of genes and cells. Furthermore, such inference method based on a mechanistic model has the great advantage to make quantitative predictions that can then be easily tested by simulating the model. The simplicity of the regression step is particularly remarkable, and appears close to the learning step of a neural network. We believe that in addition to efficiently infer GRNs from timestamped datasets while keeping a high interpretability, CARDAMOM, which combines explicitly machine learning methods with mathematical modeling, could pave the way for new adaptive methods.
The next step would be to conceive bigger and more complex networks for which all the nonzeros entries of the GRN matrix do have an impact on the data when simulating the model, which is in itself a challenge, and to compare the performances of CARDAMOM to state-ofthe-art algorithms used for GRN inference. As the problem of inference from a mechanistic model like (1) has been recently studied elsewhere using distinct mathematical tools  [15] , a collaboration is in progress for realizing an important benchmark of our method together with another algorithm specifically developed for facing transcriptional bursting and well-known methods such as GENIE 3  [19]  and PIDC  [8] , and studying the ability of the mechanistic model to reproduce in vitro expression datasets when the model is calibrated by CARDAMOM.
θ ij θ •,0 θ •,1 θ •,2 θ •,3 θ •,4 θ 0,• 0 Figure  6 : Inference of a 4-genes network (G1 to G4) consisting in two independent symmetric toggle-switch networks with parameters described in Table  1. The stimulus (S) has no effect on the network, but is nevertheless represented in order to verify whether the inferred network takes it into account or not. The network used for simulating the datasets in (A) is compared with the network inferred by CARDAMOM in (B).
Acknowledgment This work was supported by funding from  French agency ANR  ( SingleStatOmics ;  ANR-18-CE45-0023-03 ). I would like to thank especially  Thibault Espinasse  for enlightening discussion and statistical advices, as well as  Ulysse Herbach  for having highlighted the notion of bursts modes for the model of gene expression,  Thomas Lepoutre  and  Olivier Gandrillon  for critical reading of the manuscript. I also thank all members of the SBDM and  Dracula teams , and of the  SingleStatOmics  project, for providing such stimulating working environment. I finally thank the  BioSyL Federation , the  LabEx Ecofect  ( ANR-11-LABX-0048 ) and the  LabEx Milyon of the University of Lyon  for inspiring scientific events.
Prakash and Curd provide a re-analysis 1 of individual datasets taken from our report 2 demonstrating MINFLUX 3D imaging in cells. Their evaluation confirms the unique localization precision provided by MINFLUX 2,3 featuring a standard deviation of σ = 1-3 nm. We appreciate their confirmation and also welcome the opportunity to clarify their remaining points. The hitherto almost unconceivable 3D localization precision attained by MINFLUX is likely to hold the key to an all-optical dynamical structural biology.
"The average z distance between cyto-and nucleoplasmic layers of Nup96 localizations was 40. 5 nm instead of ~50 nm, in the dataset on which this claim was based". Unfortunately, in their analysis of our data it appears that Prakash and Curd neglected to account for the local inclination of the nuclear envelope (Figs. 3f,g of Gwosch et al. ). Calculating a relative position distribution (RPD) along the z axis over the entire field of view (see their Fig. 2g-j ) cannot yield the correct distance between the two Nup96 rings. It biases the RPD towards lower values. We demonstrate this effect by repeating their analysis (Fig. 1a ). If the membrane is assumed to be flat and the nuclear pore tilts are not accounted for -i. e. , the 3D dataset of Fig. 3f  of Gwosch et al. is used as is -the RPD(Δz) indeed peaks at the erroneous value of ~40 nm (Fig.
" The mean or best-fit Nup96 ring diameter varies between datasets and the spread of diameters in each dataset is broader than that found by [PALM/STORM]. " Even if we consider that fixation may add to the variability of the many individual snapshots of NPCs in various samples, the average diameters (107, 108, 111 nm) determined by Prakash and Curd still fall within the respective error margins, and are fully compatible with the 107 nm found by cryo-electron tomography after extensive averaging  11. Therefore, although they are based on small datasets (N = 20 in each case), the diameter measurements actually agree very well. 3. "The eightfold symmetry of NPCs is rarely visible at a single nuclear pore level and was not clearly determined in structure-based modelling of the localisation datasets".
