dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with a feature-based model In order to compare our processing-based approach to predict beauty to a traditional featurebased approach, we studied to which extent the neuronal activations themselves, rather than the sparsity of their distribution, can explain variation in beauty scores (following the method described by Iigaya  [30] ). In a DCNN, each artificial neuron describes one feature in a given region of the image, through its weighted connections to previous neurons. The magnitude of its activation indicates the importance of that specific feature in that region of the image. We then used neuronal activations as predictors in multivariate linear ridge regression models, fitting one model per layer at a time. However, because there were too many activations for fitting the models (the first convolution layer includes 3,211,264 activations), we reduced the number of activations using a principal component analysis (PCA), keeping only those components that explain a total of 80% of variance in activations (between 125 and 2,162 principal components depending on layers and datasets; see Supp Inf.
). For all layers and all datasets, the R 2 score was significant (p<10 -6 for all models), indicating that features, from simple and localized to complex and holistic, do explain variation in beauty scores for both face images and artistic paintings. However, activations explained a low fraction of variance in beauty scores, with R 2 score never exceeding 4% (Fig  2 ). One major exception is for the abstract painting dataset MART, with R 2 score ranging from 6% to 42%. To analyze how encoded features could predict beauty at the entire network level, as for sparsity we used a single multivariate Ridge regression model using the PC scores of all layers. However, to keep the model tractable, we considered only the first three components of each layer. Again, except for MART, we found that the first three principal components explain a relatively low fraction of variance in beauty (R 2 score < 5%; Table  1 , column "R 2 (one model for all layers, PC scores only)"), in line with the previous result obtained when using all principal components that explain 80% of the variance in activations.
Last, we analyzed the benefits of combining both feature-based and processing-based metrics to predict beauty by fitting one multivariate ridge regression model per dataset, including the first three principal components of the PCA applied to activations plus sparsity for each layer, and considering all layers in the same model (i. e. , 60 predictors). The R 2 score was always significant (p<10 -6 ) and varied from 14% for the figurative paintings (JEN dataset) to up to 48% for abstract paintings (Table  1 , column "R 2 (one model for all layers, PC scores + sparsity)"). Comparing these results to those with the model including sparsity only (Table  1 , column "mean R 2 (one model per layer, sparsity only)"), the increase in explained variance from adding the feature-based metrics is marginal (SCUT-FBP5500: +0.
Using CFD as the reference distribution, the authors found that facial typicality explained 15% of variance in facial attractiveness. Crucially, sparsity also captures the concept of statistical typicality because, during training, the weights of a DCNN are tuned to the statistical distribution of features of the training dataset (in our case, ImageNet), such that images with a similar distribution are sparsely encoded during inference  [52]. However, while typicality specifically refers to familiarity, sparsity more explicitly characterizes the use of neuronal resources, that is, the efficiency of information processing per se, a presumably important component of fluency  [8]. Regardless of the level of overlap between typicality and sparsity, both  [34]  and this study considered a static perceptual system.
In contrast, a classical approach to study fluency in psychology is to investigate how the system varies when manipulating fluency, for example using matching prime (see  [36] ). In the future, it would thus be interesting to apply similar dynamic approaches to DCNNs, for example by analyzing how progressively tuning the DCNN to features that are specific to the target stimuli increases the ability of sparsity to explain beauty. Comparing the predictive capacity of a model including sparsity only with a model including both sparsity and features suggests that information processing could be more important than features in determining beauty. One exception is for the abstract paintings MART dataset, for which features explained more variation in beauty scores than sparsity did, and sparsity did not improve prediction.
The fact that sparsity did not significantly improve the model with PC scores only, while it could alone explain 25. 7% of variance of beauty scores, suggests that, for abstract paintings, most of the information conveyed by processing efficiency is redundant with information conveyed by features. Given that the negative-positive emotions were evaluated in the abstract paintings dataset, one explanation to the difference between this dataset and other datasets could be that the hedonic marking of efficient information processing in general is not mediating the link between beauty and emotions. This, however, contradicts previous accounts suggesting at least a mild effect of fluency on aesthetic emotions (for a review, see  [53] ). For this dataset, beauty was scored by lay people who thus based their judgment on the design of stimuli only, and not on representations encoded in as in figurative paintings.
