dataesr/scientific-paragraphs-categorization huggingface dataset:
For FindMin2 operators and union-find, we jump out of GPU parallel BFS or DFS algorithms for tree traversal, propose to apply linked list data structure on GPU and profit from the CUDA atomic operations to achieve higher running speed. Though GPU parallelism prefer coalesced memory access on arrays, here because size of independent component can not be predicted, linked list is a compromised option. The proposed methods provide basis for some higher level distributed or parallel algorithms in both 2D and 3D Euclidean space. Application to other Euclidean problems can be envisaged with high efficiency and within divideand-conquer scheme, for example 3D traveling salesman problem, hierarchical 2D/3D data clusters and apply them to 2D/3D image processing such as optical flow or stereo matching problems.
The Poisson DFN model, also known as the Baecher model, is employed to generate fractures with random trace lengths and orientations. The fundamentals of the Poisson DFN model are beyond the scope of this paper but can be found in the reference  (Baghbanan and Jing, 2007). To implement the DFN model into DDA, the numerical model with generated NF networks needs to be meshed into block elements. The mesh quality is very important because it may greatly influence the convergence and computational efficiency of simulations. Here, the unstructured triangular grid is adopted to address the complex and dense pre-existing fractures within the model. The COMSOL Multiphysics software  (Comsol, 2014)  is very good at meshing the model containing numerous pre-existing fractures. It can maintain the complex geometry of the fracture network and avoid generating grids with bad quality.
The workflow The workflow of the proposed hydromechanical model is shown in Figure  6. First, the reservoir model is established and the NF network is generated by a homemade DFN generator. Then, the model containing numerous pre-existing fractures is meshed in COMSOL, and a homemade interface program helps introduce the triangular grid from COMSOL to DDA. After that, the fracturing calculations can begin. An initial equilibrium under in-situ stresses and other boundary conditions should be obtained first, and then the fluid injection can start. The fluid pressure p within fractures is calculated, and then, the fluid pressure is introduced into the global force vector  [F]. After this, the global equilibrium equation (equation (  4 )) is solved, and the block system deformation [D] can be obtained.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the  Second Tibetan Plateau Scientific Expedition and Research Program (STEP)  (No. 2019QZKK0904 ), the  National Natural Science Foundation of China  (No. 51734009 ), the  Strategic Priority Research Program  of the  Chinese Academy of Sciences  (No. XDB10030000 ), the  Science Foundation of Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences  (No. KLSG201708 ), the  Key Research Program of the Institute of Geology & Geophysics ,  CAS  (No. IGGCAS-201903 ), and the  Joint PhD Training Program  of  University of Chinese Academy of Sciences (UCAS).
, 2015; Flora & Curran, 2004). MPLUS (Version 6. 12. [Computer Software]. Los Angeles, CA:  Muthén & Muthén)  was used to estimate the parameters of the model. Although the test of exact fit proved to be significant (χ 2 = 631. 413, df = 536), the test of close fit (RMSEA =. 013) is inferior to the minimum of. 05 required to prove an acceptable adjustment  (Browne & Cudeck, 1993). This hypothesis can be accepted at a high probability threshold (p RMSEA <=. 05 >. 99). In his "Guidelines Concerning the Modelling of Traits and Abilities in Test Construction",  Schweizer (2010)  advises on the reporting of CFI (Comparative Fit Index;  Bentler, 1990)  and SRMR (Standardized Root Mean Square Residual) in addition to the χ 2 and the RMSEA. As our data are categorical, the WRMSR (Weighted Root Mean Square Residual) will be reported instead of the SRMR  (DiStefano et al.
, 2018; Flora & Curran, 2004; B. Muthén, 1978). The Comparative Fit Index is acceptable (CFI = 0. 958) and is superior to the. 95 limit  (Hu & Bentler, 1999). The WRMSR is acceptable as it is inferior to the cut-off score of 1  (WRMSR =. 978; DiStefano et al. , 2018). Dimension Level A CFA conducted with MPLUS (Version 6. 12 [Computer Software]. Los Angeles, CA:  Muthén & Muthén)  confirmed that the correlated multi-dimensional structure of QEPro presents a better fit than a hierarchical structure with a general second order factor saturating the seven firstorder factors (one for each dimension of QEPro). Indeed, QEPro measurement model indicates a good fit of the data to the correlated multidimensional model  (Barrett, 2007; Browne & Cudeck, 1993; Hu & Bentler, 1999; Kline, 2005)  (X 2 = 8.
