dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion When analysing all extracted concepts, we found out that, in addition to retrieving the greatest number of concepts, Met-aMap extracted multiple useful data for the cancer domain, e. g. , hormonal receptors values or tumor size when mentioned in the text. cTAKES did not exhibit as good results as MetaMap on our selection but this might be explained by the fact that we used the default clinical pipeline. cTAKES allows to create and customize pipelines according to the context. With adaptations, it could therefore deliver better results, since it has already demonstrated good performance for a breast cancer extraction system  [15]. We also observed from the intersections between annotators of the same language that they have a shared conceptual coverage, larger than between annotators of different languages.
For the French language, SIFR extracted most of the concepts extracted by ECMT, but also retrieved more concepts. The reason may originate from the large number of medical terminologies it uses. The fact that the version of ECMT we used did not provide the UMLS CUIs was constraining for comparing annotators, but using SIFR to extract CUIs was a practical solution to overcome this difficulty. Finally, concepts that were extracted by all systems for all patients represent common concepts that can be systematically found in clinical summaries of breast cancer patients. We noticed that the most frequent concepts extracted by the four annotators were represented by therapeutic procedures (chemotherapies, therapeutic radiology procedures, mastectomy, etc. ), pathologic functions (axillary lymphadenopathy, inflammation, etc.
), diagnostic and imaging procedures (mammography, MRI, biopsy, etc. ), body parts and associated diseases, and that the less frequent concepts were related to tissues, cell functions, or lesion information. This latter point suggests that such information, which is indeed of critical importance for cancer care, might be expressed in varied ways that not all annotators detect. It is also important to mention that each annotator can extract different specific categories of information about the patient: x MetaMap could produce more information like HER status, hormonal receptors and numeric information like tumor size. x ECMT can annotate phrases that are specific to the French language since it uses French terminologies for the indexation. x SIFR offers access to multiple terminologies and ontologies, including those that are specific to the oncological field (e.
Study Design and Study Population The design of the study is a prospective cohort study. The study population was made up of HCV RNA-positive PWID who were successfully treated with combinations of sofosbuvir, daclatasvir, and/or ribavirin for 12 weeks (negative sustained viral response at 12 weeks, known as the SVR12 HCV viral load) from the DRIVE-C study (NCT03537196)  [10]. An HCV-seronegative PWID control population was obtained from the initial HCV screening phase of the previously mentioned study. PWID were recruited during a respondent-driven sampling survey (RDSS) conducted by community workers in Hai Phong, Vietnam, in 2018. This RDSS enrolled a total of 1444 PWID  [10].
At the time of his passing, it was Dibble's intention to produce a paper pulling together and summarizing the results to date, and here we do our best to fulfill his wish in his absence. In addition to integrating his papers, we also discuss some of the limitations of the experimental design and highlight areas and approaches that could be constructively explored in subsequent experiments. With this summary, we also include a complete database coming from these experiments (see the Supplementary Information for field descriptions).
As  Magnani et al. , (2014: 41)  noted, given that in bending flakes the fractures begin some distance away from the point of hammer impact, there would be a certain amount of additional noise, or error, in the relationship between platform depth and flake size. Related to this, the fourth factor that complicates the EPA-PD model is the use of soft hammers. Studies have noted that the EPA-PD model performs better for hard-hammer flakes than soft-hammer flakes  (Shott et al. , 2000). Echoing these earlier observations, data from the  Magnani et al. (2014)  experiment show that the explanatory power of the EPA-PD model is lower for flakes made with the synthetic bone hammer compared to flakes made with the steel hammer. Both soft hammer percussion and on-edge strike tend to produce a higher frequency of non-conchoidally fractured flakes (i.
Improving the EPA-PD Model In a recent study,  McPherron et al. (2020)  examined the Dibble experimental dataset and observed that the fracture propagating from the point of percussion out towards the exterior core surface tends to occur at a more or less constant angle. This angle, which they termed the platform surface interior angle (or PSIA), is likely related to the Hertzian cone angle (which is a constant that varies with raw material type). The authors showed that, instead of the actual platform depth measured from the platform surface, estimating platform depth based on the PSIA and platform width can lead to much more accurate predictions of flake mass, particularly among flakes with beveled platforms. This finding indicates that the actual platform depth on flakes is an imperfect proxy for how far into the core a flake is struck, and it may be possible to improve the EPA-PD model and its performance by incorporating the PSIA  (McPherron et al.
