dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we investigate to what extent the major dimensions underpinning the global spectrum of plant form and function can be attributed to global gradients of climate and soil conditions; and to what extent these factors can jointly or independently explain the global spectrum of form and function. We compiled and analysed a dataset of 17 functional traits with a sufficient number of records in the TRY database  9  to characterize the main ecoregions of the world  30  , that is, environmentally homogeneous areas with distinct biota (Extended Data Fig. 1 ). The dataset is based on 225,206 georeferenced observations comprising records of 20,655 species. The trait data were complemented with 21 climate variables and 107 soil variables (Methods; Supplementary Tables  1  and 2 ). Trait-environment relationships were analysed for species medians aggregated to ecoregions using ridge regression  31  , a robust method (Supplementary Figs.
Latitudinal trait variation. As an investigation of broad-scale gradients among size and economics traits, we analyse latitudinal gradients of the first (PC1) and second (PC2) principal components. PC1-representing primarily size-related traits-shows a strong linear latitudinal signal (on the basis of species: r 2 (PC1) = 0. 37, at the ecoregion level r 2 (PC1 aggregated ) = 0. 84; Fig. 2a ). By contrast, the axis representing primarily economic traits, PC2, shows little response to latitude (on the basis of species: r 2 (PC2) = 0. 01, at the ecoregion level r 2 (PC2 aggregated ) = 0. 08; Fig. 2b , for woody non-woody species Supplementary Fig. 9 ), except for a dip at 35 ° and declining sharply at 60 ° where the species density also drops (but see Supplementary Fig. 10  for comparison to an independent dataset from arctic latitudes which shows the same pattern).
We interpret these results as evidence for the importance of both joint and independent effects of climate and soil variables for whole-plant strategies  2, 37, 38  which we show here at the global scale along with a dichotomous tendency of a stronger imprint of climate factors on size traits and of soil conditions on economics traits (Fig. 3 , Supplementary Figs. 13-38 and Supplementary Table  3 ). We propose that the dominance of joint effects implies that interactions between soil and climate properties are of primary importance in plant trait ecology; as opposed to trait syndromes being defined by single environmental variables in isolation. We next ask how the climate and soil datasets are interdependent and which predictors add the most relevant information. For this purpose, we related all traits to environmental variables in a redundancy analysis (RDA; Methods; Fig.
The respective data are too scarce to yet be integrated with global datasets. If such data were available they would have the potential to fundamentally change our perception of global plant form and function, and their relation to ecosystem functioning. Variation in size traits, represented by PC1 in Fig. 1b , shows a clear latitudinal gradient (Fig. 2b ). In contrast, variation in economics traits (represented by PC2) does not show a latitudinal trend. Only a dip is apparent at around 35 ° (absolute), in addition to a decrease at high latitudes above 60 ° (absolute) where available data become increasingly limited. However, comparison to a recent arctic dataset indicates that this decrease in variation at high latitudes reflects available observations (see Supplementary Fig. 10  for a comparison to independent data).
These patterns might represent a response to nutrient limitation and drought  42, 43  in water-scarce and nutrient-scarce deserts and Mediterranean regions (Supplementary Fig. 40 ) or boreal and arctic areas characterized by short growing periods slowing down mineralization. The dip at ~35 ° indeed can be related to low water availability (Supplementary Fig. 41 ). At high latitudes, cold winters and short growing seasons constrain plant height  13  and require on average more conservative nutrient-use strategies (like evergreen leaves) and protection against frost damage than the global mean, despite the high functional diversity in economics traits observed at these latitudes  13. Additional datasets may shed more light on specific conditions, for example see Bjorkman et al. 19. Future studies should quantify how individual stressors, for example radiative stress or water stress, relate to global patterns of trait variation.
Thirdly, soil heterogeneity within ecoregions-both abiotic and biotic-may weaken the relationship between economics traits and environmental variables  12, 54, 55. Reasons for small-scale soil variation are, for example, topography, soil age and thus fertility  56  but also abundance of microbial communities and mycorrhiza that interact with climate, pH, soil properties and also plant traits  50. Trait-environment relationships due to smaller scale variation require well-resolved soil data. However, we note that soil physics and chemistry explain a large portion of variance along the trait PC axis three (which itself explains slightly less than 10% of variance in the PCA (9. 36%); Supplementary Figs. 5, 6 and 38). We expect that with improved soil datasets and a higher resolution, the joint control of climate and soil on trait variation will probably appear even stronger and more evenly distributed between the two groups of driver variables.
