dataesr/scientific-paragraphs-categorization huggingface dataset:
If we analyze the "U-Net" performances on both DSHC and ALL datasets it is clear how such a model is not able to exploit successfully the information carried by the topographical layers. On the other hand, the ability of the "ADSMS U-Net" to get higher accuracies can be attributed to the fact that the model uses a soft attention mechanism that enables retention of the low-level information during "convolution", and at the same time, "multi-scale" inputs allow the model to gather that class information that is more readily available at many scales. We feel that this framework can be used to its full capacity, particularly in circumstances when the data resolution is quite high as in such cases. DL-based automated approaches for sinkhole mapping are still scarce in the literature  (Hoai et al.
Acknowledgments This study was supported by  "Iran National Science Foundation (INSF) " (Grant No. 99011991 ). We wholeheartedly thanked the  International Association of Geomorphology (IAG)  for bringing us as young geomorphologists together to do valuable scienti c research and projects. We are grateful to  Lorenzo Nava  and his scienti c team for their constructive remarks on methods that signi cantly improved the quality of the manuscript.
Objective The objective of this study was to assess the impact of treatment response to the ileal bile acid transporter inhibitor maralixibat on health-related quality of life (HRQoL) in children with Alagille syndrome. Study design This analysis used data from the ICONIC trial, a phase 2 study with a 4-week double-blind, placebocontrolled, randomized drug withdrawal period in children with Alagille syndrome with moderate-to-severe pruritus. Clinically meaningful treatment response to maralixibat was defined a priori as a ³1-point reduction in the Itch-Reported Outcome (Observer) score, from baseline to week 48. HRQoL was assessed using the Pediatric Quality of Life Inventory Generic Core, Family Impact, and Multidimensional Fatigue scale scores, which were collected via the caregiver. The minimal clinically important difference for HRQoL ranged from 4 to 5 points, depending on the scale.
, Foster City, CA;  3  Analysis Group, Inc. , Boston, MA;  4  H epatologie et Transplantation H epatique P ediatriques, Centre de r ef erence national de l'atr esie des voies biliaires et des cholestases g en etiques, FSMR FILFOIE, Hôpital Bicêtre, AP-HP, Universit e Paris-Saclay, Kremlin-Bicêtre; INSERM UMR-1193, Hepatinov, Universit e Paris-Saclay, Orsay, France; and  5  European Reference Network on Hepatological Diseases (ERN RARE-LIVER), Hamburg, Germany Maralixibat is owned by Mirum Pharmaceuticals, Inc. , Foster City, CA, USA. The ICONIC study and this subanalysis were funded by Mirum Pharmaceuticals, Inc. The sponsor, Mirum Pharmaceuticals, Inc. , was involved in the study design, data collection, analysis, and interpretation, and was jointly responsible for the decision to submit the manuscript for publication.
Methods This analysis used data from the ICONIC trial (LUM001-304; NCT02160782), an international, multicenter, long-term, phase 2b, placebo-controlled, randomized drug-withdrawal study with an open-label extension, in children with Alagille syndrome experiencing moderate-to-severe pruritus. 9 Patients were included from 9 locations in Europe and Australia. This prespecified subanalysis of ICONIC compares HRQoL data at baseline and at week 48, in children between the ages of 1 and 18 years with a clinical diagnosis of Alagille syndrome. Key inclusion criteria of the ICONIC trial were evidence of cholestasis (total serum bile acid >3x the upper limit of normal for age [defined as 8 mmol/L], and intractable pruritus [defined as an Itch-Reported Outcome (Observer) [ItchRO{Obs}] score >2 for 2 consecutive weeks during the screening period, prior to dosing]).
jpeds. com). Absolute changes in PedsQL Generic Core Scale, Family Impact, and Multidimensional Fatigue Total Scale scores, according to change in ItchRO(Obs) score from baseline for each patient, are shown in Figure  3. A 5-point change in the PedsQL Generic Core Total Scale score was observed in 13 of 20 (65%) responders and 3 of 7 (43%) nonresponders; P =. 39; a 10-point change in the PedsQL Generic Core Total Scale score was observed in 10 of 20 (50%) responders and 2 of 7 (29%) nonresponders; P =. 41. A similar pattern was reported for the Family Impact Total Scale score and Multidimensional Fatigue Scale score, with a higher proportion of responders achieving 5-and 10-point changes compared with nonresponders (data not shown). Multivariable linear regression analysis demonstrated that the ItchRO(Obs) treatment response was associated with a clinically meaningful improvement in all 3 HRQoL measures (defined as a change greater than the MCID of 4-5 points)  13  from baseline to week 48.
