dataesr/scientific-paragraphs-categorization huggingface dataset:
38 6. 75 Rikenellaceae_RC9_gut_group OTU546 rs429334236 5 72,324,510 0. 24 5. 81 Fibrobacter OTU1335 rs398177414 5 79,732,944 0. 18 6. 56 Ruminococcus OTU1191 rs411645323 5 83,683,348 0. 20 5. 30 Ruminococcus OTU1191 rs418870684 5 83,724,689 0. 12 5. 29 ENSOARG00000025325 Lachnospiraceae_UCG-008 OTU655 rs403619685 6 28,689,782 0. 08 5. 92 Lachnospiraceae_XPB1014_group OTU947 rs400970883 6 36,655,091 0. 05 6. 20 SPP1 Prevotella OTU1843 rs416815759 6 45,906,622 1. 43 5. 59 TBC1D19 Prevotella OTU440 rs406261149 6 50,058,344 0. 20 5. 30 Acetitomaculum OTU612 rs430647780 6 75,425,021 0. 20 5. 58 Acetitomaculum OTU612 rs415319007 6 85,637,777 0. 29 5. 51 Prevotella OTU399 rs428296445 10 16,139,981 0. 33 5. 57 W5PCY9_SHEEP Clostridia UCG-014/uF/uG OTU1386 rs423990418 10 16,216,118 0. 21 6. 28 W5PCZ6_SHEEP Clostridia UCG-014/uF/uG OTU1386 rs404163943 10 16,222,665 0.
10 6. 28 W5PCZ6_SHEEP Prevotella OTU13 rs408448649 10 46,573,078 0. 25 5. 58 DACH1 Muribaculaceae/uG OTU803 rs403822645 10 80,687,261 0. 08 4. 87 ENSOARG00000026327 Ruminococcaceae/uG OTU155 rs408532009 10 84,678,999 0. 35 6. 41 ANKRD10 Ruminococcaceae/uG OTU501 rs422890024 11 7,826,874 0. 04 5. 40 MSI2 Christensenellaceae_R-7_group OTU596 rs414408260 11 58,151,039 0. 14 5. 63 CAG-352 OTU304 rs421085019 11 61,946,606 0. 23 5. 48 CAG-352 OTU304 rs429659626 11 62,034,261 0. 04 5. 48 HELZ Prevotella OTU943 rs412819070 15 5,606,060 0. 13 5. 71 MMP20 Prevotella OTU423 rs409362751 a 15 37,553,506 0. 18 6. 97 PDE3B Prevotella OTU80 rs409362751 a 15 37,553,506 0. 16 5. 84 PDE3B Prevotella OTU399 rs409362751 a 15 37,553,506 0. 19 5. 52 PDE3B Prevotella OTU399 rs426932216 15 44,445,054 0. 14 5. 13 LOC101105776 Blautia OTU1025 rs425070593 15 80,043,367 0.
Surgery and tissue sampling The surgery was performed by two neurosurgeons. In order to guide sampling, surgeons evaluated MRE data prior to surgery to plan for biopsies covering a range of MRE signal. During resection, 2-7 biopsies were taken from different parts of the tumor. All biopsies were situated in tumor or adjacent parts of the brain included in the planned resection prior to MRE evaluation, and covered both contrast-enhanced T1-weighted (CE-T1w) images and more diffusely infiltrating tumor demarcated by non-enhancing T1-weighted and pathological T2-FLAIR signal. Biopsies were taken early in the resection, prior to any major shift in the navigational accuracy. The biopsy locations were chosen according to varying stiffness as evaluated by the neurosurgeon. The surgeon evaluated tumor consistence according to a modified version of the grading scale from Zada et al.
MR Image Processing From the MRE scan, maps of the magnitude of the shear storage modulus |G*| (tissue stiffness) and the shear phase angle φ (related to tissue viscosity, i. e. its ability to dissipate energy) were produced. Details about the MRE reconstruction can be found in Svensson et al. 7. The volumetric CE-T1w images were co-registered to the MRE image space, using a nearest-neighbor interpolation in the nordicICE software (NordicNeuroLab AS, Bergen, Norway). The resulting transformations were applied to the binary ROI masks in CE-T1w space, resulting in a one-voxel seed point in MRE space. To make the analysis more robust to brain shift and co-registration issues, a ROI consisting of a trimmed mean of nine MRE voxels around the seed point was used, where the voxel with the highest and the lowest value were removed before averaging.
