dataesr/scientific-paragraphs-categorization huggingface dataset:
2) Training We proceeded to train with different machine learning algorithms to analyze which of them would be the most suitable to use as classifier for bot detection. We use the library scikitlearn  6  in Python to create a script to train, test, and visualize the results obtained by each algorithm. For each language, datasets are divided by 80% for training and 20% for testing. The training set is used in the cross-validation process, while the test set is subsequently used in the validation phase. Due to the cross-validation process, each algorithm is performed four times (i. e. , cv = 4, the parameter for the crossvalidation) with the training set. This cross-validation returns stratified folds -i. e. , the folds are made by preserving the percentage of samples for each class. The hyperparameter space is visited to search for the best cross validation score.
1) Internal validation Table  9  summarizes metrics resulting of the prediction with each best classifier (Table  7 ) fitted to the testing datasets (from the splitting of the training datasets). Likewise, Figure  6  and Figure  7  show the confusion matrices corresponding to these classifiers. In terms of precision and accuracy in the validation phase, the worst values are for Bagging with 55. 76% and 52. 98%, respectively for the Spanish dataset, and Decision tree with 64. 79% and 72. 42% for the English dataset. While the rest of algorithms got metrics over 93% for the same metrics on both datasets (except for Adaboost with 56. 39% and 54. 10% for the Spanish dataset). The best results were Random Forest and SVC for Spanish, and Random Forest and MLP for English dataset. In order to use the best classifier for the following tests, and include a model in further implementations, we select Random Forest.
A. CREDIBILITY MODEL EVALUATION To evaluate the extended credibility model, we implemented it with the Random Forest model, because of its better performance (see Section III) and integrated into T-CREo framework  [28]. For the experiments, we designed two scenarios of tests. For the fist scenario, fourteen Twitter accounts were selected, including 10 human and 4 bot accounts for each language. The human accounts correspond to accounts that have more followers, according to Wikipedia 7  and they are verified (7 verified accounts), and other accounts from genuine dataset (Genuine accounts -Table  3 ), which are not verified (3 not verified accounts). The bot accounts correspond to accounts known as bots from the experimentation dataset (spambot #1 -Table  3 ). For the second scenario, 22 accounts were randomly selected from the fake followers dataset (Table  3 ), which were not used in the training or post-training validation process.
0, 1. 5, 2. 5]; kernel = [poly, linear, rbf]; degree = [2, 3, 5]; gamma =[scale, auto]; decision_function_shape = [ovo, ovr] Parameters of the best results in Spanish and English datasets ; splitter = best; max_depth = 200; min_samples_split = 2; min_samples_leaf = 1penalty = l2; C = 2; solver = liblinear, dual = True 0. 94883 0. 00214 MLP activation = relu; alpha = 0. 0001; hidden_layer_sizes = (5,2), max_iter = 1000, random_state = 8; solver = lbfgs 0. 99662 0. 00091 K-Neighbors algorithm = ball_tree; leaf_size = 15; metric = minkowski, n_neighbors = 5, weights = distance 0. 95869 0. 00198 SVC C = 2. 5; decision_function_shape = ovo; degree = 2, gamma = scale, kernel = rbf; 0. 97133 0. 00213 Random Forest n_estimators = 100; criterion = gini; max_depth = 180; min_samples_split = 20; min_samples_leaf = Tree Classifier; n_estimators = 10; learning_rate = 1; algorithm = SAMME; ; splitter = best; max_depth = 120, min_samples_split = 2, min_samples_leaf = 1alpha = 0.
We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning.
Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular selfassemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations.