A key strength of this analysis is that reductions in pruritus were measured over 48 weeks. Thus, the associated increases in HRQoL are durable over a long time frame. A previous cross-sectional study found no correlation between pruritus severity and HRQoL in patients with Alagille syndrome when measured at baseline, prior to treatment with maralixibat  19  ; however, that analysis explored only baseline scores where all patients had severe pruritus with high It-chRO(Obs) scores, likely impeding demonstration of the correlation between pruritus severity and HRQoL, particularly as only one pretreatment data point was assessed. These findings are further reinforced by multivariable linear regression analysis, which showed the most striking association between ItchRO(Obs) treatment response and HRQoL improvement at week 48 in the Family Impact Scale.
In conclusion, patients with Alagille syndrome who experienced a pruritus response while receiving maralixibat treatment, on average, achieved greater improvements in HRQoL from baseline to week 48, vs pruritus nonresponders. Using multivariable linear regression analysis, changes in the Family Impact Total Scale score were statistically significant and clinically meaningful. Individual sleep-related items also showed a statistically significant improvement, possibly because the reductions in pruritus resulted in fewer disruptions to, and a better quality of sleep. These data demonstrate that the significant improvements in pruritus seen with maralixibat at week 48 of the ICONIC study are clinically meaningful and are associated with improvements in patients' HRQoL. As pruritus is a leading indication for liver transplant among patients with Alagille syndrome, ongoing evaluation of outcomes in this pediatric population should include assessment of the future impact of treatment on the burden of liver transplantation.
Even though different sampling positions of H&E-stained pathology images may lead to different fat accumulation ratios in the tissues, the overall differences in fat content observed in histology images is consistent with the observations in the in vivo MSOT and SoS images. TROPUS was then used to image NAFLD and control mice in vivo. The good anatomical contrast provided by RUCT facilitated identification of the liver cross-sections (Figures  3A-B ). The SoS images further provided sufficient contrast and resolution to differentiate the liver from other surrounding tissues (Figures  3C-D ). Segmentation of the liver was then done by an experienced biologist considering both the RUCT and SoS images. The data was blinded to avoid bias in the segmentation procedure and served to define binary masks to quantify differences in SoS between NAFLD and control mice.
Speed of Sound (SoS) Imaging Data acquisition for SoS mapping was based on the same STA-based method described above. Quantitative images of the SoS distribution in m/s were reconstructed from the US waves that traversed the imaged object. Specifically, signals collected from 171 elements on the opposite side of each transmitting element were considered. A gradient-descent FWI algorithm was used to iteratively vary the estimation of the SoS values in the defined image grid to minimize the mean-squared error between the estimated waves and the actual measurements (Figure  1E )  [32]. In this work, 40 iterations were used in all cases. FWI methods are able to improve resolution and contrast in the transmission US imaging mode when compared to the less precise bent-ray-based approach previously reported for TROPUS reconstructions  [25].
Ex Vivo Liver Imaging Ex vivo imaging was performed to validate that the quantitative readings provided by TROPUS enable the differentiation between the diseased and normal liver. For this, ex vivo liver samples from 3 NAFLD and 3 control mice were imaged. The samples were embedded in a 20 mm cylindrical agarose phantom (1. 3% w/v agarose powder). The same data acquisition protocol was executed for the in vivo animal imaging experiments. Cross-sectional images from two different slices were acquired by vertically shifting the electronically controlled stage with a 1 mm step size. Fat accumulation in liver tissues was further validated with tissue histological sections. Specifically, a Leica ASP300S tissue processor (Leica, Heerbrug, Switzerland) was used for paraffin embedding. Then, a microtome (Model: Microm HM 335 E, Thermo Scientific, Walldorf, Germany) was used to generate 3 μm thick tissue samples.