RNA Sequencing For each patient, 2-4 biopsies were selected for RNA sequencing. Total RNA sequencing returned sequence counts for 22510 genes and other transcripts. The analyzed biopsies were classified as 'stiff' or 'soft' based on MRE, i. e. , higher or lower |G*| norm than the mean value of all biopsies within each tumor, respectively. Batch correction of RNA sequencing data was performed with the ComBat-seq package in R  15. Normalization and differential expression of RNA sequencing data was done with the DESeq2 package in R  16. The differential expression results were corrected for multiple testing using the Benjamini and Hochberg method, and adjusted p-value threshold was set to 0. 05.
Statistical analysis Comparison of MRE measurements in tumor and normal-appearing tissue was performed using a Wilcoxon signed-rank test. MRE measurements and tumor volumes were compared using a Spearman's rank order test. |G*| norm measurements were compared to the surgeon's evaluation using ordered logistic regression and to the radiological tissue type using multinomial regression. A significance level of P = 0. 05 was assumed for all tests. Logistic regression was performed using Stata (version 17. 0, StataCorp LLC, College Station, Texas, USA). Over-representation (OR) analysis and gene-set enrichment analysis (GSEA) were performed using the clusterProfiler package in R  17. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and sparse PLS-DA were performed with the mixOmics package in R  18.
Survival analysis Raw RNA sequencing reads from the The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects were downloaded from the NIH Genomic Data Commons Data Portal along with sample metadata. Normal samples, control samples and duplicate samples were excluded, leaving 265 patient samples. Values were batch-corrected with ComBat-seq followed by normalization and rlog transformation in DESeq2. A sparse PLS-DA model containing 22 genes was trained using 22 patient samples, annotated as 'stiff' or 'soft', using the mixOmics package. Expression of these 22 genes was used to classify the external data using the 'predict()' function in the mixOmics package. Kaplan-Meier survival curves were produced with the survminer package in R. Cox regression analysis on survival data was performed in SPSS (version 28.
1C ). Differential gene expression analysis between 'stiff' and the 'soft' biopsies per patient found that 196 genes were differentially expressed based on an adjusted p-value of 0. 05 (Fig. 2A , Supplementary Table  2 ). Of these, 122 were upregulated in 'stiff' biopsies, while 74 were upregulated in 'soft' biopsies. Normalized expression levels of differentially expressed genes in every biopsy show that 'stiff' or 'soft' biopsies tend to cluster together, and biopsies within individual patients also show similar expression profiles (Fig. 2B ). Due to the limited size of the dataset, differential expression may depend on samples from a single patient. Therefore, to explore the robustness of the differential expression, we performed sequential differential expression analysis, leaving out all samples from a single patient in each iteration.
Functional annotation of stiffness-associated gene expression To evaluate the structural and functional importance of differentially expressed genes, we performed a gene set enrichment analysis (GSEA) of genes associated with increased biopsy stiffness using the Gene Ontology (GO) and Reactome databases  19, 20. The GO-terms with the highest association to 'stiff' biopsies represent components of the extracellular matrix, cellular adhesion and innate immunity (Fig. 3A ). Similarly, among the most significantly enriched Reactome pathways associated with 'stiff' biopsies were extracellular matrix organization, integrin cell surface interactions, and neutrophil degranulation (Supplementary Fig. 2 ). In contrast, GO-terms and Reactome pathways with highest association with 'soft' biopsies largely represented normal neuronal functions such as regulation of membrane potential and neurotransmitter receptor complex although associations to DNA methylation and rRNA regulation were also found.
CR1, PLAGL1, COL4A1, and COL5A2 have all been shown to correlate with shorter survival  [40] [41] [42] [43]. When our data was compared to previously published transcriptomic profiles of GBM samples, we found that median survival was significantly shorter in patients with tumors that exhibited the gene expression signal associated with 'stiff' biopsies. This indicates that the genetic and molecular processes we detect in stiff tumor biopsies play a role in the malignant progression of tumors. It should be noted that while our data consists of multiple biopsies per patient with MRE stiffness evaluations, only one tissue biopsy per patient was available in the external data sets. We assume that this tissue biopsy is representative of the entire tumor. We do not claim that the tumors in these datasets are stiff or soft, rather, we examined the prognostic relevance of stiff or soft expression signals for GBM patients.