While these studies highlight that features can theoretically encode most of the information about beauty, in practice, they are not biologically realistic because, contrary to DCNN, the visual cortex as a whole has been shaped by evolution and development to perform many tasks, not to predict beauty only. More work is clearly needed both to quantify the relative importance of processing fluency and stimulus features in predicting beauty, and to understand the factors influencing this relative importance. Interestingly, for a given dataset, the most predictive layers differ when considering either information processing or features. For MART and SCUT-FBP5500, for example, beauty is best predicted by the last two layers, Fc1 and Fc2 (describe whole-image features) when considering PCA features, and by the first convolutional layers (describe textures and local color contrasts) when considering sparsity.
The dissociation between processing-based and featurebased contributions to beauty highlights the richness of the aesthetic experience and myriad possibilities that naturally or culturally shaped communication signals have to increase their attractiveness. Although our results validate our main hypothesis that beauty can be explained in part from information processing, they reject the prediction that high sparsity is associated with high scores of beauty. Indeed, we found that depending on layers and datasets, high beauty scores could be associated with either highly efficient, or the opposite, highly energy-demanding coding (see  Fig 1). This result contradicts the existence of a universal positive correlation between processing efficiency and beauty, and thus the original (and most frequently cited) formulation of the fluency theory of aesthetics.
Datasets We used four different, publicly available datasets consisting of images and associated mean opinion scores (MOS) of beauty (Table  2 ). The Chicago Face Database (CFD v3. 0) includes 827 standardized photographic portraits (centered faces, identical outfits, identical camera settings) of American and Indian adults of both genders, from various self-reported ethnic origins  [40]. Image size is 2,444x1,718 pixels. For each image, a MOS of attractiveness was obtained by averaging ratings from 1,087 American participants, along a Likert scale from 1 to 7, with 1 being not attractive at all and 7 extremely attractive. (Question R013 from CFD Codebook). See S2 Table for more information on the evaluators. The SCUT-FBP5500 database (South China University of Technology-Facial Beauty Prediction) includes 5,500 photographic portraits of Asian and Caucasian adults of both genders  [41].
Portrait images were retrieved from various sources and thus are not standardized. Image size is 350x350 pixels. Beauty MOS were obtained by averaging ratings from 60 Chinese volunteers, along a Likert scale from 1 to 5 with 1 being the least attractive and 5 the most attractive. See S2 Table for more information on the evaluators. The MART database (Museum of Modern and Contemporary Art of Trento and Rovereto) includes images of 500 abstract paintings by different artists from Trento museum in Italy  [42]. The width and height of images varies from 59 to 812, and 45 to 1,036 pixels, respectively. One hundred Italian laypersons were asked to rate their emotional response to each image along a Likert scale from 1 to 7, with 1 being the most negative and 7 the most positive. Previous studies have shown that beauty is mostly associated with positive emotions (e.
g. ,  [71] ). See S2 Table for more information on the evaluators. Last, the JEN database (from the German town of Jena) contains 1,563 figurative artistic paintings by different artists representing different movements  [43]. Image size is 1,456-30,000x1,351-23,803 pixels. For each image, beauty was evaluated by 134 observers along a Likert scale from 0 to 100, with 0 being not beautiful and 100 beautiful. See S2 Table for more information on the evaluators. For all analyses, images were resampled to 224x224 pixels (input size of VGG16), and scores of beauty or their proxy were all standardized to the same scale varying between 0 (lowest attractiveness/beauty score) and 5 (highest attractiveness/beauty score).
