dataesr/scientific-paragraphs-categorization huggingface dataset:
Objectives This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. Data description Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data.
Maize (Zea mays subsp. mays L. ) plays an important role in the global economy. As a crop, it displays a variety of uses such as food, feed, and fuel. At the same time, and due to its versatility and relevance, maize has been widely studied. The Genomes to Fields (G2F) is a collaborative initiative involving scientists from the public sector that support growers, consumers, and society. G2F researchers generate phenotypic, genotypic, environmental, and metadata datasets to facilitate the understanding of the potential and challenges of maize production in different environments. Individual genotype performances differ across environments, and the magnitude of this difference dictates the importance of the Genotype by Environment (G × E) interaction. Understanding and harnessing G × E interactions improves the efficiency in the use and allocation of resources, and it facilitates the identification of genotypes with higher stability across a range of locations, the identification of locations where the effect of G x E is minimized, and the identification of mechanisms affecting the differential response of phenotypes to variable environments.
Furthermore, advances in our understanding of the fundamental components contributing to the differential response of plants to environmental cues will also improve genomic and phenotypic predictabilities for traits of interest. Therefore, this data release provides a unique resource of combined agronomic, phenological, and morphological information to dissect G × E interaction. In the 2018 and 2019 experiments, 1153 publicly available hybrids were evaluated through a network of collaborators in 32 different locations. The main group of hybrids was produced by the cross of doubled-haploid (DH) inbred lines from a collection of three biparental populations that share one parent in common (PHW65) and PHN11, Mo44, and MoG as the alternative parent, to two ex-PVP inbred testers, LH195 in Midwest to Southern locations, and PHT69 in Northern locations.
Data description The 2018 and 2019 datasets are publicly available via CyVerse/iPlant and structured as described in Table  1. Briefly, the datasets included here are: • Phenotypic dataset: Phenotypic measurements that follow a standard set of instructions, available in the G2F webpage  [1]. Standard traits include days to anthesis, days to silking, ear height, plant height, stand count, stalk lodging, root lodging, grain moisture, test weight, plot weight, and estimated grain yield. Raw data and quality-controlled data are reported. Out of range observations were set to missing following the rules described in the readMe and data description files. • Genotypic dataset: Inbred parents of the tested hybrids were genotyped using the Practical Haplotype Graph (PHG)  [2, 3]. The data is minimally filtered, allowing the public to perform their own quality control steps prior to using it.
The raw sequencing reads are available under BioProject ID PRJNA530187  [4]. The code used to create the genotypic data is also available at https:// bitbu cket. org/ buckl erlab/ g2f_ 2018_ phg_ genot yping/ src/ master/. • Environmental dataset: WatchDog 2700 weather stations (Spectrum Technologies) were placed at each field site. Data was collected at 30-min intervals from planting through harvest at each location. The geographic locations of the experiments are not identical across years due to crop rotation management practices; thus, the locations of the weather stations vary across years. Each station measured wind speed, direction, and gust; air temperature, dewpoint, relative humidity; soil temperature and moisture; rainfall and solar radiation. Additional measurements taken at selected sites included soil electrical conductivity, ultra-violet light, carbon dioxide, and photosyntheti-cally active radiation.
Instructions for weather station maintenance activities including pre-season tasks, field setup, maintenance throughout the growing season, and removal are available in the G2F webpage  [5]. • Soil dataset: Each field location collected soil samples that represent the experiment field. Collaborators follow instructions available on the G2F webpage for sample collection  [5]. • Supplemental dataset: Supplemental information consists of metadata (any field-level data collected at planting, in season, and/or at harvest), agronomic information (list of pesticides, nutrients, and irrigation applied), and cooperator list (collaborators responsible for the field locations in 2018 and 2019).
