dataesr/scientific-paragraphs-categorization huggingface dataset:
We first evaluate HNN capacity on Matlab to validate Hebbian and Storkey learning rules for three HNN sizes (25, 50, and 100 neurons), then we implement Storkey and Hebbian in the on-chip learning architecture to extract the real capacity metric for a 25neuron ONN because the resource utilization limits the ONN size. A test flow is set up and automatized for testing the digital ONN on-chip learning architecture using Matlab to send commands and patterns to the system through a UART communication protocol (see Figure  4 ). We measure the latency of the 25-neuron ONN for onchip learning. The latency is divided into three parts, the ONN computation latency, the weight computation latency, and the transmission latency. The ONN computation latency is by default stable no matter the weights and size of the network, so we expect it to stay stable.
Results This section presents results obtained with both HNN on Matlab and ONN on FPGA. First, we explain the choice of the most suitable learning rules to implement for ONN on-chip learning. Then, we test the learning rules with ONN on-chip learning constraints in Matlab to study the impact of the weight precision on the HNN capacity and decide which weight precision to apply to the digital ONN design. After, we implement the learning rules in our digital ONN on-chip learning architecture and report on resource utilization, capacity, and latency of our solution for various weight precision.
Future work will first explore alternative solutions to try to overcome the current limitations of the ONN on-chip learning architecture. Furthermore, the next developments will focus on possible applications with the ONN on-chip learning architecture. The digital ONN design has already been used for sensor data treatment in various applications, like interfacing with a camera for image recognition  (Abernot et al. , 2021)  or using proximity sensor data to perform obstacle avoidance  (Abernot et al. , 2022a) , so we are confident on the integration of our architecture with different sensors. Possible applications for the digital ONN on-chip learning architecture could be in the robotics domain where realtime continual learning is often necessary, and where the digital ONN design already showcased good performances  (Abernot et al.
At 11 mM, DiPyrroTriPol-OMe performs reasonably well ( ,-/,// ≈ 73 and 78 at 9. 4 and 14. 1 T, respectively) but is less efficient that PyrroTriPol-OMe because of slower DNP build up times (TB= 1. 8 and 3. 8 s at 9. 4 T and 14. 1 T). In the case of DiTEMTriPol-I-OMe, the DNP performance are clearly drops with  ,-/,// ≈ 20 and 16 at 9. 4 and 14. 1 T, respectively. As such, this first experimental dataset does not verify the previously developed hypothesis: chemically tethering two or three nitroxide to a trityl does not necessarily enhance  ,-/,// or shorten the buildup time  $. This seems to indicate that special conditions are required; not all triradicals made of two nitroxides and one trityl may outperform their biradical counterparts and special design may be required. We show in Figure  S6  that MAS-DNP simulations  26  predict that PyrroTriPol should moderately surpass DiPyrroTriPol-OMe though the reason behind is beyond the scope of this article.
Acknowledgements This work was supported by the  French National Research Agency  ( CBH-EUR-GS and Labex ARCANE   ANR-17-EURE-0003 ,  Glyco@Alps   ANR-15-IDEX-02 , and  ANR-16-CE11-0030-03 ) and the  European Research Council  Grant  ERC-CoG-2015  No. 682895  to G. D. P. Part of this work, carried out on the Platform for Nanocharacterisation (PFNC), was supported by the " Recherches Technologiques de Base" program  of the  French National Research Agency (ANR). This work was supported by the  Icelandic Research Fund , grant No. 173727 , and the  University of Iceland Research Fund  (S. Th. S). T. H. thanks the  Deutsche For-schungsgemeinschaft (DFG)  for a postdoctoral fellowship (414196920). The  National High Magnetic Field laboratory (NHMFL)  is funded by the  National Science Foundation Division of Materials Research  ( DMR-1644779 ) and the  State of Florida.
A portion of this work was supported by the  NIH   P41 GM122698 ,  NIH   S10 OD018519  and from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101008500. Financial support from the  TGIR-RMN-THC Fr3050  CNRS for conducting DNP experiments at high magnetic fields (18. 8 T) is gratefully acknowledged. FMV thanks  Alexander Lomzov  for his initial help with respect to trityl force-field.