Methods We extracted data on 17 plant functional traits from a gap-filled version of TRY database 9 (Supplementary Table  5 ; www. try-db. org, accession date July 2017, request no. 3282) which includes published literature  11,. Quality contro  was conducted according to the published protocol of TRY. 9 ,  311  Traits with z-score > 4 were excluded and those with z-score > 3 were checked for plausibility. Before this, missing data were imputed using a Bayesian hierarchical probabilistic matrix factorization (BHPMF) algorithm  8, 312  for an extended dataset, derived from TRY (Supplementary Table  6 ). Imputation was done to be able to include the maximum number of species in our analyses. Then the 17 traits were selected among the traits with the largest total number of entries. T e data were attributed to ecoregions  30  (Supplementary Table  7  and Extended Data Fig.
1 ) and aggregated to species median values. The imputed values were calculated using the whole dataset at the individual record level. BHPMF calculates the imputations from 1,000 Gibbs sampler (Markov chain Monte Carlo) imputations by taking the mean of every twentieth imputation of these 1,000 'versions' , after the first 200 are removed. Then the species median was calculated at the ecoregion level. We excluded observations that were not georeferenced because we could not attribute them to ecoregions. Accor ing to TRY regulations, data from experimental treatments (for example, fertilization) or from botanic gardens were also excluded. In total, we included 225,206 observations from 20,655 global unique species (36,197 unique species to ecoregion combinations). Throughout this study we used one of two aggregation levels: either species median per ecoregion (ER)  30  resulting in unique species values per ecoregion (termed A1, n = 36,197 with n = 20,655 globally unique species) or the aggregation to median ecoregions calculated from median species per ecoregion (termed A2, n = 220).
Implementation. The largest possible dataset was retrieved at the time when study was conducted, including 172 traits of 652,957 individuals (Supplementary Table  6 ). For data preparation before BHPMF, all individual-level trait data were firstly log-transformed and secondly normalized via zlog transformation ( z = x-x s. d. ). Log transformation was chosen to achieve a closer-to-normal distribution of values per trait 311 ,  313. This transformation is considered necessary because a given difference for small trait values (absolute value) is likely to be physiologically more relevant than the same difference (absolute value) for large trait values. BHPMF internally splits the datasets randomly into a training dataset (80%), a test dataset (10%) and a validation dataset (10%). The training dataset is used during training of latent vectors, while the test data are tested against to improve the latent vectors, and finally the validation dataset serves as the basis for calculation of the root mean square error (RMSE) and stopping the optimization of latent vectors within BHPMF  8.
The validation dataset ensures ongoing amelioration of the model performance during the training process and stops the process after five consecutive iterations with stable RMSE. The test dataset is used only on the lowest taxonomic level (individuals × traits). BHPMF was run with a maximum of 1,000 iterations, whereas the first 200 were discarded during the 'burn-in' phase, as predictions of these iterations are likely to be influenced by the initialization of BHPMF rather than being part of the probability density distribution to be sampled by BHPMF. To avoid autocorrelation, only every twentieth iteration was used to calculate the resulting trait values. The mean of these predictions result in the final trait values used as the output. Compared to the original data, the imputed values are similar in terms of trait-trait correlation, according to the Procrustes test provided in ref.
10. Trait clustering. To define groups of correlated traits, we clustered species' traits (species median per ecoregion, A1) on the basis of absolute pairwise Pearson correlation coefficients using a hierarchical clustering algorithm ('complete linkage clustering'). Variables were transformed into distances previous to the clustering. Hierarchical clustering then attributes variables (here, traits) to groups of least distance and highest similarity. Traits were more like each other if they exhibited similar correlation patterns with all other traits. We set a distance between traits of 1 as the threshold for defining trait clusters. We used the R package 'stats' function 'hclust' included in R (ref. 315 ).
