dataesr/scientific-paragraphs-categorization huggingface dataset:
G. S. acknowledges support through  Chandra  grants  AR9-20013X  and  NASA  contract  NAS8-03060. Basic research in radio astronomy at the  Naval Research Laboratory  is supported by 6. 1 Base funding. E. O. S. acknowledges support through  Chandra  grant  G08-19112X. We thank the staff of the GMRT, who have made these observations possible. The GMRT is run by the  National Centre for Radio Astrophysics  of the  Tata Institute of Fundamental Research. H. A. P. acknowledges support by the  National Science and Technology Council of Taiwan  under grant  110-2112-M-032-020-MY3. Software: AIPS (Greisen 1990(Greisen , 2003)), astropy (The Astropy Collaboration et al. 2013Collaboration et al. , 2018)), CASA (v5. 6. 0 McMullin et al. 2007), emcee (Foreman-Mackey et al. 2013), MIR (Gurwell et al. 2007), PyBDSF (Mohan & Rafferty 2015), SPAM (Intema et al.
CEERS Observations and Data Reduction The first set of CEERS observations were taken on 2022 June 21 in four pointings, hereafter referred to as CEERS1, CEERS2, CEERS3, and CEERS6. We performed an initial reduction of the NIRCam images in all four pointings using version 1. 7. 2 of the JWST Calibration Pipeline 48 with some custom modifications. We used the current set of NIRCam reference files, 49 though we note that the majority were created preflight, including the flats. For details on the reduction steps, see  Bagley et al. (2022). To summarize briefly, we applied detector-level corrections using stage 1 of the pipeline with default parameters and used custom scripts to remove 1/f noise, wisps, and snowballs from the count-rate maps. We then processed the cleaned count-rate maps with stage 2 of the pipeline and used a custom version of the TweakReg step to calibrate the astrometry.
We then coadded the images using the drizzle algorithm with an inverse variance map weighting  (Casertano et al. 2000; Fruchter & Hook 2002)  in the Resample step in the pipeline. The final mosaics for each pointing in all filters have pixel scales of 0 03 pixel -1. We then used a custom script to background subtract the images. These final background-subtracted images were used for the morphology measurements described in Section 3.
This corresponds to the secure spectroscopic redshift if one is available or the mFDa4_z_weight photometric redshift otherwise. We use this photometric redshift value because it produces the most accurate confidence intervals (see  Kodra et al. 2022  for further details). We then use the  Stefanon et al. (2017)  photometry and the above estimated redshift to determine stellar masses for galaxies in the CANDELS EGS field. This was done using two different codes: Dense Basis 50 and P12. Dense Basis  (Iyer & Gawiser 2017; Iyer et al. 2019 ) is a pythonbased code that uses flexible stellar population synthesis to generate model spectra that correspond to a wide range of physically motivated nonparametric star formation histories, metallicities, and dust attenuation values. P12  (Pacifici et al.
Source Extractor Setup Galaxies were detected in the NIRCam images using Source Extractor 52 version 2. 25. 0  (Bertin & Arnouts 1996). The setup was optimized to detect the HSTselected z > 3 galaxies without over-deblending. We created empirical point-spread functions (PSFs) for each filter by stacking stars, and then the F115W, F150W, F200W, and F277W images were PSF-matched to the F356W image using the Python-based code PyPHER 53  (Boucaud et al. 2016). In order to detect galaxies that may be very faint in some of the NIRCam filters, we used an inverse variance weighted combination of the PSF-matched F150W, F200W, F277W, and F356W images as the detection image. We first ran Source Extractor in "cold" mode to deblend nearby galaxies, then in "hot" mode to detect faint galaxies, following  Stefanon et al.
To reflect the overall complexity seen in high-redshift galaxies, these classes are not mutually exclusive, so a classifier can choose multiple options to best reflect the overall morphology of the galaxy. For example, a galaxy can have both a disk and a spheroid in the case where it is a disk galaxy with a bulge component. A galaxy can be both a disk and 51 https://ceers. github. io/releases. html#hdr1 52 https://sextractor. readthedocs. io/ 53 https://pypher. readthedocs. io/en/latest/ 54 https://www. zooniverse. org/lab irregular if, for example, it is an asymmetric disk or a disk involved in an interaction. The exception is that if a galaxy is "unclassifiable" then it cannot also be one of the other classes. This level of complexity can make the interpretation of the various classes challenging, but the extra information provides an important level of nuance to the classifications.