However, the SoS indicator is potentially able to assess other anthropogenic pressure such as other bottom fishing activities, if the species sensitivity to the pressure is known. Fig. 1  outlines the method to generate the list of sentinel species and can be applied through a publicly available R function (https://github. com/Gonzalez-Irusta/SoS) which uses part of the code applied in  Farriols et al. , (2015) , but adapted and extended to the SoS characteristics. The SoS function follows six steps to obtain the list of sentinel species for each habitat (Fig. 1 ):.
Case studies A1 and A2. Bottom trawl survey DEMERSALES DEMERSALES is one of the surveys coordinated by the International Bottom Trawling Survey Working Group (IBTSWG) of the International Council for the Exploration of the Sea (ICES). Its design covers the northern Spanish shelf from the Portuguese border in the west to the French border in the east (Fig. 2 ). The survey has a random stratified (by depth and geographic strata) sampling according to the standard IBTS methodology for the western and southern areas (ICES, 2017). In each haul, all species caught are identified, counted, and weighed. These measures provide the biological data used to select the list of sentinel species for the assessed habitats. The DEMERSALES sampling strategy (random stratified) incorporates several MSFD broad habitats, including the two selected as case studies A1 and A2 (based on its extent) for testing the SoS indicator: 'offshore circalittoral sand' and 'upper bathyal sediment' respectively.
The distribution of hauls between 2013 and 2019, carried out in these habitats is shown in Fig. 2. These years were selected to assure consistency between available fishing effort data (2010-2019) and biological data. Trawling effort was computed as the mean fishing effort of the four previous years, including the year when the biological samples were taken. For instance, for biological data sampled in 2013, we used the mean fishing effort from 2009 to 2013. These fishing effort maps were derived using vessel GPS locations from the Vessel Monitoring Systems (VMS) and logbook data (gear information). Both were provided by the Spanish Ministry of Agriculture, Food and Environment for the period 2010-2019. Gear and GPS location data were linked using ship code and trip date fields. VMS pings not related to fishing activity were removed using speed and other criteria  (González-Irusta et al.
Case study D. Flemish Cap This case study was conducted in the Flemish Cap area, a high-seas zone off the Canadian coast (Fig. 2 ). The effect of trawling disturbance on the proportion of sentinel species of the target MSFD broad habitat 'mid bathyal sediments' located at depths ranging from 600 to 1300 m was analysed. The trawling impact on the target habitat was analysed using data from the 2007 EU Flemish Cap bottom-trawl research survey  (Durán Muñoz, et al. , 2020) , using standardised sets of a Lofoten bottom trawl (with a swept area of ≈0. 04 km 2 each) following a depth-stratified sampling design. For more information about the sampling area or method see  Murillo et al. , (2016 Murillo et al. , ( , 2020)). After filtering out hauls located in the depth range of the selected MSFD broad habitat (600-1300 m), and removing 4 hauls located in the south side of the bank, 26 hauls distributed across a trawling gradient were analysed; 6 of them were located in no pressure areas (0 pings by km 2 ), 5 in low pressure (0.
Case study E. Seco de los Olivos seamount This case study is located in the Site of Community Importance (SCI) of the European marine Natura 2000 network "Sur de Almería -Seco de los Olivos", in southern Spain (Fig. 2 ). The effect of trawling disturbance on the proportion of sentinel species of the target MSFD broad habitat 'upper bathyal sediment' was analysed using data from VMS for the period 2010-2012 with the same categories and methods explained in the previous case studies. Species data were obtained from three ROV (Seaeye Falcon & FalconDR) surveys conducted by OCEANA on board the Oceana Ranger between 2010 and 2012 (for more information about the sampling area or method see de la  Torriente et al. , 2018; De la Torriente et al. , 2019; de la Torriente et al. , 2020). The sampling unit consisted of 1-minute continuous movement ROV tracks at a speed of 0.
2-0. 4 knots, covering an average distance of 13 m (mean = 13. 16 ± 5. 74 SD). The final data set selected for analysis was composed by 86 samples located in the target MSFD broad habitat (upper bathyal sediment) across a trawling effort gradient. To select the 'typical species set', we used samples located in those areas with the lowest trawling effort and with the presence of the biological habitat EUNIS "A6. 514-facies of compact muds with Isidella elongata". To compare this typical set with samples located in areas exposed to higher levels of pressure, we used the rest of the samples which corresponded with environmentally similar areas but without the presence of the biogenic habitat. Trawling effort was calculated using the same methodology and threshold for the trawling levels described for the case study A.