Acknowledgements We gratefully acknowledge contributions from many field managers and data collectors including:  Dustin Eilert ,  Marina Borsecnik ,  Rachel Perry ,  Renata Barcelos  and  Ben Fischer (de Leon/Kaeppler labs, University of Wisconsin-Madison). Amanda Gilbert (Hirsch lab, University of Minnesota ). Christine Smith ,  Brandi Sigmon ,  Connor Pedersen ,  Nathaniel Pester ,  Isaac Stevens  ( Schnable Lab, University of Nebraska-Lincoln ). Trevor Perla ,  Amy Deariso ,  Paige Coffee ,  Steven Hughes , and  C. J. Dudley  ( USDA Tifton ). Naomi Rodman ,  Spencer Caro ,  Coleman Grindle ,  Allison McCabe ,  Samuel Morris , and  Bamidele Sangoyomi  ( Wallace lab-University of Georgia ). William Widdicombe  and  Linsey Newton  ( Singh and Thompson Labs, Michigan State University ). Susan Melia-Hancock  and  Jim Elder  ( Flint-Garcia Lab ,  USDA-ARS, Columbia MO).
Methods Observational and model data. The model simulations used here are fully-coupled, transient climate simulations from the MPI-GE  41   Observational data from the E-OBS 50 dataset version EOBSv24. 0e for the period of 1950-2021 are used for comparison and evaluation of the MPI-GE simulations against current climate conditions. E-OBS data, with a native regular grid of 0. 25°, are regridded to the coarser resolution of MPI-GE simulations. All spatial averages are calculated over land-only grid cells in the European area defined by the [35-63 N, 10W-55E] latitudelongitude domain. When comparing against observations, model data are subsampled to grid boxes where observations are available. Note that this version of the E-OBS dataset includes input of several station series that have not been homogenized, and should be used with caution for the evaluation of trends  50.
Acknowledgements This project was supported by the  Max Planck Society for the Advancement of Science  and by the  German Ministry of Education and Research (BMBF)  under the  ClimXtreme  project, subproject  DecHeat  (Grant number  01LP1901F ). L. S. G. has also received funding from the  European Union 's  Horizon Europe Framework Programme  under the  Marie Skłodowska-Curie  grant agreement No  101064940. We thank  Leonard Borchert  and three anonymous reviewers for providing insightful comments and suggestions to improve this work. We acknowledge  Sebastian Brune  and  Johanna Baehr  for producing and processing the historical and RCP4. 5 MPI-GE simulations used in this study, and the  Swiss National Computing Centre (CSCS)  and the  German Climate Computing Center (DKRZ)  for providing the necessary computational resources.
Data availability The data required to interpret the findings reported in this article is available in the public long-term archive repository of the German Climate Computing Center (DKRZ-LTA; https://www. wdc-climate. de/ui/entry?acronym=DKRZ_LTA_1152_ds00903) and in the public repository Zenodo (https://doi. org/10. 5281/zenodo. 10044343). The source MPI-GE simulations and model output are partially available for download at https:// esgf-data. dkrz. de/projects/mpi-ge/ or by contacting grandensemble@mpimet. mpg. de. E-OBS observational data are available for download at https://www. ecad. eu/download/ ensembles/download. php.
Code availability The scripts used to perform this analysis and other supporting information that may be useful in interpreting and reproducing this work are available in the public long-term archive repository of the German Climate Computing Center (DKRZ-LTA; https://www. wdc-climate. de/ui/entry?acronym=DKRZ_LTA_1152_ds00903) and in the public repository Zenodo (https://doi. org/10. 5281/zenodo. 10044343). The analysis and figures in this article were performed using Climate Data Operator (CDO) software  57  , Python and NCAR Command Language (NCL, NCAR 2019; Version 6. 6. 2).
13 en we quantified interactions of centrosome-and cilia-related proteins with spliceosome factors, we unexpectedly discovered that a wide range of pre-mRNA-processing factors (e. g. , PRPF4), Serine and Arginine Rich Splicing Factors (SFRS3, SFRS7), RNA Helicases (DDX24), and ubiquitously expressed hnRNPs (e. g. , HNRNPM), displayed a robust interaction predominantly with CEP250 (Figures  2B  and S2D ). CEP250-associated splicing factors showed a significant correlation with genetic disorders including retinitis pigmentosa and microcephaly that are associated with mutations in centrosome genes (Figure  2C ). 15, 22, 23 Similarly, a large number of RNA-processing proteins including factors involved in mRNA splicing and regulation of translation at the centrosome were identified in an independent database of human neural cells (Figure  2D  and Table  S2 ).
