dataesr/scientific-paragraphs-categorization huggingface dataset:
To test this possibility, we carried out bioinformatics analyses using a publicly available gene expression dataset: the Cancer Genome Atlas (TCGA). LCN2 (but not NUPR1) mRNA expression was upregulated in the PDAC tumor group compared to the normal group (Fig. 7a ). An overall survival assay revealed that a low expression of NUPR1 or LCN2 was correlated with increased survival of PDAC patients (Fig. 7b ). These analyses indicate that activation of the NUPR1-LCN2 pathway may contribute to the development of human PDAC.
Each sample was scanned for 600 FOV on the nCounter Digital Analyzer. NanoString results were produced from RCC files using nSolver Analysis Software 3. 0. qPCR analysis. Total RNA was extracted and purified from cultured cells or tissues using the RNeasy Plus Mini Kit (QIAGEN, 74136) according to the manufacturer's instructions. The RNA was quantified by determining absorbance at 260 nm. One microgram of total RNA from each sample was reverse-transcribed into cDNA using the iScript cDNA synthesis kit (Bio-Rad, 170-8891) in a volume of 20 μl; cDNA from cell samples was amplified. The qPCR was performed using SsoFast EvaGreen Supermix (Bio-Rad, 172-5204) on the C1000 Touch Thermocycler CFX96 Real-Time System (Bio-Rad) according to the manufacturer's protocol. Analysis was performed using Bio-Rad CFX Manager software 3.
2 (Bio-Rad). Source data of images of western blot bands are provided as a Source Data file. Biochemical assay. Commercially available enzyme-linked immunosorbant assay (ELISA) kits were used to measure the concentrations or activity of HMGB1 (Shino Test Corporation, ST51011), amylase (BioVision, K711), MPO (BioVision, K744), iron (Abcam, ab83366), MDA (Abcam, ab118970), and 8-OHdG (Cell Biolabs, STA-320) in indicated samples according to the manufacturer's instructions. Data were normalized to protein or DNA concentration. In addition, C11-BODIPY probe (Thermo Fisher Scientific, D3861) was used to detect lipid ROS in cells. Secrete-pair luminescence and ChIP assay. Dual-reporter promoter clones or controls were transfected into two cell lines in duplicates. Indicated WT and Nupr1 -/-cells were transfected with pEZX-PG04-Lcn2 promoter Gaussia luciferase/secreted alkaline phosphatase (GeneCopoeia, MPRM39850-PG04).
Data availability All the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. GEPIA (http://gepia. cancer-pku. cn/index. html)  75 was used to analyze the TCGA data of NURP1 (Ensembl ID: ENSG00000176046 [https://www. ncbi. nlm. nih. gov/gene/26471]) or LCN2 (Ensembl ID: ENSG00000148346 [https://www. ncbi. nlm. nih. gov/gene/3934]) gene. Source data are provided with this paper.
Value of the Data • The detailed data related to the EnergyPlus model guarantees a better and deeper understanding of the building addressed in the research paper  [1]  , grounding the study developed and providing more information to aid in reading the paper. • Both building modelling and simulation are performed using EnergyPlus software. Different configurations/ simulation techniques could be employed based on this available data, so different studies might be compared. • The data presented in this article can assist designers and researchers who deal with the modelling of naturally ventilated buildings, especially with Airflow Network and multizone approach. • The use of Energy Management System (EMS) to model hybrid ventilation operation could be adopted as a reference for further research on naturally ventilated buildings.
Data Description The data in this article present the input data regarding the EnergyPlus model used at the investigations addressed in the research paper. General simulation settings are summarised in Table  1  , while the building materials properties are listed in Table  2. Table  3  shows the Airflow Network opening settings used in the annual investigation. Finally, the occupancy schedule, which specifies when the living and bedrooms were occupied, as well as its respective number of people, can be seen in Fig. 1. The model set up is based on consolidated practices used in studies involving INES' experimental houses, and therefore does not use the EnergyPlus database. Since they are originally unoccupied, a classic family occupancy schedule was established, which would represent an extreme/worse possible scenario.