Parametric Fits We perform parametric fits on the NIRCam images using both Galfit 55  (Peng et al. 2002 (Peng et al. , 2010) )  and GalfitM 56  (Häußler et al. 2013). Galfit is a least-squares fitting algorithm that finds the optimum Sérsic fit to a galaxy's light profile, and GalfitM is a modified version that uses images at different wavelengths to allow one to constrain the fit parameters that vary smoothly as a function of wavelength. The benefit of using GalfitM is that it fits all bands simultaneously and produces more physically consistent models. We performed fits using both codes to test for selfconsistency, but since we use the rest-frame optical fit throughout this paper, we focus here on the GalfitM fits and describe the Galfit fits in the Appendix. GalfitM fits were performed using the IDL program Galapagos-2 from the MegaMorph Project 57  (Bamford et al.
Nonparametric Measurements We measure nonparametric morphologies using the Python package Statmorph 58 (Rodriguez-Gomez et al. 2019). For each NIRCam filter, we create 100 × 100 pixel cutouts of the 850 galaxies in our z > 3 sample to use as input to Statmorph, along with a cutout of the segmentation map, the empirical PSF, and the gain. Statmorph measures a wide range of morphology statistics commonly used in astrophysics. The ones that we use for the analysis in this paper are concentration (C), asymmetry (A), and clumpiness/smoothness (S;  Bershady et al. 2000; Conselice et al. 2000; Conselice 2003) ; the Gini coefficient (G) and the second moment of the region of the galaxy containing 20% of the total flux (M 20 ;  Abraham et al. 2003; Lotz et al. 2004) ; the Gini-M 20 bulge and merger statistics (Rodriguez-Gomez et al.
Comparison with Expectations from Theory We compare the results of our surface brightness profile fits and nonparametric fits with the results from mock images and catalogs based on several different simulations in Figure  12  in three different redshift bins. First, we use a mock galaxy catalog based on the Santa Cruz semianalytic model (SAM) and publicly available as part of the CEERS simulated data release SDR3. 59 The CEERS mock galaxy catalog is an augmented version of the EGS light cone presented by  Yung et al. (2022) , which spans 782 arcmin 2 and contains galaxies with -16  M UV  -22 in the range 0 < z  10. The physical properties of the galaxies are modeled with the physics-based Santa Cruz SAM  (Somerville et al. 2015 (Somerville et al. , 2021;; Yung et al. 2019). The sizes of the disk components of the galaxies are computed based on the ansatz that the specific angular momentum of the halo gas is equal to that of the dark matter halo and that it is conserved during disk formation  (Mo et al.
Error bars represent the 1σ binomial confidence limits given the number of objects in each category, following the method of  Cameron (2011). Figure  11. HST and JWST postage stamps for five example galaxies with different morphologies in HST F160W images compared to JWST images or differences across the JWST filters. The F150W, F277W, and F356W filters are shown along with an RGB combination of these three filters. Each stamp is 2″ on a side. 59 https://ceers. github. io/sdr3. html#catalogs 60 https://ceers. github. io/ancillary_data. html 61 https://www. tng-project. org/  Naiman et al. 2018; Nelson et al. 2018; Pillepich et al. 2018; Springel et al. 2018 ) is a series of large cosmological magnetohydrodynamical simulations of galaxy formation and is an update to the original Illustris-1 simulation  (Vogelsberger et al.
Sample datasets We obtained the RNA sequencing datasets of chicken and human liver samples from the sequence read archive database (https://www. ncbi. nlm. nih. gov/ sra) (Table-1) (Accession numbers GSE185051  [17]  and GSE111909  [9]  for humans and chickens, respectively). In detail, the chicken datasets were derived from biopsies of the first generation of Jingxing-Huang chickens (three birds with a fatty liver condition and three control birds)  [9]. For human datasets, clinically indicated ultrasound-guided liver biopsies were collected from NAFLD patients (n = 15) and healthy candidates (n = 5)  [17].
Experimental design The experiment consisted of three sections: (1) Data pre-processing; (2) DGE and DTU analyses; and (3) evaluation of the DGE and DTU results (Figure  -1 ). We pre-processed human and chicken raw messenger RNA-Seq datasets in the "Data pre-processing" section. For DGE and DTU analyses, we employed these pre-processed datasets to identify the differentially expressed genes and transcripts. Finally, we explored the relationships between DGE and DTU results and identified the human-chicken orthologous genes with significant DTU results in the "Evaluation of DGE and DTU results" section.