PCA. Values for all 17 traits (unique species per ecoregion, A1) were natural log-transformed and then projected onto components (PCA). We used the R package FactoMineR 316 that scales data internally. 8] [319] [320] [321]  ), function testdim). For the analysis (ridge regression package 'glmnet'  322, 323  ) for Fig. 3 , all environmental variables (climate and soil) were first reduced with this package to 20 PCs.
A total of 220 ecoregions met these criteria and were included in the analysis. On average, these ecoregion-level trait values were based on 164 species-level trait medians (with a maximum of 1,245 species in Tapajós-Xingu moist forests; Supplementary Table  7 ). In total, we aggregated 36,197 median species trait values to ecoregions. These ecoregions cover the global latitudinal gradient (Fig. 2 ) as well as a substantial fraction of the geographic space (Extended Data Fig. 1 ). To aggregate environmental variables to ecoregions, we associated each trait observation with its corresponding values of climate and soil variables. Then, we averaged over all values within one ecoregion. Thus, the selected environmental variables represent averages that are weighted by the number and locations of trait observations within ecoregions.
Model building. Ecoregion trait values (natural log-transformed, A2) were related to all environmental variables using ridge regression  31  , which is a well-established linear regression method that is suitable to deal with a large number of collinear predictors and uneven numbers of predictors for climate and soil. We used the R package 'glmnet'  322, 323. From aggregating trait values to ecoregion medians we obtain 220 samples for each trait. The environmental predictors of climate and soil were reduced to 20 each by means of a PCA. In addition, the environmental predictors show relatively high collinearity, thus duplicated information. Ridge regression addresses collinearity among predictors by shrinking (regularizing) regression coefficients according to a penalty on the L2 norm of the vector of regression coefficients.
Acknowledgements The study was supported by the TRY initiative on plant traits (http://www. try-db. org). The TRY database is hosted at the  Max Planck Institute for Biogeochemistry (MPI BGC, Germany ) and supported by  Future Earth  and the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. We would like to thank all PIs contributing to the TRY database, whose efforts allowed this analysis. In detail, we thank:  J. H. C. Cornelissen ,  R. Milla ,  W. Cornwell ,  K. Kramer ,  S. Gachet ,  Ingolf Kühn ,  P. Poschlod ,  M. Scherer ,  J. Pausas ,  B. Sandal ,  K. Verheyen ,  J. Penuelas ,  N. Soudzilovskaia , We appreciate the discussions at the  MPI BGC. We thank  F. Fazayeli  for preparing the gap-filled trait data. We thank  F. Gans  and  U. Weber  for preparing ancillary data and  B.
Ahrens  for pointing out some soil data availability. We acknowledge  Environmental Systems Research Institute (ESRI)  and its licensor(s) for the Geodata product of the Missions Database '  ArcWorld Supplement' (GMI), published by Global Mapping International and originated from Global Mapping International  for producing Extended Data Fig. 1 and Supplementary Fig. 7 and available in ArcGIS software by  ESRI. ArcGIS and ArcMap are the intellectual property of  ESRI  and are used herein under license. For more information about ESRI software, please visit www. esri. com. The authors affiliated with the MPI BGC acknowledge funding by the  European Union 's  Horizon 2020 project BACI  under grant agreement no. 640176. We are thankful to the data providers for the SoilGrids, hosted by ISRIC. J.
S. J. acknowledges the  International Max Planck Research School  for global biogeochemical cycles. J. S. J. , M. E. S. and M. C. S. acknowledge support from the  University of Zurich University Research Priority Program on Global Change and Biodiversity. P. B. R. , M. E. S. and M. C. S. acknowledge membership in the US NSF 20-508  BII-Implementation  project, 'The causes and consequences of plant biodiversity across scales in a rapidly changing world'. M. E. S. acknowledges the NOMIS grant of Remotely Sensing Ecological Genomics that funds J. S. J. and M. C. S. C. W. acknowledges the support of the  Max Planck Society  via its fellowship programme. N. R. was funded by a research grant from  Deutsche Forschungsgemeinschaft DFG  ( RU 1536/3-1 ). K. K. was supported by the project  Resilient Forests  ( KB-29-009-003 ) of the  Dutch Ministry of Economic Affairs.