Acknowledgements We thank emlyon business school and The Adecco Group for their invaluable help in data collection. We also thank  Olivier Le Courtois  for his relevant feedback on the draft of this paper and  Kevin Charras  and  Nicolas de Zamaroczy  for the copy-editing. Finally, we thank the editor and the reviewers for their suggestions that have been very helpful in improving the manuscript. Funding The tool (QE Pro) was developed with funding from  Badenoch & Clark , a subsidiary of The Adecco Group, as part of an academic research project. Neither the test nor the data collected to calibrate the tool belong to the company that funded the research project.
Data collection and response assessment 2. 3. 1. Multidisciplinary risk assessment Patients underwent a multidisciplinary evaluation including consultation with an oncologist and a dietitian. Such an assessment includes subjective and objective parameters such as medical history, weight loss, current dietary intake (including energy and protein balance), physical examination and anthropometric measurements, functional and mental assessment, medications, REE measurement and laboratory values. REE was determined prior to immunotherapy initiation, under standard resting conditions, i. e. after 12 h of fasting (overnight), between 8 and 9 a. m. , in a thermo-neutral environment, by a trained nurse. Prior to commencement of the measurements, patients rested quietly for 15 min, during which time the indirect calorimeter was calibrated and a steady state obtained  [13].
Anonymized clinical data were recorded by local investigators using electronic case report forms (eCRF) in a password-protected secure online portal (Cleanweb). We counted the number of metastatic sites according to the radiologists' reports of the computed tomography scan and other image examinations taken before the ICI therapy. Best response to immunotherapy, achieved at least once during the course of therapy, was based on Response Evaluation Criteria in Solid Tumors (RECIST) version 1. 1. For each tumor, we performed PD-L1 immunostaining on freshcut slides from representative blocks using an anti-PD-L1 antibody (E1L3N, Cell signaling) on Bond automat (Leica) as previously described and validated by the PATTERN French thoracic pathologists group.
Statistical analyses Calculations were performed using R statistical software (version 3. 6. 2. , R Stats, epiR, survival and survcomp Package). Comparisons between groups were performed with Student t-test for quantitative variables and with chi-square test for qualitative variables. The primary endpoint of analysis was 6-month PFS  [16]. We first performed a retrospective exploratory study on 38 patients to estimate the effect of hypermetabolism on 6-month PFS. Then we conducted a prospective validation study in two centers to validate our hypothesis. We calculated that we would need to enroll 50 patients in normo-and hypermetabolic groups to show a 30% difference of 6month PFS (50% vs 20% respectively) with a two-sided 5% significance level and a 90% statistical power. Logistic regression was used to test the association of clinical and biological variables with 6-month PFS.
Hypermetabolism was associated in this study with lower albumin and altered Glasgow Prognostic Score (albumine h 35 g/L and CRP i 10 mg/L). As a consequence of systemic inflammation concomitant with a tumor, findings report an association between inflammation and REE  [20, 21]  and weight loss  [21, 22]. Although hypermetabolism is part of precachexia and cachexia syndroms, hypermetabolism may be observed while the patient has no evidence of weight loss nor presence of systemic inflammation  [23, 24]. Several limitations of these results may be pointed out: the study is not randomized and therefore we cannot discriminate between the relative predictive and the relative prognostic value of hypermetabolism. The prognostic influence of REE and its predictive capacity to identify patients sensitive to ICI therapy are not visible in a dataset, where all patients have been treated with ICI.
Acknowledgments This research was supported by grant from  Baxter  ( 04012016 ). We would like to thank the nurses  Florence Astorg ,  Catherine Du Mortier  and  B eatrice Musengeshi  for their assistance in performing calorimetry and the dieticians  Sophie Gentile ,  Camille Le Bris ,  Lauren Leduc ,  Aurore Mages , for their systematic early analysis and intervention.