Data pre-processing We pre-processed the liver sample datasets based on a previously described procedure with slight modifications  [18, 19]. Specifically, we utilized the "TrimGalore" program (https://github. com/ FelixKrueger/TrimGalore) for sequence trimming, which removed any contaminated adapter and disqualified sequences that were <110 nucleotides long with a mean Phred score below 30. We used the "STAR" software (https://github. com/alexdobin/STAR) to align the passed sequences to their corresponding genomes: GRCg7b and GRCh38 for the chicken and human data, respectively. During the alignment process, the software counted the number of reads per gene for all datasets. The transcript isoform expressions were quantified from the aligned results using the "RSEM" software (https://github.
Differential gene expression analysis We conducted DGE analysis using both the read count data of chickens and humans using the "DESeq2" package with batch correction through the "svaseq" method (false discovery rate [FDR] ≤0. 001 and log2-fold-change ≥2)  [19]. In this study, we referred to the significant differentially expressed genes obtained from the DGE analysis as "DGE genes. " To obtain the batch-corrected gene expression matrices for both chicken and human data, we applied the "removeBatchEffect" function of the "limma" package  [18, 19].
Differential transcript usage analysis We employed the "DRIMSeq" and the "DEXSeq" packages for DTU analysis, following the guidelines  [12]. After filtering genes with only one transcript isoform and the genes with a very low abundance of transcripts, we applied the test of DTU between normal and fatty liver samples using both "DRIMSeq" and "DEXSeq" models. We utilized the "stageR" package to screen and confirm the significant genes obtained from the DTU analysis (FDR ≤0. 05 with SD per sample proportion ≥0. 1). In this study, we referred to the genes with significant differences in their isoform transcript usage, based on the DTU analysis, as "DTU genes. ".
Exploring cross-species DTUs We evaluated all the orthologous DTU genes from both chicken and human DTU analyses  [21]  using the "ComplexHeatmap" package to illustrate their expression patterns across both species. As this study focuses on the genes with homogeneous expression patterns in normal and fatty liver sample groups across the human and chicken datasets, we only targeted these genes and created expression level bar graphs for each transcript isoform using the "ggplot2" package. We have summarized all transcript isoforms with the same expression trend as their respective genes (Kruskal-Wallis-test, p ≤ 0. 05). Furthermore, we reviewed and discussed the possible roles of these selected DTU genes in fatty disease development or fat metabolism.
Exploring the interaction between DGE and DTU genes in fatty liver samples from both humans and chickens The Venn diagrams in Figure  -2a  demonstrate the interaction between DGE and DTU genes acquired from analyses of both chicken and human samples. The plots also separated the results obtained from the "DRIMSeq" and "DEXSeq" models for DTU genes (labeled as DRIM and DEX in Figure-2a, respectively). While the number of DGE genes with no difference in transcript isoform usage obtained from both species were comparable ("only DGE" in Figure  -2a ), the DTU results were remarkably different in both gene numbers and proportions (all labels other than "only DGE" in Figure  -2a ). To further investigate the expression patterns of chicken and human DTU genes across normal and fatty liver samples, we used circular heatmaps (Figure  -2b ).
The primary concern was the limited availability of chicken datasets, which could The left-side bar plots demonstrated the mean expression differences between fatty and normal liver samples in both chicken (C) and human (H). The significant differentially expressed genes were plotted in pink or green bar colors, as described in the legend. Based on their comparable cross-species expression patterns, we selected seven candidate DTU genes: ALG5, BRD7, DIABLO, RSU1, SFXN5, STIMATE, TJP3, and VDAC2 (highlighted in red rectangles).
Table-3: Summary of the significant transcript isoforms with same trend of their genes in fatty liver samples. contribute to a restricted number of chicken DTU genes (Figure  -2 ). In addition, the severity of fatty liver samples between chickens and humans could not be compared as no practical guidelines are available for such cross-species comparison. These factors might reduce the sensitivity of the cross-species analyses by reducing the number of candidate genes obtained. In addition to these limitations, the scarcity of chicken DTU genes acquired from this study also limited the biological reasoning. The Ensembl database obtained from the "BioMart" package  [39]  revealed that the number of identified genes in chickens was only about half of that in humans. Furthermore, the approximate ratio of transcript isoform numbers per gene in chickens was almost half that in humans.
Therefore, we should anticipate a lower number of DTU genes in chickens when compared to humans. Due to this limitation, our study included all the Figure-4: Expression levels of transcript isoforms for candidate differential transcript usage genes. The blue and pink bars depicted the expression levels of normal and fatty liver samples, respectively. We identified all transcript isoforms using Ensembl ID, with "ENSGALT" and "ENST" as the identifiers for chicken and human transcript ID, respectively. The transcript isoforms that exhibited statistical differences are in Table-3 (Kruskal-Wallis-test, p = 0. 05). chicken DTU genes obtained from "DRIMSeq" and "DEXSeq" models for subsequent cross-species analysis. The previous studies have indirectly suggested the existence of cross-species DTU genes between mice and humans  [6, 15].