Introduction Wordom is a world-widely used software for fast manipulation and analysis of molecular structures and trajectories from molecular simulations. The original version of Wordom and its first update were published in 2005  [1]  and 2011  [2] , respectively. Other available software for structural dynamics analysis include: a) molecular simulation and analysis packages, like CHARMM  [3, 4] , Gromacs  [5, 6] , and Amber  [7] ; b) molecular viewers like VMD  [8]  and Pymol  [9] ; c) command-line analysis software and script-suites like MMTSB  [10] , carma  [11] , and pcazip  [12] ; and d) R or Python packages like Bio3D  [13, 14] , MMTK  [15] , Biskit  [16] , ProDy  [17] , MD-TASK  [18] , MDTraj  [19] , and MDAnalysis  [20]. For Wordom, the implementation of basic manipulation tools paved the way for the building of a modular framework to easily add analysis routines.
The first update of Wordom implemented tools for secondary structure assignment, surface area calculations, and elastic network model (ENM) building, which were already available in other software packages or via web servers with impractical or time-consuming usage and/or complex input setup. Other modules introduced procedures and algorithms not available elsewhere, such as the protein structure network (PSN) analysis  [21] , the kinetic grouping analysis (KGA)  [22] , and the mincut-based free energy profile (cFEP)  [23]. Significant technical improvements concerned simplification of the input syntax, the procedure for atom selection, gain in speed, and implementation of multi-threading for a subset of modules. In this article, we illustrate the novelties added in this second update. We expanded the existing CORR module and added 21 new modules, divided in those suitable for the analyses of atomic fluctuations and structural communication in biomolecular systems, those specific for the study of ion channels, and those for computation of geometrical indices of structural deformation (see Table  1 ).
Biomolecule flexibility and structural communication A relevant fraction of the new Wordom modules concerns the analysis of flexibility and structural communication in biomolecules. The new adds significantly extend the CORR module to infer correlated motions, identify regions that move as dynamic units, identify the dynamically stable core for proper superimposition and comparison of trajectory frames, distinguish intra-domain from interdomain motions, infer the multi-scale nature of macromolecular motions by distinguishing low-frequency large-scale collective motions from high-frequency short-scale fluctuations, compare collective motions, infer collective motions related to function, identify mechanically stable residues that may be central in the allosteric communication and resist to mutation, and infer dynamically coupled regions and structural communication pathways.
CORR -correlations of atomic fluctuations The CORR module computes the correlations of atomic fluctuations in a molecular dynamics (MD) trajectory. Two methods were already implemented in Wordom: dynamic cross-correlation (DCC)  [47]  and linear mutual information (LMI)  [48]. The second update features two new methods: the distance correlation coefficient (DiCC)  [24]  and the atomic movement similarity (AMS)  [25]. The DiCC method is able to detect long-range concerted motions in a protein and is a well-known and established measure of dependence between two random vectors  [24]. DiCC is defined as the ratio between the distance covariance of two-atom positions along an MD trajectory and the product of their distance standard deviations as summarized in the following equation: = = = = DiCC ij I J N I N J N k l N kl kl k l N kl k l N kl , 1 2 , 1 2 , 1 2 (1) where DiCC ij is the distance correlation coefficient between atom i and j, respectively.
CORRCLUST -determination of geometrically stable domains The CORRCLUST module of Wordom implements the geometrically stable substructures (GeoStaS) method, which can be used to find dynamic protein domains that move as coherent units in a simulation  [25]. The identification of geometrically stable domains is founded on the clustering of atoms based on the similarity of their fluctuations, i. e. , by using a matrix of correlation coefficients. The original method employed an AMS-based similarity matrix  [25]. Wordom allows the choice of one of the four different algorithms implemented in the CORR module (i. e. , DCC, LMI, DiCC, and AMS) for calculating the correlations of atomic fluctuations on conformational ensembles from molecular simulations or the DCC algorithm for computing correlations of motions on the pseudo-trajectories inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) on a single structure.
