dataesr/scientific-paragraphs-categorization huggingface dataset:
Platelet Aggregation Assay Analysis The TL, TNL, and TPL of each extract were assessed for their ability to inhibit PAF and thrombin-induced platelet aggregation in human PRP. The results of the platelet aggregometry assay for all lipid samples are expressed as an IC 50 value, which is the mass of the lipid sample in micrograms (µg) required to inhibit half (50%) the maximum-reversible PAF/thrombin-induced platelet aggregation. The IC 50 results for each extract tested against PAF-induced platelet aggregation are presented in Table  3. It is clear from the data presented that overall the TL and TPL are the most bioactive fractions in the various brewing-related extracts. Overall, the TNL extracts generally exhibited poor bioactivity. Notably in the MG, the TNL was slightly more bioactive than the TL, but was less bioactive than the TPL.
Acknowledgments: The authors would like to express their sincere gratitude to  Padraig Hyde and Adrian Hyde of Munster Brewery  for their kind donation of their time, expertise, beer, and brewing materials for this study. The authors would also like to thank all the volunteers for their donations,  Elaine Ahern  for her phlebotomy support, and the  Department of Biological Science, University of Limerick, Ireland  for their continued support.
T-distributed Stochastic Neighbor Embedding analyses Flow cytometer LSRII measured parameters were normalized and randomized. Data were first analyzed using FlowJo software. After doublets, debris, and dead cells exclusions, living cells were gated. Downsampling function of multiple intra-and interindividual measurements was applied before data concatenation. Resulting FCS files were analyzed with the tSNE module. Specific plots were generated to model global DP T cell immune responses to C. albicans challenge. The following input settings were used before tSNE reduction: up to 300,000 concatenated cells per file, 1000 iterations, 20 perplexity, 0. 5 Theta. Pairwise comparations of all events were mapped in a low dimension space, arranging similar events nearby and dissimilar cells farther away.
Statistics Statistical analyses were all carried out with Prism V6. 0a software (GraphPad Software). The fungal burdens after challenge with each Candida isolate were compared using a two-way ANOVA test with Tukey's correction for multiple comparisons. For the quantification of intra-and inter-subject variability at baseline, we pooled data from all individuals and we used a data vector X={x st } (individual s, time-point t) where each x st was associated with a subject and each subject had a total three measurements obtained from each of the timepoints. Then we used the ANOVA model to evaluate the total variance (total sum of squares), subject-to-subject differences (between column variation) and the fraction of variance explained by subject relative to the residual of the fit. The surface immune cell markers and cytokine secreting cells were expressed in percentage.
Preserving the divergence-free condition Historically, one of the first ideal MHD, general purpose codes, is ZEUS-2D developed specifically for astrophysics by Stone and Norman  (Stone et al. , 1992a). It is a finite-difference code using constrained transport and artificial viscosity to handle shocks, The continuity and divergence-free conditions are therefore satisfied by construction, but since artificial viscosity is used to handle shocks, instabilities can occur in fast rarefaction waves  (Falle, 2002). The other popular strategy for grid-based methods is to maintain all MHD variables as volume-averaged quantities, allowing for discontinuities across all cell faces. In the late 90s, a series of papers presented finite-volume MHD codes with proper upwinding of numerical fluxes using Riemann solvers  (Dai and Woodward, 1994; Ryu et al.
The balance between the ambipolar diffusion and the Hall resistivity determines the excess Hall resistivity. The HDS integrates the induction equation for the excess Hall using a dimensional splitting which is explicit for the first dimension, i. e. the x component B n+1 x of the magnetic fields is given by B n y and B n z at time n, explicit-implicit for the second dimension, B n+1 y is given by B n+1 x and B n z , and finally implicit for third dimension, B n+1 z is given by B n+1 x and B n+1 y. Both STS and HDS can be subcycled to reach the time step limit given by the hyperbolic system. The HDS scheme has been also implemented in ATHENA by Bai (2014) using CT. An alternative for finite volume methods is to include the Hall effect into the conservative integration scheme. Tóth et al. (2008)  proposed a first implementation in the AMR code BATSRUS  (Tóth et al.
