dataesr/scientific-paragraphs-categorization huggingface dataset:
0  [28]. The RNA-seq data are uploaded in Gene Expression Omnibus (GEO) repository (GSE186820). var Genes Transcripts var transcripts were assembled de novo from RNA-seq reads using a published script (https://github. com/PapenfussLab/as-semble_var) [5]. The nature of each subdomain (eg, DBLα1. 1-2-3/2) was assessed by performing an alignment with MAFFT  [29]  against the Vardom database  [11]. The FPKM (fragments per kilobase million) values were evaluated with edgeR. To evaluate var genes assembly quality, reads were aligned with Hisat2 v2. 2. 1  [30]  to a database composed of ATS domains from Vardom's 7 reference genomes  [11] , varDB  [31] , and RNA-seq assembly and to assembled exon 1 sequences in each sample. To evaluate var genes abundances, reads mapped to ATS were counted and were normalized accounting for both the library size and the parasite's stage proportions, based on synchronized 3D7 strain RNA-seq data  [20].
RT-qPCR Validations Expression of 8 genes was measured to confirm the RNA-seq results on the Rotor-Gene qPCR system (QIAGEN). Gene levels transcripts were evaluated with serial dilutions of plasmids (GenScript). Results were normalized with a housekeeping gene expression and accounting for the parasite's developmental stage (Supplementary Method). var genes expression was assessed by 3 independent methods. Published DBLα primers  [12]  and newly developed primers targeting the ATS domain (ATS-2 primers based on varDB dataset  [31] , Supplemental Data 2) were used on the Rotor-Gene qPCR system (QIAGEN) following the same methodology. The qPCR-TaqMan developed by Hofmann et al  [32]  targeting the ATS domain (ATS-1) was performed on a Viia7 qPCR system (Applied Biosystems) (Supplementary Method).
Samples Analysis Parasite RNA was extracted from a total of 73 CM and 98 UM cases. Based on RNA quantification and quality criteria, 21 UM and 16 CM samples were subsequently selected for highthroughput sequencing (Supplemental Data 1). From RNAseq mapping statistics, 30 samples (15 CM and 15 UM) were retained for transcriptome analysis (Supplemental Data 3). Selection criteria are detailed in the methods. Clinical and biological features are displayed in Table  1. Children with CM presented a higher parasitemia (P <. 001, Mann-Whitney U test), and children with UM were older (P =. 004, Mann-Whitney U test). Hemoglobinemia and blood glucose were lower in CM (P <. 001, Mann-Whitney U test). Total antibodies titers, reflecting previous exposure to Plasmodium sp, did not differ between CM and UM (P =.
Circulating Parasites' Gene Expression The all-gene analysis was focused on the parasite core genome (VSAs were excluded). Parasite developmental stage proportions were evaluated in each sample based on total gene expression measured by RNA-seq  [5]  (Figure  1A , Supplemental Data 5). The mean parasite age expressed in hours post infection (hpi) was statistically lower in CM cases, with a median age of 8 hpi (8-11. 2) compared with 12. 6 hpi (8. 8-16. 7) in UM (P <. 001, Mann-Whitney U test). The difference was confirmed by the microscopic reading of thin blood smears ). The all-gene expression principal component analysis (PCA) showed that both clinical presentation and parasite developmental stage affect the separation by the first component (Figure  1B ). After correcting gene expression for both life-cycle stage effect and unwanted variations, CM and UM isolates were perfectly separated based on clinical presentation (Figure  1C ).
(D) Heatmap of differentially expressed genes. An upregulated expression is represented in red and the downregulation is in blue. IX and plasmepsin X (pmIX, PF3D7_1430200 and pmX, PF3D7_0808200), and erythrocyte binding antigen-175 (eba175, PF3D7_0731500). Concerning KEGG pathways results, we found 3 associated pathways, with at least 40% deregulated genes. A PPI network analysis was performed on 546 of 551 DE genes that were annotated in the STRING database. Results showed a very dense network with 212 nodes and 2976 edges with a significant PPI enrichment (P <. 001), reflecting biologically connected proteins (Figure  2B ). Four highly interconnected clusters were found (Figure  2B , Supplemental Data 8). Cluster 1, containing 26 nodes and 323 edges, was composed exclusively of ribosomal proteins (all downregulated), reflecting peptide biosynthesis process deregulation.
