dataesr/scientific-paragraphs-categorization huggingface dataset:
Data procurement and psuedotime ordering The original data set comes from a cross-sectional breast cancer study (GEO accession GSE7390  [38] ) consisting of microarray expression values for 22000 genes from 198 breast cancer patients, which we aimed to sort along a pseudotime axis. We noted that the same data set was also used in the PROB  [8]  paper. PROB is a GRN inference method that infers a random-walk based pseudotime to sort cross-sectional samples and reconstruct the GRN. For consistency and convenience in pseudotime inference, we obtained the same version of this data that was already preprocessed and sorted by PROB. We normalized the expression values to be between 0 and 1. We limited our analysis to the genes that had measurable expression and appeared in our prior model, and obtained a pseudotrajectory of expression values for 11165 genes across 186 patients.
Sequence motif mapping was performed using the FIMO software  [62]  from the MEME suite  [63]  and the R package GenomicRanges  [64]. Note that W 0 carries no sign information so that we cannot infer whether TFs inhibit or activate the expression of a gene. Hence we assigned each of the non-zero entries as +1 or -1 with uniform probability. Validation of explainability was challenging, since there are only few data sets that have ChIP-seq data for many TFs from the same cells. We used ChIPseq data from the MCF7 cell line (breast cancer, 62 TFs) in the ReMap2018 database  [39]  to create a validation network of TF-target interactions. We calculated AUC values by comparing the encoded GRNs retrieved from the trained models (see Supp. Methods 2) to the validation network. PHOENIX.
Acknowledgments. The authors thank  Marouen Ben Guebila ,  Dawn DeMeo ,  Jonas Fischer ,  Kimberly Glass ,  Camila Lopes-Ramos ,  Panagiotis Mandros ,  Soel Micheletti ,  Enakshi Saha , and  Katherine H. Shutta  for thoughtful critiques and discussions. We also thank  Daniel Karlsson  and  Olle Svanström  for sharing with us their starter code [66] for basic NeuralODE training. Declarations. The authors declare the following: VF, and JQ were supported by a grant from the  US National Cancer Institute  ( R35CA220523 ). JQ and VF have additional funding from the  National Human Genome Research Institute  ( R01HG011393 ). • Conflict of interest : All authors declare no competing interests. • Availability of data and materials : Relevant data are made available through the Supplemental Methods (SM) and Supplemental Results (SR) sections, as well as the PHOENIX release: https://github.
com/ QuackenbushLab/phoenix. • Code availability : An open-source implementation [34] and documentation is available through https://github. com/QuackenbushLab/phoenix. • Authors' contributions : IH, JQ, and RB conceived of the project. RB and IH designed the modelling framework. IH carried out the bulk of the work including data processing and coding of the methodology. VF contributed to the breast cancer pathways modeling and interpretation. All authors contributed to the writing and editing of the manuscript.
Coupling refers to communication and interchange of information between sub-modules. As noted by  Siad et al. (2019) , the way the sub-modules are coupled varies greatly, depending on the expected outcomes but also on the possible computational techniques. We have distinguished a gradient in the levels of coupling (Fig. 2 ). The first case concerns where models are chained: the principle of modelling chains or workflow. In this case, there is no real coupling as each model is fully independent. The second case concerns so-called weak coupling where the models will exchange information through files or a shared database. The third case consists of completely integrating models so that the included model becomes a simple module or sub-module. The fourth case consists of integrating different modules within the same model, at the level of the computer code.
If modelling frameworks such as APSIM  (Holzworth et al. , 2018) , BioMA  (Donatelli et al. , 2010) , DSSAT  (Hoogenboom et al. , 2019) , OpenAlea  (Pradal et al. , 2015) , RECORD  (Bergez et al. , 2013)  and Simplace  (Gaiser et al. , 2013)  helped to produce models with a good level of Published by Burleigh Dodds Science Publishing Limited, 2023. modularity, this was rarely the case for models with legacy code. Modularity has long been a recommended practice in the crop modelling community  (Reynolds and Acock, 1997; Jones et al. , 2001). Most models today have developed a modular approach  (Brown et al. , 2018) , which facilitates their readability, their evolution by adding new modules and their sharing with the modeller community. In addition, to facilitate model sharing, these communities have worked for the implementation of semantic interoperability.