5 "N, 5 °52 27. 4 "E Data accessibility With the article EnergyPlus files (. idf): https://data. mendeley. com/datasets/rp6xy7rfhn/1 Related research article Sakiyama NRM, Mazzaferro L, Carlo JC, Bejat T, Garrecht H. Natural ventilation potential from weather analyses and building simulation. Energy and Buildings 2021;231:110596. https://doi. org/10. 1016/j. enbuild. 2020. 110596.
For patients coinfected with SARS-CoV-2 and HRV, demographic and clinical data were retrospectively retrieved when they were available from medical files, including comorbidities, main symptoms, in/outpatient status, transfer to an intensive care unit (ICU), and death. To evaluate the relationship between SARS-CoV-2-HRV coinfections and the clinical profiles and outcomes of the patients, we selected patients monoinfected either with SARS-CoV-2 or with HRV as controls groups, matched with cases by, in order of priority, gender, age, comorbidities, and date of diagnosis. Infections with SARS-CoV-2 and/or another respiratory virus diagnosed during the period from 1 March to 30 April 2020 for 1711 patients from the present study were described in a previous work that only focused on diagnoses performed in March and April 2020  [2].
Moreover, we discuss the quality of the fit to the SIDIS data with low virtuality Q 2 showing that, as expected, low-Q 2 SIDIS measurements are generally harder to describe within a NLO-accurate perturbative framework. † MAP is an acronym that stands for "Multi-dimensional Analyses of Partonic distributions" and that we adopted as a name for a collaboration of people engaged in the study of the threedimensional structure of hadrons. Visit https://github. com.
The structure of the paper is as follows. In Sect. 2 we introduce the data sets used in this analysis, its features, and the criteria applied to select the data points. In Sect. 3  we discuss the setup used to compute the theoretical predictions, focusing on the description of SIDIS multiplicities. In Sect. 4  we illustrate the methodological framework adopted in our analysis, specifically the treatment of the experimental uncertainties and the details of the neural network parametrisation. In Sect. 5 we present the results of our analysis, we assess the interplay between SIA and SIDIS data sets and the stability of FFs upon variation of the kinematic cuts. Finally, in Sect. 6  we provide a summary of our results and outline possible future developments.
Experimental data This analysis is based on a comprehensive set of measurements of pion-production cross sections in electron-positron SIA and in lepton-nucleon SIDIS. In the first case, the data corresponds to the sum of the cross sections for the production of positively and negatively charged pions, differential with respect to either the longitudinal momentum fraction z carried by the fragmenting parton or the momentum of the measured pion p π ; the differential cross section is usually normalised to the total cross section (see Sect. 2. 2 in Ref. [4]  for details). In the second case, the data corresponds to the hadron multiplicity, that is the SIDIS cross section normalised to the corresponding inclusive DIS cross section (see Sect. 3 for details). Multiplicities are measured separately for the production of positively and negatively charged pions.
In the case of SIA, we consider measurements performed at CERN (ALEPH  [21] , DEL-PHI  [22]  and OPAL  [23] ), DESY (TASSO  [24] [25] [26] ), KEK (BELLE  [27]  and TOPAZ  [28] ), and SLAC (BABAR  [29] , TPC  [30]  and SLD  [31] ). These experiments cover a range of centre-ofmass energies between √ s ∼ 10 GeV and √ s = M Z , where M Z is the mass of the Z boson. In the case of SIDIS, we consider measurements performed at CERN by COMPASS  [32]  and at DESY by HERMES  [33]. The COMPASS experiment utilises a muon beam with an energy E µ = 160 GeV and a 6 LiD target. The HERMES experiment utilises electron and positron beams with an energy E e = 27. 6 GeV and hydrogen or deuterium target. Both experiments measure events within a specific fiducial region. The features of SIA and SIDIS data are summarised in Tab.