Reasons for that may be insufficient labeling, tilts and structural variabilities  7, 8. The visibility of the channel (ring) requires a good top view, which was easier to accomplish in the referenced STORM study  12  because that study used isolated Xenopus nuclei instead of complete mammalian cells, as we did. Nonetheless, the ring-like structure mapped out by WGA can also be extracted from more recently recorded MINFLUX data (Fig. 1d-g ) for selected top (x-y) views. An overlay of the aligned MINFLUX data showed a narrowly defined WGA ring of ~30 nm diameter in fixed U-2 OS cells. Yet, care should be exerted when taking these 'rings' for granted. Considering the overall distribution and accessibility of the putative WGA binding partners, further research will be needed to answer if the observed rings are biologically relevant, or whether they are due to labeling and fixation.
Data analysis The described analysis was performed in MATLAB. In brief, for Fig. 1a  the RPD(Δz) was calculated for three separate sets of 3D localization coordinates: (1) All localizations within the field of view, unaligned; (2) 14 segmented NPCs, unaligned; and (3) the same 14 segmented NPCs after horizontal alignment (rigid transformation of their coordinates), removing the tilts estimated from a comparison to a template of the average positions of Nup96 C termini based on the cryo-ET structure  11. For the localization coordinate set (1), all Δz across the entire field of view were calculated, but adding a filter in the Euclidian distance R < 200nm for meaningful interpretation of the RPD histogram, due to the substantial non-flatness of the nuclear membrane over the field of view. Figure  1b  shows direct, normalized z histograms of the aligned localizations of set (3).
Cues The task included auditory and tactile spatial cues. The auditory cue was a 1-second tone of 500 Hz with 0. 1 seconds fading in and out to reduce the sound's sharpness. The spatial information of the auditory cue was generated by the audio spatializer software development tool kit that is part of the Unity game development platform  [41]. The head-related transfer function of the spatializer is based on the KEMAR data set (set of pre-ear impulse recordings of a dummy head; Bill Garnder at MIT Media Lab  [43] ). In the task, the sound was realistically perceived as coming from the left (due to different sound volume levels between the 2 ears) if the sound source was on the left side of the head and as coming from the front if the head was oriented toward the sound source. The tactile cue (1-second vibration) was applied using punctual vibration that came from a special inlay of the HMD that contained 6 symmetrically positioned individually controllable coin vibrators  [29].
Acknowledgments This work was supported by  Swiss National Science Foundation (SNSF) Grants  ( P2BEP3_195283  to BCK,  32003B_196915  and  320030_169789  to TNyffeler, and  Z00P3_154714/1  to DC). We are grateful to the patients who took part in our study and to the clinical teams of the  Inselspital  and the  Luzerner Kantonsspital  for their support.
16  In this work, we transfer the well-established method (and implementation) of molecular docking to the field of MOF-guest complexes. The software AutoDock v4. 2. 6, originally designed for protein-ligand binding, is used in the initial screening of various adsorption sites of guest molecules inside MOF pores. We employ a genetic sampling algorithm in combination with additional force field parameters, needed to describe MOFs (see Methods). Subsequently, the soobtained structures are used as inputs for DFT-based geometry optimization. With these structures, in turn, host-guest interaction energies are computed. This new approach is validated by comparing the structures, obtained here via multiscale simulations, to five experimentally determined MOF-guest structures deposited in the Cambridge Structural Database (CSD).