6 (PF3D7_0902500), and semp1 (PF3D7_0702400). Downregulated genes, including exported protein 2 and exported protein 3 (exp2 PF3D7_1471100; exp3 PF3D7_1024800), were also identified. var Genes Expression var genes from each isolate were assembled from RNA-seq data (Supplemental Tables  3  and 4 ). Fifteen CM and 13 UM samples were analyzed with at least 1 var genes domain identified. As we obtained more P falciparum mapped reads in CM isolates compared with UM (P =. 016, Mann-Whitney U test), we sought to assess the quality of the assemblies by comparing the number of mapped reads to exon 1 and ATS semiconserved sequence (Figures  3A  and B ). With a 0. 3 exon1/ATS counts proportion threshold, we considered 15 CM and 8 UM for downstream analysis. The number of var genes assemblies with at least 1 predicted domain did not differ between CM and UM (96.
DISCUSSION Although recent studies have focused on host transcriptome signatures associated with CM  [33] [34] [35] , characterization of the gene expression profile of P falciparum isolated from CM needs further improvement. In this context, we analyzed the gene expression of 15 CM and 15 UM P falciparum isolates from a Beninese pediatric population, to better understand the parasite factors associated with CM. A greater proportion of ring stage parasites was measured in CM, suggesting a decrease in parasites circulation time. Recent studies that included datasets with various disease severity also demonstrated that lower circulating parasites hpi was correlated with increased severity  [5] [6] [7] [8]. Andrade et al  [7]  predicted that cytoadhesion alone may explain the parasites age difference observed in febrile compared with asymptomatic malaria isolates in a cohort of Malian individuals.
However, humoral immunity is not the only host protection against malaria infection and the role of complement component 1s, modulating iE adherence, has recently been demonstrated  [42]. The decreased circulation time observed in CM may result in iE avoiding splenic clearance, leading to higher parasitemia  [7] , as we found in our dataset. A correlation between decreased circulation time and increased parasitemia was retrospectively demonstrated from different studies, by Thomson-Luque et al  [8]. Because adhesion capacity does not fully explain CM pathophysiology, we then focused on all-gene expression to decipher CM specificities. After normalization accounting for parasite developmental stage, gene expression profile was a signature of malaria severity. More than 500 genes were deregulated in the CM group compared with the UM isolates.
Acknowledgments. We are grateful to all patients and their parents for participating in this study. We thank the nurses of the CHU-MEL hospital ( CHU-Mère et Enfant de la Lagune ) and  Hôpital de Zone de Calavi  for help in the collection of samples. We also thank the health center of  So-Ava. We thank  Adrian Luty  for help in reviewing the manuscript. We acknowledge  Antoine Claessens  and all of his team ( Prince Nyarko ,  Marc-Antoine Guéry , and  Camille Cohen ) for help and counseling regarding data analysis.
I. INTRODUCTION Understanding the brain and cognition has always been one of the fundamental research areas of science. One of the ways researchers approach this task is by establishing neural encoding and decoding methods. New ways to decode information from brain signals have emerged with recent developments in modeling and computation. Funded by AI-REPS grant ANR-18-CE37-0007-01 and ANITI grant ANR-19-PI3A-0004 In vision research, many studies have used statistical methods and machine learning to decode specific information like position  [1]  or orientation  [2] ,  [3] , to classify image categories  [4] ,  [5] , to retrieve the closest images from a candidate set  [6] , or even to reconstruct images with low-complexity like basic shapes and structures  [7]. With the emergence of deep learning, and in particular advanced deep generative models, reconstructing more complex images like handwritten digits  [8] , faces  [9] , and natural scenes  [10]  has become possible.
g. reflecting the size, position or orientation of an object, details of the background, etc. ) are obtained via a two-stage optimization of the IC-GAN "noise" and "dense" latent vectors (inspired by the method of Pividori et al. [13] ). Next, we train three ridge regression models to predict these latent image representations from the corresponding fMRI patterns, recorded while human subjects viewed the same training images (Figure  2 , Step 1). Finally, for each image in the test set, we predict the instance feature, noise vector, and dense vector from fMRI data (using the previously learned regression models), and then reconstruct an estimate of the image using IC-GAN's generator (Figure  2 , Step 2 and 3). The code of this paper can be found in the official GitHub repository 1. Our method establishes a new state-of-the-art performance for capturing the semantic attributes of the images, while preserving a reasonable amount of low-level details.