Full resistive implementation Currently, a handful of 3D codes benefit of a full implementation of the three resistive terms: PLUTO  (Lesur et al. , 2014) , ATHENA  (Bai, 2014) , RAMSES  (Masson et al. , 2012; Marchand et al. , 2018) , the SPH code by  Tsukamoto et al. (2017) , PHANTOM  (Price et al. , 2017) , ZeusTW  (Li et al. , 2011) , and GIZMO  (Hopkins, 2017). A very recent full implementation for solar physics can be found in  González-Morales et al. (2018)  in the MANCHA3D grid code. It combines STS for ambipolar and ohmic diffusion, and HDS for the Hall effect. Currently, the ATHENA, PLUTO, RAMSES, and ZeusTW implementations rely on CT algorithms for the resistive MHD equations integration. All other works use divergence cleaning algorithms. Given the variety of the methods developed in the literature, a quantitative comparison (in standard tests as well as in star formation applications) of all these implementations would be welcome in the coming years.
, 2017). A similar scheme based on the Monte-Carlo approach has been developed for SPH in  Altay et al. (2008)  and  Pawlik and Schaye (2008). A similar approach is provided by the long characteristics method. This method can describe light rays emanating from a single point source like the ray-tracing method, but also diffuse radiation by adopting for each direction a set of parallel rays whose spacing matches the local grid resolution. This method has been implemented in the FLASH AMR code by  Rijkhorst et al. (2006) , with however an important addition we will discuss later. Diffuse radiation using long characteristics was included later by  Buntemeyer et al. (2016)  in the FLASH code, with an emphasis of describing both the optically thin and optically thick regimes. These methods exploit the particular property of the AMR implementations of FLASH and ENZO, namely large rectangular patches of various sizes and resolution.
This can be achieved efficiently using any gravity solver. Once the corresponding Eddington tensor is computed, the moments equations are solved using a finite difference scheme. In  Jiang et al. (2012) , on the other hand, the authors developed a moment-based solver for the ATHENA MHD code, using VET together with the short characteristic method of  Davis et al. (2012)  to compute the Eddington tensor. Obviously, this leads to significantly more accurate results in optically thicker environments. An intermediate solution has been implemented in the TreeCol code  (Clark et al. , 2012) , for which a gravity tree code is used to collect column densities between particles. The second approach relies on simple but approximate models for the Eddington tensor. The simplest one, called M0, assumes the Eddington tensor is isotropic, with M0 closure : D = 1 3 I, ( 50 ) as it is the case for example in the diffusion limit (see below).
The M1 model has also some major caveats, such as radiation shocks, in case multiple strong sources are present, or incorrect radiation geometry, in case of sharp shadows. This model was introduced for the first time in astrophysics by  González et al. (2007)  in the HERACLES code. It was then adapted to cosmic reionization by  Aubert and Teyssier (2008)  in the ATON code, and ported to GPU into the RAMSES-CUDATON code  (Aubert and Teyssier, 2010) , used now routinely for simulations of the Epoch of Reionization (EoR) (e. g. Ocvirk et al. , 2016). Later on, the M1 method has been ported to the AMR code RAMSES, leading to the design of the RAMSES-RT solver  (Rosdahl et al. , 2013; Rosdahl and Teyssier, 2015). The M1 method has also been ported to ATHENA by  Skinner and Ostriker (2013)  and recently to AREPO by  Kannan et al.
Here again, the ZEUS 2D code was a precursor  (Stone et al. , 1992b). For AMR, the ORION code was developed specifically for FLD in collapsing star forming core by  Krumholz et al. (2007a) , using the mixed frame formulation outlined above or in  Mihalas and Klein (1982). A version of the RAMSES code with FLD was developed later by  Commercon et al. (2011) , with a particular emphasis on adaptive time stepping together with implicit time integration described in  Commercon et al. (2014). Because of its simplicity, FLD has been used for many detailed studies of gravitational collapse with the effect of radiation included. The current frontier for FLD is probably the multigroup treatment of radiation, allowing for more accurate models of the full spectral energy distribution  (Shestakov and Offner, 2008; Zhang et al.