Acknowledgements and funding sources We thank the ARPEGE platform facilities at the  Institut de Génomique Fonctionnelle  for all fluorescence-based assays. J. -P. P. was supported by la  Fondation pour la Recherche Médicale  (ref. DEQ20170336747 ),  Cisbio Bioassays  (EIDOS collaborative team IGF-CISBIO, ref. 039293) and  LabEx MAbImprove  (ref. NR- 10-LABX-5301 ). P. R. was supported by the  Agence Nationale de la Recherche  (ref. ANR-15-CE18-0020-01 ). J. -P. P. , L. P. and P. R. were further supported by the  Centre National de la Recherche Scientifique  and the  Institut National de la Santé et de la Recherche Médicale. P. C. and D. N. were supported by the  Fond Unique Interministériel of the French government (FUI ,  Cell2Lead  project). F. A. was supported by the  Centre National de la Recherche Scientifique  and the  Science Ambassador Program from Dassault Systèmes BIOVIA.
J. H. was supported by a fellowship from la  Région Occitanie  and  Cisbio Bioassays  (TransACT, ref. 156544) and la Ligue contre le cancer (PhD grant, ref. IP/SC-16487). J. F. was supported by la  Fondation pour la Recherche Médicale  (ref. DEQ20170336747). R. Q. was supported by a grant from the  Agence Nationale pour la Recherche  ( ANR 18-CE11-0004-02 ). The CBS belongs to the  France-BioImaging  national infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04 , "Investments for the future").
Protein co-expression networks in shoots and roots The shoot proteomics data set consisted of 6453 proteins quantified in at least one sample across six genotypes and two phosphate treatments. From roots, 5146 proteins were quantified. In total, 8219 proteins from two data sets were analyzed (Table  S2A ). To identify proteins with concordant abundance changes (co-expressed) across genotypes and phosphate conditions, we applied a weighted gene co-expression network analysis (WGCNA) analysis  (Plaxton and Tran, 2011)  using 2714 proteins from shoots and 1677 proteins from roots that were quantified in all genotypes under both phosphate conditions with at least one quantification among biological replicates. The protein-protein co-expression network was generated based on the topological overlap matrix in which protein interconnectivity was indicated in a heatmap (Figure  1 ).
The WGCNA analysis inferred 19 protein co-expression modules in shoots and 11 coexpression modules in roots that were inferred from hierarchical clustering and represented by different colors  (Figure 1a, d). Representative enrichment of protein functions within each module was based on MAPMAN4 annotation  (Schwacke et al. , 2019 ) and Fisher's exact test. Representative functional enrichments for each module are shown in a global network visualization containing all proteins from shoots and roots (Figure  1b, e ). The most prominent co-expression module within the shoot proteome (shoot module 'black') was overrepresented with proteins from photophosphorylation in photosynthesis (bin 1. 1), glycolysis (bin 2. 1), oxidative phosphorylation (bin 2. 4) and primary carbohydrate metabolism (bin 3).
Transcript co-expression network in root We also analyzed gene expression changes by RNAsequencing (RNA-seq). Thereby, we focused on the roots because, based on previous research  (B€ untgen et al. , 2021) , root processes in some genotypes appeared more critical for adaptations to low P than processes in shoots. The high quality of our triplicate RNA-seq datasets per genotype and P treatment allowed robust comparisons between individual conditions. To remove the noise associated with poorly expressed transcripts, we considered genes expressed above a minimal threshold for normalized fragments per kilobase million reads (FPKM). This yielded 23 382 protein coding genes. Small RNAs and long intergenic non-coding RNA represented only a small fraction of the total RNA reads (<4%); thus, the majority of reads was protein coding.
Different splice variants played a minor role and were neglected. Gene expression changes associated with phosphate limitation were overall similar to the responses in other maize genotypes  (De Bigault et al. , 2016)  and in other plant species. To obtain a number of co-expression modules similar to that for the much smaller proteome dataset, the WGCNA analysis settings for gene expression modules were adjusted to the proteome data. This revealed 14 coexpression modules for root transcripts of which the module 'turquoise' and 'brown' were the largest. The transcript module 'turquoise' contained genes coding for proteins with functions in protein biosynthesis, solute transport, nutrient uptake cell wall and lipid metabolism. The transcript module 'brown' contained genes coding for proteins in vesicle trafficking, protein biosynthesis, protein modification and photosynthesis (Figure  1g, h ).