We discard the bins with z < 0. 2, which are used to control the model dependence of the smearing-unfolding procedure, and with z > 0. 8, which lie in the region where the fractional contribution from exclusive processes becomes sizeable. The kinematic coverage of the data sets included in this analysis is displayed in Fig. 1. As is apparent, SIA and SIDIS data sets cover two different regions in Q: the former range from the centre-of-mass energy of the B-factory measurements, Q ∼ 10 GeV, to that of LEP measurements, Q = M Z ; the latter, instead, lie at lower energy scales, Q ∼ 1 -6 GeV. The two data sets are nevertheless complementary. On the one hand, SIDIS data widens the Q lever arm needed to determine the gluon FF from perturbative evolution effects. On the other hand, SIDIS data provides a direct constraint on individual quark and antiquark FFs, that are otherwise always summed in SIA data.
As expected from kinematic considerations, experiments at higher centre-of-mass energies provide data at smaller values of z. Kinematic cuts are applied to select only the data points for which perturbative fixed-order predictions are reliable. For SIA, we retain the data points with z in the range [z min , z max ]; the values of z min and z max are chosen as in Ref. [4] : z min = 0. 02 for experiments at a centre-of-mass energy of M Z and z min = 0. 075 for all other experiments; z max = 0. 9 for all experiments. For SIDIS, we retain the data points satisfying Q > Q cut , with Q cut = 2 GeV. This choice maximises the number of data points included in the fit without spoiling its quality and follows from a study of the stability of the fit upon choosing different values of Q cut , see Sect.
5. 3. 3. Overall, after kinematic cuts, we consider N dat = 699 data points in our baseline fit almost equally split between SIA (N dat = 377) and SIDIS (N dat = 322). Each of the two processes is dominated by measurements coming respectively from LEP and B-factories, and from COMPASS. Information on correlations of experimental uncertainties is taken into account whenever available. Specifically, for the BABAR measurement, which is provided with a breakdown of bin-by-bin correlated systematic uncertainties, for the HERMES measurement, which is provided with a set of covariance matrices accounting for correlations of statistical uncertainties obtained from the unfolding procedure, and for the COMPASS measurement, which is provided with a correlated systematic uncertainty. Normalisation uncertainties available for the BELLE, BABAR, TASSO, ALEPH and SLD experiments are assumed to be fully correlated across all data points in each experiment.
We use the NNPDF31 nlo pch as 0118  [14]  as a reference PDF set. In Sect. 4 we will explain how the PDF uncertainty is propagated into SIDIS observables and in Sect. 5. 2. 2 we will discuss the impact of alternative PDF sets on the determination of FFs. The coefficient functions C in Eq. (  4 ) admit the usual perturbative expansion C(x, z, Q) = n=0 α s (Q) 4π n C (n) (x, z) , (6) where α s is the running strong coupling for which we choose α s (M Z ) = 0. 118 as a reference value. Presently, the full set of coefficient functions for both F 2 and F L is only known up to O(α s ), i. e. NLO  [38, 39]. Explicit x-and z-space expressions up to this order can be found for instance in Ref. [40]  and are implemented in the public code APFEL++  [41, 42]. A subset of the O(α 2 s ), i. e. NNLO, corrections has been recently presented in Refs.
Data sets with 10 or less data points are instead fully included in the training set. The χ 2 of the validation set is monitored during the minimisation of the training χ 2 and the fit is stopped when the validation χ 2 reaches its absolute minimum. Replicas whose total χ 2 per point, i. e. the χ 2 computed over all data points in the fit, is larger than 3 are discarded. The minimisation algorithm adopted for our fit is a trust-region algorithm, specifically the Levenberg-Marquardt algorithm as implemented in the Ceres Solver code  [52]. This is an open source C++ library for modelling and solving large optimisation problems. The neural-network parametrisation and its analytical derivatives with respect to the free parameters are provided by NNAD  [20] , an open source C++ library that provides a fast implementation of an arbitrarily large feed-forward neural network and its analytical derivatives.
The MAPFF1. 0 set In this section we present the main results of this analysis. In Sect. 5. 1 we discuss the quality of our baseline fit that we dub MAPFF1. 0. In Sect. 5. 2 we illustrate its features: we compare our FFs to other recent determinations and we study the stability of the fit upon the choice of input PDFs and of the parametrisation scale µ 0. In Sect. 5. 3 we study the impact of some specific data sets: we discuss the origin of the difficulty in fitting SIA charm-tagged data, we investigate the impact of the SIDIS data on FFs, and finally we study the dependence of the fit quality on the low-Q cut on the SIDIS data.
