dataesr/scientific-paragraphs-categorization huggingface dataset:
structure of the tag was consistent with that of X-ray crystallography with 75% accuracy (data not shown), which is the same as the average performance for the 30 reference proteins  (15). Comparisons between the secondary structure sequences of SUMO-tagged NCYM and the isolated SUMO tag revealed that NCYM contains seven b-strands and four a-helices (Figure  1D ). Three of the four a-helices were localized in the central region of NCYM. In addition, the protein database UniProt revealed the presence of compositional bias to Arg/ Lys-rich (basic) at the C-terminal (68-109 aa) of NCYM (UniProt KB-P40205 NCYM Human, Figure  1D ). According to the amino acid sequence only, NCYM was previously predicted to be a basic helix-loop-helix protein (28) (Figure  S3 ), but the predicted regions of the helix in the present study differed from those in the previous report.
Substitution of the 35th serine, a phosphorylation site (Japan Proteome Standard Repository/Database, ID: PSM204_1_28892), of NCYM to cysteine (S35C) was detected at a frequency of 0. 0001 in African people. A frameshift mutation causing a deletion in the NCYM (I19N*17) was found in Japanese, African, and Non-Finnish European people, and it causes the protein to have similar amino acid sequences as the ancestral form of NCYM (Table  2 ). A previous study reported that NCYM stabilizes the MYCN protein (1) and promotes Myc-nick production (3). To investigate the effect of amino acid substitutions on the function of NCYM, variants of NCYM including the D90N mutant were overexpressed in the neuroblastoma cell line SH-SY5Y. Western blotting revealed that the expression of MYCN increased upon overexpression of the NCYM variant of D90N (Figure  2B ).
SNPs in populations are also useful for functional characterization of de novo evolved proteins because they occasionally include amino acids that are essential for protein functions. The existence of nonsynonymous SNP that affects NCYMmediated Myc-nick production supports that NCYM is a bona fide protein-coding gene  (1, 5) , rather than a long noncoding RNA. Nonsynonymous SNPs at the phosphorylation site of NCYM (S35C) further support that NCYM encodes proteins. In addition to noncoding variants of NCYM/MYCNOS (NR_110230, transcript variant 1 in the National Center for Biotechnology Information database)  (30, 31) , the transcript variants of coding NCYM (NR_161162 and NR_161163, transcript variants 2 and 3) have been proposed to function as long noncoding RNAs that enhance MYCN expression  (32, 33)  and promote metastasis  (32) , as reported for protein function  (1).
g. density). Consequently, a gradient of concentration, temperature and other properties may exist inside the reactor. These affect the mechanisms of reaction of HTC, also makes difficult their hydrodynamics analyses and thermal studies. The inclusion of a stirrer reactor can help avoid these problems, and the use of CFD simulation can contribute to the design, optimization, as well as the scaling up to industrial level. In addition, CFD can assist in the analysis of hydrodynamic and thermal effects inside the reactor during the HTC process. Hence, the main objectives of this study were to use the CFD technique to produce a 3D simulation of HTC stirred reactor for an open-loop controller system by using COMSOL Multiphysics software: (a) to study the hydrodynamic and determine the distribution of biomass inside the reactor, high mixing intensity and stagnant zones.
(b) to determine the heat of the reaction and the type of chemical reaction. (c) to combine kinetics, temperature, mass, and velocity fields to predict the conversion of the biomass inside the reactor using the model proposed by Álvarez-Murillo et al. [24] , considering the thermal properties of the AS and heat of reaction, and the effect of biomass to water ratio on heat transfer. These results will be compared with the experimental data of the HTC of AS.
Reactor simulations in HTC condition The geometry of the reactor simulated is shown in Figure  1b. Inside the reactor, water and biomass are mixed in the function of biomass-to-water ratio. The thermal properties of the reactor and continuous phase (water) inside the reactor and its relationship with temperature were taken from the COMSOL Multiphysics database. The thermal properties of the biomass determined in section 2. 3 were used. The fluid was considered weakly compressible to take into account variations in fluid density for temperature change. Since the system is closed, the pressure point was constraint. The equations used for this simulation are presented into supplementary document.