There are two types of wavelet transforms, discrete (DWT) and continuous (CWT), the latter being implemented in Wordom  [59]. CWT, or simply WA, was employed in combination with statistically significant tests to analyze MD simulation databases (the Dynameomics database  [29, 58, [60] [61] [62] [63] ), thus highlighting the multiscale nature of protein motion. WA was indeed able to distinguish long scale, low frequency, movements associated to large structural rearrangements and changes in secondary structure from short scale, high frequency movements characteristic of thermal vibrations. The continuous wavelet transformation of the displacements of an atom in an MD simulation is given by: = WT IFT D W [ ] a s a s , ( 8 ) where a is an atom, s is the wavelet scale, D a is the frequency domain of the displacement time series of atom a, W s is the frequency domain of the wavelet function at scale s and IFT denotes the inverse Fourier transform.
PCATOOLS -tools for PCA This Wordom update adds a module, PCATOOLS, for post-processing the output of the principal component analysis (PCA) and ENM-NMA modules. This module allows to compare the conformational subspaces by a number of similarity metrics, i. e. , the dot product of pairs of single or combined eigenvectors, the cumulative square overlap between two sets of eigenvectors, and the fraction of variance described by a set of eigenvectors. The module allows also calculation of the mode involvement coefficient, i. e. the overlap between an eigenvector and a deformation or displacement vector, which is useful to investigate conformational transitions. PCATOOLS was used in a number of investigations that identified function-related similarity/differences in the collective motions inferred from MD trajectories  [50, 64, 65]  or from sets of crystallographic structures  [66, 67]  of several biomolecular systems.
Finally, the maximum of the absolute value of R can be found by solving: = = = v p p f p j d cov( , ) cov( , ), 1. i d i i j j 1 (12) The FMA modules of Gromacs or Wordom have been used to address a variety of mechanisms of proteins function, e. g. , potassium channel gating  [68] , aquaporine water permeability and regulation  [69] , transthyretin amyloidogenesis  [70] , microtubule assembly  [71] , formation of the reactive state of the Holliday junction (HJ) resolving enzyme RecU  [72] , cadherin adhesion  [73] , mechanical signal transmission in a flagellar motor switch complex  [74] , protein-protein binding by allosteric switch  [75] , allosteric transition in hemoglobin  [76] , guanine nucleotide-exchange factor (GEF) binding and GEF-catalyzed nucleotide depletion by Ras GTPases  [50, 77] , structural features of transducin mutations linked to disease  [78] , arrestin activation by the G protein coupled receptor (GPCR) vasopressin V2 receptor (V2R)  [79].
Ion channel dynamics An important addition in Wordom is the implementation of a number of structural indices to investigate the function of oligomeric transmembrane (TM) proteins like ion channels. Although the new modules may work on any TM ion channel of class α (i. e. , αhelical oligomers), here, pentameric ligand-gated ion channels (pLGIC) are presented as a case study. pLGICs are oligomeric TM proteins that open an ion pore in response to increased levels of neurotransmitters at chemical synapses. Structural, functional, and modeling data indicate that these receptors transduce signals by cycling through distinct conformational states corresponding to the resting (R), active (A), and one or more desensitized (D) states  [112]. Recent high-resolution structures in combination with all-atom MD opened to the structural characterization of the functional motions with atomic resolution  [113].
Analysis tools to display the morphology of the ion pore and quantify both ion and water fluxes through it are critically important for a structure-to-function annotation as well as the exploration of the ion-permeation mechanism (s)  [116]. To analyze the properties of ion channels in all-atom MD simulations, four new modules have been added in Wordom: 1) HOLE, to calculate the radius of an ion pore along the vector describing its direction; 2) FLUX, to compute the number of ions or water molecules translocating across the membrane during the simulation; 3) TWIST, and 4) TILT, to measure the global twisting and blooming of the receptor.
Miscellaneous indices of structural deformation Wordom includes new modules for the computation of geometrical, size/shape indices like the angle between main axes, perimeter, area, volume, β-sheet curvature, radial distribution function and mass center to explore the conformational changes of a biomolecule or a supramolecular architecture more generally. These indices can be used as hallmarks of protein functional states by allowing comparisons between different functional states of the same protein or between homologous proteins in the same functional state. A ring perception module is also included, useful to monitor supramolecular self-assembly.