Encoding images with a convolutional neural network All images were encoded using the VGG16  [44]  deep convolutional neural network pretrained on the ImageNet dataset  [45]. VGG16 includes 13 convolution layers and two fully connected layers. For each image, we thus extracted 15 encodings corresponding to the neuronal activations (after ReLU transformation) of these 15 different layers. For convolution layers, encodings are three-dimensional matrices of size H*W*C, with H, W corresponding to the height and the width of the feature maps, respectively, and C to the number of feature maps (i. e. , channels). H and W vary from 224 to 14, and C from 64 to 512, between the first and the last convolution layer. For both fully connected layers, encodings are one-dimensional vectors of size 4,096.
Statistical analyses The four datasets were studied separately. For each layer, measurements of sparsity were ztransformed prior to statistical analyses. We first conducted simple linear regression models to analyze the correlation between the empirical scores of beauty (response variable) and the sparsity measured for each layer (one model per layer). Then, we analyzed the ability to predict beauty from sparsity measurements of all layers with a single multivariate linear regression model and a Ridge penalization, that included 15 explanatory variables (a separate sparsity measurement for each of the 15 layers). The strength of the Ridge penalization (Lambda parameter) was set by cross-validation. Last, we used linear Ridge regression models to analyze the ability of image features to predict beauty scores.
However, encodings have too many dimensions to be included in a regression model. For example, in the first convolution layer, the encoding corresponds to H*W*C = 3,211,264 features. We thus first reduced the dimensionality of encodings by applying a PCA (one PCA per layer per dataset), keeping the principal components explaining 80% of variance (see S1 Table ). PC (principal components) scores were then z-transformed and included in the ridge regression model as explanatory variables. We performed one Ridge regression for each layer separately. In addition, we performed one global Ridge regression including the first three principal components of each layer (model with 45 explanatory variables), and another global Ridge regression including the first three principal components and the sparsity measurement of each layer (model with 60 explanatory variables).
For all linear regression and linear Ridge models, we performed a 10-fold cross-validation, repeated 100 times to ensure that the results were stable, calculating the coefficient of determination (R 2 score for univariate models and adjusted R 2 score for multivariate models) between predicted and empirical beauty scores for each test fold, and then averaging the R 2 score over the 10 folds and then over the 100 repetitions. Statistical analyses were performed using R software v4. 2. 1  [74]. We used the glmnet method of package glmnet  [75]  for the Ridge regression models, and the caret package for the cross validation  [76].
This study was funded by the  Agence Nationale de la Recherche  ( ANR-20-CE02-0005-01 ) received by JPR and WP, by the  National Science Foundation  ( NSF   IOS 2026334 ) received by TM and JPR, and by the  CNRS  through the  MITI interdisciplinary programs  ( Programme Interne Blanc MITI 2023. 1 -Projet:  DEEPCOM-L'intelligence artificielle pour etudier la communication ) received by WP. ND and ST received a salary from  ANR-20-CE02-0005-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Bruker, Berlin, Germany). Representative areas or defined lines of the samples were analyzed and mapped for elemental distribution based on the EDX-spectra data using QUANTAX ESPRIT Microanalysis software (version 2. 0, Berlin, Germany) SEM images were taken from selected regions (the conditions are shown in the figures). Comparative SEM-EDX analyses of the control sample and iron-spongin after ultrasound treatment were carried out using a scanning electron microscope (Quanta 250 FEG; FEI Ltd. , Brno, Czech Republic) correlated with an energy-dispersive X-ray spectrometer (EDX Team Software) to determine the elemental composition and surface morphology of the samples studied. Moreover, SEM and supplementary EDX measurements were carried out using a low-vacuum scanning electron microscope, JEOL JSM-6610LV, with a LaB6 cathode, which was also equipped with an energy-dispersive X-ray spectrometer (10 mm 2 Silicon Drift Detector (SDD) X-Flash 6|10, Bruker Co.