The film morphology is independent of the nature of the substrate. A magnification of the surface of a film obtained on a glass substrate is shown in Fig. 2c. Again, large grains and porosities can be observed. Such porosities may explain the difference between the expected and measured film thickness. The average grain size derived from Fig. 2c  and estimated using the ImageJ software is 260 nm. This value is very high compared to the value estimated by the Scherrer formula, and we will elaborate in the following on the origin of this difference. An interesting point is the appearance of some straight lines on the copper iodide grains. Such behavior has already been observed in the literature  [1, 35]  but there is no clear description of the meaning of such lines. In order to obtain relevant information about these lines, the films were characterized by transmission electron microscopy.
g. , bilayer leaflets and aggregates, and offer some of the desired level of abstraction. However, these tools were developed to deal with relatively simple compositions and topologies, and they may fail as systems contain more than one or two segments (see Figure  S1  in the Supporting Information). To avoid this problem, we developed a robust method for consistent sequential segmentation with a voxeloriented analysis paradigm. No assumptions are required regarding the number of segments or molecules, and it natively supports all-atom or coarse-grained (CG) simulation data. ] [18] [19]  To guide the reader through this work, we will first present pseudo code to illustrate the general implementation of our leaflet segmentation algorithm, which is implemented in a tool coined MDVoxelSegmentation.
Pseudo Code for Spatial Segmentation of Leaflets. The aim is to segment a lipid density into leaflets (i. e. , a single membrane leaflet is a segment). To successfully segment lipid densities into leaflets, we require three things: a reference file, a trajectory file, and a selection file. The reference file should contain information regarding atoms and their naming (e. g. , GRO, PDB). The trajectory file should contain coordinates for each point in the reference file in every frame (e. g. , GRO, XTC, TRR). Finally, the selection file should contain the selections (lipid heads, lipid tails, lipid linkers, and exclusion beads). By default, most CG Martini lipids are supported, 20 meaning no selection is required to be specified for the leaflet segmentation of those systems. However, if you are working on a custom lipid, it can easily be added to the default selections file (an example for CHARMM lipids is given in the methods for the leaflet segmentation).
If one of the identities is maintained, the Jaccard score between one of the identified segments and the unidentified segment has exceeded the Jaccard threshold. This indicates that a large segment is merging with one or more smaller segments. For example, this could be the result of a transient fusion between two leaflets. If a new identity is created, this means none of the previous segments contained enough atoms to attain a Jaccard score higher than the Jaccard threshold, when compared to the unidentified segment. In this case, all previous identified segments are merged into a new segment with a new identity. To allow for the reappearance of identities, the disappeared identities are stored in a database. This database contains the identity and the atoms present in the identified segment at the current frame.
This could indicate a leaflet losing a small group of lipids, which form their own segment. The other unidentified segments will then either gain a new identity or take the identity of an old segment from the database. The new segment takes the identity of the segment with the highest Jaccard score in the database, but only if the score exceeds the Jaccard threshold. If the threshold is not exceeded, the segment is assigned a new identity. In the other case, the identified segment could not match with one of the unidentified segments. This will lead to the disappearance of the identified segment. This could be caused by a leaflet splitting into two roughly equal parts. For example, in the case of a bilayer with a transient pore, closing of the pore would result in a split of the bilayer in its previous two leaflet segments.
The disappearing segment will be stored in the database, while the new segments will each be compared to the database. Each segment that has a match with the database will continue with the identity of this match, while the unmatched segments continue with new unique identities. The result after temporal segmentation is a consistent sequential segmentation over the trajectory, both spatially and over time. Identities between time frames are consistent, based on the atoms within each segment. Merging segments are detected and identified in a size-dependent way. Splitting segments are detected as well. A database of previous segments allows identities to persist over a longer time frame.