Materials We have selected five MOF-guest systems from the Cambridge Structural Database, as examples for which the structures of the framework and the guest have been determined. Additionally, new MOF with two guests was synthesized and characterized experimentally and computationally. The six investigated systems are (see Figure  1 ): System 1. MOF [Zn2(bdc)2(bimx)] (H2bdc = 1,4-benzenedicarboxylic acid, bimx = 1,4bis(imidazol-1-ylmethyl)-2,3,5,6-tetramethylbenzene) with guest 1,4-benzenedicarboxylic acid (CSD refcode: DOGZIJ). 17  The guest molecules reside in channels along the a-axis. System 2. {[Zn3(µ3-OH)3(2-stp)(bpy)1. 5(H2O)](EtOH)(2H2O)}n (2-stp = 2- sulfonylterephthalate, bpy = 4,4'-bipyridyl) (CSD refcode: JITPOS). 18  This three-dimensional MOF consists of zinc-oxygen secondary building units forming chains, which are in turn connected by bipyridine and 2-sulfonatoterephthalate in the other two directions (cf.
Crystallographic section. A single crystal of UB-MOF-1 with an approximate dimension of 0. 080 × 0. 045 × 0. 030 mm was chosen for the diffraction experiments. The experiment was carried out at ID15B beamline at ESRF, using a convergent monochromatic beam (30 keV). Data were collected at room temperature by an Eiger2 9M CdTe detector. The sample-to-detector distance (180 mm) was calibrated using a Si powder standard and a vanadinite [Pb5(VO4)3Cl] single crystal. The data collection strategy consisted of a ω-scan (-70° ≤ ω ≤ +70°), with 0. 5° step width and 0. 5 s exposure. The structure was solved with the dual-space algorithm, as implemented in SHELXT. All non-hydrogen atoms were refined anisotropically using the full-matrix, least-squares method on F 2 by the SHELXL software. All hydrogen atoms were refined using the riding model.
This high conformational flexibility is further complicated by the residual electron density in the pores in the experimental structure. Moreover, the computed interaction energy is only insignificantly affected by the orientation of the ethanol molecule (see Figure  S2  in the SI), hampering a distinction between different conformations based on energy alone. Nevertheless, with molecular docking, a hydrogen bond between the guest and the sulfonate group of the linker was identified correctly. Our findings for system 2 resemble those of Korb et al. 11. Using the software GOLD  36  , which, like AutoDock, is optimized for protein-ligand binding, the authors also located a hydrogen bond between ethanol and the framework's sulfonate group, but could not correctly predict the XRD position. System 4 is illustrative of an alternative, yet equally challenging scenario: the 1,2-bis(4pyridyl)ethane guest occupies a large portion of the pore space since it is present during the synthesis.
We present the results of geological mapping of Mercury's Derain (H10) quadrangle (0°-72°E and 22. 5°N-22. 5°S) using data from the MESSENGER spacecraft. The map is presented on a scale of 1:3,000,000, for which linework was drawn at 1:300,000. We distinguish three major morphological plains units: Smooth, Intermediate, and Intercrater Plains. We produced two versions of the map, with craters classified according to a 3-and 5-class degradation system. This allows compatibility with other MESSENGER-era maps and Mariner 10-era maps.
BDR (Basemap reduced data record) 166 mpp mosaic basemap The primary mosaic for mapping was the BDR monochrome basemap with an average resolution of 166 mpp (metres per pixel) (Figure  1(A) ). This is the highest resolution and most recent mosaic, composed of images from the NAC and WAC with predominantly moderate incidence angle close to 74°. This allows good visibility of features with topographic relief, and was the primary dataset used in the production of the map.
Low incidence angle 166 mpp mosaic The low incidence angle basemap is a mosaicked data set composed of low incidence angle (i. e. small angle between the sun and surface normal) images (Figure  1 (C)). Low incidence angle imagery accentuates albedo differences and minimizes obscuration by shadows, but surface relief and texture are difficult to see. 2. 1. 4. High incidence angle (east-and westfacing) 166 mpp mosaics These high incidence angle mosaics comprise images with an incidence angle of close to 78°, with consistent illumination from east or west. These basemaps were useful for revealing detail within areas shadowed on the BDR mosaic. 2. 1. 5. Enhanced colour 665 mpp mosaic 'Enhanced color' is a standard MESSENGER product that accentuates subtle colour differences (Figure  1(B) ). It was created using MDIS-WAC images in the 430, 750, 1000 nm bands.
