dataesr/scientific-paragraphs-categorization huggingface dataset:
In these files, the query peptide in SMILES format (and the corresponding amino acids) are reported and so are the ChEMBL compounds found to be (relatively) similar to the queries, the Tanimoto coefficient, the putative targets, the possible links to diseases, the UniProt IDs and the Reactome identifiers. Images of the compounds are present. These files can be sorted by columns, and search by keywords. Hyperlinks to the ChEMBL, UniProt and Reactome databases have been inserted. Obviously, users can apply other types of fingerprints and different Tanimoto coefficients or use consensus scoring via the selection of several fingerprints. We compared the chemical space covered by the 714,780 ChEMBL29 bioactive compounds (orange spheres), the 160,0 0 0 tetrapeptides (blue spheres) and approved drugs (magenta spheres) extracted from DrugBank 5.
Experimental Design, Materials and Methods The SQLite version of the ChEMBL29 database was downloaded from the ChEMBL website. SQLite standard terminal shell commands were applied to collect the molecules. The canonical SMILES strings of the compounds were extracted for small molecules with an associated biological assay involving a single protein or a protein complex and an assay type named "binding" (assays measuring binding of compound to a molecular target, e. g. Ki, IC50). Thus small molecules with functional assays measuring biological effects without associated targets were not considered; only bioactivities with pChEMBL values were chosen. This term refers to all the comparable measures of half-maximal responses (e. g. , IC 50 , EC 50 , Ki, Kd…) on a negative logarithmic scale. The considered activity value for a compound-target pair corresponds to a value of 20 micro-molar or less.
Additional filtering steps were then applied to this initial raw data file. A confidence_score was already assigned to each assay-to-target relationships in the ChEMBL database with ranges from 0 (uncurated data entries) to 9, with 9 representing a single macromolecular binding target that has been assigned to a compound. We selected small molecules with a confidence_score running from 6 (homologous protein complex subunits assigned) to 9. Mixtures were curated manually while salts were removed using MayaChemTools. Duplicates were investigated by text mining over the ChEMBL compound IDs and using Open Babel. The resulting filtered file contained 714,780 unique ChEMBL29 compounds acting on 6477 different macromolecular targets. By contrast with the previous ChEMBL dataset (extracted from ChEMBL25, it contained 524,810 unique ChEMBL25 compounds active on 4811 targets) that is presently embarked with FastTargetPred package.
Fingerprints could not be computed for 19 compounds present in the extracted ChEMBL29 dataset because of unusual chemistry (compound IDs: CHEMBL1160519, CHEMBL2012126, CHEMBL2012127, CHEMBL2012128, CHEMBL2313980, CHEMBL2313981, CHEMBL2373427, CHEMBL2373486, CHEMBL2373969, CHEMBL2373970, CHEMBL269742, CHEMBL3354724, CHEMBL3354729, CHEMBL3354735, ). 2509 approved drugs were initially collected but 287 very small compounds (molecules with less than 10 heavy atoms) were removed (e. g. , several compounds have only 1 atom). In addition, 124 other molecules (unusual chemistry and some mixtures) were deleted. Six physicochemical properties were computed with DataWarrior and a PCA plot was generated. The explained variance percentage of the first 3 principal components are PCA1: 81. 79%, PCA2: 13.
27%, PCA3: 2. 52%. CHEMBL384970, CHEMBL409633, CHEMBL412073, CHEMBL414976, CHEMBL440623), leading to a final number of 714,761 small molecules with associated fingerprints. The Tanimoto coefficient was used as a metric to measure the similarity between a query compound and an annotated compound present in the ChEMBL database  [10] [11] [12]. This coefficient ranged from 0 (molecules are not similar) to 1 (molecules are identical or very similar). ChEMBL target IDs were matched with UniProt IDs via the UniProt website and the IDs of about 100 targets had to be manually fixed. When available, protein UniProt names, the gene names, the organism, the involvement in diseases of the target, gene ontology data and cross-references to the Reactome pathway database were also compiled. The CSV file containing these annotations is provided (file name: uniprot_database_ChEMBL.