Hence, we focus on the methods presented in Section 3. 2. We do not present an adaptation of the PLS method (iv) to mixed models, because we are not aware of any proposed method or package that would provide this. Thus, we focus on methods (ii), (iii), and (v). (ii) Following  Nabugoomu et al. (1999) , the CCR approach of  Digby (1979)  is easily extended in a mixed model framework. In the criss step, estimating θ hk k ¼ 1,…, p; h ¼ 1,…,q ð Þ , we fix the variance parameters at their current estimates to save computing time because this usually has fewer parameters to be estimated as fixed effects than the cross-step. These variance parameters are re-estimated in the cross step, estimating α i and β ih i ¼ 1, …, n; h ¼ 1,…, q ð Þ , using residual maximum likelihood (REML). For an application of this method, see  Macholdt et al.
| EXAMPLE To illustrate the random-effects modelling discussed in Section 5, we consider the lettuce data reported in van Eeuwijk (1992) as means per genotype and environment. All analyses were implemented in both SAS and R, and the full code for both packages is found in the Supporting Information. The response is nitrate concentration. The data is balanced and stems from a single replicated trial, in which eight genotypes were evaluated at 18 points in time, which are regarded as environments for our analyses. The data comprise eight observed environmental covariates, which are scaled here to zero mean and unit variance for all regression analyses. We start by fitting model (  16 ), dropping the covariate terms, leaving the structure α i þ ε j. Our baseline model in Table  1  has independent (ID) residual effects e ij with constant variance.
Background Eosinophilic esophagitis (EoE) is a chronic immune-mediated rare disease, characterized by esophageal dysfunctions. It is likely to be primarily activated by food antigens and is classified as a chronic disease for most patients. Therefore, a deeper understanding of the pathogenetic mechanisms underlying EoE is needed to implement and improve therapeutic lines of intervention and ameliorate overall patient wellness. Methods RNA-seq data of 18 different studies on EoE, downloaded from NCBI GEO with faster-qdump (https:// github. com/ ncbi/ sra-tools), were batch-corrected and analyzed for transcriptomics and metatranscriptomics profiling as well as biological process functional enrichment. The EoE TaMMA web app was designed with plotly and dash. Tabula Sapiens raw data were downloaded from the UCSC Cell Browser.
Transcriptomics analysis RNA-seq data were downloaded from NCBI GEO (18 different studies, listed within the EoE TaMMA web app, in the metadata tab > analysis overview subtab, and metadata tab > sample characteristics subtab; Fig. 1A, B , and Additional file 2: Table  S1 ) with faster-qdump (https:// github. com/ ncbi/ sra-tools). FASTQ sequencing reads were adaptor-trimmed and quality-filtered with Trimmomatic  [11] , prior to mapping to the hg38 human reference genome with STAR  [12]. Gene count normalization and differential gene expression were performed with DESeq2  [13]. Functional enrichment analysis was done with GeneSCF  [14]. Low-dimensional embedding of high-dimensional data was performed by either Uniform Manifold Approximation and Projection (UMAP) or t-distributed stochastic neighbor embedding (t-SNE) machine learning algorithms, within R (https:// cran.
Web app design The web app, available at https:// eoe-meta-analy sis. herok uapp. com/ (username: ungaro; password: steams), was designed with plotly and dash (https:// plotly. com/ dash/). Processed data and code are hosted by GitHub and are available at https:// github. com/ DU-omics/ eoemeta-analy sis_ data and https:// github. com/ DU-omics/ eoe-meta-analy sis. The complete guide and the related documentation are available at https:// ibd-tamma. readt hedocs. io/. The user interface is described in Additional file 1: Fig. S1. Newly released EoE datasets will be continuously searched and implemented in this platform to maintain it as timely updated. A suggestion link is available at the bottom of the home page of the platform for the users to notify newly released data sets.
Single-cell RNA-seq data analysis and deconvolution Tabula Sapiens raw data  [20]  were downloaded from the UCSC Cell Browser (https:// cells. ucsc. edu). Esophageal single-cell raw data were downloaded from https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE17 5930  [21]. The analysis was performed within the Automated Single-cell Analysis Pipeline (ASAP)  [22]. Single-cell data-driven bulk RNA-seq data deconvolution was performed with MuSiC and CIBERSORTx  [23, 24].