24 To assess the validity of our observations, we next analyzed an independent centrosome proteome database where the Human Centrosome-Cilium interactome has been captured for a large subset of centrosome proteins (n = 58), resulting in more than 6,000 interactions (Table  S2 ). 25  The global centrosome interactome was enriched for Gene Ontology (GO) terms related to RNA binding and the regulation of mRNA export, expanding the significance of specific RNA-processing proteins at this location (Figure  S3A ). We reasoned that extrapolating highly reliable and experimentally validated interactors (n = 418, FDR 1%) would result in more precise predictions of the real binding partners. The findings revealed no interactions with translation-related proteins. Notably, we discovered that crucial satellite/cilia basal body (SSX2IP and OFD1) and centrosome linker (CEP135) components established a robust protein interaction network with several spliceosome components alone or in complexes   S2 ).
A variety of nuclear spliceosome components localize at the centrosome To further assess the consistency of the protein interaction networks at the centrosome, proteomic data of centrosomes (>700 proteins) isolated from lamb thymocytes were analyzed. 26 As expected, the thymocytes centrosome proteome was enriched for Gene Ontology (GO) terms related to ribosome and mRNA splicing components. Notably, a set of RNPs including PRP19, RPS11 and SRSF7 showed concordance with human centrosome-associated proteins (Figures  S3C  and S3D ). Proteomic data of centrosomes isolated from HeLa S3 cells revealed that, relative to ribosomes and eIFs factors, splicing proteins at centrosomes increased five-fold in M phase compared to S phase (Figure  S4A ). 27 The subcellular distribution of ribonucleoproteins localized at centrosomes and centriolar satellites (n = 564) was then mapped using imaging data from the human proteome atlas (Figures  S4B  and S4C ).
29 The analysis of additional subcellular organelles revealed that proteins associated with splicing localized predominantly at the centrosome and vesicles, supporting a possible nucleo-cytosolic shuttling of splicing factors at distinct intracellular sites (Figure  3D ). Altogether, these findings suggested that centrosome-associated factors may have celltype specific relevance at the centrosome. A subset of centrosome-associated splicing factors are co-regulated in a cell-specific manner To determine how centrosome and RNP-related transcripts are regulated, we investigated gene expression data across the human GTEx database. 30 As expected, ribosomal-related transcripts were stably expressed in a variety of tissues. Instead, a subset of splicing and centrosomal genes tended to be similarly downregulated in heart, brain, and liver tissues, indicating a tissue-specific co-regulation (Figures  S6A-S6C ).
Of note, these tissues function without a minor spliceosome. 31 Some ciliogenic genes with minor introns such as TCTN3 and CEP170 that were downregulated in liver tissues established a protein interaction network mainly with the identified centrosome-associated splicing factors (CTNNBL1, BUD31, and BCAS2) (Figure  S6D ). To ensure our observations are not merely explained by a species-specific effect, we collected single cell RNA-seq data from mouse embryonic stem cells and developed a gene regulatory network using SINCERITIES. 32, 33 By focusing our attention on the top 2,000 interactions with the highest score, we found that centrosome transcripts were highly related to splicing genes as compared to translation-related transcripts (Figure  S7A ). Notably, Fbf1 and Ofd1 were the most prominent centrosomal genes of the regulatory networks (Figures  4A  and 4B ).
Tissue-specific alterations in centrosome-associated splicing factors To better understand the reciprocal relationship between splicing and centrosomes in human diseases, we interrogated gene expression profiles in the TCGA database using as a reference non-neoplastic tissue. 38, 39 As expected, ribosome-associated transcripts exhibited higher expression patterns in a variety of blood, brain and gastrointestinal cancers. In contrast, the splicing and centrosome expression profiles were highly tissue-specific (Figure  S7D ). Co-expression networks were evident in liver cancers (LC), especially in a rare and aggressive variant of the bile duct termed cholangiocarcinoma (CHOL) (Figures  S8A  and S8B ). For instance, CEP250 expression was significantly higher in liver cancers ($ three times higher) than in normal liver (Figure  S8C ).