, 2020). Another important step in refining the EPA-PD model is to integrate proxies for the angle of blow into the model. As mentioned already, the Dibble experiments have demonstrated the importance of the angle of blow in determining flake size. Where the angle of blow is known in the Dibble experimental dataset, adding the variable into the EPA-PD regression model (which we term the EPA-PD-AOB model) increases the model's adjusted R-square from 0. 80 to 0. 91. The improvement in the model performance can be seen in Fig. 12 , where the predicted flake mass from the updated EPA-PD-AOB model plots more tightly against the actual flake mass. Thus, efforts to explain flake variation need to consider the effect of the angle of blow. In a recent study,  Li et al. (2022)  introduced a proxy for the angle of blow, what they termed the bulb angle, that can be directly measured on the flake's bulb of percussion.
, 2019; Dibble, 1997; Dogandžić et al. , 2020; Lin et al. , 2013; Režek et al. , 2018) , the effect of many other variables that were examined in the Dibble experiments is left unexplained. For instance, variables such as core surface morphology, platform width, hammer material, the angle of blow, hammer strike location, and raw material type are all found to influence the flake size and/or shape, but they are not included in the EPA-PD model because (1) the effect of these variables is overshadowed by EPA-PD and (2) it is difficult to quantify the effect of some variables in the form of linear regression. Multivariate approaches such as general linear modeling (as conducted in  Dogandžić et al. , 2015)  and principal component analysis (as conducted in  Rezek et al. , 2011)  should be applied more to the Dibble experimental dataset to better understand the influence of the various experimental variables and to create a more comprehensive model of flake formation.
comments. We dedicate this paper to  Harold. Funding Open Access  funding enabled and organized by Projekt DEAL. The experiments that generate the dataset were funded by the  National Science Foundation  ( BCS-0649673  and  BCS-1153192 ),  University of Pennsylvania Museum of Archaeology and Anthropology and School and Arts and Sciences , the  European Union 's  Framework Programme for Research and Innovation Horizon 2020  under the  Marie Skłodowska-Curie  grant agreement No. 751125 , and the  Institutional Strategy of the University of Tübingen (Deutsche Forschungsgemeinschaft ,  ZUK 63 ).
Each assembly was hot-pressed for 13-15 hr at 600°C and experimental confining pressure before deformation. After the deformation experiment, the sample was quenched from 600°C to 200°C at a rate of 25°C/s to prevent back-reactions and preserve any transformed phases. In this work, the sample-average mechanical variables (classically reported in experimental rock-deformation literature) are indicated by "sa" superscripts (Figure  1b ). The measurements of the confining pressure in both machines were corrected as shown in Text S2 in Supporting Information S1. The mechanical experimental data (stress and displacement) were corrected using the RIG AB program  (Heilbronner et al. , 2020; Richter et al. , 2016). This includes corrections for: decreasing piston-overlap area, friction during lead run-in, and deformation and machine compliance.
Electron Backscatter Detection A field emission gun scanning electron microscope (JEOL IT800) at the Heidelberg University, equipped with an Oxford Instruments Symmetry2 EBSD was used for the EBSD analysis. The machine was operated in high vacuum mode, with 18 kV acceleration voltage, ca. 50 nA beam current, an aperture of 30 μm and sample tilt of 70°. The patterns were binned to 156 × 128 px and dynamic background correction was applied. The orientation data were pre-processed in Aztec crystal (auto-clean), by removing wild spikes and applying a Kuwahara filter and merging twinned grains (plane orientation). Grain-size (area-equivalent diameter) maps and distributions were calculated using the MTEX toolbox  (Hielscher & Schaeben, 2008) , with a grain-boundary angle of >7°. For discrete values, we report the modal value of a Weibull fit to the grain-size distribution.