The trait data supplied were co-funded by the  EU-FP7 - KBBE  project:  BACCARA-Biodiversity and climate change , a risk analysis (project  ID   226299 ). I. W. acknowledges support from the  Australian Research Council  ( DP170103410 ). J. P. acknowledges financial support from the  European Research Council Synergy  grant  ERC-SyG-2013-610028 IMBALANCE-P. N. A. S. is financed by a  VIDI grant  ( 016. 161. 318 )issued by the  Netherlands Organization of Scientific Research. The data V. M. provided were funded by  II. Oldenburgischer Deichband  and the Wasserverbandstag e. V. ( NWS 10/05 ). We thank  M. Kleyer  for his critical input. P. H. and V. D. P. have been supported by  CNPq  (grant nos  369617/2017-2  and  307689/2014-0 , respectively). C. B. was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  ( 2018R1C1B6005351 ).
Data availability Plant trait data were accessed from the TRY database (https://try-db. org, request no. 3282, date accessed July 2017, see also Extended Data Fig. 1 ). All TRY data required to reproduce this analysis, and the corresponding R scripts, are provided in an open TRY File Archive (https://www. try-db. org/TryWeb/Data. php). Climate data WorldClim are publicly available via https://www. worldclim. org/ (accessed May 2018). Soil data, namely SoilGrids (https://soilgrids. org/, accessed June 2018) are publicly available. Ecoregion information 30  shapefiles are publicly available (accessed January 2014, Sciencebase. gov), The estimate of species richness per ecoregion  174  is publicly available (accessed January 2014, databasin. org. Data for this study can be accessed on Github (https://github.
Introduction Cervical cancer (CC) is a significant public health problem affecting women in countries with low resource settings. According to the Global Cancer Observatory database, there were 570,000 cases and 311,000 deaths due to CC in 2018  (Ferlay et al. , 2018). Over a third of for better management of CC. Genome-wide genetic and epigenetic studies using normal and tumor samples lead to identifying cancer-associated genes and related pathways for diagnostic and prognostic applications in cancer. Studies have shown that the genome-wide data's reanalysis may provide (i) molecular markers for diagnosis, prognosis, therapy, and (ii) a novel gene-gene regulatory networks, mechanisms, and pathways facilitating CC. Interestingly, previous studies have indicated that the integrated bioinformatics analysis of big data generated from cancer studies can develop reliable and novel biomarkers, networks, and mechanisms related to CC pathogenesis  (Lin et al.
The anterograde and retrograde signaling between mitochondria and the nucleus is critical for the normal functioning of the cell  (Pfanner et al. , 2019). Many nuclear-encoded genes linked with mitochondrial function (NEMG) show a significant difference in their expression in normal and CC conditions  (Sun et al. , 2020). Therefore, profiling the expression of NEMG can be useful as a sensitive and specific biomarker for diagnostic and prognostic applications in CC. In the present study, we have investigated the differentially expressed nuclear-encoded mitochondrial genes (DE-NEMG) and the associated network for diagnostic and prognostic applications in CC. We have analyzed The Cancer Genome Atlas, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) datasets for DE-NEMG in CC to evaluate its prognostic significance.
TCGA-CESC datasets The TCGA contains molecularly characterized epi (genomics), transcriptomics, and proteomics data for over 20000 primary tumors and matched normal samples across 33 different cancer types. The TCGA-CESC dataset contains clinical and molecular data for 310 samples (307 tumors and 3 normal samples)  (Tomczak et al. , 2015). The differentially expressed genes in TCGA-CESE data sets were retrieved using the Transcriptome Alterations in Cancer Omnibus (TACCO: http://tacco. life. nctu. edu. tw/) online tool. The TACCO is a freely available online tool for identifying differentially expressed genes (DEG) and miRNAs from TCGA datasets. The TACOO can be used for Gene Ontology (GO), pathway enrichment, and prognostic model construction using a user-defined gene list  (Chou et al. , 2019).