A. Instance-Conditioned GAN We utilized an Instance-Conditioned GAN (IC-GAN) model, pretrained for natural image generation on the Ima-geNet dataset  [20]. IC-GAN can be considered as a generic framework rather than a single model, because it can be applied to different GAN backbones, e. g. StyleGAN  [21]  or BigGAN  [22]. In the usual conditional GAN setting  [23] , class labels are provided along with noise vectors sampled from a normal distribution to generate images. Images belonging to that specific class are labeled as "real", and generated images from the generator are labeled as "fake". Both the generator and discriminator are trained with these images and labels in an adversarial learning framework. In the instance-conditional setting, instead of giving a class label, instance features that capture the semantic attributes of a given image are extracted (via a pre-trained feature extractor) and provided to the generator as conditioning, alongside a sampled noise vector.
B. Extracting Latent Variables from Training Stimuli We illustrate the computation of latent variables in Figure  1. We first extracted a 2048-dim instance feature vector for each training image in our dataset (see dataset details below) by presenting it to a SwAV ResNet-50 feature extractor. We then provided these instance features to the IC-GAN generator, and optimized the 119-dim noise vector for the same image using the covariance matrix adaptation evolution strategy (CMA-ES)  [28]. We used this method because we empirically observed that global optimization strategies worked better than local optimization strategies (like gradient-based methods) for the noise vector. The loss function for this optimization was the distance between the generated image and the original training image in Layer-4 of SwAV ResNet-50; this representation level, hierarchically lower than the instance feature level, encodes more spatially structured information.
C. Generic Object Decoding Dataset In this study, we used previously published fMRI recordings of five human subjects presented with images from the ImageNet dataset  [31]. The dataset contains training and testing image perception sessions where subjects looked at 1200 training samples drawn from 150 categories (8 samples each) and 50 testing samples chosen from 50 categories (1 sample each), respectively. Training and testing categories were chosen independently and were non-overlapping. Each training image was presented only once, while testing images were repeated 35 times during the whole experiment. All fMRI runs followed a similar design: fixation (33s), 50 image presentations (9s per image flashing at 2Hz), fixation (6s). Moreover, subjects were also asked to perform a one-back task by pressing a button whenever the same image was presented two times in a row (five such events occurred per run).
The fMRI data were pre-processed for each subject by three-dimensional motion correction followed by coregistration to the high-resolution anatomical image. Then, the brain representation of each image was calculated by averaging the percent signal change values of each voxel over the 9-s presentation window. Additionally, the dataset provides functional regions of interest (ROIs) that cover the entire visual cortex, including V1-V4, the fusiform face area (FFA), parahippocampal place area (PPA), and lateral occipital complex (LOC). The pre-processed data is available to download at brainliner. jp 2.
D. fMRI Decoding and Image Reconstruction Details of fMRI decoding and image reconstruction are depicted in Figure  2. The procedure involves two separate stages for training and testing the brain decoding system of each subject. First, we trained three separate ridge regression models to predict the latent variables (instance features; noise vectors; dense vectors) for each of the 1200 training images based on the corresponding fMRI patterns. Since both the fMRI data and the latent variables are high-dimensional, we applied L 2 regularization on the regression weights during training. At test time, we averaged the 35 repetitions of fMRI signals corresponding to each test stimulus. Next, we used the previously trained regression models to predict the instance features, noise vectors, and dense vectors from these averaged fMRI signals.
B. Semantic Analysis of Visual Encoding in Brain ROIs Our brain decoding model, relying on the latent space(s) of the IC-GAN network, can reconstruct the high-level content of perceived images better than all prior methods, while retaining more low-level details than at least some of these methods. From a neuroscience viewpoint, can this brain decoding model also help us understand the neural coding of visual information in the brain? Here, we use our model to explore and directly visualize the types of information that are preferentially represented in various brain regions-of-interest (ROIs). The fMRI dataset counts seven distinct ROIs across visual cortex for each subject-in hierarchical order: V1, V2, V3, V4, LOC (Lateral Occipital Complex), FFA (Fusiform Face Area) and PPA (Parahippocampal Place Area).
In FFA, a high-level region known for its selectivity to face images, IC-GAN generates human and animal faces. The presence of animal faces is not unexpected, since the ImageNet dataset (on which IC-GAN was trained) contains many more animal images than human images. Some previous experimental and computational work  [35] ,  [36]  also suggests that fusiform regions may show a preferential response to animals, and particularly dogs. Nonetheless, the model still -7. generates human face images for two of the subjects. The last ROI from the higher visual cortex is PPA, known for its selectivity to environmental scenes like indoor and outdoor places. IC-GAN also generates indoor and outdoor places when the voxels of this region are activated. Some of the images have an object in the center of the scene; this might be caused by the training of the IC-GAN model on ImageNet-an object-centered dataset.