As mentioned in the Sec. 2, ideal MHD was introduced early in protostellar collapse models using grid-based codes (e. g. ,  Dorfi, 1982; Phillips and Monaghan, 1985). The first calculations of second collapse incorporating magnetic fields were thus done on a grid-based code by  Tomisaka (2002)  using a nested grid with cylindrical coordinates assuming axisymmetry  (Tomisaka and Bregman, 1993). Tomisaka (2002)  code solved the ideal MHD equations using a second-order "monotonic scheme"  (van Leer, 1977; Norman and Winkler, 1986 ), a constrained transport scheme for the induction equation  (Evans and Hawley, 1988) , as well as an angular momentum preserving scheme. To capture the huge range in spatial scales,  Tomisaka (2002)  used up to 16 levels of refinement. He found that two different types of outflows were launched at the first and second cores scales.
As a consequence, the infall velocity increases along with the incident kinetic energy, which then modifies the thermal budget through the accretion shocks. The next step in the increasing complexity is to perform full 3D RMHD calculations. The first SPH RMHD calculations of protostellar core formation were performed in  Bate et al. (2014) , extending their previous work to ideal MHD. Tomida et al. (2013) ;  Tomida et al. (2015)  performed the first 3D full RMHD calculations using a grid-based code, extending the work of  Machida et al. (2006)  to account for radiative transfer with the FLD approximation, as well as the Ohmic dissipation and the ambipolar diffusion. They used a finite-volume nested-grid code with standard MUSCL predictor-corrector scheme to achieve second-order accuracy. More details on  Tomida et al.
Radiative feedback The first class of sub-grid models were designed to account for the radiative feedback of the nascent protostars. The radiative feedback term includes the accretion, the internal, and the ionisation luminosities. When integrating over discrete timesteps, the luminosity is an energy or radiative flux input. Radiative feedback was introduced early in 2D models (e. g. ,  Bodenheimer et al. , 1990). They used a 2D axisymmetric hydrodynamical code  (Rozyczka, 1985) , which was primarily designed for stellar winds. Bodenheimer et al. (1990)  used the grey FLD approximation for the radiation transport. They combined the evolution of their central zone, or sink cell, with a pre-main sequence evolution model based on Maclaurin spheroids in hydrostatic equilibrium. They computed the structure of the protostar according to the central (ρ, T ) path tracks reported by  Winkler and Newman (1980)  from 1D spherical calculations.
Numerical Methods in Star Formation Last, we note the developments made to capture ionising radiation feedback from massive stars:  Dale et al. (2007)  used a Strömgren volume method in SPH,  Peters et al. (2010)  ray tracing in the FLASH code,  Kuiper and Hosokawa (2018)  an hybrid ray-tracing and FLD irradiation module in the PLUTO code,  Geen et al. (2015)  the M1 moment method in the RAMSES code, and  Harries (2015)  Monte Carlo radiative transfer in the AMR code TORUS. These schemes are applied first in isolated collapse calculations  (Kuiper and Hosokawa, 2018)  and, mostly, in cluster formation studies  (Peters et al. , 2011; Dale and Bonnell, 2011; Dale et al. , 2012; Geen et al. , 2015; Gavagnin et al. , 2017; Ali et al. , 2018; Geen et al. , 2018).
Most of the implementation of outflows and jets have a similar construction, with flavors depending on the sink particle algorithm. Wang et al. (2010)  implemented in the MHD ENZO code a simplified version for protostellar outflows where they only account for the collimated jet component. They adopt a continuous momentum injection ∆P = P ∆M where P is a proportionality constant which depends on the stellar mass ∼ M 1/2 ). If the momentum injection occurs in a cell with a too low density, they take 10% of ∆M out of the sink particle and redistribute it evenly between the injection cells in order to avoid too large outflow speeds (and too small integration timestep). Along the same line,  Cunningham et al. (2011) ;  Hansen et al. (2012)  extended the work of  Krumholz et al. (2005 Krumholz et al.
Second collapse and pre-main sequences Ideally, the sub-grid models should be designed to reproduce the results of high resolution studies, which resolve the protostar scales. The short time integration after stellar core formation, dominated by the adiabatic evolution, does not allow to design sub-grid models for protostellar feedback from full 3D RMHD calculations with a high level of confidence. For these reason, all recent works mostly rely on the results of 1D spherical symmetry results to set their protostellar core evolution models. For instance,  Kuiper and Yorke (2013)  use the pre-main sequence evolution code STELLAR  (Bodenheimer et al. , 2007; Yorke and Bodenheimer, 2008)  to determine the protostellar luminosity of the non-resolved protostars. Pelupessy et al. (2013)  developed the open source Astrophysical Multi-purpose Software Environment (AMUSE), a component library of simulations involving different physical domains and scales.
