dataesr/scientific-paragraphs-categorization huggingface dataset:
The data were initially processed using the LCO BANZAI pipeline  (McCully et al. 2018). The multi-aperture photometry on the reduced data was performed using "X-ray Binary New Early Warning System (XB-NEWS)", a real-time data analysis pipeline that aims to detect and announce new X-ray binary outbursts within a day of the first optical detection of an outburst (e. g. ,  Russell et al. 2019; Goodwin et al. 2020; Pirbhoy et al. 2020). The XB-NEWS pipeline downloads new images of all targets of interest from the LCO archive along with their associated calibration data and performs several quality control steps to ensure that only good quality images are analyzed. XB-NEWS then computes an astrometric solution for each image using Gaia DR2 positions, 36 performs aperture photometry of all the stars in the image, solves for zero-point calibrations between epochs  (Bramich & Freudling 2012) , and flux calibrates the photometry using the ATLAS All-Sky Stellar Reference Catalog (ATLAS-REFCAT2,  Tonry et al.
Archival Optical Data We also use the archival data of the source obtained in the G spectral filter with the Gaia telescope 37  during the recent outburst. Gaia first detected the source on 2017 January 27 (MJD 57780. 8) at G = 16. 44. Prior to that, the last observation it had on 29 October 2016 (MJD 57690) was a non-detection (typical detection limit of Gaia is ∼20. 7 mags,  Brown et al. 2016). Gaia detected GRS 1716-249 on 13 days during the outburst, with the last detection on 2017 September 23 (MJD 58019). We use these public data in Figure  2 , while studying the optical light curve of the source.
Nevertheless, standard stars taken just before and after GRS 1716-249 were used to achieve accurate flux calibration on April 21. The detailed VISIR observing log and the photometric results are reported in Table  1. (Masetti et al. 1996). The target is indicated with hash mark in both the panels. The right panel shows the quiescent optical finding chart (MJD 58388. 4) with the 2 m LCO telescope in the i′ band with 100 s exposure time (an image taken under excellent conditions, with seeing of 0 82). The counterpart is just 1 6 from a star of similar magnitude to the north of GRS 1716-249. The data were reduced using the VISIR pipeline in the gasgano environment. We combined the raw images from the chop/nod cycle and performed aperture photometry in IRAF using a large enough aperture to minimize the effect of small seeing variations on the fraction of flux in the aperture (the method is the same as that used in  Baglio et al.
Archival Near-IR Observations We use the archival near-IR photometric observations of GRS 1716-249 during the outburst. Bassi et al. (2020)  reported near-IR detections of the source obtained with the Rapid Eye Mount telescope (REM) on 2017 February 9 (MJD 57793) of J = 14. 18 ± 0. 22, H = 13. 81 ± 0. 14, and K = 13. 84 ± 0. 29 and 13. 59 ± 0. 16, with exposure times of 300 s, 150 s, 75 s, and 75 s, respectively (the same observation is also included in the data set presented in Section 2. 2. 2). Later,  Joshi et al. (2017)  observed the source with the Mount Abu 1. 2 m telescope and the Physical Research Laboratory (PRL) near-IR Imager/Spectrograph, and reported near-IR magnitudes on 2017 March 20 (MJD 57832) of J = 14. 3, H = 14. 0, and K S = 13. 7, with typical errors of 0. 1 magnitude, for a total integration time of 15, 15, and 17.
Archival Swift/UVOT Observations We gathered publicly available Swift UV/Optical Telescope (UVOT) pointing observations of the source during its entire outburst from the NASA/HEASARC data center. We used the pipeline processed images and obtained the magnitude of the Note. The reported fluxes are not de-reddened. Standard stars used are 1, HD039523; 2, HD046037; 3, HD047667; 4, HD061935; 5, HD075691; 6, HD097576; 7, HD099167; 8, HD111915; 9, HD133774; 10, HD145897; 11, HD151680; 12, HD178345; 13, HD082668; 14, HD108903; 15, HD123139; and 16, HD163376. source using the uvotsource HEASOFT routine, with an aperture of 5 arcsec centered on the source. An empty region close to GRS 1716-249 was chosen as the background region. We select only those 81 pointings where the source flux estimate is at least 5σ above the sky background.