■ DISCUSSION Future Development of MDVoxelSegmentation. In this manuscript, we show the use of voxel-based approaches for the analysis of lipid-and surfactant-based systems. However, our implementation is not optimal yet. What we hope to achieve is to stimulate developers of MD software who have a Figure  7. Single frame scalability. The time required to segment a single frame, relative to increasing particle count. Exact data is represented in Table  1. Some variation from linearity is expected due to the fact that the specific geometry of a system can have an impact on the complexity. large user base (MDAnalysis,  12  VMD,  15  Pymol,  23  UnityMol 24 ) to embrace voxels and add user intuitive voxel APIs with common components analysis to their packages. VMD already has a well-optimized grid density function, which could be made more accessible for users, or even contain some GUI.
The VMD code could probably perform all operations in this manuscript at runtime for systems of considerable size due to their efficient usage of GPU(s). How the segmentation API could best be merged into the current selection syntax is not trivial and will require careful consideration. Potential Extra Features. As shown here, leaflet segmentation can be solved with high fidelity, using only local checks and chronological segmentation. We showed accurate segmentation of a wide range of lipid systems with or without the presence of cholesterol, protein, and or (extreme) curvature. Although the focus of this manuscript was lipid leaflet detection, we would like to highlight some possible additional features that further broaden the scope of the applications. One of those features is the tracking of membrane pores and stalks.
In our tests, we show many examples of CG Martini lipid systems, mostly performed with the same parameter settings. These settings show very few artifacts, indicating that, in many cases, no time is required to be spent on parameter tweaking. Besides CG systems, we also examined an atomistic system for which segmentation showed to be of high quality with only minor changes needed to the default settings. Our algorithm practically scales linearly with the increase in particles and is able to handle millions of particles within minutes on a desktop machine. 7] [18]  The resulting segmentation is easy to work with in the python or VMD programs and can directly be used for visualization, because of its supported segmentation data format. The MDVoxelSegmentation package will be expanded by our own attempts to address problems within this framework.
Characterization of the mcr-1-Carrying Plasmids The replicon content was determined from the whole-genome sequencing (WGS) data using PlasmidFinder 2. 0. 1 (see text footnote 2). Plasmids carrying the mcr-1 gene were assigned in silico when the mcr-1 gene was located on the same contig as the plasmidic marker. When in silico data showed no cooccurrence on the same contig, plasmids carrying the mcr-1 gene were detected using PFGE-S1 gels (6 V/cm for 20 h with an angle of 120 • at 14 • C with pulse times ranging from 1 to 30 s) followed by Southern blot using adequate probes as previously described  (Saidani et al. , 2019). Plasmid co-localization was assessed by comparison between the bands corresponding to the resistance gene and those corresponding to the Inc type of the plasmid. When Southern blots did not lead to interpretable results and for all isolates that could not be typed by PFGE (smearing profile), the plasmid of interest was transferred by conjugation in an E.
The mechanism of colistin-resistance in mcr-negative isolates was not elucidated here. Numerous mutations were observed compared to the K12 reference strain, most of which were detected in both mcr-positive and mcr-negative isolates, if not only in mcr-positive ones. The rare mutations that have been associated with colistin-resistance (R81S in PmrA; T156K, A159V, G161V in PhoP; E375K in PhoQ) were not detected in our dataset  (Jeannot et al. , 2017). Likewise, the N346K modification highlighted by  Luo et al. (2017)  as a possible colistin-resistance-related modification was found here associated to mcr-1-positive isolates. We might hypothesize that colistin-resistance-related mutations in PmrAB and PhoPQ may also arise in mcr-1-positive isolates and contribute to an increased MIC to colistin, but the highest MICs to colistin (≥16mg/L) observed in this study did not correlate with a specific modification pattern.
, 2011). Thus, there is a need to better understand how the physical and chemical changes caused by a variety of anthropogenic stressors affect freshwater ecosystems. Incomplete knowledge of these factors hampers the sustainable management of freshwater ecosystems and biodiversity globally, especially in developing countries. Whereas the relationship between fauna and environment has been studied in various Nearctic and Palearctic freshwaters, our understanding of how changes in various water physical and chemical variables affect freshwater invertebrate species, notably molluscs, remains very fragmentary for the Afrotropics. Two important limiting factors are the lack of accurate taxonomic identifications  (Odountan et al. , 2019b)  and the challenges to develop large spatial and temporal datasets (biological and physicochemical), which both result in a lack of systematic monitoring.