Methods X-ray diffraction measurements. Our structural study was performed on a single crystal of CsV 3 Sb 5  [25]  loaded into a diamond anvil cell filled with neon gas as pressure transmitting medium. Pressure values were independently calibrated using ruby luminescence and the lattice parameter of gold powder placed into the same cell. X-ray diffraction (XRD) measurements were performed at room temperature on a six-circle diffractometer of the CRISTAL beamline at SOLEIL. Diffraction data were collected with the x-ray wavelength of 0. 42438 Å by 1 • rotations of the cell about the ϕ axis. The data were processed using Crysalis Pro  [29]  and corrected for the absorption in Absorb  [30]  by taking crystal shape into account. About 420 reflections with 70 unique reflections were used to refine 5 parameters of the CsV 3 Sb 5 structure with the P 6/mmm symmetry: z-coordinate of Sb1 and isotropic displacement parameters of Cs, V, Sb1, and Sb2  [31].
The refinements were performed in Jana2006  [32]  against the data collected at pressures up to 20 GPa. Further compression led to a rapid deterioration of the crystal quality, such that only lattice parameters could be determined at 22 GPa. Note that all diffraction measurements were performed at room temperature in order to assess crystal and electronic structures of CsV 3 Sb 5 in its normal state. Electronic instabilities of the normal state would then lead to the formation of a charge-density wave upon cooling, as observed at ambient pressure. Likewise, the evolution of the normal-state band structure should be responsible for changes in the superconducting state as a function of pressure. DFT calculations. Full-relativistic DFT calculations were performed in the FPLO  [33]  and Wien2K  [34, 35]  codes using Perdew-Burke-Ernzerhof (PBE) exchange-correlation potential  [36]  and the 24 × 24 × 12 k-mesh that ensured convergence of the Fermi energy.
Acknowledgements We are grateful to  Gabriele Untereiner  for preparing single crystals for the XRD experiment as well as  Alain Polian  for preparing the pressure cells for measurements. We also acknowledge  SOLEIL  for providing the beamtime. Funding information S. D. W. and B. R. O. gratefully acknowledge support via the  UC Santa Barbara NSF   Quantum Foundry  funded via the  Q-AMASE-i program  under award  DMR-1906325. B. R. O. also acknowledges support from the  California NanoSystems Institute  through the  Elings fellowship program. The work has been supported by the  Deutsche Forschungsgemeinschaft (DFG)  via  DR228/51-1  and  UY63/2-1. E. U. acknowledges the  European Social Fund  and the  Baden-Württemberg Stiftung  for the financial support of this research project by the Eliteprogramme.
High-resolution respirometry At the end of incubation time, fibers (10 mg) were transferred into 2mL of fresh ice-cold BIOPS relaxing solution and 20μL of saponin (freshly-prepared stock solution 5mg/mL) are added to obtain final concentration of 50μM. Bundles were maintained for 30 min with saponin solution, and then washed for 5 min three times in ice-cold respiration medium MiRO5 containing 110 mM sucrose, 20 mM HEPES, 10 mM KH 2 PO 4 , 20 mM taurine, 3 mM MgCl 2 6H 2 O, 60 mM MES-K, 0. 5 mM EGTA and 0. 1% bovine serum albumin; pH 7. 1. Respiration rates were measured (O 2 k oxygraph, Oroboros, Innsbruck, Austria) and expressed in picomoles O 2 per second per milligram wet weight. Data acquisition and analysis were performed with Datlab4 software (Oroboros, Innsbruck, Austria). Substrate and/or inhibitors for respiratory experiments were added within MiRO 5 in a step-by-step manner using micro syringes.
Chemical agents (Sigma Aldrich, France) were sequentially prepared according to Oroboros manufacturer data sheet and administered as we have previously described  [12]. Respiration rates were measured (O 2 k oxygraph, Oroboros, Innsbruck, Austria) and expressed in picomoles O 2 per second per milligram wet weight. Chemical agents (Sigma Aldrich, France) were sequentially prepared according to Oroboros manufacturer data sheet and administered as described below: • Complex I-dependent state 2 respiration (V 0 ) was determined as the respiration rate in the presence of 10 mM L-glutamate and 2 mM L-malate dissolved in H 2 O, which activate the Krebs cycle enzyme malate dehydrogenase providing NADH to complex I of the respiratory chain. • Complex I-dependent state 3 respiration (V glut+mal ) was determined as a phosphorylationstimulated respiration rate in the presence of 5 mM ADP added to the V 0 medium providing ADP to F 1 -F 0 ATP synthase.