Archival Data from X-Ray Telescopes We acquired X-ray monitoring data of GRS 1716-249 from the Swift/BAT and Swift/XRT telescopes. Swift/BAT has observed the source almost daily from 2016 December 1 (MJD 57723) in the 15-50 keV flux range. We extracted the daily average light-curve data of this source from the Swift/BAT transient monitor 38   (Krimm et al. 2013). To convert the countrates to flux, we used the hydrogen column density N H = (0. 70 ± 0. 01) × 10 22 cm -2 and a photon index of Γ = 1. 68 ± 0. 01, as reported in  (Bassi et al. 2020). Swift/ XRT observations were made every few days between 2017 January 28 (MJD 57781) and 2017 October 20 (MJD 58046), mostly in the window timing mode, with target IDs 34924 and 88233 (see Table  1 , of  Bassi et al. 2019 , for a detailed observation log).
We used the on-line Swift/XRT products generator 39   (Evans et al. 2007 (Evans et al. , 2009) )  to extract the 2-10 keV count rate of GRS 1716-249 from each XRT observation, after correcting for instrumental artefacts. GRS 1716-249 was also observed with Nuclear Spectroscopic Telescope Array (NuSTAR) during the 2016-2017 outburst. We calculate the NuSTAR flux density of the source using NUPIPELINE V0. 4. 6 in HEASOFT V6. 25, with the calibration file version v20171002. Both Science Mode and Spacecraft Mode data were considered. The flux was calculated in the Swift BAT energy band (15-50 keV) using the best-fit spectral models provided in  Jiang et al. (2020). A relativistic disk reflection model with a variable disk density parameter was used  (Ross & Fabian 2007). Detailed descriptions of spectral modeling can be found in  Jiang et al.
Long-term Monitoring and Quiescent Magnitude The only report of any optical quiescent magnitudes of GRS 1716-249 in the literature is a single weak constraint of B ∼ 21. 0-21. 5 mag  (della Valle et al. 1993 ). The source was not detected with Gaia in quiescence, and it only appears in EDR3 after the new outburst data were included (see Section 2. 1. 2). This provides a 20. 7 mag limit in the G band  (Brown et al. 2016). We have been monitoring GRS 1716-249 in quiescence with LCO (mostly using the 2 m Faulkes Telescope South) for the last 15 yr, since 2006 February 3 (MJD 53769; see Section 2. 1. 1 for details). The monitoring continues past the data we report on here through 2022 February. During quiescence, all the measurements obtained with XB-NEWS are forced-photometry points centered at the position of GRS 1716-249.
Conclusion The 2016-2017 outburst of the BHXB GRS 1716-249 (or GRO J1719-24) is well-studied at X-ray and radio wavelengths. In this work, we investigate the optical, near-IR, mid-IR, and UV wavelength monitoring data of GRS 1716-249 in outburst using LCO, REM, VLT (VISIR), and Swift's UVOT, and compare them with the multiwavelength archival data from Gaia, Mount Abu 1. 2 meter telescope, Swift XRT, NuSTAR, MAXI, ATCA, VLA, and LBA. We also report the long-term (∼10 yr) optical light curve of the source using LCO and find that the quiescent i′-band magnitude is 21. 39 ± 0. 15 mag. We find that the optical and UV emission of the source in outburst is mainly originating from a multi-temperature accretion disk, with X-ray reprocessing dominating at high luminosities, and with some contribution at the fainter end from the viscously heated disk.
distance. D. M. R. and D. M. B. acknowledge the support of the  NYU Abu Dhabi Research Enhancement Fund  under grant  RE124. J. J. acknowledges the support of the  Leverhulme Trust , the  Isaac Newton Trust , and  St. Edmund 's college, University of Cambridge. R. S. acknowledges grant No. 12073029  from the  Natural National Science Foundation of China (NSFC). S. M. is thankful for support by  Dutch Research Council (NWO)   VICI award , grant Nr. 639. 043. 513. T. M. B. acknowledges financial contribution from the agreement  ASI-INAF n. 2017-14-H. 0  and from  PRIN-INAF 2019 N. 15. T. D. R. acknowledges financial contribution from the agreement ASI-INAF n. 2017-14-H. 0. T. S. acknowledges financial support from the  Spanish Ministry of Science, Innovation and Universities (MICIU)  under grant  PID2020-114822GB-I00.