Mollusc species-environment relationship analysis Prior to in depth analysis, we examined whether the measured physicochemical variables affect mollusc community composition (Fig. 2 , step 2) with multiple regressions distance matrices (MRM) on species and environmental. MRM is a flexible non-parametric and non-linear multiple regression method in which the response matrix (here, mollusc species occurrences) is regressed on an environmental matrix (physicochemical variables) and subjected to permutation to evaluate whether the observed correlation differs from the expectation under randomisation  (Manly, 2006). The MRM analysis was performed with the function multi. mantel in the "Phytools" package  (Revell, 2012). The strength of the relationship between physicochemical variables and occurrence data of species was evaluated with the R-squared statistic and p-value (corrected for the false discovery rate;  Benjamini and Hochberg, 1995).
Self-organising maps (SOM) As correlation between physicochemical variables and occurrence data were significant, we subsequently explored the association between mollusc species through the SOM. The SOM is a powerful data-mining tool used for clustering high-dimensional data (here mollusc abundances). Detailed information on the SOM approach  (Kohonen, 1990 (Kohonen, , 2013) )  and how to construct a robust SOM can be found in the literature (e. g. Adandedjan et al. , 2013; Liao et al. , 2019). Here, the batch algorithm of the "Kohonen" 3. 0 package  (Wehrens and Kruisselbrink, 2018)  was used to train a set of 2160 elements (15 mollusc taxa for each of the 144 samples) (Fig. 2 , step 3). Mollusc abundance data were subjected to range normalisation to standardize weights of abundant and rare species  (Park et al.
, 2018). Based on the relevant heuristic rule of  Vesanto et al. (2000) , a grid of 6 × 10 cells was considered appropriate for our data. We selected the number of clusters from a diversity of scenarios with the Davies-Bouldin index (DB), by identifying the minimal index for the sum of squares within clusters. Hierarchical cluster analysis, using the cutree and hclust functions of "dendextend" package  (Galili, 2015) , was performed to define clusters (based on SOM output), that were delineated by different colours.
Biological features of SOM clusters To quantify differences in mollusc communities among the clusters obtained with the SOM, i. e. , the mollusc assemblages, four biodiversity indices were used: species richness, taxon abundance, the Reciprocal Fig. 2. Workflow followed in this study. Steps 1-5 relate to the analyses that were undertaken and step 6 to the discussion of the results. Berger-Parker index and the Shannon-Wiener index (Fig. 2 , step 4). Differences in the biodiversity indices among clusters were tested with a Kruskal-Wallis test from the "dplyr" package  (Wickham et al. , 2020) , followed by pairwise Dunn's tests, using the dunnTest function in the "FSA" package, with Benjamini-Hochberg correction  (Derek and Wheeler, 2020). To identify mollusc species that are characteristic for each mollusc assemblage we used IndVal  (Dufreˆne and Legendre, 1997).
This method interrogates the affinity of taxa (here mollusc species) to groups of samples (here the mollusc assemblages) by analysing occurrence data and relative abundances of individual taxa. The algorithm provides per species and per assemblage an IndVal statistic, which ranges between 0 (not an indicator) and 1 (good indicator), with an associated probability. The indicator analysis was performed with the "indicspecies" package version 1. 7. 9 (De Caceres and Jansen, 2016) using 1000 permutations and α = 0. 05 significance level.
Mollusc species and water quality variables relationships To determine which of the 22 physicochemical variables explain the differences in mollusc composition among mollusc assemblages, we performed LDA  (Legendre and Legendre, 2012)  as implemented in the "ade4" package version 1. 7-15  (Dray et al. , 2020). Prior to LDA, we tested for multivariate homogeneity of within-group variances  (Borcard et al. , 2018)  and the heavy metals required square root transformation. Using standardized factorial coefficients, we identified the ten physicochemical variables that explain most of the differences among mollusc assemblages. Cross-validation was used to evaluate the predictive performance of the LDA model. The overall quality of the LDA model and the accuracy of the classification prediction for the samples of each cluster were evaluated using Cohen's Kappa Statistic with the "caret" package  (Kuhn et al.