Acknowledgments We thank  Johanna Erdmenger ,  Marius Gerbershagen ,  Antti Kupiainen ,  Diego Liska ,  R. Loganayagam  and  Anna-Lena Weigel  for useful discussions during the completion of this work. JdB is supported by the  European Research Council  under the  European Unions Seventh Framework Programme  ( FP7/2007-2013 ),  ERC  Grant agreement  ADG 834878. VG acknowledges the postdoctoral program at  ICTS  for funding support through the  Department of Atomic Energy, Government of India , under project no. RTI4001. JK is supported by the  Osk. Huttunen foundation  and by the  Magnus Ehrnrooth foundation. EKV 's research has been conducted within the framework of InstituteQ -the  Finnish Quantum Institute.
(C) Schematic representation of cholesterol consensus motif and several corticosteroid receptors binding site. CHOL + CCM: cholesterol in the cholesterol consensus motif of human β2-Adrenergic receptor (pdb: 3D4S). CRT + MR: corticosterone in interaction with human mineralocorticoid receptor (pdb: 2A3I). The residues involved in hydrogen bonds with the corticosteroid are represented. DEX + GR: dexamethasone in interaction with human glucocorticoid receptor (pdb: 1M2Z). The residues involved in hydrogen bonds with the corticosteroid are represented. DEX + AQP: dexamethasone in interaction with human AQP2 (pdb: 4NEF). Residues involved in hydrogen bonds with the corticosteroid and with a similar nature and position as in MR and GR are colored in purple. Residues mimicking cholesterol consensus motif are colored in yellow.
Three experimental setups were carried out. Experimental setup 1 corresponds to Figures  3 4 5 6 : The tetrameric assembly of AQP2 (pdb: 4nef) was inserted into the POPC bilayer, solvated with TIP3 water molecules and 150 mM of KCl for "control" conditions. For the "COR" condition, additionally, 4 cortisol molecules were manually placed inside the extra-cellular vestibules of AQP2 following the same procedure as in  [18] , hence, leading to one cortisol per monomer. The two systems were then simulated for 200 nanoseconds. Experimental setup 2 corresponds to Figure  7. The same starting tetrameric assembly as for the setup 1 condition "control" (i. e. , without the cortisol fixed inside the extra-cellular vestibules) is used. The tetramer is then inserted into the POPC bilayer and solvated with TIP3 water molecules.
Structural Alignment of Experimentally Solved Structures and Delineation of a Putative Conserved Binding Site to Corticoids The superposition was achieved for each structure on the AQP2 structure with the "iterative magic fit" routine of the Swiss PdbViewer-4. 1 software  [89]  by selecting "all atoms". The ligand (COR) was previously placed in a bound state from the AQP2-COR dynamics by superposition on the AQP2 structure to determine candidate residues to be a corticoid binding site. A 50 ns trajectory segment (50 to 100 ns from experimental setup 2) displaying a docked subunit (chain B) with COR was used to grasp the effective residues in retaining the cortisol in a bound state.
Molecular Composition from DNP SENS Over the last decade, Dynamic Nuclear Polarization Surface-Enhanced NMR spectroscopy (DNP SENS) has emerged as a powerful approach for an in-depth structural characterization of functionalized surfaces and materials. [34, 35]  Following the first proofs of concepts reported on mesoporous silicas,  [36, 37]  recent applications have successfully covered a much wider range of materials. [38] [39] [40] [41] [42]  In particular, we and others have demonstrated the relevance of this methodology for the investigation of the backbone structure of POPs. [23, [43] [44] [45]  One-and two-dimensional DNP SENS spectra were recorded to address the molecular composition of Cp*Rh@IV(NO 2 ) and Cp*Rh@V(NH 2 ). NMR data were also recorded on the non-functionalized Cp*Rh@BpyMP-1.
The agreement of the  15  N chemical shift between monomers and polymers as well as the absence of any further peak excludes any chemical modification or metal coordination of the NO 2 /NH 2 groups (Figure  2b  and Figure  S6 ). The molecular composition of the materials was further confirmed using 1 H-  13  C dipolar-based heteronuclear correlation (HETCOR) experiments. These data were acquired at a higher magnetic field to improve resolution in the indirect (proton) dimension. The 1 H-  13  C HETCOR spectrum of Cp*Rh@V(NH 2 ) (Figure  2c ) shows strong correlations between aromatic protons centred at 7 ppm and the carbon resonances at 151. 6, 143. 6, 135. 5, 131. 3, and 122. 0 ppm. In addition, a cross-peak is observed between the methyl carbon and proton resonances centred at 8. 5 ppm and 1 ppm, respectively.