The designs were done in Verilog at the gate level. These results are presented in Table  II. In order to assess the quality of the approximation, the evaluation employed the following error metrics: • The error distance (ED): is the arithmetic difference between the accurate result and the result from the multiplier for a given input. • The mean error distance (MED): is the mean of all possible EDs. • The error rate (ER): is the probability of producing an incorrect result. • The normalized mean error distance (NMED): is the normalization of MED by the maximum output of the accurate design. • the mean relative error distance (MRED), which is the average value of all possible REDs. For the error analysis, we have simulated the multipliers using the dataset from the hamming high pass filter as one of the operands and uniform random input for the other.
The following parameters were analyzed with Solomon Coder software (András Péter, Keele, UK): (1) the number of food pellets collected, (2) the time spent in interaction with the food pellets, (3) the time spent in food pellets zone, and (4) the interaction with food pellets time and time in food pellets zone ratio. (A) Timeline of experiment 1 (hoarding). After baseline assessment of motor function (stepping) and of hoarding behavior, bilateral lesion of the substantia nigra pars compacta was achieved via viral-mediated bilateral overexpression of α-synuclein. Stepping was performed again at 4 and 9 weeks after surgery. Hoarding behavior was assessed at 9 weeks postsurgery (Off PPX) and at 11 weeks, during 10 days of PPX administration (0. 3 mg/kg/day, s. c. ). Hoarding behavior was assessed during 30 min session in a 100 × 50 cm open field.
Stereology TH-positive neurons were counted using the optical fractionator method on every 6th section of the SNc as previously described  [8]. Systematic random sampling was performed with the Mercator Pro V6. 5 (Explora Nova, La Rochelle, France) software coupled with a Leica 5500B microscope. Following delineation of the SNc with ×5 objective, counting was done with ×40 objective.
1 ). Hence, the first three introns located in the open reading frame of the elasmobranch scpp are in phase 0 (fig. 1 ), which is the phase most commonly observed in osteichthyan scpp genes (fig. 1 ; see  Kawasaki and Weiss 2003). A fourth intron is located in phase 1 in scpp and sparc-L (fig. 1 ). We searched genomic and transcriptomic data and identified a similar scpp gene in other elasmobranch species belonging to selachimorphs (the sharks Scyliorhinus torazame, Chiloscyllium punctatum, and Carcharodon carcharias) and batoids (the ray Raja clavata and the skate Okamejei kenojei, supplementary material S1, Supplementary Material online). In all cases, the encoded elasmobranch Scpp protein displays a signal peptide conserved with Sparc, Sparc-L/L1/L2, and the osteichthyan Scpp sequences, together with an enrichment of aspartate/glutamate (D/E) residues and the presence of conspicuous putative Fam20°C phosphorylation SxE sites (fig.
Materials and Methods Animal care, staging, histology, and in situ hybridization procedures of embryonic lesser spotted catshark S. canicula were performed as previously described  (Enault et al. 2018 ). The scpp and fam20°C sequences were amplified and cloned using lesser spotted catshark vertebrae cDNA and the following primers (5 ′ -3 ′ ): scpp-Forward GATTTGGG CAGCAACAGTCA, scpp-Reverse TGTCTAACCCCGGTG TGAAA; fam20°C-Forward GGCTGCTGATCATCATGG TG, fam20°C-Reverse GGAAAGCAGCAATCTCCGAG, to be used for RNA probe synthesis. Refer to  Enault et al. (2018)  for sparc-L probe synthesis. All in situ hybridizations included two negative control slides (no probe and sense probe), which were devoid of signal (supplementary material S13, Supplementary Material online). All accession numbers and genomic coordinates are available in supplementary material S14, Supplementary Material online.