2. 5. Using the SoS indicator to assess the extent of the habitat affected by a physical pressure (MSFD criterion D6C3) The SoS indicator can be directly applied to assess D6C3 by converting pressure maps into percentage of sentinel species using correlative approaches. For example, the SoS indicator was used to assess the environmental status of the MSFD broad habitat 'upper bathyal sediment' in the north coast of Spain (case study A2, see below). The correlation between the proportion of sentinel species and trawling effort was analysed using General Additive Models (GAMs). Since the response data was a proportion, they were analysed using a binomial GAM with Logit as link function  (Zuur et al. , 2009). The statistical models were then applied to the GIS layers of trawling effort (supplementary Fig.
3 ). Case studies based on otter trawl data (A1, A2, B and D) showed similar initial and final values in the proportion of sentinel species (proportions from 0. 35 to 0. 6 of total biomass in no pressure areas and values close to zero in the high-pressure areas) but with slightly different trends. Case study A2 (DEMERSALES: upper bathyal sediment) and the case study D (Flemish Cap) showed an acute decrease in the proportion of sentinel species with a reduction under values lower than 0. 1 at medium pressure levels, whereas the case study A1 (DEMERSALES: offshore circalittoral sand) showed a less severe reduction for intermediate pressure levels (values higher than 0. 2). Case study B (Ría de Vigo) which tested the impact of chemical pollution on the proportion of sentinel species also showed a clear decrease with pressure, although the initial proportion values of the sentinel species were much lower than in other case studies (<0.
Acknowledgements The present study was funded by the project  ESMARES2  from the  Instituto Español de Oceanografía  ( IEO-CSIC ), under the framework of the tasks ordered to the IEO by the  Ministerio de Transición Ecológica y Reto Demográfico (MITERD)  of the  Spanish government  for the application of the  Marine Strategy Framework Directive (MSFD)  in Spanish waters. The initial development of this indicator was one of the results of the project  EcApRHA (Applying an ecosystem Approach to (sub) Regional Habitat Assessments ) co-financed by the  European Union 's  DG ENV/MSFD/Action Plans2014 , under the agreement No. 11. 0661/ 2015/712630/SUB/ENVC. 2OSPAR. Testing of applications to GES assessment has been carried out within the  NEA-PANACEA project  ( North East Atlantic project on biodiversity and eutrophication assessment integration and creation of effective measures ) financed by the  European Union 's  DG ENV/MSFD 2020 , under the agreement No.
| Study participants and study drug The study complied with current International Conference on Harmonization-Good Clinical Practice guidelines and was approved by the applicable regulatory authorities and independent ethics committee/institutional review board at each institution. All patients or their legal representatives provided signed informed consent before enrollment, and assent where applicable. Patients with a confirmed LGS diagnosis who completed the phase 3 randomized clinical trial (aged 2-35 years at entry into the core study) were eligible to enroll in the OLE study (NCT03355209). In accordance with the International League Against Epilepsy criteria, confirmed LGS diagnosis included abnormal background activity accompanied by a slow spike-and-wave pattern on the electroencephalogram (<2.
Specifically, many subjects who received study drug under the protocol were unable to attend a final in-person study visit within the protocolspecified time period (365 ± 4 days), and their treatment was extended until the visit could take place and suitable transition of care could be arranged. Therefore, an interim analysis was performed based on a snapshot of the clinical database taken on October 19, 2020, a date chosen to ensure that the analysis included at least 365 ± 4 days of exposure in the OLE for all but one of the patients who remained in the trial. As a result, 142 patients in the OLE had data captured at Month 13-15, which  (i. e. , last treatment dose) in the modified intent-to-treat population.
| Statistical analyses The OLE safety population was defined as all patients who received ≥1 dose of fenfluramine during the OLE. Effectiveness analyses were performed on the modified intent-to-treat population, defined as all patients with ≥1 dose of fenfluramine and ≥1 month of valid drop seizure data during the OLE. Continuous variables were summarized by mean, SD, median, and range. Categorical variables were summarized by frequency and percentages. For effectiveness endpoints, Wilcoxon signed-rank tests assessed the statistical significance of median percentage change from prerandomized baseline, where statistical significance was defined a priori as p <. 05. No imputation was made for missing data or dropouts in any of the efficacy endpoints. Standardized z-scores were calculated for patients <20 years of age based on the most recent publicly available weight data from the US Centers for Disease Control and Prevention.
| Patients A total of 263 patients were randomized in the RCT. A total of 247 patients had enrolled in the OLE as of October 19, 2020 and had at least one dose of study drug and 1 month of diary data in the OLE (Figure  1A  and Table  1 ). Eightythree (33. 6%) patients withdrew; the most common reason for withdrawal was lack of efficacy (n = 55, 22. 3%; Figures  1B  and S1 ). Per protocol, concomitant ASM regimens were to remain stable for the first 6 months of the study, after which investigators were permitted to adjust the doses of one or more of the other concomitant ASMs as per routine clinical practice. Deviations from the protocol during the first 6 months included dosage changes (n = 23 patients), discontinuation of an ASM (n = 2), and new ASM added (n = 1); the most frequent ASM changes.