Impact of SIDIS data In this section, we study the impact of the individual SIDIS data sets included in our analysis. To this purpose we have repeated our baseline fit by removing either the COMPASS or the HERMES measurements. The three fits are compared in Fig. 6. We note that the number of data points that survive the kinematic cuts defined in Sect. 2 is 8 for HERMES and 314 for COMPASS. Despite the limited amount of data points, HERMES still provides a sizeable constraint in the region of its coverage. As shown in Fig. 6  the impact of the HERMES data in the region 0. 2 z 0. 6 can be summarised as follows: • Overruling the COMPASS data for D π + d and partly for D π + ū. When excluding HERMES, these FFs are respectively suppressed by approximately 2-σ and enhanced by 1-σ. • Competing with the COMPASS data for D π + s + because both datasets have a comparable impact on this FF combination.
The entirety of the results presented in this paper have been obtained using the public code available from: https://github. com/MapCollaboration/MontBlanc. On this website it is possible to find some documentation concerning the code usage as well as the FF sets in the LHAPDF format. We provide three sets of FFs with N rep = 200 replicas each and of their average for the positively and negatively charged pions and for their sum. These are correspondingly called MAPFF10NLOPIp, MAPFF10NLOPIm, and MAPFF10NLOPIsum and are made available via the LHAPDF interface  [62]  at: https://lhapdf. hepforge. org/.
Acknowledgments We thank  W. Vogelsang  for confirming that the misprint in the C (1),nm L,qg expression reported in Ref. [45] was corrected in Ref. [46], and  Rodolfo Sassot  for helping us debug the APFEL++ implementation and for providing us with the predictions of Ref. [2]. We are grateful to  Gunar Schnell  for support with the interpretation of the HERMES data and to  Nobuo Sato  and the  JAM collaboration  for providing us with their theoretical predictions, PDF and FF sets. We thank  J. Rojo  for a critical reading of the paper. The work of R. A. K. is partially supported by the  Netherlands Organization for Scientific Research (NWO). V. B. is supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement № 824093. E. R. N. is supported by the  UK STFC  grant  ST/T000600/1  and was also supported by the  European Commission  through the  Marie Sk lodowska-Curie Action ParDHonSFFs.
Background RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data. Methods In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset.
When applied to medical RNA-seq datasets using appropriate statistical methods, this strategy identifies any sub-sequence whose increased abundance is associated to a given clinical label. This may include novel splice variants, long non-coding RNAs (lncRNAs) or RNAs from repeated retroelements  18, 19  which are ignored by conventional protocols based on reference gene annotations. Although attractive in principle, k-mer derived prognostic signatures pose two major challenges. First, a single RNA-seq dataset commonly contains tens to hundreds of millions distinct k-mers. Therefore false positive and replicability issues encountered with gene expression profiles  21, 22, 23, 24  are expected to worsen with k-mer count matrices. The second challenge is related to the transfer of a k-mer signatures across independent datasets.
Signatures inferred from an initial discovery set are expected to generalize to any independent dataset. In the absence of a unifying gene concept, independent validation requires matching signature k-mers to read sequences from the new dataset. This may cause significant signal loss if sequencing or library preparation technologies differ. Our main objective here was to compare the characteristics and performances of referencebased and reference-free classifiers for PCa risk and relapse prediction. We built both types of classifiers using the same discovery dataset and assessed their performances in independent datasets using equivalent pipelines and parameters. For the reference-free approach, this required special developments to reduce the number of variables and to transfer expression measures between datasets.
Data acquisition and outcome labelling We used tumor samples from the TCGA-PRAD data collection 25 (N=505) for signature discovery and from the ICGC-PRAD data collection 26 (N=284) and from Stelloo et al. 27  (N=91) for independent validation. All three datasets used similar technologies for library preparation (frozen samples, poly(A)+ RNA selection) and Illumina sequencing, however they differed by read-size, read depth, strandedness and use of single or paired ends sequencing (Table  1 ). TCGA-PRAD RNA-seq data were retrieved from dbGAP accession phs000178. v9. p8 with permission. ICGC-PRAD-CA RNA-seq data (EGAD00001004424) were downloaded from the European Genome-Phenome Archive (EGA) with permission. The RNA-seq files from the "Porto" cohort  27  were retrieved from GEO, under accession GSE120741.