Identification of DE-NEMG We have downloaded a list of 1,158 NEMGs from the MitoCarta2. 0 database. We downloaded the list of differentially expressed protein-coding genes (DEGs) between normal and tumor samples in the TCGA-CESC dataset via the TACCO online tool. We have identified the DEGs in the TCGA-CESC data set using the "select DEGs" function with cut-off criteria of adjusted p-value < 0. 05 and expression value log 2-fold change of +2 and -2 between tumor and normal tissue samples. We have compared the DEGs in TCGA-CESC dataset with that of NEMG downloaded from the MitoCarta2. 0 using Venny 2. 1 (https://bioinfogp. cnb. csis. es/tools/venny/) online tool to identify the DE-NEMGs.
Identification of differentially methylated NEMG We have used UALCAN (http://ualcan. path. uab. edu/ index. html) and DNMIVD (http://119. 3. 41. 228/dnmivd/ index/) online resources for identification of methylation regulated NEMGs. UALCAN online tool can identify differentially expressed, methylation-regulated genes, along with clinical attributes such as survival, age, histology, tumor grade, and nodal metastasis. A beta values of 0 and 1 are considered as unmethylated and completely methylated respectively. Beta value: 0. 7 -0. 5 and 0. 3 -0. 25 and a P<0. 05 considered significantly hypermethylated and hypomethylated  (Chandrashekar et al. , 2017). DNMIVD is a user-friendly interactive visualization of the DNA methylation profile of genes in TCGA datasets. The tool has a module for gene expression analysis, methylation and expression correlation, survival analysis, diagnostic and prognostic model generation.
Evaluation of metastatic potential of DE-NEMG The Human Cancer Metastasis Database (HCMDB https://hcmdb. i-sanger. com/) is a freely available online tool for assessing gene metastatic potential. The interactive web tool contains metastatic data from 38 metastasis sites and 29 cancer types  (Zheng et al. , 2018). In the present study, the HCMDB database was used to test the metastatic data. Next, we analyzed the expression of 10 HGs at the protein level using The Human Protein Atlas (HPA: http:// www. proteinatlas. org/) (Thul and Lindskog, 2018).
Drug and DE-NEMG interaction analysis The drug-gene interaction database (DGIdb, https:// www. dgidb. org/) was used to screen the drugs that interact with DE-NEMGs. The DGIdb is a user-friendly online resource for screening a druggable genome. The tool has browse and search options to identify DGI and enrichment. Besides, the DGIdb has options for searching approved, antineoplastic, and immunotherapies options to filter the drugs  (Cotto et al. , 2018).
Data Retrieval and differential gene expression analysis Figure  1  depicts the workflow of the study. We first retrieved the DEGs in the TCGA-CESC dataset using the TACCO database. Our analysis identified 2020 (Figure  2A ) DEGs with a fold change of 2 and P<0. 05. Among those 2020 DEGs, 802 and 1218 genes were upregulated and downregulated, respectively. The MitoCarta2. 0 database contained 1158 NEMG. Figure  2B  shows the expression of 1158 NEMG in the TCGA-CESC dataset. The comparison between 2020 DEGs with that of 1158 NEMG lead to the identification of 52 (27 upregulated and 25 downregulated) DE-NEMG (Table  1 , Figure  2C , and 2D). The list of all DEGs in the TCGA-CESC dataset and NEMG retrieved from Mitocarta2. 0 are shown in Supplementary Tables 1, 2 and 3.
Functional enrichment analysis (FEA) of DE-NEMG The FEA included prediction of gene ontology (GO) [biological process (BP), cellular component (CComp) and molecular function (MF)], and pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG: http://www. genome. jp) (Kanehisa, 2000). The analysis and visualization of functional and pathway enrichment for 52 DE-NEMG were performed using the clusterProfiler tool in the Bioconductor package in the R statistical environment  (Yu et al. , 2012).
Identification of driver gene and enrichment analysis The driver gene analysis was carried out using DriverDBv3 (http://driverdb. tms. cmu. edu. tw/) online tool. The DriverDBv3 uses genetic and epigenetic changes in OMICS databases to compare against clinical data to identify cancer driver genes  (Liu et al. , 2020). We have used the CCDB database (https://webs. iiitd. edu. in/ raghava/ccdb/index. php) (Agarwal et al. , 2011)  to identify the DE-NEMG, which are already reported in CC. Also, the disease enrichment analysis of 52 DE-NEMG was performed using Comparative Toxicogenomics Database (http://ctdbase. org) (Davis et al. , 2019).