Acknowledgements This study was financially supported by the ministry of higher education and scientific research of Benin. ZCK and OHO thank the  Belgian National Focal Point  to the  Global Taxonomy Initiative of the CEBioS programme , at the  Royal Belgian Institute of Natural Sciences , and financed by the  Belgian cooperation for development (DGD). BVB thanks the  French Agence Nationale de la Recherche  ( ANR-17-CE02-0015 ).
The authors concluded that for some configurations, the outer bolts are not loaded and that the prying force was applied between the inner and outer bolts, thereby reducing considerably the contribution of the outer bolts to the resistance of the joint. The aim of this paper is to analyze numerically the behavior of T-stub with two (Fig. 1a ) and four bolts (Fig. 1b ) per row under monotonic and cyclic loading. Both the global load-displacement curves and the evolution of the bolt forces by considering the prying forces were analyzed. All the T-stubs configurations were modeled using the finite elements software CAST3M  [35]. The mesh densities were selected on the basis of convergence study and the evaluation of symmetry conditions effect (Figs. 2  and 3 ). The load-displacement curves obtained by the numerical model represent well those of experimental tests under monotonic and cyclic loading available in the literature  [3]   [36, 37].
1, [18] [19] [20]  In this signaling pathway, the high number of BMP ligands (≃20) compared to the low number of BMP receptors (four type-I and three type-II receptors) indicates the presence of a promiscuous mechanism in which a given BMP can bind several receptors with distinct binding affinities. 6, 21  Furthermore, it has been reported that high affinity ligands can compete with low affinity ligands for the binding of BMPRs and, therefore, can antagonize their signaling. 22  The previously described structures of several BMPs (BMP-2 pdb: 3BMP, BMP-7 pdb: 1BMP, BMP-9 pdb: 1ZKZ) lead us to gain insights into the structural differences between them. For example, it was described that most of the residues existing in BMP-2 wrist epitope are invariant or replaced by isofunctional side chains in BMP-7.
B. BLI kinetics interaction experiments All the BLI experiments were performed using an OctetRED96e apparatus from Pall/FortéBio (California, USA) and data were recorded with the manufacturer software (Data Acquisition v11. 11). All proteins were solubilized following the supplier instruction in the HEPES-NaCl buffer. The analysis protocol was adapted from previous studies. 29, 36 In detail, prior to any capture, the BMPR-Fc samples were first diluted in the HEPES-NaCl buffer. For the association phase, the BMPs were diluted in twofold serial dilutions in HEPES-NaCl buffer. 0. 2 ml of each sample and buffer were disposed in wells of black 96-well plates (Nunc F96 MicroWell, Thermo Fisher Scientific), maintained at 25 °C and agitated at 1000 rpm the whole time. Prior to each assay, all biosensors were prewetted in 0.
All measurements were performed three times in independent experiments. Kinetics data were analyzed using the manufacturer software (Data analysis HT v11. 1). The "blank" signal from the biosensor in the presence of the HEPES-NaCl buffer was subtracted from the signal obtained from each functionalized biosensor and each analyte concentration. The kinetic signals were then fitted using a global/local method and 1:1 Langmuir or 2:1 heterogeneous ligand model. Affinity constants were calculated from the ratio k d /k a values. The reported values are given as mean ± SD obtained from three independent experiments.
C. Surface plasmon resonance experiments All surface plasmon resonance experiments were performed using a Biacore T200 apparatus (GE Healthcare Life Sciences/ Biacore, Illinois, USA) and data were recorded using the manufacturer software (Biacore control software v2. 0). All protein samples were solubilized following the supplier instruction in the analysis buffer prior to any experiment. Prior to capture, the BMPR-Fc samples were first diluted in analysis buffer. For the association phase, BMP samples were diluted at concentrations between 0. 2 and 6. 4 nM in twofold serial dilutions in the Hepes-NaCl buffer. Sensor chips and system were pre-equilibrated in the Hepes-NaCl buffer prior to any injection. The protein A sensor chips (GE Healthcare Lifesciences) were loaded by injecting each ligand for 100 s until they reach a signal level between 100 and 120 arbitrary response units depending of BMPR-Fc, followed by an additional equilibration step of several minutes in analysis buffer.