UniProt mapping file is in CSV format. The predicted targets for the tetrapeptides are reported in two files that were generated with the open source DataWarrior software (dwar files). These files are searchable (e. g. , using amino-acid sequence query…), they can be sorted by column values and hyperlinks to the ChEMBL, UniProt and Reactome databases were added when available. while file format conversions were carried out with Open Babel [9]. Data format Raw and processed data: Extracted ChEMBL compounds (canonical SMILES and ChEMBL compound IDs) in TXT format. ChEMBL compound IDs and corresponding ChEMBL target IDs in TXT format (name of the file = chembl29_active_all. tlt). Computed fingerprints in CSV format with the corresponding binary files (to accelerate the similarity search computations) readable by FastTargetPred (four bfp files are provided).
In  (Marquardt et al. , 2011)  the authors presented a proximic toolkit that enables prototyping proxemic interaction. It consists of a collection library conceived in a component-oriented architecture and considers the proxemic variables between people, objects, and devices. The toolkit involves four main components: (1) Proximity Toolkit server, (2) Tracking plugin modules, (3) Visual monitoring tool and (4) Application programming interface. Another proxemic designers' tool for prototyping ubicomp space with proxemic interactions is presented in  (Kim et al. , 2016). It is built using software and modeling materials, such as: Photoshop, Lego and paper. Interactions can be defined with photoshop based on proxemic variables. The tool uses an augmented reality projection for miniatures to enable tangible interactions and dynamic representations.
In order to respond to these shortcomings, we introduce in this paper SUIL (Spatial User Interaction Language) and SIMSIT (Spatial Interface Modeling and SImulation Tool) which are respectively a modeling language and a modeling & simulating framework for spatial interfaces. The remainder of this article is structured as follows: Section 2 describes The SUIL modeling language, and section 3 presents the SIM-SIT tool. Section 4 provides a conclusion and perspectives for future works.
SIMSIT: TOOL SUPPORT FOR SUIL SIMSIT (Spatial Interface Modeling and SImulation Tool) is a framework for modeling and simulating spatial interfaces. Through SIMSIT we can first, manipulate graphical representation of SUIL concepts to specify spatial user interfaces. Then, the modeled interface can be generated to provide a simulation of the spatial interface through a web page. Visual studio 1  and C# 2  language were used for the development of SIMSIT.
Example of applications modeled and simulated with SIMSIT To check SIMSIT framework, we present in this section a case study. It consists of manipulating the VLC media player software through simulated spatial user interactions into a graphical interface. Before starting the modeling and the simulation, we need to define system functions, spins and spatial attributes that will be used in the example (Table  3 ). We choose to use only one M-spin illustrated by a cube that can be manipulated using the mouse. The Modeling process is carried out into two parts: (a) modeling the graphical interface and (b) modeling of the functional model. (a) The graphical interface corresponds to the interface that simulates the execution scene. In the example (Figure  6 -a), a cube (represents the M-spin) is manipulated by the user to launch system functions.
CONCLUSION The presented work proposes a language and a framework for modeling spatial user interaction. Literature analysis showed that existing works aim to demonstrate the relevance of the spatial interaction technique rather than to provide tools and methods to specify it in a generic and reusable way. Hence, we noticed a lack of tools supporting the process of realizing spatial interfaces, starting from the design phase until the development and deployment ones. Based on the aforementioned concerns, we introduced SUIL (Spatial User Interaction Language), a modeling language and SIMSIT (Spatial Interface Modeling and SImu-lation Tool), a tool for modeling and simulating spatial interfaces. A case study using proposed tool was also presented. In future work, we aim first to use the SUIL language for developing an application in a real ambient environment.