VOLUME -volume This module computes the total, occupied, and free volume of a box defined by an atom selection. Wordom provides three different ways to define the box: a) as large as needed to contain all selected atoms; b) centered on the geometrical center of an atom-selection and with user-defined size; and c) the same as b) but the center of the box is directly defined by its three spatial coordinates. The box can be either updated at each frame of a trajectory or computed only once on a reference structure and kept fixed. The module properly estimates the contribution of the atoms located across the box boundaries, by considering only the fraction of their volumes inside the box.
RING -ring perception module On the exploration of the mechanisms of molecular self-organization at surfaces and interfaces  [120, 121] , it is interesting to investigate the structure of the supramolecular intermediates visited on the path to the 2D crystalline architecture with atomic resolution. For this purpose, in the case of 2D supramolecular networks the number of supramolecular rings as well as the number of building blocks per ring are useful parameters to be monitored along MD simulations of self-assembly  [122]. The RING module in Wordom implements the ring perception algorithm by Hanser and coworkers  [46] , which allows to identify, enumerate, and characterize the number of cyclic structures (i. e. , supramolecular hexagons, pentagons, or other polygons) from a contact map. The RING module takes a contact map as an input (i.
Conclusions The second update of Wordom brings a significant enrichment in tools for investigating biomolecule flexibility and allosteric communication as well as detecting structural deformations of biosystems. The new modules integrate an already rich platform for structural dynamics analysis, holding features for: a) coordinate reading/writing; b) analyzing molecular degrees of freedom (e. g. , bond lengths and angles, dihedrals), structural flexibility by computation of RMSD (root mean square deviationa), RMSF (root mean square fluctuations), DRMS (distance root means square), correlation of motions, quasiharmonic entropy, collective motions by PCA, and clustering according to structural similarity; c) investigating structural deformations by monitoring secondary structures, molecular surface, order parameters; d) investigating structural communication by protein structure network (PSN) analysis; e) inferring the thermodynamics and kinetics of complex molecular processes by analyzing the free energy surface; and f) deciphering the dynamic features encoded in a protein structure by ENM building  [1].
PSN analysis is now developed in a dedicated standalone software, PSNtools, that employs Wordom to read the atomic coordinates and compute the correlations of atomic fluctuations on MD trajectories or on pseudo-trajectories from ENM-NMA to infer the structural communication pathways  [123]. The combination of Wordom and PSNtools constitutes also the engine of a webserver (webPSN) for high-throughput prediction of the allosteric communication in any bio-system  [124]. Like the original project, the updated version is released under the general-public license (GPL, https://www. gnu. org/licenses/gpl-3. 0. html). From 03-01-2008 to 01-01-2023 the software underwent 12073 downloads and, up to January 2023, the two articles describing Wordom  [1]  and its first update  [2]  received, respectively, 286 and 242 citations according to Google Scholar.
We present the first results from the JWST program A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE). This program represents an imaging and spectroscopic survey of 25 reionization-era quasars and their environments by utilizing the unprecedented capabilities of NIRCam Wide Field Slitless Spectroscopy (WFSS) mode. ASPIRE will deliver the largest (~280 arcmin 2 ) galaxy redshift survey at 3-4 μm among JWST Cycle 1 programs and provide extensive legacy values for studying the formation of the earliest supermassive black holes, the assembly of galaxies, early metal enrichment, and cosmic reionization. In this first ASPIRE paper, we report the discovery of a filamentary structure traced by the luminous quasar J0305-3150 and 10 [O III] emitters at z = 6. 6. This structure has a 3D galaxy overdensity of δ gal = 12.
6 over 637 cMpc 3 , one of the most overdense structures known in the early universe, and could eventually evolve into a massive galaxy cluster. Together with existing VLT/MUSE and ALMA observations of this field, our JWST observations reveal that J0305-3150 traces a complex environment where both UV-bright and dusty galaxies are present and indicate that the early evolution of galaxies around the quasar is not simultaneous. In addition, we discovered 31 [O III] emitters in this field at other redshifts, 5. 3 < z < 6. 7, with half of them situated at z ∼ 5. 4 and 6. 2. This indicates that star-forming galaxies, such as [O III] emitters, are generally clustered at high redshifts. These discoveries demonstrate the unparalleled redshift survey capabilities of NIRCam WFSS and the potential of the full ASPIRE survey data set.