Generally, the observed protein abundance changes for the protein kinases were little related to their transcript changes. We propose that the abundance of protein kinases as regulatory elements are likely important in defining the general and genotype-specific protein abundance differences observed in response to low phosphate and that these are not well predicted from their transcripts. To predict putative substrates of these kinases, for each kinase, we extracted the most highly co-expressed proteins from the protein co-expression network. In total, the top 1200 correlations associated with protein kinases within the WGCNA network covered 16 protein kinases and 646 coexpressed proteins (Figure  7b ). For example, the receptor kinase Zm00001d048053 of the 'blue' module was highly co-expressed with RNA-regulatory proteins within the blue module.
DISCUSSION In the present study, we use state-of-the-art proteomic datasets and 31 different traits from six selected maize genotypes in a WGCNA  (Plaxton and Tran, 2011)  to reveal a co-expression network. Comparison with root RNA-seq transcriptomes revealed only partial overlap in the responses and of genotype-specific patterns. We identified the Founder Flint lines EP1 and F2 with highly distinct molecular responses to low P supply revealing the specific regulation of central carbohydrate metabolism, specifically sucrose metabolism, lipid metabolism and respiration processes.
WGCNA approach for deriving coexpression networks This proteomic and transcriptomic analysis not only confirmed many known relationships of low-phosphateinduced protein abundance differences and their correlation to typical traits in response to low phosphate supply, but also revealed further candidates. The WGCNA  (Plaxton and Tran, 2011)  approach has initially been developed for construction of transcript co-expression networks but, in recent years, been applied to integrate different 'OMICS' data types, exemplified by studies of a developmental series of maize kernels  (Walley et al. , 2016). This principle of weighted co-expression was applied here to protein abundance as well as mRNA abundance differences across two phosphate conditions and six genotypes. Protein abundance changes were integrated with low phosphate induced phenotypes and revealed common responses of old and new maize lines (Figures  1  and 2 ).
Candidate proteins and over-represented categories were obtained from robust statistical tests and describe more categories than DEPs only. More importantly, the WGNA converted the association of thousands of proteins with traits into the association of several protein sets (modules) with traits, eliminating the problem of multiple hypothesis testing correction, and provided potential targets that played central roles in these associations. Different measures of co-expression include Pearson's or Spearman's correlations, least absolute error regression or a Bayesian approaches  (Dam et al. , 2018). The former two methods were provided in WGNCA and we chose Pearson's correlation because it is robust with respect to analyzing linear relationships and avoids the pitfall of overfitting. A weighted co-expression in WGCNA allowed all proteins interconnected to be attributed with a measure of different strength instead of partial connections represented by '1' for each connection.
Concordant and discordant responses on mRNA and protein level upon low phosphate The transcriptomic comparison revealed much larger numbers of regulated genes that, formally, were similarly represented by analogous WGCNA modules, although direct comparisons with the proteome were biased (Figure  5 ). Generally, on a proteome/transcriptome-wide scale, there were only moderate correlations between log 2 -fold changes of protein abundances and mRNA abundances. This limited overlap between transcript abundance and protein abundance had already been observed also for the Arabidopsis root under low nitrogen  (Menz et al. , 2016) , as well as in mammalian cells, where mRNA and protein changes showed a similar correlation coefficient of approximately 0. 4  (Schwannh€ ausser et al. , 2011; Selbach et al.
Protein identification and statistical analysis Protein identification, ion intensity normalization and quantitation were determined using MAXQUANT, version 1. 6. 7. 0  (Cox and Mann, 2008 )  (Tyanova et al. , 2016). Spectra were matched against the maize proteome (Zea_mays. B73_RefGen_v4. pep from Gramene, Ensembl plants release-44 with 131 585 entries) using Andromeda, with intensity quantification determined and normalized by the LFQ procedure  (Cox et al. , 2014). Unmodified peptides were used as well as peptides with oxidation (M), acetyl (Protein N-term). Mass tolerance for the database search was set to 4. 5 ppm on full scans and 20 ppm for fragment ions. For label-free quantitation, retention time matching between runs was chosen within a time window of 0. 7 min over a 20-min section.
RNA extraction and transcript analysis Total RNA was extracted from ground root tissues with an innu-PREP Plant RNA Kit (Analytik Jena AG) in accordance with the manufacturer's instructions. The RNA concentration was determined using a Nanodrop 2000c Spectrophotometer (Thermo Fisher Scientific). Three replicates of each genotype, both at low and high P, were sent to Novogene Co. , Ltd (Beijing, China) where 36 RNAseq libraries were constructed using the NEBNextâ Ultra TM RNA Library Prep Kit for Illuminaâ (NEB, Ipswich, MA, USA). The Novaseq 6000 instrument was used for sequencing. Reads were normalized using DESeq (https://bioconductor. org/packages/deseq2), which accounted for differences in the sequencing depth of the samples, dispersion values for each gene and fitted the model counts ~treatment + genotype + intercept across the 36 samples.