Amino acid enrichments were detected with CAST  (Promponas et al. 2000) , internal repeats were searched with XSTREAM  (Newman and Cooper 2007) , and repeat logos were generated with the GGSEQLOGO R package  (Wagih 2017). Isoelectric point was computed using Expasy online tool  (Gasteiger et al. 2003). Highly similar sequences between the Sparc-L and Scpp loci were searched using BLAST 2. 2. 19 with default parameters  (Altschul et al. 1997). Sequences used for phylogenetic reconstructions were aligned using MAFFT  (Katoh et al. 2002; Katoh and Standley 2013) , using standard parameters. Alignments were cleaned using HmmCleaner with standard parameters  (Di Franco et al. 2019). Phylogenetic analyses were performed using Maximum Likelihood under IQ-TREE 1. 6. 12 (Nguyen et al. 2015). Node support was estimated by performing five thousand ultra-fast (UF) bootstrap replicates  (Hoang et al.
Acknowledgments We acknowledge the  MRI platform  ( ANR-10-INBS-04 ), the  GenSeq platform, Labex CEMEB  ( ANR-10-LABX-0004 ), and  NUMEV  ( ANR-10-LABX-0020 ), and the  RHEM facility  ( INCa_Inserm_DGOS_12553, FEDER-FSE 2014-2020 Languedoc Roussillon ) for histology technical expertise, the  IRP (LIA) MAST (CNRS)  and the  ANID  ( FONDECT 1190926  to S. M. ). We thank  Felipe Aguilera  for help with the GGSEQLOGO R package,  Benoit Nabholz  for discussion on Ks, and  Jake Leyhr ,  Tatjana Haitina , and  Didier Casane  for critical reading of the manuscript. We are grateful to the anonymous reviewers for their constructive comments that improved the quality of the present study.
The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0. 43-0. 51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods.
The paucity of high-quality Caco-2 permeability data has limited the development of accurate models with a comprehensive applicability domain. It is very common to observe variations during in vitro permeability protocols among different research groups because the cultured cells may vary depending on culture conditions, the number of passages, age of monolayer, seeding density, differentiation stage, and the transport buffer used  [11, 12]. Recently, Lee et al. found substantial differences for absolute apparent permeability coefficients (P app ) of compounds between datasets from various laboratories with high normalized RMSE values in the range of 0. 46 to 0. 58  [13]. In addition, involuntary misprints due to erroneous transformations of values or units and the poor information related to the assay conditions have caused a decline in the quality of the data.
Considering the above factors, the collection of Caco-2 datasets for modeling purposes needs a good balance between quality (experimental consistency) and size. If the experimental variability in the training dataset is high, building a reliable computational quantitative structure-property relationship (QSPR) model is likely to be difficult. Numerous classification and QSPR models have been developed to predict Caco-2 permeability based on a variety of physicochemical and physiological descriptors  [14]. Even when regression models have shown good performance on small datasets (<500)  [15] [16] [17] , most published work refers to classification algorithms that have been able to classify Caco-2 permeability with acceptable statistics according to the cut-off value used to transform numerical values into categorical classes.
The main drawback of these classification models is that no consensus has been reached on the best cut-off for modeling purposes and from the perspective of the practical application of the model  [14]. While there are many studies that predict Caco-2 permeability, there are few that propose a pathway for making predictions of new molecular entities. This issue has limited the applicability of published models. Considering the importance of a numerical value for decision-making, more robust and useful Caco-2 regression models are still needed, capable of covering a broad spectrum of chemical diversity and a wide range of magnitude. It is also required to make available automatic systems that can quickly and easily predict new molecules, to allow the development of virtual screening procedures and to implement the FAIR principles to make data findable, accessible, interoperable, and reusable  [18] [19] [20].
Here, we present an automated platform for predicting permeability in the Caco-2 cell line. This work shows the potential of recursive machine learning models to perform variable and data selection with the aim to obtain interpretable and reliable regression models. A new method for developing a consensus model was implemented too. The model was developed on a curated dataset of more than 4900 molecules and was validated for the early estimation of permeability rate for BDDCS and preliminary evaluation of absorbed fraction. This platform provides a free tool for virtual screening of Caco-2 permeability in large compound libraries. The early evaluation of this property during the drug design and discovery stages should facilitate decision-making, minimize the number of experiments, and promote a rational selection of potential orally active drugs.