Numerical Modeling of Sample Deformation The actual variations of mechanical variables within the sample cannot be resolved in a deformation apparatus and therefore, sample-average values are used. To compute these local variations in the sample throughout the experiment, a numerical model is performed. We used a finite difference numerical model to calculate the local mechanical variables (e. g. , stress and strain rate) within the experimental assembly  (Cionoiu et al. , 2022). The model has a numerical resolution of 551 × 851 grid points. We solve Stokes' equations for slow, viscous, incompressible flow. The code uses the MATLAB ® direct solver, employs a staggered grid for discretization and the marker in cell method for model advection  (Gerya & Yuen, 2003). For characteristic timescales that are larger than the effective viscoelastic deformation timescale, the incompressible approximation yields accurate stress/pressure values (fig.
In this model, we used a suboptimal dose of rapamycin, which found that, compared to higher doses given alone, it has a minor effect on the clinical course of the disease, as we previously showed that there is a synergistic effect of rapamycin and IL-34 on a model of acute graft rejection in rats  6  and to benefit from the promoting effect of rapamycin on Tregs. Rapamycin has been used in the EAE and transplantation models in this manuscript and although it is not the main treatment in the corresponding disease in humans, multiple sclerosis and aGVHD, there have been studies showing that its use can be of interest. Rapamycin has been used to alleviate EAE in mice  29, 30  and has been very recently proposed as a treatment to screen for new therapies in multiple sclerosis. 31  More importantly, rapamycin has been used in multiple sclerosis patients  32  (https://clinicaltrials.
gov/ct2/show/NCT00095329). In aGVHD, rapamycin has been used in recent translational work, which has been done in the humanized NSG model  33  as well as in clinical trials. 34, 35  When we specifically studied the impact of the deficiency in Treg subsets, we revealed a clear defect in the IL-34-deficient CD4 + Tregs-suppressive function where they fail to protect from the wasting disease. This could be due to an insufficient differentiation of M2-like macrophages. This is reminiscent of our observations that IL-34 induces immune tolerance in a model of organ transplantation through M2 macrophages and downstream induction of Treg, 9 creating a positive regulatory loop. In in vitro suppressive assay, we could not find a significant defect of suppressive capacity of CD4 + and CD8 + Tregs, but this might be due to the setting in which this assay is performed using irradiated allogeneic T cells depleted-splenocytes and analysis after 6 days incubation (data not shown).
For stimulation, splenocytes were incubated with PMA (50 ng/ml) and ionomycin (1 μg/ml) for 4 h in the presence of Brefeldin A (10 μg/ml). Cells were permeabilized with a Fix/Perm kit (Thermo Fisher, Waltham, MA). Abs were used to stain cells, and fluorescence was measured with a BD FACSCanto II flow cytometer (BD Biosciences, Mountain View, CA), and FlowJo software was used to analyse data.
A C K N O W L E D G E M E N T S We thank  Séverine Battaglia  ( Inserm UMR1238, Nantes, France ) for performing the Micro-CT analysis and  Ms Maire Pihlap  for the LIPS analysis. We thank the biological resource centre for biobanking of  Nantes Hotel-Dieu University hospital (CRB Nantes , F-44093, France -BRIF:  BB-0033-00040 ) for their help in the preparation of the samples. We thank  Laure-Hélène Ouisse  ( ITUN CRTI UMR1064, Nantes, France ) for the Ig quantification. This work was partially funded by  Labex IGO  project (no. ANR-11-LABX-0016-01 ). This work was funded by the  Agence Nationale de la Recherche   ANR-17-CE18-0008 , the  Fondation du rein 'Don de Soi -Don de Vie 2017 FdR-Trans-Forme/FRM ' and the  Agence de la Biomedecine. This work was also realized in the context of the support provided by the  Fondation Progreffe.
One hypothesis to explain this restricted response is that the DNA/nuclear receptor/ligand complex has allosteric properties  (12, 13). Under this hypothesis, the exact nucleotide sequence of the DNA response element indirectly would not only determine its affinity for receptor binding but also influence the capacity of the distant ligand-binding domain to recruit transcription corepressors or coactivators. It is therefore important to provide a functional characterization of the response elements, in addition to assess their capacity to bind the nuclear receptors in vitro. As an example, we selected the well-studied TRα1/retinoid X receptor (RXR)α heterodimer, which activates transcription when TRα1 bind the thyroid hormone (3,3′,5-triiodo-L-thyronine or T3)  (2). In vitro studies  (14)  and motif analysis of chromatin immunoprecipitation sequencing (ChIP-seq) datasets  (5, 7, 9, 11)  previously established that this heterodimer preferentially binds the so-called DR4 element, in which 2 half-sites are separated by a 4-nucleotides spacer (5′AGGTCANNNNAGGTCA3′).