Deregulation of splicing factors influences centrosome-related transcripts Pathogenetic mutations in splicing genes often cause phenotypic abnormalities associated with centriole defects. 15 Analyzing RNA-seq data from retinal organoids and fibroblasts mutated in the pre-RNA processing factor PRPF31 identified CEP250 as a common target of splicing alterations (Figure  S10A ). 22, 23 In addition, by exploring an online tool (https://portals. broadinstitute. org/ctrp/)  44 we found that CEP250 and DHX35 were similarly targeted by indisulam, a drug known to induce degradation of the splicing factor RBM39 via the proteasome (Figure  S10B ). In an independent database, we confirmed that a large number of RBM39 mRNA targets comprised a cytoskeleton and microtubule related-pathway (Figure  S11A ).
The numerous disordered regions identified could confer extreme flexibility and enable centrosomal proteins to interact with many partners. Consistent with this, our protein-protein interaction network study identified ribosome and spliceosome components as the most abundant interactors of CEP250. The abundance of intrinsically disordered regions in CEP250 may provide a higher degree of plasticity and promote multivalent interactions at the centrosome. Spatial proteomics analysis from independent databases confirmed that spliceosome factors expected to localize to the nucleus also localized to the centrosome or centriolar satellites, refining the protein interaction network data. The identified centrosome-associated RNA processing factors were assigned to distinct spliceosome sub-complexes: SR proteins, complexes A and B, Prp19 complex, and potential components of the catalytic complex C (Figure  6A ).
49 We propose that centrosome-associated splicing factors could facilitate the transport and assembly of RNP particles contributing to cell division and ciliogenesis in a cell-type specific manner. Many challenges remain in studying the native RNP aggregates surrounding centrosomes in a cell-free context. Thus, our study opens an exciting area of future research on the cytosolic compartmentalization of the RNP condensates in the context of cellular differentiation and development trajectories in metazoans. patterns were compared with known spliceosome proteins (n=257) extracted from the Oncomine database. The spatial proteomes of Golgi (n=1127 proteins); mitochondria (n=1139 proteins); vesicles (n=2247 proteins); endoplasmic Reticulum (n=523 proteins), and cytoskeleton elements including microtubules (n=479 proteins), intermediate filaments (n=180 proteins) and actin filaments (n=365 proteins), were analyzed for comparison.
Ribonucleoproteins localized at centrosomes were cataloged as eukaryotic initiation factors (eIFs), ribosome, and spliceosome proteins. For the protein-protein interaction maps, both baits and interactors were hierarchically clustered, using the Spearman rank correlation. To visualize the strength of the molecular interaction, the tool significance analysis of interactome (SAINT) was used to create dotplots, heatmaps, and graphical representations of interacting partners. 25 The software platform WEBGestalt at (http://www. webgestalt. org/) was used to independently verify and visualize important protein-protein interaction networks at centrosomes. 51 Overlapping characteristics and dataset discrepancies were analyzed using Venn diagrams. To investigate the molecular pathways and functional profiles of protein-protein interaction at the centrosome, DAVID (Database for Annotation, Visualization, and Integrated Discovery) and g:Profiler were used.
52 Significant pathways and gene ontology (GO)-Terms were identified using a Benjamini-Hochberg adjusted P-value less than 0. 05. GO-Terms that scored significant in two different bioinformatics tools were included. indisulam targeting RBM39) were analyzed to determine the effect of splicing inhibition on centrosomerelated genes (Table  S2 ). The therapeutics response portal at https://portals. broadinstitute. org/ctrp/, which links genetic, lineage, and cellular aspects of cancer cell lines to small-molecule sensitivity, was used to investigate drugs that target gene clusters of interest. 44 The Cancer Genome Atlas (TCGA) database at https://cancergenome. nih. gov/was used to evaluate the transcript levels of centrosome and RNAbinding protein genes. The differential expression of tumor/normal tissues was investigated using ANOVA (analysis of variance).
The gene expression profiles in cancer cells were investigated in two independent databases, the Cancer Cell Line Encyclopedia (CCLE) and the NCI60 panel of 947 and 60 cancer cell lines, respectively (Table  S2 ). Explorative immunohistochemistry (IHC) for centrosome (CROCC) and pre-mRNA processing factors (SNRPD3) in non-pathological and cancer tissues were collected from the Human Proteome Atlas. 28 GEPIA, a bioinformatics web application for analyzing RNA sequencing expression data across cancers and normal tissues, was used to import the graphical representation of gene expression data. A P-value less than 0. 05 was considered as statistically significant. The correlation between gene expression data was verified independently using UALCAN, an interactive web platform for assessing cancer OMICS and proteomic data (Table  S2 ).