Geometric morphometrics and statistical analysis To describe the shape of the tip of the trunk, we used a 2D geometric morphometric (GM) approach  (Rohlf & Marcus, 1993; Adams, Rohlf & Slice, 2004; Gunz & Mitteroecker, 2013). For each image of the trunk tip, the trunk tip was outlined with tpsDIG2 software, version 2. 32  (Rohlf, 2016). To obtain these outlines, we placed an anatomical landmark on the top of each trunk tip and placed 100 sliding semilandmarks on the curves  (Gunz & Mitteroecker, 2013)  to outline the distal part of the trunk (Fig. 1F ). These semilandmarks defined the borders of the trunk tip. Every characteristic (the subject, food item, and number of failures) that might influence the trunk tip contour was annotated. The film in the Supplementary Material shows a part of this methodology (video recording of the grasp as well as the landmark and semilandmarks on the trunk tip).
We performed a Generalised Procrustes Analysis (GPA)  (Rohlf & Slice, 1990) , using the ''gpagen'' function of the package ''geomorph'' version 3. 2. 1  (Adams, Collyer & Kaliontzopoulou, 2018)  in R version 3. 6. 1 (R Core  Team, 2018). This GPA aligned the different distal trunk shapes with a Procrustes superposition to remove the effects of translation, rotation, and scaling. We visualised the GPA using the ''plotTangentSpace'' function and retained 90% of shape variability after reducing the dimensionality of the variables with a principal components analysis (PCA)  (Baylac & Frieß, 2005). The PCA components were used as shape parameters in the following analyses. To detect significant differences in the shape of the trunk according to the object properties, individual, or number of grasping failures, we performed a MANOVA using the ''manova'' function in R.
The deformation isolines indicated the location of the strongest differences in trunk tip form, and the deformation vector fields showed the direction of these differences. These TPS visualisations were performed with the functions ''tps_arr'' and ''tps_iso'' in the ''Momocs'' package (version 1. 3. 2) in R  (Klingenberg, 2013). The mean number of grasping failures per individual and per object for all trials were also calculated. Linear regressions were performed to determine the change in the number of failures over time for each individual and each object. The number of failure are shown along all the grasping attempts (all objects included). Finally, a GPA of the trunk tip shapes of all individuals for each object was performed and paired with trajectory plots. These plots were obtained in the Procrustes space by joining for each individual the dots corresponding to the outlines of the first and last grasping movement.
ACKNOWLEDGEMENTS We would like to thank  ZooParc of Beauval (France) , which enabled us to conduct this experiment with their elephants. The experiments were conducted with assistance from the keepers  Amaury Boutier ,  Nathan Durand ,  Matthieu Fromet ,  Mathieu Hysbergue ,  Clément Langles ,  Mégane Marron  and  Matthieu Villemain  and under the direction of  Yann Ménager. We warmly thank the entire team. We also thank  Arnaud Delapré  for his technical help. Finally, we would like to thank our two reviewers,  Andrew Schulz  and the anonymous reviewer, for their helpful comments, which greatly improved our paper.
However, not all situations are amenable to super-resolution fluorescence imaging, because of computational limitations, or because one does not have access to advanced optical instruments and experienced personnel. Fast and efficient algorithms for resolution enhancement of conventional (fluorescence) images remain highly desirable 8. Examples include interpolation techniques 9 , learning-based algorithms 10 and reconstruction approaches based on deconvolution and regularization 11. Modern computer languages like Matlab, R and Python provide efficient matrix operations that make it easy to implement convolution with little programming. Deconvolution is the inverse operation, estimating a source image from the observed image. It is a so-called ill-conditioned problem, but it can be tamed by using constraints, implemented as penalties  1, 12.
A simple and effective remedy is provided by ridge regression. The objective function is Q = |y -Sx| 2 + κ|x| 2 , where |. | 2 indicates the squared norm; e. g. |x| 2 = j x 2 j. Here κ , a positive number, is the regularization parameter. Increasing κ decreases the norm of x , shrinking the solution towards zero. It is simple to find the x that minimizes Q, because an explicit formula is available: x = (S ′ S + κI) -1 S ′ y , with I the identity matrix. As Fig. 1  shows, the best results were obtained with κ = 0. 01 and, with κ < 0. 01 , essentially the same results are obtained. Only for very low values, less than 10 -10 , instabilities occur. For κ = 0. 1 the esti- mate of x is strongly shrunk towards zero. Larger values of κ will lead to even more shrinkage. For κ = 0 , Matlab reports a solution, but warns that the system is singular, and produces a result that looks more or less random.