Wang) to search for Hβ+  [O III]  emitters in the fields of 25 quasars at z > 6. 5 by utilizing the spectroscopic capabilities of NIRCam/WFSS  (Greene et al. 2017). In this paper, we provide a brief overview of the ASPIRE project and present the discovery of a filamentary structure traced by the quasar J030516. 92-315055. 9 (hereafter J0305-3150) and 10 [O III] emitters at z ∼ 6. 6. We also report the discovery of 31 additional [O III] emitters in this field with half of them clustered at z ∼ 5. 4 and 6. 2. In Section 2, we present a brief program design of ASPIRE, JWST observations, and data reduction. In Section 3, we report on the line emitter search and the discovery of 41 [O III] emitters in this field, and in Section 4, we discuss the properties of the filamentary structure and complex environment of J0305-3150.
ASPIRE Program Overview and Observations The science drivers of the ASPIRE program are to resolve the long-standing question of whether the earliest SMBHs reside in massive dark matter halos and inhabit galaxy overdensities, detect the stellar light from quasar host galaxies, understand the SMBH growth and AGN feedback, and constrain cosmic reionization and metal enrichment in the early universe. The NIRCam/WFSS, with unprecedented spectroscopic capabilities and remarkable imaging sensitivity and resolution, is ideal for these purposes. To put the Hβ +[O III] lines of both quasars and physically associated galaxies at the sweet spot of NIRCam/WFSS sensitivity, we focus on quasars at 6. 5 < z < 6. 8. The ASPIRE program targets 25 known quasars at 6. 5 < z < 6. 8 having both high-resolution (i.
The resolving power of the NIRCam/WFSS observations is about R ∼ 1300-1600 from 3 to 4 μm. The dispersion of the NIRCam/WFSS data for our configuration is about 10 Å pixel -1. The main observations are performed with a threepoint INTRAMODULEX primary dither pattern, and each primary position includes two subpixel dithers. This gives a survey area of ∼11 arcmin 2 for the imaging and slitless spectroscopy. We use the SHALLOW4 readout pattern with nine groups and one integration, which gives a total on-source exposure time of 2834. 5 s. To maximize the sky area coverage, both NIRCam modules are used by ASPIRE. The quasar is put at a carefully designed position (X offset = -60 5, Y offset = 7 5) in module A to ensure that we have full WFSS wavelength coverage and a full imaging depth for the quasar.
(2017). In addition, HST program GO 15064 (PI: Caitlin Casey) observed J0305-3150 with ACS imaging in the F606W and F814W bands and WFC3 imaging in the F105W, F125W, and F160W bands, making J0305-3150 the only ASPIRE quasar field covered by existing multiband optical and infrared imaging from HST and an ideal target for early ASPIRE data analysis. The detailed description of the HST observations, data reduction, and characterization of selected LBGs is presented in  Champagne et al. (2023).
The master backgrounds were then scaled and subtracted for individual exposures. After that, we aligned the LW images to a reference catalog from the DESI Legacy Imaging Surveys 45  (Dey et al. 2019)  and then aligned all SW images to the calibrated LW images using tweakwcs. 46 The purpose of this step is to remove astrometric offsets between different detectors, modules, and filters. The aligned and calibrated individual files were then passed through the CALWEBB stage 3 pipeline to create drizzled images. During the resampling step, we used a fixed pixel scale of 0 0311 for SW images and 0 0315 for LW images and adopted pixfrac = 1. The mosaicked images are further aligned to the reference catalog from the DESI Legacy Imaging Surveys for absolute astrometric calibration. Our procedure yields precise relative alignment (rms ; 15 mas) and absolute astrometric calibration (rms ; 50 mas).