Multi-omics factor analysis The different omics datasets were integrated with the Multi-Omics Factor Analysis (MOFA) framework  [25]  which interprets multi-layer (different data modalities) high-dimensional data and infers an interpretable lowdimensional representation in terms of a few latent factors. Variance stabilization and z-scoring, followed by feature selection to select the most informative variables, namely those explaining more variance, were performed to ensure that all the molecular layers were equally represented. Variance decomposition was then performed between groups to find differences in terms of variance explained within factors and groups, thus stratifying patients into cohorts, each one of them displaying a specific molecular signature.
Results The EoE TaMMA web app identifies EoE-specific markers EoE aetiopathogenesis is not fully explained, even if a major shift toward antigen-mediated T H 2 response has been accepted as the most relevant characteristic  [1]. Although some RNA-seq studies have been performed, the complete survey of all transcriptomics collections to advance EoE-related research has not been compiled yet. For this purpose, we analyzed and batch-corrected a total of 18 different studies, including 660 samples from esophageal mucosa and blood, combined into the EoE TaMMA web app (Fig. 1A  and B ). We included blood and esophageal tissues from EoE and GERD patients and IBD-derived blood samples. Of note, we included also IBD samples because it helped to better correct the batch variability, a normal consequence of the combination of different studies coming from a variety of data sources generated by different operators, sequencers, and analytic platforms  [9, 10].
Indeed, this methodological approach mitigates intersubject variability thanks to the discovery of the principal sources of variation in multi-omics data sets. In this regard, the possibility to perform such an analysis recently became effective thanks to the machine learning-based tool Multi-Omics Factor Analysis (MOFA). MOFA infers a set of (hidden) factors that capture biological and technical sources of variability  [25]. Therefore, by applying MOFA for processing the six different types of omics data, encompassing the human transcriptome, virome, eukaryome (fungi and protists), bacteriome, and archaeome from EoE and healthy control samples (Additional file 1: Fig. S7A ), the source of variation between the EoE and healthy (control) esophageal mucosa was identified mainly among the metatranscriptomics (microbiome) factors.
Availability of data and materials Transcriptomic data are accessible at https:// eoe-meta-analy sis. herok uapp. com/. Processed data and code are available at https:// github. com/ DU-omics/ eoe-meta-analy sis_ data/ master/, and https:// github. com/ DU-omics/ eoemeta-analy sis, respectively. Data, analytic methods, and study materials will be made available upon request to the authors.
Two quasars exhibit significantly broad and blueshifted [O III] emission, thought to trace galactic-scale outflows, with median velocities of -610 and -1430 km s -1 relative to the [C II] 158 μm line. All eight quasars show strong optical Fe II emission and follow the eigenvector 1 relations defined by low-redshift quasars. The entire ASPIRE program will eventually cover 25 quasars and provide a statistical sample for the studies of the BHs and quasar spectral properties. Unified Astronomy Thesaurus concepts: Quasars (1319); Supermassive black holes (1663); Reionization (1383).
g. , Shen 2013;  Wang et al. 2020). The existing Mg II-based scaling relation is indirectly calibrated based on the Hβ relation derived from RM, which introduces additional uncertainties. Therefore, high-sensitivity infrared (IR) spectroscopic observations covering the rest-frame optical emission are needed to better estimate the Hβ-based BH masses of quasars at the highest redshift. A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE) is a James Webb Space Telescope (JWST) Cycle 1 GO program that will observe a sample of 25 z > 6. 5 quasars with JWST/NIRCam imaging and Wide Field Slitless Spectroscopy (WFSS;  Wang et al. 2023). The WFSS observations will provide the spectra in the Hβ region for reionization-era quasars for the first time. In addition, the highquality JWST spectra will allow us to study the spectral properties (e.
Observations and Data Reduction The eight quasar spectra used in this work are part of the medium-size JWST Cycle 1 program ASPIRE (PID 2078, PI: F. Wang;  Wang et al. 2023) , which carries out a spectroscopic survey in 25 reionization-era quasar fields using JWST/ NIRCam WFSS using the F356W filter, as well as imaging observations in the F115W, F200W, and F356W bands at the same time. This program allows us to statistically investigate both reionization-era quasars and high-redshift galaxies in the quasar fields. The NIRCam spectral and imaging data set probes the rest-frame optical emission in a large sample of z > 6. 5 quasars and host galaxies for the first time, which will improve our understanding of early SMBH activity and their host galaxies. The spectroscopic and imaging observations of the quasar fields allow studies of the environments of reionization-era quasars in a statistical manner, which will address the long-standing question of whether luminous quasars reside in the most massive dark matter halos and inhabit large-scale galaxy overdensities in the early universe.