In a modern computer language like Matlab, deconvolution of vectors by ridge regression is not challenging. One line of code does the work: X = (S' * S + kappa * eye(n)) \ (S' * y). Unless we have very small images this will not work with vectorization of matrices Y and X. As we have noted before, too much memory space and computing time are needed when we form vectors y and x and work on them. Q = |y -( S ⊗ S)x| 2 + κ|x| 2. Conjugate gradients (CG) is an algorithm for iteratively solving systems of linear equations  14. It was developed for the standard case with a matrix and vectors. Its key property is that no matrix inversion is used, only products of matrices and vectors. For our application to images, we can adapt CG in such a way that only products of matrices are needed, and that these matrices are never larger than Y, S, S or X.
The Supplemental Information shows Matlab code for one-dimensional and two-dimensional CG. Here we discuss some key points. The conjugate gradients algorithm saves memory space, because we can work directly on the matrix X. Constructing a classical system of penalized equations would take much time and solving it even more. Another advantage is that we can set the number of iterations at a value that suits our purpose. For initial exploration of images, to locate interesting regions, a handful iterations will be enough. This is illustrated in Fig. 5. We show the core of the code here. Listing 1 The core of the conjugate gradients algorithm for solving a linear system. The matrix G = S ′ S + κI + D ′ D and two vectors p and q are computed beforehand, starting from the observed y and setting x = 0.
The result of super-resolution by a factor 8 is shown in the right panel. The PSF of the microscope was approximated by a two-dimensional Gaussian function with full-width at half-maximum (FWHM) of approximately 350 nm. Linearized DNA molecules that could hardly be distinguished on the original image can now be clearly resolved. This is of crucial importance as most methods employed for optical mapping of DNA rely on the quality of the signal traces extracted from these images (e. g. for database matching) and require a high accuracy of signal conversion. Another key aspect for DNA high throughput technologies is of course speed. The computation time to obtain the result in Fig. 6  was approximately one second. Maize stem cross section. The image in Fig. 7  was obtained using a macroscopy imaging system to observe the global organization of vascular bundles in cross sections of maize stems.
Our algorithm needs no training. Only two parameters, for the ridge and the roughness penalty, need tuning. This can be done quickly by visual feedback. An interesting competitor to our proposal is the work by Zhao et al. 11. It is fast and Matlab code is available. It uses a special interpretation of the way a low-resolution image was formed: as picking every second, fourth etc. row and columns of pixels of an underlying high-resolution image. Our model assumes averaging groups of 2x2, 4x4 etc. pixels. Zhao et al. 11  investigated modified algorithms to allow sparse super-resolution. The key is to replace the sums of squares by the sum of absolute values (L 1 norm) in the ridge penalty. In addition an L 1 norm can be used in the difference penalties to obtain sharp edges. It will be interesting to explore if these modifications can be combined efficiently with the conjugate gradients algorithm.
Acknowledgements The authors thank  R. Vitale ,  J. Hofkens ,  M. F. Devaux  and  M. Sliwa  for having provided their data. CR acknowledges funding from the " Laboratoire Associé International High-Performance Fluorescence Microscopy, U Lille -KU Leuven" and from the project  " ANR-21-CE29-0007 ,  Agence Nationale de la Recherche ".
Survey administration The survey was a web-based survey administered using Qualtrics software (Qualtrics™, Provo, UT). Due to the descriptive nature of the survey, there was no randomization of the questions. Adaptive questioning was used in the survey design to ensure that respondents answered questions relevant to their experience when possible. The questionnaire had a total of 34 questions, with two to eight questions displayed per page. A completeness check was performed before the survey was distributed. All questions on the survey were optional, except the first two questions, which were mandatory ("Did your simulation center conduct distance simulation activities prior to the COVID-19 pandemic?" and "Does your simulation center currently conduct distance simulation activities?"). If a respondent answered "no" to both mandatory questions, the survey logic skipped to a short demographics section.