A control was performed in which reverse transcriptase was omitted to ascertain the absence of contaminating DNA. Amplicons were sequenced both by Sanger sequencing or by deep sequencing (Illumina Microkit V2 300 cycles MiSeq). Paired-end reads contained in the. fastq files were merged (fastq-join; Galaxy Version 1. 1. 2-806. 1) and converted to tabular format using FASTQ Groomer (Galaxy Version 1. 0. 4) and FASTQ to Tabular converter (Galaxy Version 1. 1. 5). Tab-delimited table files were curated from sequences that differ from the expected sequences at the conserved positions around the DR4 elements (20/20 identity). An R-script was used to produce counts tables from the sequence tables.
Statistics The χ 2 test was used to detect significant changes in the observed distribution of nucleotides at specific positions, with Bonferroni correction. Specific response elements with differential response to T3 were identified using count tables and Deseq2 Galaxy Version 2. 11. 40. 7  (19). Overlap with ChIPseq datasets were analyzed by searching for Intervals intersection (Galaxy Version 2. 30. 0).
Based on these 9129 sequences, which represent only 28% of the 32 768 possible combinations, we calculated the frequency of each nucleotide at each variable position and addressed whether T3 can influence this distribution (Table  S2 )  (22). A significant enrichment, indicating an influence on transactivation, was observed for specific nucleotides at each position, which was highly significant according to χ 2 tests. Because the enrichment was more pronounced when 1. 2 μg of DNA was transfected, we concentrated the analysis on this experiment using   the other datasets only to confirm the main results. The conclusion from this analysis is that there is a clear preference for specific nucleotides at several positions. In particular, A or G at position 1, T or C at position 9, A at position 11, and C at position 15 are clearly favorable to transactivation (Table  1 ).
We used for this published ChIP-seq datasets describing chromatin occupancy by TRα1 in the GABAergic neurons of mouse striatum  (11)  and in mouse cardiomyocytes  (10). We also included a ChIP-seq dataset obtained for TRβ1 in liver  (6). Using FIMO (Find Individual Motif Occurrences) search, from the MEME suite  (23) , we listed 51 231 matches in the mouse genome (GRCm38/mm10) for the DR4 motif (5′NGGTCANNNNRGGNNA3′) used for our STARR-seq screen. A small fraction of these were occupied by TRα1 (2099 in neurons, 1266 in cardiomyocytes) or TRβ1 (499 in liver). We then asked whether the nucleotide composition of the occupied and unoccupied DR4 motives differ. Table  2  reveals striking similarities between the outcome of this differential analysis and of the STARR-seq screen. There is therefore a strong correlation between the capacity of a specific DR4 element to recruit TRα1 in chromatin, and the capacity of this DR4 to mediate transactivation in transfected cells.
We pursue the comparison between the outcome of ChIP-seq and STARR-seq analyses and tested the influence of combination of nucleotides for the largest dataset (GABAergic neurons). We also found that some specific combinations at position 14 and 15 are also more favorable to chromatin occupancy by TRα1 (Table  3 ). With only 2099 TRα1 binding sites in chromatin, this statistical comparison was not applicable to the spacer combinations, or to the whole sequence.
Finally, a modified version of the pre-trained Convolutional Neural Network (CNN) is fine-tuned and used to predict the quality score through the selected patches. Five distinct pre-trained models were analyzed and compared in term of results. The performance of the proposed metric has been evaluated on four commonly used datasets (3D LIVE phase I and phase II databases as well as Waterloo IVC 3D Phase 1 and Phase 2). Compared with the state-ofthe-art metrics, the proposed method gives better outcomes.