The eight quasars reported in this work were observed with JWST/NIRCam in 2022 August and September. These targets were observed using the R-grism with F356W filter in long wavelength (LW) and F200W imaging in short wavelength (SW) simultaneously. We performed a three-point INTRAMO-DULEX primary dither with a 2-POINT-LARGE-WITH-NIRISS subpixel dither. A SHALLOW4 readout mode was used with nine groups per integration and one integration per exposure, which resulted in a total exposure time of 2834 s. After the grism observation, direct image and out-of-field images were taken in F356W (LW) and F115W (SW) with a total exposure time of 1417 s. For the analysis in this work, we only use the WFSS F356W spectral data of the quasar targets. Analysis of the spectra of galaxies in the quasar fields and broadband imaging data will be presented in a set of subsequent papers.
Rest-frame Optical Spectra of Reionization-era Quasars In this section, we describe our spectral fitting procedure and the main results derived from this early sample. We measure the BH masses based on the Hβ line width and continuum luminosity and compare them with the measurements derived from their rest-frame UV spectra. We also discuss the profile of the [O III] emission lines and search for potential galactic-scale outflows. We note that the data reduction procedure of this new instrument is still in development, and the spectra might be slightly affected by future changes of the reduction pipeline, but the main findings in this paper will not be significantly affected. The final JWST ASPIRE data set will be presented after this program has been completed.
g. ,  Dubois et al. 2013; Costa et al. 2014 Costa et al. , 2022;; Lupi et al. 2022 ). However, observational evidence of large-scale outflows in quasar hosts remains anecdotal at this redshift. The JWST data set provides us with the first chance to search for [O III] outflows in reionization-era quasars. Two quasars, J0218+0007 and J0224-4711, have prominently strong broad and blueshifted [O III] components, with a velocity shift of -630 km s -1 and -1690 km s -1 relative to the narrow [O III], respectively. Figure  4  shows their zoomed-in [O III] λ5008 lines, with other components subtracted out according to the best-fit models. We compute the median velocity, v 50 , the velocity at the 50th percentile of the line flux; v 10 and v 90 , the velocities at the 10th and 90th percentiles; and w 80 (=v 90v 10 ), containing 80% of the total line power, which is close to the commonly used FWHM (w 80 = 1.
Summary In this paper, we present the results from an early sample of ASPIRE JWST/WFSS spectral data set consisting of eight quasars at z > 6. 5, observed in 2022 August and September. With these high-quality WFSS spectra, we are able to study the rest-frame optical emission in reionization-era quasars for the first time. We measure the virial BH masses using the Hβ lines and compare them with the Mg II-based measurements. We find that, in general, the Hβ BH masses are consistent with the Mg II BH masses within the systematic uncertainties of the BH mass estimators. The discrepancy is within the scatter between The blue solid lines denote the median velocity (v 50 ), and the dashed lines mark the part containing 80% of the line power (w 80 ). J0218+0007 has a v 50 of ∼-610 km s -1 and a w 80 of 1590 km s -1 ; quasar J0224-4711 has a more remarkable [O III], with a v 50 of ∼-1430 km s -1 and a w 80 of 4140 km s -1.
Acknowledgments J. Y. and X. F. acknowledge support from  US NSF  grants  AST 19-08284. F. W. acknowledges support by  NASA  through the  NASA   Hubble Fellowship  grant # HST-HF2-51448. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. Research at  UC Irvine  was supported by  NSF  grant  AST-1907290. F. S. and E. E. acknowledge funding from JWST/ NIRCam contract to the University of Arizona,  NAS5-02105. J. T. S. acknowledges funding from the  European Research Council (ERC)   Advanced Grant program  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 885301 ). L. B. acknowledges support from  NSF  award  AST-1909933  and  NASA  award # 80NSSC22K0808.