Acknowledgements Financial support of this work by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) project -ID WI 4721/1-1 & WI 4721/3-1 (F. M. W) and within  SPP 2248  and  FOR 2433  (E. B. ), by the  CNRS  ( Momentum 2018 excellence  grant, F. M. W. ), by the  Royal Society  ( RG160498 , C. W. A. ), by the  Commonwealth Scientific and Industrial Research Council  ( C2017/3108 , C. W. A. ), by the  IR INFRA -NALYTICS FR2054  (F. M. W. , A. L. ) and by the  European Union 's  Horizon 2020 research and innovation program  (no. 101008500 , " PANACEA ") are gratefully acknowledged. The calculations were performed using the HPC resources from GENCI (CINES/TGCC/IDRIS) through the grant  A013097343  (C. M. D. ). The authors are grateful to  Dr. E. Jeanneau  for single crystal measurements, to  C.
Acknowledgements We would like to thank  Prof. Carl Johan Wallentin ,  Prof. Morten Grötli , and  Prof. Henrik Sundén  (all  University of Gothenburg ) for being the academic supervisor/examiner for J. K. , J. F. , and S. V. and for stimulating discussions throughout the project. We are grateful to  Prof. Sabine Choppin  ( University of Strasbourg ) for being the academic tutor during L. B. 's internship at  AstraZeneca. We would also like to thank the analytical and separation science teams at  AstraZeneca Gothenburg  for purification of final compounds and analytical support and colleagues at  AstraZeneca  and  Pharmaron  for measuring the key in vitro properties of compounds 20 and  32-36. We are also grateful to  Dr. Yantao Chen  ( AstraZeneca Gothenburg ) for discussions at the beginning of the project.
Toward the development of an ePRO-based model of care Development of the IePRO model of care took place between November 2020 and November 2021. A team of four physicians and five nurses of the participating institutions' oncology departments and one patient-representative collaborated in the creation of its core components and their integration in the existing workflows of each hospital. All members had previous experience in collecting and interpreting PRO data in clinical oncology trials. Two nurses have published research on PROM aimed toward patients treated with ICI  [26]. The patient representative was identified by screening Swiss and French patient advocacy groups related to oncology. A brief in-person interview allowed to assess their knowledge of ICI and their side effects, expertise in using PROs, and experience in collaborating in clinical trials.
The impact on the burden of clinical teams would also need to be considered, as it may result in more frequent prompts to intervene than a system where the decision to initiate contact lies with the provider. Other eHealth interventions have used automated written feedback  [35] , which could be integrated in this model as well. Ongoing data collection from patient interviews may highlight the strengths and limitations of the application in its current version. Patient feedback will be addressed in future publications. Data collection concerning the acceptability of the model of care from the provider's perspective will take place after the trial and will be analyzed and disseminated in a later stage of the project. It is unclear how patients will perceive the novel role of the triage nurse and how it may interfere in their relationship with other providers like the CNS.
IV. RESULTS The network was initially simulated in Cadence using models that followed DW-MTJ behavior  [36] -  [38]. Larger network sizes took a prohibitively long time to simulate, so a specialized simulation program was created which was able to perform the calculations significantly faster. The values calculated at each step of this program were verified against those generated by Cadence to confirm simulation correctness. Simulations were then run to solve the associative memory problem over multiple network sizes and the max-cut problem for a 60 neuron network.
ACKNOWLEDGMENT The authors thank  R. Iyer  for editing,  E. Laws ,  J. Mc-Connell ,  N. Nazir ,  L. Philoon , and  C. Simmons  for technical support, and  H. -J. Drouhin  for arranging the research exchange. This research is sponsored in part by the  National Science Foundation  under  CCF  award  1910800  and the  Texas Analog Center of Excellence undergraduate internship program.