For instance, it enables one to couple hydrodynamical codes (GADGET, ATHENA, AMRVAC) with pre-main sequence and binary evolution codes such as MESA  (Paxton et al. , 2011). Recently,  Wall et al. (2019)  used AMUSE to perform a star cluster formation model that includes individual star formation from self-gravitating, magnetized gas, coupled to collisional stellar dynamics. It couples different tools: the AMR code FLASH  (Fryxell et al. , 2000) , the N-body code ph4  (McMillan et al. , 2012) , and the stellar evolution code SeBa (Portegies  Zwart and Verbunt, 1996). We anticipate to see more works using this strategy of coupling tools and scales in the near future. Recent work from  Vaytet et al. (2018)  have managed to resolve the second core formation with nonideal MHD (Ohm diffusion and ambiploar diffusion) from collapse 1 M dense core (see Figure  3 ).
Phylogenetic analysis The complete envelope gene of DENV-2 strains isolated in 2019 from five inhabitants of Tahiti, including three locally acquired infections (GenBank accession numbers: MK905539, MK905540 and MK905541) and two imported cases from New Caledonia (MK905538 and MK905542) were sequenced as previously described  [3]. Phylogenetic analysis showed that all DENV-2 strains collected in Tahiti belonged to the Cosmopolitan genotype and were more closely related to other strains isolated in French Polynesia in 2017 and 2018 (KY782125, KY782126, KY782127, MH807159 and MH807160), with percentages of homology ranging from 99. 5% to 100% (Figure  2 ). The French Polynesia strains belonged to the same cluster as DENV-2 strains isolated in Tuvalu in 2014 (MG967223) and in Fiji during 2014-17 (KM279392, MG967229 and MG967231), with percentages of homology of more than 99.
Despite multiple importations and autochthonous transmission of DENV-2 detected in French Polynesia during 2017-18, no infection was reported until 2019. Concomitant circulation of DENV-1 could have played a negative role in the transmission dynamics of DENV-2, as there may be competition between different serotypes for transmission by the mosquito host  [14]. Another possible explanation for the absence of The phylogenetic tree was constructed using the MEGA7 software  [17]. The evolutionary history was inferred by using the Maximum Likelihood method based on the Kimura 2-parameter model  [18]. The percentage of trees in which the associated taxa clustered together is shown for values over 90 next to the branches (1,000 replicates). Each strain is labelled by country _ date of origin/GenBank accession number.
Study Design and Setting ISCOLE is a multinational, cross-sectional study designed to examine the relationships between lifestyle behaviors and obesity in 12 countries from all inhabited continents of the world (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, United Kingdom, and United States). ISCOLE study sites were chosen because they represent a wide range of human development index, economic development, and income inequality. More information about the design and methods of ISCOLE can be found elsewhere  (14). Children from urban and suburban areas only (n=256 schools in total) were recruited in ISCOLE in order to facilitate comparisons across sites and for logistical reasons. Data collection occurred between September 2011 and December 2013 and was conducted during the school year.
Participants ISCOLE included 9-11 year-old children, and each site aimed to recruit a sex-balanced sample of at least 500 children. Of the 7372 children who participated in ISCOLE, a total of 5873 remained in the present analytical sample. Reasons for exclusion included invalid accelerometry data (n=1214) and missing information about screen time (n=25), level of parental education (n=255) and body mass index (BMI) z-scores (n=5).
Movement behaviors MVPA was assessed with the use of an Actigraph GT3X+ accelerometer (ActiGraph LLC, Pensacola, FL, USA) worn on the waist. Children were instructed to wear the device 24 hours per day (except for water-related activities) and for a period of at least 7 days (including 2 weekend days). The average wear time in this study was 22. 8 hours per day. We included in our analyses only children with at least 4 days of waking wear time with at least 10 hours per day (including at least one weekend day). Data were downloaded in 1-s epochs using the ActiLife software and were later reintegrated to 15s epochs to determine MVPA levels. Sleep duration was assessed using 60-s epochs and with the use of an automated algorithm validated for this study  (18). The algorithm has been shown to provide more precise estimates of sleep duration than previous ones based on 24-hour accelerometry  (18, 19).