The interoperability is also essential because simulation results are, and will remain, uncertain despite all the progress on the models themselves. This is reinforced by the fact that, in order to assess the impact of climate change or to explore possible adaptation pathways, it is necessary to consider uncertainties in future climate predictions. One way to reduce uncertainty is to cross-reference information sources and this relies, in particular, on semantic interoperability. In connection with AgMIP, researchers have worked on the definition of standards to facilitate the interoperability of modules of models and data. Interoperability corresponds to one of the FAIR principles  (Wilkinson et al. , 2016) , now extensively promoted in the agrifood community  (Top et al. , 2022) , and uses the International Consortium for Agricultural Systems Applications (ICASA) vocabulary  (White et al.
, 2013). Recently, a module specification standard and a metalanguage Crop2ML  (Midingoyi et al. , 2021)  were proposed as a standard to express module equations. Beyond the AgMIP community, we also observe the development of ontologies and their use in modelling work. They help in sharing data used as model inputs for model calibration and documentation. The agronomic community has developed many controlled vocabularies and ontologies such as AGROV OC  (Subirats-Coll et al. , 2022)  from FAO or AGROPORTAL  (Jonquet et al. , 2018). Some ontologies are very specific, such as OntoHydroAgro, which is dedicated to the impact of climatic changes and agricultural activities on water resources  (Bonacin et al. , 2016). To facilitate the access to crossreferenced sources, it is also necessary to have open and collaborative software systems enabling the sharing of large-scale, multifaceted data and models (e.
In  Launay et al. (2021)  e. g. cells are the intersection of climate and soil data which are heterogeneous in terms of surface. It is then necessary, for each of these cells, to run one or more instances of the model with its own inputs based on soil, climate, management, etc. Gridded simulations require increasingly large computing resources. This can range from a high-performance workstation to a cluster (e. g. where information needs to be provided in real time, such as for water flows in a watershed) or a computing grid (independent simulations between cells, with their own resources on each node). Moreover, these gridded simulations require specific databases at the territory scale. There are numerous sources for these databases and there is ongoing work to develop them, e. g. soil data at 10 km² resolution  (Han et al.
e. tensile, shear. ), the development of a simultaneous measurement of strains, strain rates and temperatures at the tool chip interface to understand the chip formation genesis is extremely difficult and few studies have been carried out in the case of cutting  [37, 13]. If the measurements allow to build large experimental databases, some informations remain inaccessible. Thus, experimental technique does not allow to obtain the stress field during the cutting operation. Moreover, the experimental techniques listed above provide only partial informations, notably because that the measurements are obtained on the lateral surfaces of the workpiece and the formed chip. It is possible to overcome these difficulties by using the numerical simulations of chip formation  [1, 20]. On the one hand, a primary interest of the numerical simulations is the ability to access the thermal, kinematic and mechanical fields  [9, 28].
Declarations Funding: This work is supported by the the French State (ANR) through the program "Investment for the future" referenced by ANR-11-LABX-0008-01 (LabEx DAMAS). Authors would like to acknowledge the LabEx DAMAS for the financial support. Conflict of interest/Competing interests: The authors declare no conflict of interest. Ethics approval: Not applicable Consent to participate: Not applicable Consent for publication: Not applicable.
Prototyping and Experimental Validation After manufacturing the various mechanical organs of the platform via an additive manufacturing technique, all these parts, including the sensors and actuators, have been assembled to make the first prototype of the CochleRob manipulator (Figure  35 ). This manipulator is controlled via a computer using LabVIEW software and Maxon controllers. The motors are Maxon as well. They were chosen after a trade-off was done between its length (which should ideally be short) and its torque (which should be higher). This implemented closed loop uses a classic PID controller. The experimental results are illustrated in Figure  36. As we can see, the motors' angles very accurately follow the given references. This experiment validates the operation of the platform and the different models developed in this work.