Data cleaning and compilation To account for incomplete responses and respondent attrition while completing the survey, we dropped records with less than 5% of other answered questions. It accounted for about 12. 7% of total responses removed from the compiled data. Data were also verified for consistency, checking for straight-lining errors (respondent checking the same response option throughout the survey) and examining data quality for skip patterns and lagged responses (e. g. , survey responses lasting over one day). Responses to survey items were dichotomized to facilitate analysis and interpretation, combining the top two and the bottom three response options in the fivepoint ordinal scale. Because we used an open survey design where anyone with access to the link could respond to the survey, we could not calculate a response rate.
Results We analyzed and organized the survey results to answer the research questions outlined in the "Aims and objectives" section. In the following paragraphs, we will describe who reported using distance simulation, as well as how this changed over time: from before the pandemic, to the beginning of the pandemic, and then to the latter part of 2020. We will then examine the driving factors motivating programs to initiate distance simulation, and the main reported purposes for distance simulation. Next, we explore the modalities of distance simulation being used, and how programs reported they were implementing these modalities. Then, we will present results related to personnel training and recruitment for distance simulation. Finally, we will report survey data on the specific challenges of distance simulation relative to in-person simulation and the perceived satisfaction of various stakeholders with distance simulation.
Sample demographics We gathered data from 708 respondents. Following data cleaning and compilation, 618 responses were included in this analysis. The median time to complete the survey was 9 min 17 s, with an interquartile range of 2 min 23 s to 16 min 48 s. The respondents came from 32 countries and within the USA from 44 states. Forty-one percent of respondents dedicated more than 75% of their time to simulation, 16% dedicated 50-75% of their time to simulation, and 43% dedicated less than 50% of their time to simulation. The survey allowed respondents to select multiple applicable roles: 65% described themselves as simulation educators, 42% as simulation program directors and/or administrators, 21% as simulation researchers, 14% as simulation operation specialists or technicians, and 7% as standardized patient program directors or administrators.
The survey was written with perspectives regarding the simulation center (e. g. , What professions does your simulation center support?) and specific types of simulations (Currently, where are the following participants located for the majority of procedural simulation at your simulation center?). Survey data were analyzed at the respondent level, generalizing responses to reflect independent program-or center-level inferences. Given the anonymity of survey responses, coupled with the voluntary nature of identifying institutional affiliation, disaggregating multiple responses possibly from the same institution was challenging to verify nor compile at the time of analysis. As such, future studies may be designed to better reflect the complexity of individual-and program-level data as part of additional data collection for more nuanced analysis.
Acknowledgements This study was one study supported and established by the  Distance Simulation Survey Committee. Byline Authors: Alzoraigi ,  U. , Calhoun ,  A. , Charnetski ,  M. , Clark , L. , Duff, J. , Elkin,  R. , Li ,  D. , Miller , J. , Ramachandra, G. , Stapleton, S. , Wagner, M. , Zanin, A. The authors would like to thank the many organizations who supported the dissemination of this work:  SSH, INSPIRE, IPSS, NKS, SESAM, NLN, INACSL, PediSTARS, ASPE, GNSH, SimGHOSTS, Sim Online 2020 Facebook Group, COVID19 Sim WhatsApp Group, Simulation Canada, SoFraSimS, California Simulation Alliance, ESPNI, GISP, CMS, WISER, KidSim, Kentucky Simulation Alliance, Simulation Professionals of Texas, SimPOW, Maryland sim organization, SAEM Sim Academy, ACEP SIM, CORD Sim group.
On the other hand, and evolving over a much longer arch (a long histories account), Merton found scientists responding to the 'practical problems of the time' determined externally, such as an external demand for research on weapons technology. The responsibility storyline of the time was one of protecting the empire. De-facto responsible innovation is not always pretty, indeed. The significance of the above vignette is two-fold. First, most directly related to our paper is that quantitative metrics that analyses the words of only one group in society such as scientists, in the case of Scopus databasecan only provide a partial account of the formation of discourse coalitions. In the above example, there is an inference that the scientists in question do not have the self-regulating autonomy that Polanyi depicts.