Training-free approaches have an internal generalization potential, and yet, their performances are currently inferior to machine learning-based methods. Instead, using machine learning techniques such as Support Vector Regression (SVR) and Random Forest (RF), image feature values can be simply mapped to the image quality index, assisting machine learning-based NR-IQA models to obtain comparatively higher evaluation performance. Furthermore in latest years, deep-learning-based algorithms that directly map an image or image structure to a quality index have achieved promising results. But, there are several flaws to this latter, such as fixed input pixel resolution, pixel attack sensitivity, and large scale training data requirement. Regardless of the learning approach employed in the SIQA system, modeling Human Visual System (HVS) is important to simulate the visual judgment.
The PSNR is then computed between the reference and test edge maps to assess the quality. Instead of using the disparity/depth information, Gorley et al. [5]  calculate quality scores on corresponded feature points of the left and right images provided by SIFT  [6]  and RANSAC  [7]. However, full-reference metrics continue to pique the interest of researchers. For instance, metrics based on binocular receptive field properties have been proposed in  [20, 21]. In  [20] , to determine the best features that imitate the reactions of basic cells in the brain, an Independent Component Analysis (ICA) technique was applied. While the scheme in  [21]  tends to learn a multi-scale dictionary from the training database. In the quality estimation phase, they calculate a sparse feature similarity index based on the estimated sparse coefficient vectors.
Datasets and training protocol To examine the consistency and effectiveness of our method, four databases have been used to evaluate the performance of our metric. These datasets are listed in Table  2  and briefly described below: 3D LIVE phase I (LIVE-P1)  [56] : It consists of 365 distorted stereo images of size 360 Â 640 pixels generated from 20 stereo image scenes. Five degradation types are considered (White Noise: WN, JPEG2000: JP2K, JPEG, Fast Fading: FF and Blur). All the distortions are carried out symmetrically. The subjective evaluation scores are given in the term of DMOS within the range of [À10,70]. 3D LIVE phase II (LIVE-P2)  [3] : It contains 360 distorted stereo images with the same size and distortion types as phase I. This database includes symmetric as well as asymmetric distortions.
Subjective evaluation scores are given within the range of  [20]  in the DMOS term. Waterloo IVC 3D Phase 1 (P-1)  [57] : It has 330 full HD (1920 Â 1080 pixels) distorted stereo images derived from six pristine stereo images collected from the Middlebury Stereo 2005 Datasets. Three forms of distortion are present in this database: additive white Gaussian noise, Gaussian blur, and JPEG compression. Subjective evaluation scores are given in term of MOS and distributed in the interval of  [10]. Waterloo IVC 3D Phase 2 (P-2)  [58] : It contains 460 full HD stereo images created from 10 pristine stereo image pairs. The stereo images carry the same distortion types as Phase 1, and both of them include symmetric and asymmetric distortions. Subjective assessment scores are in term of MOS and the range is the same of Waterloo-P1  [10].
It is worth noting that the asymmetric degradations in the Waterloo P-1 and P-2 databases are different from those in the LIVE-II database. LIVE-II uses only one type of distortion to perform  the asymmetry, while the two Waterloo databases consider the possibility of multiple types of degradation in which the left and the right images are affected by different distortions. Generally, the above-described SIQA databases have smalllimited labelled images. To increase the amount of data, data augmentation is often applied. The available data augmentation techniques except horizontal flipping, affects the subjective quality ratings. The rotation and re-sizing approaches often applied change the observers perception of spatial details and are thus not appropriate for SIQA methods. Therefore in this work, neither rotation nor translation or re-sizing were applied.
Instead of, we allow a maximum of 80% overlapping between patches. During the learning, each pre-trained CNN model is fine-tuned for 50 epochs using a learning rate of 0. 01. Stochastic Gradient Descent (SGD) with a momentum equals to 0. 9 is used as optimization function. The human scores are normalized in the form of DMOS/ MOS to min-max normalization [0,1]. The closer to 0 the better quality of the stereo image is for DMOS and the opposite for MOS. After-all, the expected quality rating for each scene is the average of quality scores obtained from patches, described as follows: Q ¼ 1 N X N n¼1 P n ð4Þ where P n is the predicted score for the n th patch, N is the number of patches, and Q is the final quality score. We have carried out 10-fold validation test by randomly splitting the dataset into training (80%) and test (20%) at each time.