The proposed robot has been successfully prototyped and its components were manufactured using the additive manufacturing process. The robot was effectively con-trolled using Labview software, further demonstrating its viability as a solution for treating hearing loss. The motors chosen for the robot met the necessary specifications for torque and volume. It is important to note that, while our proposed solution presents a promising step towards safe and effective treatment of hearing loss, further research and development are necessary to fully exploit its potential and to enhance the feasibility and reliability of our proposed robot. For example:.
DATA Our strong-lensing analysis of RXJ0437 is based on a combination of imaging and spectroscopic data sets. While much of our analysis focuses on MUSE and Hubble Space Telescope ( HST ) data (Fig. 1  ), we also derive ancillary colour information from ground-based imaging taken with the Dark Energy Camera (DECam) on the Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory (CTIO).
MUSE The MUSE data used in our analysis co v er a 1. 5 arcmin × 1. 5 arcmin region of the sky centred on the brightest cluster galaxy (BCG) of RXJ0437 at ( α = 69. 289677, δ = 0. 73114470), spanning the cluster's strong-lensing zone and its immediate surroundings. A mosaic of nine partially o v erlapping 1-h MUSE pointings forms the full field of view, which has a combined exposure-time depth ranging from 6. 5 h in the centre (near the BCG and the H-U lens systems), to 1-2 h in the outer regions (depending on position and orientation). An initial central pointing (total exposure time 2910 s) was observed on 2020 February 15 as part of the ESO Kaleidoscope Clusters surv e y (PID 0104. A -0801; PI A. Edge), a large 'filler' surv e y programme designed to quickly identify bright strong-lensing features in shallow (snapshot) exposures of galaxy clusters.
Throughout this survey, MUSE operated in its WFM-NOAO-N mode characterized by widefield nominal wavelength coverage (wavelength range 4750-9350 Å, mean resolution R = 3000) without adaptive optics correction. The data were also obtained during grey time. Examining the data from this initial observation, we detected the features of the primary H-U ring (System 1 in Section 5 ), along with four other multiply-imaged galaxies, allowing us to create a preliminary lens model and estimate the extent of the multiple-image region. Using this preliminary model as a guide, we obtained the additional data to create our final mosaic (which co v ers the full multiple-image footprint) in ESO programme 106. 21AD (PI D. Lagattuta) between 2021 January 15 and February 13 (total exposure time ranging from 2544 s to 20352 s).
Unlike in the initial observ ation, ho we ver, we did apply AO corrections (WFM-AO-N mode) during this programme, and the data were obtained with stricter observational constraints: exposures were acquired in dark time (moon brightness < 40 per cent of maximum), under clear skies ( < 10 per cent cloud co v er), with natural seeing limited to < 0. 8. All pointings -both in the filler surv e y and in dedicated follow-up campaign -consist of a series of three (rotated) exposures taken at 0, 90, and 180 degree roll angles, respectively; each exposure is also shifted by a small dither offset (0. 3) to minimize the effect of bad pixels and other systematics during data combination. Ho we ver, because of differences in o v erheads between NOAO and AO modes, the individual AO-corrected exposures are slightly shorter (848 s versus 970 s) and have a coverage gap between 5805 and 5965 Å due to laser contamination.
Keck RXJ0437 was observed with the near-infrared multiobject spectrograph MOSFIRE on the Keck-I 10 m-telescope on Maunakea on 2022 January 8. We targeted two images of the multiple-image System 1 (see Section 5 , Fig. 1  , and Table  1  for details) for 24 min in the K band (1. 92-2. 40 μm); a 5 arcsec offset was used between observations in an ABBA pattern to facilitate background subtraction. Similarly, Systems 2 and 5 were observed for 72 and 24 min in the K and H band (1. 46-1. 81 μm), respectively, using a 1. 5 arcsec offset. For the 0. 7 arcsec slit used in these observations, the spectral resolution is 4. 5 and 6 Å in H and K , respectively. All data were reduced with the MOSFIRE data reduction pipeline designed by the MOSFIRE commissioning team and written by Nick Konidaris with e xtensiv e checking and feedback from Chuck Steidel and other MOSFIRE team members.