Permeability Data Collection Experimental values of Caco-2 permeability were collected from three publicly available datasets  [22] [23] [24]. Three criteria were followed for this selection: to have more than 1000 instances, to have been used for regression tasks, and to have been published within the last 5 years. The first dataset (Set A) was published by  Wang et al. in 2016 [22]. They used a relatively large dataset consisting of 1272 compounds to develop several models for the prediction of Caco-2 permeability using different machine learning methods in combination with a genetic algorithm for variable selection. The second dataset of 1827 compounds (Set B) was collected by Wang and Cheng  [23]  to develop several QSPR models based on neural networks and other machine learning approaches.
The third dataset of 4464 compounds (Set C) was reported by Wang et al. in 2020 to model the Caco-2 permeability using neural networks approaches  [24]. All permeability measurements were converted to cm/s × 10 -6 and were transformed to a base 10 logarithmic scale to form a Caco-2 permeability dataset for modeling. Missing entries for permeability values were removed. To minimize the uncertainty and evaluate the experimental variability, the mean value and the standard deviation (STD) were calculated for the repeated entries. Considering that the validation dataset should have the highest possible quality, the MERGED data were split according to the standard deviation computed from molecules with multiple measurements. Samples with standard deviation known (STD = 0) and equal to or less than 0.
Permeability Data Permeability data were collected from three public data sources, which have been used independently to build QSPR models in previous studies  [22] [23] [24]. Rigorous chemical and experimental data curation steps were performed before modeling to ensure the development of reliable models (see Methods for chemical and experimental data curation). This issue is of particular interest in this study, due to the combination of different data sources. After the chemical data curation procedure, the three original datasets A, B, and C consisted of 1267, 1863, and 4462 molecules, respectively (see Figure  1a ). Before the removal of duplicates, the grade of agreement between the datasets was evaluated by comparing the log P app values reported for the same molecules. Figure  1a  shows the intra-and interset RMSE statistics based only on molecules with different values reported in the same or different data sources (results are shown in the RMSE matrix).
This comparison provides us with an estimation of the expected lower limit of root mean squared error prediction (RMSEP), since experimental errors should be always lower than computational errors from in silico models. Set B showed the best degree of agreement with the others, with RMSE values below 0. 35 for all cases. The most critical value reported corresponds to the RMSE analysis between duplicate pairs in set C (RMSE intraset = 0. 49). Note that the RMSE values obtained from the comparison of the three main datasets versus the external set ranged from 0. 23 to 0. 52. Pharmaceutics 2022, 14, 1998 6 of 21 a standard deviation of 0. 79 (Pravastatin). This molecule was excluded from the modeling dataset. The rest of the stereoisomers were in the 0. 0-0. 5 standard deviation range. Finally, the analysis of 2D duplicates identified 5000 unique molecules.
Molecular Properties and Caco-2 MERGED Data Figure  1c  shows the distribution of the count of failures of the extended rule-of-five (Ro5) on the MERGED data. Following this rule of thumb, more than 60% of the MERGED data satisfied the extended Ro5 guidelines for oral bioavailability and the rest failed in at To analyze the predictive limits of the QSPR model on the whole dataset (excluding the external set), we added a random error to the target property by sampling from a Gaussian distribution of zero mean and standard deviation σ: log P noise app = log P true app + N(0, σ) (2) Two levels of standard deviation were evaluated in correspondence with the mean and maximum values of RMSE reported in the matrix depicted in Figure  1a. After 10 replicates for each level, the coefficient of determination (r 2 ) for the whole set ranged from 0.
55 to 0. 82. The same procedure was applied to the external set. For this set, the r 2 values ranged from 0. 42 to 0. 84. To reduce experimental uncertainty, only those molecules with the same chemical structure and standard deviation ≤0. 5 were considered as duplicates. Manual inspection confirmed that the pairs corresponded to truly duplicate structures. Among 1836 molecules with more than one experimental value, only 9 pairs corresponded to stereoisomers. From this list, only one pair presented a significant difference in the log P app values with a standard deviation of 0. 79 (Pravastatin). This molecule was excluded from the modeling dataset. The rest of the stereoisomers were in the 0. 0-0. 5 standard deviation range. Finally, the analysis of 2D duplicates identified 5000 unique molecules.