Flow Cytometry. Cells were transfected with the different mutants of the receptor fused to HA tag and grown until 70% confluence and washed using complete phosphate buffered saline (PBS) (PBS with 1 mM of CaCl 2 and 0. 5 mM of MgCl 2 ). Cells were then detached and incubated with antihemagglutinin (HA) from Roche Diagnostics for 60 min, followed by an additional incubation with goat anti-rat (FITC) antibody (ab6840) for 60 min. Isotypic controls were done in parallel by incubation with each corresponding immunoglobulin isotype. After washing, stained cells were analyzed by flow cytometry using a BD LSR cytometer (BD Biosciences) and results from 10,000 cells were analyzed by Cell Quest Pro software. Results are presented as the difference in fluorescence relative intensity between the cells labeled with the antibody versus the cells labeled with the corresponding isotype, and by the overlay histograms displaying the isotypic control and the antibody labeling, for one representative experiment out of two.
SDS-PAGE and Western Blot. After lysis, samples were resolved by electrophoresis on 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and transferred electrophoretically to PVDF membranes (GE Healthcare Life Sciences). Membranes were washed in Tris-buffered saline (TBS; pH 7. 4) containing 0. 1% Tween-20 (TBS-T) and blocked with 5% (wt/vol) dry milk TBS-T for 30 min. Blots were probed with anti-arrestin, antiphospho-ERK, or anti-ERK, antiphospho-SRC, anti-SRC, anti-HA, or anti-GFP, or anti-actin antibodies (1:2,000). Horseradish-peroxidase-conjugated goat anti-rabbit, anti-mouse, or anti-rat antibodies (1:33,000) were used as secondary antibodies. Immunoreactive bands were detected using the Dura detection kit. Protein quantification on blots was performed using Quantity One software (Bio-Rad).
The results are shown as mean ± SEM from three independent experiments. Data were plotted and analyzed using GraphPad Prism 4 software for Windows (GraphPad Software, Inc. ). For normalization the value of all replicates was divided by the mean of the agonist 5-CT-induced maximal responses and multiplied by 100 for any given read out. Concentration/response curves were fitted by nonlinear regression and saturation curves by a hyperbolic one-binding site equation. The method provided estimates for EC 50 values and corresponding SEM. Animals. C57BL/6 (Janvier) and 5-HTR 7 knockout (5-HT 7 R À/À ) mice maintained on a C57BL/6 background (56) as well as Sprague-Dawley rats (specific pathogen free status, Janvier) were used. For experiments, male animals (8 to 14 wk old) were housed in a temperature (20 to 24 °C)and relative humidity (45 to 65%)-controlled room and acclimated to a bright cycle (12/12 h).
3% Triton X-100. Then, cells were washed three times with TBS and incubated with the corresponding secondary antibodies, sheep anti-rabbit IgG FITC (F7512 1/500, Sigma) or goat anti-mouse IgG TRITC (T7657 1/100, Sigma) for 1 h in a wet and dark room. After three washes, cells were stained with bisBenzimide H33258 (B1155, Sigma) for 10 min and washed and mounted onto microscope slides with Fluoromount-G (0100-01, Southern Biotech). The costainings were observed using an inverted Zeiss Cell Observer 27 microscope with a 40× EC Plan Neofluar 40/0. 75 numerical aperture objective (Carl Zeiss Co. ). Images were processed with Zen software analyzed with ImageJ.
ACKNOWLEDGMENTS. We thank  Francesco De Pascali  for technical assistance. We also thank  Catherine Grillon ,  Martine Decoville , and  Pascale Cr  epieux for helpful discussions and  St ephane Charpentier  for fruitful discussions and proofreading of the manuscript. This work was supported by the  Region Centre Val de Loire  (projects Serosero7 and PAIN), the  Structure F ed erative de Recherche (SFR) FED 4226 "Neuroimagerie Fonctionnelle" , the  F ed eration de Recherche Physique et Chimie du vivant FR2708 Centre de Biophysique Mol eculaire -Institut de Chimie Organique et Analytique  and the  University of Orl  eans. C. E. K. was the recipient of a PhD fellowship from the  Region Centre Val de Loire. F. R. was the recipient of a postdoctoral fellowship from the  Fondation pour la Recherche M edicale.