DECam Multiband ground-based imaging of RXJ0437 is publicly available from the Dark Energy Camera Le gac y Surv e y (DECaLS; NOAO programme 2014B-0404, PIs D. Schlegel & A. Dey), a subset of the larger DESI Le gac y Surv e y. 1  creating wide-area mosaics co v ering ∼14 000 de g 2 of the sk y. To maximize co v erage with e xisting data, we extract a 4 arcmin 2 section of the mosaics in three optical bands ( g , r , and z ), each centred on the RXJ0437 BCG. Individual frames making up these cutouts were taken between 2016 October and 2018 No v ember, and the final stacked mosaic frames consist of 4, 4, and 5 exposures in the g , r , and z bands, with a total exposure time of 372, 184, and 513 s, respectively. These ground-based images have lower angular resolution than the HST data (with an average psf of ∼1.
G A L A X Y R E D S H I F T S Prior to modelling the RXJ0437 system, we measure the redshifts of objects within the MUSE footprint, in order to identify cluster members, multiple-image constraints, and look for structure along the line of sight. The techniques used to extract spectra and measure redshifts from the MUSE data cube are the same as those employed in previous work (e. g. Richard et al. 2021 ; Lagattuta et al. 2022  ), but we briefly describe them again here. We identify objects in tw o w ays: first by selecting sources detected in the stack ed HST imaging data (which we call prior targets), and then by scanning the muse data cube itself for prominent emission lines ( muselet targets) using the MPDAF software package  (Bacon et al. 2016 ; Piqueras et al. 2017 )  2. Combining the sources identified by each procedure -and matching objects to remo v e duplicate entrieswe then extract a spectrum for each candidate using the optimally weighted  Horne ( 1986 )  algorithm.
We use the redshift-fitting software MARZ  (Hinton 2016 )  to create an initial redshift guess for each object, then visually inspect the results and adjust (or reject) the values as needed. After the visual-inspection stage, we flag 171 objects as having high-confidence redshifts (measurements with a 'zconf' value of 2 or 3, following  Lagattuta et al. 2022 ) ; this set is reduced to 147 unique objects when accounting for multiply-imaged galaxies. We also measure low-confidence redshifts (zconf = 1) for 33 additional objects; although we do not include these more speculative measurements in all of the spectroscopic analysis described below, we still list them in the final redshift catalogue for completeness. In total, counting high-and lowconfidence measurements and including all multiple images, we have 204 redshift entries in the RXJ0437 field.
M O D E L L I N G T E C H N I Q U E We construct a model of the mass distribution of RXJ0437 using the publicly available LENSTOOL 3  software  (Kneib et al. 1996 ; Jullo et al. 2007 ; Jullo & Kneib 2009 ). The model is constructed from parametric components that represent mass haloes at large and small scales. The parameters characterizing these components are constrained by the positions of multiply imaged background sources identified throughout the field. The majority of the mass is modelled as a series of pseudoisothermal elliptical mass distributions (PIEMD; El íasd óttir et al. 2007 ), representing both cluster-and galaxy-scale haloes. Ho we ver, to account for any mass not directly observed in the available data, we also include a systematic 'external shear' term (see e. g. Keeton, Kochanek & Seljak 1997 )  as an additional component.
With only two identified clusters housing H-U systems (RXJ0437 and Abell 1703), it is difficult to know if they are representative of a larger parent distribution. To perform a truly robust statistical analysis with H-U lenses, we will thus need to increase the sample size. To  Notes. 1: For explanations of lettered footnotes, see Table  3  Note. 2: in the final three columns, gNFW components are described by β, r s , and c (the central mass density slope, scale radius, and concentration parameter), while all other mass components are instead described by the usual r core , r cut , and σ 0 parameters this end, we have already begun searching archi v al MUSE data for additional cases -focusing on Kaleidoscope clusters and the MUSE GTO lensing-cluster atlas  (Richard et al. 2021 ). Although this search is still in the early stages, we have already identified ∼10 additional H-U candidates, which will be the subject of a forthcoming paper.
Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters.
Chemical compounds and descriptors Ten HIV-1 protease inhibitors have been approved by the Food and Drug Administration (FDA), but the emergence of multidrug-resistant (MDR) strains has limited long-term treatment options  [19] [20] [21] [22] ; therefore, the search for new efficient drugs has become a necessity. Thirty-three new compounds were synthesized and evaluated in a previous study to determine their optimal biological activity  [14] (S1 Table in S1 File). Meanwhile, this work is based on computing various descriptors (Topological, Constitutional, Geometrical, Physicochemical, and Quantum) of the compounds mentioned above using several software packages (Gaussian, Chem3D, ChemSketch, and MarvinSketch).
Descriptive analysis The computed descriptors must be analyzed to generate a computational model that relates the biological activity of these compounds to the structure (descriptors). To do so, we used both methods; the first one is called Principal Component Analysis (PCA), the main purpose of which is to delete correlated descriptors, so we lower the dimension of the data representation area. The second one is a clustering method, called k-means partitioning, used to split the dataset into training sets for model generation and test sets for validation.
Statistical analysis Multiple linear regression. Multiple linear regression analysis is a statistical technique based on several analytical independent variables called descriptors to anticipate the outcome of a response variable (biological activity); it is selected to asset a linear model relating the activity (dependent variable) to descriptors having high correlation with the response (activity)  [23]. The linear model takes the form that follows: Y ¼ a 0 þ X n i¼1 ai : xi Where; Y represents the biological activity (dependent variable), a 0 is the intercept of the equation, x i is the molecular descriptors, and a i is their coefficients. Model generation. A QSAR model was generated using XLSTAT software after analyzing the data with both methods (PCA and K-means)  [24] , which after validation, were used to anticipate the activity of brand-new compounds that can be more efficient as HIV-1 protease inhibitors.
In order to assess the physicochemical influence of the substituents (structure/descriptors) on the biological activity, we introduced the dataset along with descriptors corresponding to the 33 compounds listed previously and their biological activities to an MLR analysis. To choose the first-rate regression performance, we use several coefficients; r, r 2 , r 2 adj , MSE and P value  [25, 26] , where r represents the correlation coefficient, r 2 is the coefficient of determination, r 2 adj is the coefficient adjusted for degrees of freedom, MSE is the mean squared error, and P value is the probability of Fisher statistics. Model validation. The model generated by MLR analysis must be validated to evaluate its significance and ensure its accuracy prediction ability. In order to achieve this, we use internal and external validation.
Internal validation. Also called leave-one-out cross-validation (LOOCV), whereby one element is removed from the training set, and the remaining compounds are used to rebuild a model; then it will be returned to the training set, and another compound will be removed, the model generated will be used to predict the activity of the removed one and the cycle is repeated until all compounds have been detached one by one, in the end, a correlation coefficient Q 2 is computed  [27]. External validation. Besides the internal validation, external validation is primordial; the k-means clustering method allowed us to divide the dataset into training and test sets. The second one was employed in this stage. The obtained model will be used to investigate the activities of the test set compounds, and the regression coefficient (R 2 cv ) value will be computed  [28].
Descriptors generation Several softwares were used to compute various descriptors such as Gaussian, Chem3D, ChemSketch and MarvinSketch, but only some descriptors correlated with the activity were used in minimizing the size of the data representation space. Considering the quantum descriptors, they were investigated using DFT approach performed by Gaussian 09 program package; employing for this purpose the hybrid method B3LYP combining the Becke's threeparameter and the Lee-Yang-Parr exchange-correlation functional, using as well 6-31G (d,p) basis set, performing the optimization of the compounds geometries ultimately while all the other parameters were computed using Chem3D, Chemsketch and MarvinSketch software (S2.