The strongest association was seen when children met all three movement behavior recommendations, and limiting screen time habits to the recommended amount was most strongly associated with healthy dietary patterns. The unhealthiest dietary patterns were observed in children meeting the MVPA recommendation alone, while any combinations including limiting screen time habits were those more strongly associated with less unhealthy dietary patterns (i. e. , children less frequently ate unhealthy foods if they met the screen time recommendation). Findings were also similar across study sites (data not shown).
After its introduction in 1982, the Hopfield model has been extensively applied for classification and pattern recognition. Recently, its great potential in gene expression patterns retrieval has also been shown. Following this line, we develop Hope4Genes a single-sample class prediction algorithm based on a Hopfield-like model. Differently from previous works, we here tested the performances of the algorithm for class prediction, a task of fundamental importance for precision medicine and therapeutic decision-making. Hope4Genes proved better performances than the state-of-art methodologies in the field independently of the size of the input dataset, its profiling platform, the number of classes and the typical class-imbalance present in biological data. Our results provide encoraging evidence that the Hopfield model, together with the use of its energy for the estimation of the false discoveries, is a particularly promising tool for precision medicine.
Once the subtypes have been identified, their usage for therapeutic decision-making on a new patient, requires to solve a class prediction problem. Class prediction requires in input a signature, i. e. a set of genes coherently over-or under-expressed in patients with a certain phenotype of interest, and it is aimed at associating each sample of the input dataset to a class (e. g. poor vs good prognosis) based on the coordinate behavior of the genes belonging to the signature. In the last years, transcriptomic signatures able to differentiate distinct subtypes of tumors, to identify important cellular responses, to predict clinical outcomes and to model the activation of signaling pathways have been extensively developed  [21] [22] [23]. Consequently to the spread in gene signatures production, many class prediction algorithms have been introduced.
Inspired by the promising results obtained by the Hopfiled model in the analysis transcriptomic data, we here developed Hope4Genes a new algorithm for class prediction based on a Hopfield-like model. Hope4Genes, available at https: //github. com/lcan88/Hope4Genes, preserves the three advantages implemented in NTP (absence of training phase, single-sample classification and FDR estimation). Moreover, it associates to each sample a secondary class, useful to better characterize those 5 -10% of samples that generally result to be unclassified by existing algorithms. Once tested on the same data to which NTP was originally applied, Hope4Genes obtained competing results, proving that the application of the Hopfield model for class prediction of transcriptomic data, interesting in itself, has also a great potential in precision medicine.
Hope4Gene a class prediction algorithm based on the Hopfield model We here develop Hope4Genes, available at https://github. com/lcan88/Hope4Genes, a single-sample class prediction algorithm for transcriptomic data (see Figure  1 ). Given a gene expression matrix, a set of classes in which the user wants to classify the samples together with a signature, i. e. a set of genes whose expression is correlated to a class, the general idea behind Hope4Genes is to inizialize the nodes of the Hopfield network according to the binarized expression values of each sample and let the model evolve until it reaches one of the class templates stored into the model (see Methods for a detailed explanation of the Hope4Genes algorithm). Indeed the Hopfield network consists of a set of neurons connected through symmetric bidirectional links and having two possible states: firing (+1), or silent (-1).
35 leukemia samples to be classified into AML and ALL based on the gene signature defined in  28. • Example 2: Cross-platform prediction of Estrogen Receptor (ER) positivity in breast cancer. 49 breast cancer samples from  29  to be classified according to the gene signature obtained in  24. • Example 3: Cross-species prediction of liver cirrhosis between human and rat. 12 liver cirrhosis rat samples are classified through a signature generated on human samples affected by the same pathology in  30. • Example 4: Prediction of multiple tissue types. A dataset containing 52 samples collected from four different tissues: breast, prostate, lung and colon is classified based on the signature defined in  31. • Example 5: Prediction of breast cancer molecular subtypes: Basal-like, HER2, Luminal A, Luminal B, and Normal.