Acknowledgements CCM and DLP acknowledge studentships from the  Science and Technology Research Council , and funding from the  Open University Space Strategic Research Area. CCM, DAR, MRB, DLP, and JW acknowledge the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  776276  'Planmap'. SJC thanks the  French Space Agency CNES  for supporting her BepiColombo related work. All the authors thank  Heike Apps ,  Valentina Galluzzi  and  Ivan Lopez  for their insightful reviews of the text and map.
Multiple Sequence Alignments set the basis for many biological sequence analysis methods. However, they are susceptible to irregularities that result either from the predicted sequences or from natural biological events. In this paper, we propose MERLIN (Msa ERror Localization and IdentificatioN), an object detector that consists in identifying such irregularities using visual representations of MSAs. Our model is developed using a state-of-the-art deep learning object detector, YOLOv4, and trained on a set of MSA images from an in-house built dataset with automatically annotated errors. Our object detector exhibits a mean Average Precision of 71. 18% in predicting different types of errors within MSAs. We conducted a thorough examination of the obtained results which showed that our method correctly identifies certain inconsistencies that were missed by the automatic annotation algorithm.
Introduction Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics that consists in arranging a set of biological sequences (DNA, RNA or proteins) in order to identify regions of similarity that reflect their biological relationships. However, MSAs are prone to errors which can originate either from natural biological variants or from MSA-generating algorithms. Algorithms used to predict protein-coding genes in DNA sequences, for instance, are not always accurate, and often lead to sequence prediction errors. Consequently, today's protein databases are riddled with inconsistencies  [15]. Identifying inaccuracies in multiple sequence alignments is not a trivial task, but accurate MSAs are instrumental in developing solutions to address problems spanning different biology related areas of study  [3] , such as evolutionary studies, protein structure, analysis of effects of genetic mutations, etc.
In this paper, we propose an approach to identify inconsistencies within MSAs of protein sequences. Our method, MERLIN (Msa ERror Localization and IdentificatioN), consists in applying a state-of-the-art deep learning object detector, YOLOv4  [2] , to detect and localize different types of errors (deletions, insertions/extension, and mismatches) using visual representations of multiple sequence alignments. This paper is organized as follows: Section 2 introduces related works. Our dataset is presented in Section 3. Section 4 describes the experimental protocol used for the proposed approach. Section 5 presents and discusses the experiment results. Finally, Section 6 summarizes the conducted work and concludes this paper.
As an attempt to investigate the errors introduced into MSAs, the authors in  [4]  developed EvalMSA, a software tool that allows the detection of divergent sequences and "outliers" in MSAs. The basic idea behind this technique is to evaluate the contribution of each sequence to cause gaps in the alignment using a gappiness value (gpp); the sequence with the highest gpp value is deemed strongly divergent and is more likely to introduce gaps in the remaining se-quences. EvalMSA was benchmarked with six different alignments derived from the Pfam database  [7]. Artificial outliers were added and the sequences were realigned using different alignment algorithms (MUSCLE and ClustalW  [21] , as well as Expresso algorithm  [1] ). EvalMSA was able to correctly identify outliers regardless of the algorithm used to generate the MSAs.
Dataset In order to train and evaluate our object detector, we used multiple sequence alignments from an in-house built dataset  [15]. These MSAs are extracted from the Uniprot reference proteomes  [5]  and RefSeq  [17]  databases, and were automatically annotated using SIBIS algorithm  [12]. As described in Section 2, three types of errors can be found in an MSA: deletions, insertions/extensions, and mismatches. It is important to note that, since the original MSAs were not manually annotated, some alignments may contain certain errors that the annotation program failed to detect 1. The original MSAs were converted into images using ADOMA (Alternative Display Of Multiple Alignment)  [28]  which is a graphical interface that offers a colored alignment output where amino acids with similar physico-chemical properties share the same color.