S. E. I. B. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 740246  " Cosmic Gas. " M. H. acknowledges support from the  Zentrum für Astronomie der Universität Heidelberg  under the  Gliese  Fellowship. Z. H. acknowledges support from  NSF  grant  AST-2006176. G. K. is partly supported by the  Department of Atomic Energy (Government of India)  research project with Project Identification Number  RTI 4002 , and by the  Max Planck Society  through a  Max Planck Partner Group. A. L. acknowledges funding from  MIUR  under the grant  PRIN 2017-MB8AEZ. S. R. R. acknowledges financial support from the  International Max Planck Research School for Astronomy and Cosmic Physics at the University of Heidelberg  (IMPRS-HD).
A C T Ecopath mass-balanced models are widely-used tools to address various challenges in the understanding and protection of ecosystems. To track the continuing improvements in data and the evolving environment (climate change, anthropic pressure), new models are regularly being developed. In this study, we built a Gulf of Lion Ecopath model, focused on the continental shelf, featuring enhanced representation of benthic invertebrates and a realistic assessment of catches, and which takes into account the significant changes observed after 2008-2009 in the trophic structure of this ecosystem as well as related changes in fisheries activities. The model is composed of 68 functional groups, including 6 primary producers, discards and detritus, 27 invertebrate groups, 31 fish groups, dolphins and seabirds.
New datasets were taken into account for biomasses, as well as for diets. P/B and Q/B parameters were calculated to include the most recent and geographically closest data. Model results highlight a food web diagram, ranging over 5 trophic levels and placing Prionace glauca, Squalus acanthias and dolphins as top predators. The mixed trophic impact analysis showed that the groups with the highest accumulated negative impacts are, in decreasing order, benthic trawls, nets and carnivorous echinoderms. The groups with the highest accumulated positive impacts are, in decreasing order, detritus, microphytoplankton and nanoplankton. The flux analysis shows that a major part of the flows occurs at trophic level 2 with 35. 1% of the model total throughput and 43. 8% of the total biomass. The catches have a mean trophic level of 3.
, 2014; Saraux et al. , 2019; Feuilloley et al. , 2020)  and most demersal fishes  (Bensebaini et al. , 2022). Historically-used single-species models have proven insufficient to accurately explain the processes influencing the biomass of a species  (Cochrane, 2002). Ecosystem-based modelling is a response to this complexity and has been increasingly developed over the past decades. Ecopath with Ecosim (EwE, Ecopath. org) is today among the most widely-used software in this field. Since its introduction, it has been continuously improved, starting from a static ecosystem snapshot to become a dynamic and spatialized simulation  (Polovina, 1984; Christensen and Pauly, 1992; Walters et al. , 1997; Walters et al. , 1999; Heymans et al. , 2016). Its many uses include studies on the impact of fishing and fisheries management, the effectiveness of MPAs, habitat loss and degradation, understanding food webs and the dissemination of pollution  (Coll and Libralato, 2012).
Regarding this last aspect, the representation of biological groups interacting strongly with sediment is of paramount importance for the comprehension of some continental shelf marine areas  (Cresson et al. , 2020)  as well as for the modelling of the fate of contaminants in food webs  (Hammerschmidt et al. , 2004; Ono et al. , 2015; Tateda et al. , 2020). Bȃnaru et al. (2013)  developed an Ecopath model for the Gulf of Lion (NW Mediterranean Sea) and pointed out the lack of data for benthic invertebrate groups, the need for improved diet and biomass data and catch estimates. Their model covered the period 2000-2009, however an important shift in the GoL species composition and size occurred at the end of this period  (Van Beveren et al. , 2014; Saraux et al. , 2019)  and may have led to changes in the structure and functioning of this system as well as fishing activities.
Ecopath software and balancing The Ecopath software version 6. 6. 5 has been used to build the GOLEM model and ensure its mass and energy balance  (Christensen et al. , 2008; Christensen and Walters, 2004; www. ecopath. org). The main equation driving Ecopath models is: P i = ∑ B j × M2 ij + Y i + E i + BA i + P i × (1-EE i ) with P the production of the functional group i, M2 ij the predation caused on i by a predator j and B j its associated biomass, Y and E the export terms, the first for the fisheries and the latter for the other types of export, BA the biomass accumulation in the system and EE the ecotrophic efficiency (i. e. the proportion of the production of group i that is explained by the model, either by its exports or its predation). Thus, (1 -EEi) represents other mortality, or mortality not explained in the system.