ACKNOWLEDGMENTS The authors thank the patients who participated in the study and their families; and  Dr Rachel Tham  ( UCB ) for statistical support. This study was sponsored by  Zogenix (Emeryville, CA, USA ; now a part of  UCB ). Professional medical writing and editing were provided to the authors by  Danielle Ippolito, PhD ,  CMPP ,  MWC , and  Scott Bergfeld, PhD , of  PharmaWrite (Princeton, NJ, USA ), and were paid for by  Zogenix.
5X using TE70 ECL Semi-Dry Transfer Unit (Amersham Pharmacia Biotech). Probes (Table  S3 ) were [α-32 P]dCTP-labelled. RNA-seq and transcriptomic analysis. RNAs (DNA-free) from triplicate cultures sampled at OD 600 = 7 were sequenced using NextSeq 500/550 High Output Kit v2 (75 cycles). Sequences were aligned to the reference genome (CP000253-1-NCTC8325) with Bowtie2 tool and quantified with Feature Counts program. Differential gene expression analysis was performed using DESeq2 algorithm  (90). The data for this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB50160 (https://www. ebi. ac. uk/ena/browser/view/PRJEB50160). 5'-3' RACE. 5' and 3' ends were determined using circularization method described in (51). 10 µg total RNA of wild-type strain (HG003) were extracted from samplings at OD 600 7, O/N and day 4 (D4).
Acknowledgments We are grateful to our colleague  Sandy Gruss  ( INRAE, MICALIS ) for critical reading of the manuscript. We thank  Patricia Kerboriou  ( I2BC ),  Claire Toffano-Nioche  ( I2BC ) and  Mehdi El Sadek Fadel  ( I2BC ) for technical, bioinformatics and statistical support. We are grateful to  Masaya Fujita  ( University of Houston ) who graciously provided anti-σ A antibodies. We thank  Dodo Bourbon  for helpful discussions and warm support. We acknowledge the highthroughput sequencing facility of  I2BC  for its sequencing and bioinformatics expertise and for its contribution to this study. This work was supported by the  Agence Nationale de la Recherche  [ ANR-19-CE12-0006-01  (RRARE)]. M. E. and W. L were the recipient of scholarships from the  Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation (MESRI) and Chinese scholarship council (CSC) , respectively.
A unit cell with one nanodisk was simulated. Periodic boundary conditions were applied at the edges of the unit cell to simulate the electric-field distribution of square nanodisk arrays. The mesh consisted of free triangular elements with a maximum size of 2 nm. All simulations were conducted with linearly polarized incident light along the sample normal with a wavelength ranging from 650 to 1,000 nm. A uniform embedding medium with a dielectric constant of n = 1. 5 was used. The refractive index dataset was measured as shown on Figure  S20  (Supporting Information). In particular, the optical spectra of the metasurfaces and the continuous films were simulated as presented in Figure  3c  while Figure  3d -f plot the near-field distribution in the plane of nanodisks of different sizes. Ellipsometry Measurements: The sample with linearly polarized light was illuminated and the change of polarization was probed upon reflection from the sample to obtain the complex reflectance ratio  = r p /r s.
Acknowledgements Maxime Vergès was supported by a  CIFRE Ph. D. grant from  Vinci Technologies. This work was supported by the  ANR-20-CE09-0013  UFO, by the  Institute Carnot ICEEL  for the project " CAPMAT " and FASTNESS, by the  Région Grand Est , by the  Metropole Grand Nancy , for the Chaire PLUS by the  impact  project  LUE-N4S , part of the  French PIA project  " Lorraine Université d'Excellence " reference  ANR-15-IDEX-04-LUE , by the " FEDERFSE Lorraine et Massif Vosges 2014-2020", a European Union Program , and by the  Academy of Finland  (grant no. 316857 ). The devices in the present study were patterned at MiNaLor platform that is partially funded by  Grand Est Region and Feder  through  RaNGE  project.