Clinical information was retrieved from Liu et al. 28  for TCGA-PRAD, from Fraser et al. 26  for ICGC-PRAD and from sample metadata of GEO accession GSE120741 for Stello et al. 27. We built predictors for risk and relapse using two-class prediction models. To achieve a clear separation between the two classes, we only focused on high risk (HR) samples versus low risk (LR) samples, ignoring the medium risk, and we focused on relapse prior to a given year and non-relapse after a given year. For this reason, only a fraction of samples could be labelled for a given class in each set. Risk information was not available in the Stelloo dataset and relapse labelling on the ICGC dataset led to a small validation set (only 7 non-relapse samples). We classified tumor specimens into low-risk and high-risk groups using an adaptation of d'Amico's classification which does not take into account the PSA rate but only the anatomopathological data on the basis of Gleason and TNM features as performed previously  19.
was taken from SRA accession PRJNA494345. Risk and relapse predictors were derived using a combination of feature selection and supervised learning (Figure  1 ). The predictive model was tuned over a discovery (or training) dataset and its performance was then evaluated on an independent validation (or testing) dataset, to avoid selection bias  29. The same procedure was used for reference-based and reference-free models, however two extra steps were included to obtain and validate reference-free signatures. First a procedure was implemented to reduce the k-mer matrix using a sequence assembly-like algorithm to merge k-mers into contigs based on their sequence overlap and on the similarity of their count vectors. This step led to a contig count table an order of magnitude smaller than the initial k-mer count table (see results below).
Feature selection and model fitting were performed over this contig table. A second adaptation was necessary to validate the reference-free signature in an independent dataset. This required extracting k-mers from both the signature and the sequence files of the independent set, and compute the signature expression in the independent set based on counts of matching k-mers. The pipeline is detailed in Methods. Note that we select features and train a predictive model only on the discovery dataset. The model is then applied to the validation set with no retraining (i. e. with the same coefficients) for an unbiased evaluation of the signature.
Gene and k-mer count matrices DEkupl-run  18  was used to produce gene and k-mer count matrices for each dataset. DEkuplrun converts FASTQ files to k-mer counts using Jellyfish 30 , joins individual sample counts into a single count table and filters out low count k-mers. K-mer size was set to 31, lib_type to unstranded, and parameters min_recurrence and min_recurrence_abundance were set for each dataset as in Supplementary Table  S1. K-mer size was set to 31 as commonly adopted for human transcriptome applications  31, 18. Note that contrary to TCGA-PRAD, ICGC-PRAD uses stranded RNA-seq libraries. However we could not use this information as signatures were produced from unstranded libraries. We thus built all k-mer tables in canonical mode, which amounts to consider all libraries as unstranded.
Gene expression was computed using Kallisto v0. 43. 0 31 with Gencode V24 as a reference transcriptome. Gene-level counts were obtained by summing counts for all transcripts of each gene. Gene expression matrices were submitted to the same recurrence filters as k-mer tables to remove low expression genes. After count tables were generated and filtered, the k-mer merging and differential expression analysis module of DEkupl-run were not used. Instead, tables were further processed as explained below.
Count normalization To account for differences in sequencing depth among samples, we applied a normalization step on feature counts (genes or contigs) in discovery and validation datasets. Each feature count in a sample is divided by the sum of all feature counts in this sample, then multiplied by a constant base number: e f,s ← e f,s f ∈{f eatures} e f,s • C b , where e f,s refers to count of feature f in sample s, and C b is the base constant. For genes, C b = 10 6 resulting in a conventional count per million (CPM) normalization, while for contigs, we used C b = 10 9 , or count per billion (CPB). For contigs, normalization is applied on the contig count table produced after contig extension and for genes it is applied on the recurrence filtered gene expression matrix.