Statistical analysis Statistical analyses were implemented in the R or PYTHON environment. The R package WRS2 was adopted to apply robust two-way ANOVA test using pbad2way() function with the 'mom' estimator and to apply a post-hoc test using the mcp2 atm() function to analyze each trait. An additional Kruskal-Wallis one-way test and Dunn's test were employed for multiple comparisons of between lines on each P condition and a pairwise t-test was employed to compare high and low phosphate conditions for each genotype. The t-test and Fisher's exact test of traits and protein abundance were performed using SCIPY, version 1. 4. 1 (https://pypi. org/project/scipy/). P-values were corrected for multiple testing using the Benjamini-Hochberg method carried out in STATSMODELS, version 0. 11. 1 (https://pypi.
org/project/ statsmodels/). Log 2 -LFQ intensities of each replicate were used for PCA analysis, and only proteins quantified in all lines for each tissue were loaded. PCA calculations were conducted using SCIKIT-LEARN, version 0. 23. 1 (https://pypi. org/project/scikit-learn/). Scatter plots of PCA analysis results were drawn using MATPLOTLIB, version 3. 2. 1 (https:// pypi. org/project/matplotlib/). Venn diagrams were drawn using the pseudovenn library from VENN, version 0. 1. 3 (https://pypi. org/project/ venn/), conducted in a PYTHON environment. Over/under-representation of MAPMAN4 bins were identified by Fisher's exact test in combination with the X4. 2 Zea mays database. Upregulated and downregulated proteins/genes were analyzed separately. MAPMAN4 bins with FDR < 0. 05 were listed and visualized.
Co-expression networks The averaged log 2 -transformed LFQ-intensity of biological replicates or the log 2 -transformed FPKMs and their corresponding trait values were loaded into the WGCNA R package for analysis  (Plaxton and Tran, 2011). Protein networks of shoots and roots were constructed in parallel, where the data matrix consisted of 2714 proteins from shoot and 1677 proteins from root. Transcriptomic network of root was constructed using 23 659 genes. The soft thresholding power was set as 6 for the root proteome, 10 for the shoot proteome and 7 for the root transcriptome based on the criterion to approximate scale-free topology  (Chan et al. , 2013)  (Figure  S7  ). Adjacency matrices were built using the adjacency() function with Pearson correlation. The adjacency matrices were turned into the topological overlap matrices (TOMs) using the TOMsimilarity() function with default parameters.
GECKO Loader and Plotter To allow GECKO-A outputs, which are usually highly complex and voluminous, to be explored and visualized in detail on standard (non-UNIX) personal computers, we have developed the GECKO Loader and Plotter based in the data-analyzing and graphic-making package Igor Pro 8. 0 (WaveMetrics, Lake Oswego, Oregon, USA). This tool assists on the rapid and detailed analysis of model-chamber/OFR comparison studies. Specifically, the GECKO Loader and Plotter facilitates: i) filtering the (sometimes extremely large and finely-resolved) model results time series to examine specific characteristics, ii) identifying the most abundant and/or influential species in each phase (gas, particle, and wall), iii) selecting species by specific chemical identity (molecular formula, specific formula, and/or functional group identity), iv) plotting time series of individual species and their formation/destruction rates, v) assessing and displaying aggregated properties (volatility distribution, mass spectrum, Henry's law constant distribution) of the product mixture and subsets thereof, and vi) calculating bulk characteristics of the simulation (OHexp, OHRVOC, light intensity, elemental ratios etc.
Acknowledgements This work was partially supported by  NSF   AGS-1822664  and  AGS-1740610. We thank  Sasha Madronich  and  Alma Hodzic  for useful discussions. We would like to acknowledge high-performance computing support from  Cheyenne  (doi: 10. 5065/D6RX99HX )provided by  NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation. https://doi. org/10. 5194/acp-2021-118Preprint. Discussion started: 24 February 2021 c Author(s)  2021. CC BY 4. 0 License.