001; momentum and weight decay are set to 0. 9 and 0. 0005, respectively. The training was processed for 14 000 iterations  3. The distribution of classes in the training data is as follows: Internal deletions (32. 7%), N-terminal deletions (31. 4%), Cterminal deletions (9. 57%), Internal insertions (9. 07%), N-terminal extensions (7. 10%), Mismatches (6. 76%), C-terminal extensions (3. 40%). This distribution corresponds to the actual proportions of these error types across MSAs in the primate proteomes databank  [15] : Internal deletions (36. 9%), N-terminal deletions (29. 34%), C-terminal deletions (8. 87%), Internal insertions (8. 82%), N-terminal extensions (6. 43%), Mismatches (6. 63%), C-terminal extensions (3. 01%).
Detecting inconsistencies in MSAs: Qualitative Analysis We examined the extent to which our model, MERLIN, is able to make good predictions on randomly selected MSAs from the test set: these MSAs contain different types and number of errors. MERLIN was also tested on MSAs that were annotated as error-free by SIBIS. Table  3  shows error detection results of our detector on randomly selected MSAs. The MSA identified as Q9UEE5 contains three different errors: N-terminal deletion, N-terminal extension, and internal deletion. All three errors were correctly detected by the model with high confidence scores 4  : 99%, 98%, and 93%, respectively. The second example, MSA Q9UHL4, contains an internal insertion error and a mismatch. While the model correctly identified both errors, the mismatch error has a low confidence score (40%) compared to the internal insertion (92%).
As stated in Section 3, the Bayesian model, SIBIS, used for data annotation is not 100% accurate; in terms of error detection, SIBIS exhibited a higher specificity of 92% with a sensitivity of 81%. Taking this statement into account, it is possible that certain MSAs contain at least one error that SIBIS has failed to detect. Moreover, it is important to note that MSAs may contain certain gaps as well as sequence segments that are structurally similar to deletions, extensions or insertion errors; however, these segments are simple shape variations in the MSA and do not constitute inconsistencies. In order to assess the ability of our object detector to distinguish between these variations and erroneous segments we used our model to identify errors on randomly selected examples amongst MSAs containing such characteristics.
More detection results on randomly selected MSAs are shown in Table  4. MSA Q9UBP4 has one extension error and three deletions. All detected errors are true positives identified by the object detector with high confidence scores  (98% -100%). It should be noted that the MSA contains two insertions that were not annotated as errors by SIBIS nor detected by our method. To determine whether these sequence segments constitute sequence inconsistencies, more information about the corresponding genomic sequences and the exon/intron structure is required. Three N-terminal deletions were found in the second example (MSA Q9UK12). While the first deletion is the only true positive prediction with a high confidence of 86%, a close inspection of the MSA revealed that the remaining deletions are true errors that the annotation tool has failed to identify.
In the last alignment, Q9NYB5, two mismatches were incorrectly detected by our model with low confidence scores. As mentioned above, SIBIS is an automatic algorithm that sometimes fails to identify errors within MSAs. Therefore, it is reasonable to assume that some MSAs, containing at least one error, were dismissed as error-free. In order to validate this claim, we used MERLIN on three randomly selected alignments from a set of MSAs initially dismissed as containing no known inconsistencies by SIBIS. Prediction results are reported in Table 5. In the first example, MSA Q8WTV1, the model identified a deletion error with a high confidence (84%), while in the second MSA, an extension error was detected with a relatively low confidence of 48%. In the third example, the model identified an insertion with an even lower confidence of 26%.
All the identified inaccuracies were manually confirmed by human expertise. It is important to note that, regardless of the detection confidence scores, the false positive predictions obtained by the detector warranted a manual reevaluation of the MSAs which confirmed the accuracy of these predictions. These inconsistencies were not identified by SIBIS  [12]  in the original alignments  [15] , and therefore not annotated in our dataset. Thus, it is safe to assume that the number of true positives identified by our object detector is probably higher than 78. 10%. Considering all of the above, our trained object detector is a promising tool for the quality assessment of multiple sequence alignments.