The cBioPortal database, available at https://www. cbioportal. org/, was used to confirm variations in gene expression profiles and alterations in genomic data and DNA copy number changes. The stoichiometric relationships between transcripts and proteins were investigated with CellMiner at discover. nci. nih. gov/cellminercdb. 37 To determine if the identified centrosome and cilia interactors could be categorized as minor intron-containing genes (MIGs), the tool available at https://midb. pnb. uconn. edu was used. 31 rmal and cancer tissue specimens The use of human tissue samples was approved by the ethical committee for research of the University of Verona (protocol 42160) and abides by the Declaration of Helsinki principles. FFPE (Formalin-fixed Paraffinembedded) samples were collected from the archives of Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, FG, Italy and Mater Salutis Hospital AULSS9, ''Scaligera'', 37122 Verona, Italy.
ACKNOWLEDGMENTS We are very grateful to Dr. Pietro Cirigliano (Nikon Instruments S. p. A) for support and invaluable technical assistance in the use of confocal microscopy and acquisitions of images. This work was supported by the (FFABR grant n. 4982/2018), from the Italian Ministry of University and Research (MIUR) to M. P and by the Associazione Italiana per la Ricerca sul Cancro (AIRC; Grant IG 26414/2022 to BF).
Sequence divergence and gene decay, SNP density, and TE expansion Next, we calculated the sequence difference as the percentage of mutations in 10 kb sliding windows (Fig. 4A ). This revealed a sequence difference to the corresponding region of the Z for the W long arm at 0. 93% (1st quarter to 3rd quarter: 0. 43-1. 13%; median: 0. 66%) and for the W short arm at 0. 86% (1st quarter to 3rd quarter: 0. 46-1. 04%; median: 0. 70%). Thus, no region with elevated sequence difference was apparent. We also calculated the extent of gene decay between the long (0-27. 5 Mbp) and short arm (27. 5-45 Mbp) of the W-chromosome. From a collection of 455,817 protein sequences retrieved from the vertebrate database of Swiss-Prot, 1. 284 protein-coding genes were identified on the G. affinis W with an alignment coverage > 35% and percentage of identity > 50%.
5C ). As in the CESH assay, cross-hybridization to centromeric DNA was not observed (Fig. 3A ). In summary, the long arm of the G. affinis W-chromosome harbors multiple copies of dispersed repetitive elements also found in certain segments of the amt locus, which are enriched in and are possibly specific to female and ovary-expressed DNA. These amt -segments include exons 4, 6, 7, and 8, introns 3, 6, and 7, and the 5′-UTR, corresponding to PCR products B, D, G, H, M, P, and R (Fig. 5E ) and likely represent a subset of transcribed sequences also detected by CESH (Fig. 3A ). To identify these Wq-enriched disperse repeats, we searched the published G. affinis 6498 bp amt gene sequence from  [26]  for simple repeats, TEs, long noncoding RNAs, and micro-RNAs in public databases. We identified three micro-RNAs, six sequences with partial homology to human  (5)  or mouse (1) long non-coding RNAs, eight sequences with partial homology to TEs, and three simple repeats, totaling 1680 bp (24%) (Table  S2 ).
Female enriched repeats from the G. affinis amt locus The published amt sequence (Genbank KP113677)  [26]  includes simple repeats, TEs, long non-coding RNAs, and micro-RNAs (Table  S2 ). The dispersed FISH localization of amt PCR products along Wq indicated an enrichment of certain repeats on the W-chromosome (Fig. 5A, B , Fig. S4  and summary in Fig. 5E ). Furthermore, some of these repeats were found to be highly predominant in the female genome and perhaps even absent from the male (Fig. 5B , Fig. S4B ). Moreover, RT-PCR FISH from the ovary and not from the testis cDNA hybridized to Wq, showing that the respective sequences are sex differentially transcribed (Figs. 3A  and 5C ). The ovary-specific expression in G. affinis resembles the testis-specific expression pattern of genes from the knifejaw fish neo-Y  [40].