Treatments Mice were randomized before administration of liposomal irinotecan (MM-398 4. 3 mg/ml, Ipsen) or non-liposomal irinotecan (CAMPTO 20 mg/ml, Oncodesign) using VIVO Manager Ò software (Biosystemes) when their mean tumor volume was 100-200 mm 3. Treatments were injected into the caudal vein following dilution in either phosphate-buffered saline (without Mg 2? and Ca 2? ) for liposomal irinotecan or 0. 9% NaCl for non-liposomal irinotecan. Control mice received vehicle (0. 9% NaCl) injections on the same dosing schedule as nonliposomal irinotecan. Liposomal irinotecan was administered as one injection per week (Q7D), and non-liposomal irinotecan was administered either as five injections over 5 days consecutively per week (Q1D5) (optimized exposure characteristics) or Q7D. This corresponds to one cycle of treatment for both drugs.
Therapeutic Index The therapeutic index is a quantitative measurement of the relative safety of a drug. It was calculated per treatment, as the ratio of the highest exposure dose that produces the desired antitumor response for a maximum tolerated toxicity (i. e. , the MTD) to the lowest dose that produces an effective antitumor response. To evaluate therapeutic index, mice were assessed for BWL, TV, DNA damage within tumor cells, tumor regression, plasma and tumor CPT-11 and SN-38 levels (tumor CPT-11 data not shown), and toxicity within the jejunum and bone marrow.
Prior Compliance with Ethics Guidelines. All experimental procedures were approved by the Ethics Committee of Ipsen Innovation (C2EA; registration number 32) and were performed in full compliance with the ARRIVE guidelines, EU Directive 2010/63/EU for animal experiments, and the 2013 French Regulatory Decree. All efforts were made to minimize animal suffering and to reduce the number of animals used. Data Availability. The datasets generated during and/or analyzed during the current study are available from the study sponsor (Ipsen) on reasonable request. Study data that underlie the results reported in this publication may be made available to researchers who provide a research proposal. Additional relevant information such as the study protocol and study report may also be made available.
General characterization: In-house powder X ray diffraction (PXRD) measurements were carried out using a Panalytical X'pert Pro diffractometer (Cu Kα1,2, λ1 = 1. 540598 Å, λ2 = 1. 544426 Å) using a Bragg-Brentano geometry. Variable-temperature PXRD measurements were carried out using the aforementioned in-house diffractometer, equipped with an Anton Paar XRK 900 chamber (Supplementary Fig. 22 ). Thermogravimetric analysis (TGA) data (Supplementary Fig. 7 ) of as-synthesized SU-102 were collected using a Perkin Elmer TGA 7. Secondary electron SEM images were acquired using a JEOL 7000F microscope operating at 2 kV (unless indicated otherwise in SI). Elemental analysis was carried out by Medac Ltd, Surrey, United Kingdom. Elemental analysis of as-synthesized SU-102: expected (%) for DMA2[Zr(C14H3O8)2]⋅6H2O: C 42.
Different organic molecules were analysed by HPLC: the amount of adsorbed or degraded SMT, At and DCF, as well as the released ellagic acid linker were determined using a reversed phase HPLC Jasco LC-4000 series system, equipped with a photodiode array (PDA) detector MD-4015 and a multisampler AS-4150 controlled by ChromNav software (Jasco Inc, Japan). A Purple ODS reverse-phase column (5 µm, 4. 6 x 150 mm, Análisis Vínicos, Spain) was employed. For the quantification of all chemical species, isocratic conditions were used. The flow rate was 1 mL min -1 , and the column temperature was fixed at 298 K. In all cases, the injection volume was 30 µL. The mobile phase was based on a mixture of 70:20:10 water:acetronitrile:formic acid (10% v/v) solution for ellagic acid analysis, with a retention time (rt) and an absorption maximum of 5.