For the utilized setup, the probing volume sizes were calculated as a function of the energy by calibrating the characteristics of the spectrometer parameters  [121]. Hereby, the following probing volume z-sizes can be expected in approximation: Si-Kα (77. 2 µm), S-Kα (69. 0 µm), Ca-Kα (55. 6 µm), I-Lβ (51. 8 µm), Fe-Kα (42. 0 µm) and Br-Kα (31. 4 µm). The exported measurement datasets (containing information about the location coordinates x and y and the signal count values) were then further processed using in-house software (applied in references [122,123]), providing tools like the normalization of the xy mappings to a global signal maximum, generating RGB color-coded images and signal noise correction. The final stacking of the two-dimensional distribution datasets was carried out with the Python application Mayavi, achieving three-dimensional distribution images.
Dopamine Detection For the sensor preparation, modified carbon paste electrodes (CPE) were fabricated by grinding graphite, paraffin oil as a binder, and a modifier in a mortar with a ratio of 65:15:20 (w/w/w) and a grinding time of 40 min. The components were homogenized to form a paste, which was then pressed into a holder with an inner diameter of 4 mm. Amperometric measurements were carried out using a PalmSens 4 electrochemical analyzer with the software PSTrace 5. 8 (PalmSens BV, Houten, the Netherlands) and a three-electrode setup including modified CPE as the working electrode, Ag/AgCl (3 M KCl) electrode as the reference, and a platinum wire as the counter electrode. The amperometric response of the different modified CPEs for the successive addition of DA in 0. 1 M phosphate buffer pH 6.
Acknowledgments: The authors would like to thank  Agnieszka Martyła  for her assistance in interpreting the XRD results and  Daria Tokina  and  Morphology Service of IMBE, Marseille, France , for technical support in specimen preparation for TEM. The electron microscopy studies were provided using the equipment of the  Microscopy Core Facility of IMM, Marseille, France. Special thanks to  Jean Vacelet  for the discussions and motivation of our study.
This research field therefore remains very active and also diverse, depending on the amount and level of experimental information available for the ligand considered. A recent illustration of this research thematic can be seen in the study of Ziemnak et al. on halogen derivatives of 2-deoxy-D-glucose  [1]. Through a combination of experimental methods (solid state and solution) and theoretical calculations using several quantum chemistry approaches, a comprehensive understanding of the structural features of halogensubstituted carbohydrates was acquired, as well as information on their intermolecular interactions, providing evidence of their distinct biological activity. The design of new tools, based on the utilization of the information available in the world's two major crystallographic databases, the Cambridge Structural Database (CSD)  [2]  and the Protein Data Bank (PDB)  [3] , fits perfectly into this research area.
For example, the Torsion Library is a collection of torsion motifs encountered in low-molecular-weight ligands with angle distributions derived from crystallographic databases  [4]. These features are of vital importance to drug design and may be a decisive factor in bioactivity. With them being based on experimental observations of a given motif in various intra and inter molecular environments, they encompass the corresponding conformational flexibility. Another example in this area is provided by CSD-CrossMiner, which allows for specific structural queries to be mined simultaneously in the CSD and the PDB  [5]. As the number of experimental structures grows, the application potential of such tools should increase. The investigations of integrin ligands' conformational features carried out by Piarulli et al.
Methods The pragmatic trial on the use of an NAAT in the diagnosis of TB among presumptive TB cases aged 20 years or more was conducted in five hospitals in Taiwan. The trial did not intervene with routine practice of clinicians in requesting an NAAT as a frontline test, together with smear and culture examinations for patients suspected of having pulmonary TB. These patients were classified as group A (frontline NAAT by clinician). Those who had no NAAT were randomized in a 1:1 ratio into group B for whom an NAAT as a frontline test was performed as intervention, and group C (usual care) for whom no frontline NAAT was performed. The trial was approved by the Joint Institutional Review Board of Taipei Medical University (N202002038) and registered at ClinicalTrials. gov (ID: NCT04433195). It has been specified in the trial design that incremental yield of serial sputum examinations will be evaluated.