Results We measured the sparsity of artificial neuron activations triggered by images for which an empirical score of beauty or judgment related to beauty has been obtained by psychological inlab or online experiments. These images belong to four publicly available datasets and represent photographic portraits (CFD  [40]  and SCUT-FBP5500  [41]  datasets) or artistic paintings (abstract painting: MART  [42] ; paintings from various styles and epochs: JEN  [43]  datasets; see Materials and methods). We analyzed these four datasets separately. Each image was first processed by the standard DCNN VGG-16  [44]  pre-trained on the ImageNet dataset  [45]. VGG-16 includes 13 convolutions and 2 fully connected layers. ImageNet is a large dataset of 14 million images depicting about 20,000 categories including people, plants, animals, and humanmade objects.
We found a significant R 2 score for all layers and all datasets (p<10 -6 for all models). However, the variance explained by sparsity varied strongly across both datasets (mean R 2 across layers from 2,7% for JEN to 4,8% for CFD, Table  1 ), but also across layers for a given dataset (e. g. , for SCUT-FBP5500 from R 2 = 0,3% for layer conv4_1 to R 2 = 17,5% for layer conv1_2;  Fig 1). For each dataset, we observed at least one layer with a moderate R 2 score (maximum: 11%, 18%, 8% and 9% for CFD, SCUT-FBP5500, JEN, MART, respectively). For both face datasets (CFD and SCUT-FBP5500) Table  1. Variance (R 2 score) in beauty score explained by the sparsity and principal components of activations in VGG16. R 2 score are calculated between the ground truth and the predicted values of beauty scores by a multivariate Ridge linear regression model including sparsity of one layer (first column, mean of 15 R 2 scores, one per layer), sparsity of all layers (second column), principal component (PC) scores (explaining 80% of variance in activations) of one layer (third column: mean of 15 R 2 scores, one per layer), the scores of the first three PC of all layers (fourth column), or the scores of the first three PC and sparsity of all layers (fifth column) as predictors.
R 2 score was calculated using a 10-fold cross validation procedure. The four datasets (rows) were analyzed separately. and for the abstract paintings dataset (MART), the R 2 score was the highest for the second convolution layer (conv1_2). For the figurative paintings dataset (JEN), the R 2 score was the highest for the two last convolution layers. This means that for faces and abstract paintings (CFD, SCUT-FBP5500, MART), people's perception of beauty is most influenced by the efficiency of processing texture or local color contrasts, while for figurative paintings, the efficiency of processing objects as a whole, and the spatial arrangement of their parts, is more important. We also examined the coefficients estimated by this multivariate model (blue bars in  Fig 1). Some of the layers with a significant effect of sparsity have positive coefficients while others have negative ones.
Importantly, even within a broad portion of the network (early vs. mid vs. deep layers), the sign can vary from positive to negative, especially for face datasets (CFD, SCUT-FBP5500). This puzzling result indicates that from one layer to the next, high scores of beauty could be explained by either very efficient or inefficient neural codes. We then investigated the link between beauty and the efficiency of information processing across the whole visual system. Indeed, previous studies in psychology have provided evidence that the ease of processing information at each stage of the visual system (e. g. , as measured by detection time in the early stages, and as recognition performance in the later stages) triggers micro-experiences of fluency associated to each of these stages, and that these micro-experiences aggregate into one global sensation of fluency  [5, 47].
To model the aggregation of processing efficiency at every layer, we fitted a single multivariate (one measure of sparsity per layer) linear regression model to beauty scores. Because the model includes fifteen non-independent predictors (see S1 Fig) , we constrained the optimization by applying a L2-norm penalty to coefficient estimates (i. e. , Ridge regression model). We calculated the adjusted R 2 score using a 10-fold cross validation procedure. We found a significant (p<10 -6 ) R 2 score for all four datasets. Moreover, sparsity explained approximately 25% of the variance in beauty scores for the two photographic portrait databases (CFD, SCUT-FBP5500) and for the abstract painting database (MART; Table  1 , column "R 2 (one model for all layers, sparsity only)"). With figurative paintings, sparsity explained a substantial but lower fraction of variance in beauty scores (R 2 = 14% for JEN; Table  1 , column "R 2 (one model for all layers, sparsity only)").