Acknowledgements The authors thank  Prof. Guillaume Maurin  for insightful discussions. E. S. G. and A. K. I. acknowledge support from the  Swedish Foundation for Strategic Research (SSF). A. K. I. also acknowledges support from the  Knut and Alice Wallenberg Foundation  ( KAW 2016. 0072 ). T. W. acknowledges support from the  Swedish Research Council  (VR,  2019-05465 ). A. C. , S. R. , Y. P. and P. H. acknowledge the  MOFSEIDON  project ( PID2019-104228RB-100 ,  MCI/AEI/FEDER , UE). S. R. thanks the financial support from the  Madrid Community  (Recruitment of Young doctors 2017,  Modality 2 ,  2017-T2/IND-5149 ) and  Spanish Juan de la Cierva Incorporación Fellowship  (grant agreement no. IJC2019-038894-I ). O. C and M. Å. acknowledges support from the  Swedish Research Council  ( VR 2020-04029 ) and the  Swedish Research Council for Sustainable Development  ( FORMAS 2018-00651 ).
Data availability CCDC 2167934 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge via www. ccdc. cam. ac. uk/data_request/cif, or by emailing data_request@ccdc. cam. ac. uk, or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, U. K. ; fax: + 44 1223 336033.
Docking Studies of Metabolites on the Active Sites of PTP1B (1G7F) and α-Glucosidase (3A4A) In this study, we carried out a molecular docking study on the isolates through a molecular operating environment (MOE) program to verify that they have effects on reducing blood sugar in diabetic patients. We targeted two enzymes that are directly linked to diabetes: protein tyrosine phosphatase 1B (PTP1B) (PDB ID: 1G7F)  [12, 38, 39]  and α-glucosidase (PDB ID: 3A4A)  [40] [41] [42]. This method was validated with redocking of 2-{4-[(2S)-2-[({[(1S)-1-carboxy-2-phenylethyl]amino}carbonyl)amino]-3-oxo-3-(pentylamino)propyl]phenoxy}malonic acid (INZ), the cocrystalized inhibitor for PTP1B (PDB: 1G7F), and alpha-D-glucopyranose (GLC), the cocrystalized inhibitor for α-glucosidase (PDB: 3A4A). In both cases, the RMSD values between the cocrystallized pose and the docked pose of the same ligand were determined.
Docking Studies of Metabolites on the Active Site of Lipase (PDB: 1LPB) Here, we carried out a molecular docking study on the isolates to verify that they have an effect on reducing blood sugar in diabetic patients indirectly via acting on the lipase enzyme (PDB ID: 1LPB)  [43] [44] [45] [46]. This method was validated via redocking of octyl beta-D-glucopyranoside (BOG), the cocrystalized inhibitor (PDB: 1LPB). In addition, the RMSD value was determined. As indicated in Table  7 , all the isolates (papaverine and its metabolites, 1-7) demonstrated efficient binding, with BE (binding energy) values that had negative Kcal. mol -1. Among of them, metabolite 7 also had the best docking score, Finally, when compared to the reference compound "acarbose", metabolite 7 was found to display a predicted promising effect on diabetes through its action with the glucosidase target more than the phosphatase target.
Docking Studies of Metabolites on the Active Site of Lipase (PDB: 1LPB) Here, we carried out a molecular docking study on the isolates to verify that they have an effect on reducing blood sugar in diabetic patients indirectly via acting on the lipase enzyme (PDB ID: 1LPB)  [43] [44] [45] [46]. This method was validated via redocking of octyl beta-D-glucopyranoside (BOG), the cocrystalized inhibitor (PDB: 1LPB). In addition, the RMSD value was determined. As indicated in Table  7 , all the isolates (papaverine and its metabolites, 1-7) demonstrated efficient binding, with BE (binding energy) values that had negative kcal. mol -1. Among of them, metabolite 7 also had the best docking score, with a BE value of -6. 05 kcal. mol -  , as shown in Table  7. As described in Table  7  and Figure  5 , the docking pose of metabolite 7 into the lipase active site showed electrostatic interaction between N-oxide and His30, along with several hydrophobic interactions between the methoxy groups and aromatic rings and six amino acids (Pro4, Leu16, Leu18, His30, Leu34 and Leu36).
MD Simulation A 100 ns MD simulation was used to confirm the stability of docked metabolite 7 in the active sites of the three targets of interest. To validate this goal, the RMSD for the backbone was calculated and compared to the apoprotein over the entire simulation time. As shown in Figure  6 , there was no obvious fluctuation in the RMSD of the docked metabolite, indicating stability of the formed complexes throughout the entire MD simulation in all studied targets. A detailed analysis of MD simulation of the lipase enzyme showed that the lipase enzyme alone demonstrated great stability with RMSD deviation After all, metabolite 7 was found to possess a predicted favorable effect on the lipase target more than did the reference compound "orlistat". The docking studies of the remaining metabolites on the active site of lipase (PDB: 1LPB) are illustrated in the supplementary files (SI: S60, S61, S62, S63, S64, S65, S66).