Methods Selection and Alignment of H7N9 Genome Sequences. We downloaded all H7N9 genome sequences of the HA gene segment, that were collected between 1st February 2013 and 30th April 2017 in China, from the GISAID database (Global Initiative on Sharing All Influenza Data, http://www. gisaid. org) on 1st September 2021. Sequences were selected if they were coming from humans or LBMs in the Yangtze River Delta region (including Anhui, Jiangsu, Shanghai, and Zhejiang provinces) and the Pearl River Delta region (including Guangdong, Guangxi and Hunan provinces) and were associated with a complete sampling date. Information about the LBM identifiers was shared by the submitting laboratory (Harbin Veterinary Research Institute, China). The resulting alignment of 798 sequences was made using MAFFT v7 (77) and checked using AliView v1.
The final set consisted of 238 sequences from human cases and 62 sequences from LBM outbreaks (Fig. 6  and SI Appendix, Table  S3 ). Phylodynamic Inference of H7N9 Transmission Dynamics. Bayesian phylodynamic analyses were implemented using the BDMM-Prime package for BEAST v2. 6. 3  (80)  and the BEAGLE library  (80)  to improve computational performance. The MTBD model was used as the tree prior  (81, 82). Similar to compartmental models in epidemiology, the MTBD model assumes that the underlying population is structured into discrete subpopulations (or demes), each with specific transmission, removal, and sampling rates and allows inference of the effective reproductive number R e directly from genome sequences. Under this model, demes were defined according to host type and geographical location of genome sequences: humans in the Yangtze River Delta region, humans in the Pearl River Delta region, LBMs in the Yangtze River Delta region, and LBMs in the Pearl River Delta region (SI Appendix, Table  S3 ).
The MCC tree was obtained from the multitype phylogenetic trees in TreeAnnotator v2. 6. 3 (92)  and annotated using the ggtree package (95) in R v4. 0. 2  (96). Incidence trajectories (i. e. , the number of newly infected hosts per deme over time due to within-deme and between-deme transmission) were inferred from a subsampled set of posterior phylogenetic trees and model parameters (n = 500) using a particle filtering algorithm  (50). To test the robustness of the phylodynamic analysis with respect to changes in the priors' assumptions, a separate set of analyses were performed by using a broader prior for the within-and between-deme R e parameters [lognormal  (1, 1) , corresponding to a median of 2. 7 (95% IQR: 0. 4 to 19. 3)] (SI Appendix, Fig. S5 ), as well as by reperforming the random subsampling procedures of bird and human sequences (SI Appendix, Fig.
S6 ), which did not significantly change the temporal dynamics of the R e estimates (<1 and >1) compared to the main analysis. Data, Materials, and Software Availability. All H7N9 genome sequences of the HA gene segment used in this study are available on the GISAID database (http://www. gisaid. org) (97). Case surveillance data shown in this study are available on the FAO database (Food and Agriculture Organization of the United Nations, https://empres-i. apps. fao. org) (98). The prior values and distributions of the model parameters are described in SI Appendix, Table  S5. The BEAST 2 XML file used to perform the phylodynamic analysis together with the accession numbers of the H7N9 genome sequences, and the R scripts are available at https://github. com/ClaireGuinat/h7n9_bdmm-prime  (99).
ACKNOWLEDGMENTS. We are grateful to the cEvo group ( ETH Zurich, Switzerland ) for providing useful comments on this project. We also acknowledge  Jean Artois  and  Marius Gilbert  ( Université Libre de Bruxelles, Belgium ) for providing information on the predictive number of LBMs in China and  Xiaoyan Zhou  and  Guo Fusheng  ( China Animal Health and Epidemiological Center, China ) for sharing their expertise with LBMs in China. We also thank the submitting laboratory ( Harbin Veterinary Research Institute, China ) for sharing the H7N9 genome sequences in the GISAID database, in particular  Chen Hualan  for retrieving the LBM identifiers from which the H7N9 genome sequences were collected. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no.
Acknowledgments: The authors are indebted to the local teams at  GSI , in particular of  ESR  and  CRYRING@ESR , for providing us with an excellent beam. This research has been conducted in the framework of the SPARC collaboration, experiment E138 of FAIR Phase-0 supported by  GSI. B. Z. acknowledges  CSC Doctoral Fellowship   2018. 9-2022. 2  (Grant No. 201 806 180 051 ).