iScience

Unmodified data relating to the iScience manuscript titled "Mapping the substrate landscape of protein phosphatase 2A catalytic subunit PPP2CA"

[Column E and Column F] We overlapped the location coordinates of the 81,922 0-nt to 5-nt variant 13-nt ERE or HRE DNA elements in the genome and the location coordinates of the ChIPSeq or ChIPExo peaks in an experiment (157 ER experiments at 0-nt to 5-nt variant ERE DNA elements) (194 KR experiments at 0-nt to 5-nt variant HRE DNA elements) to determine the absolute number of times each 0-nt to 5-nt variant 13-nt ERE or HRE DNA element occurred within an experiment (the entire 13-nt DNA element was required to be within the peak boundaries). [Column V] Multiple peak selection criteria were used (L4, L8, L10, L15, L20), where Lx represents an x-fold greater tag density at peaks than in the surrounding 10-kb region. This performs a low-to-high stringency analysis of the data. By studying the data with respect to a multiple spectrum of peak selection criteria, we adjust for both the risk of excess background noise and the risk of filtering out any low-amplitude information. All peaks are observed at L4, [Column V] identifies whether that specific peak is also observed at L8, L10, L15 or L20. The ChIPSeq or ChIPExo peaks were annotated to regions in the genome (annotatePeaks.pl) using Homer. [Column A] Peak ID [Column B] Chromosome [Column C] Peak start position [Column D] Peak end position [Column G] Strand [Column H] Peak Score [Column I] FDR/Peak Focus Ratio/Region Size [Column J] Annotation (i.e. Exon, Intron, ...) [Column K] Detailed Annotation (Exon, Intron etc. + CpG Islands, repeats, etc.) [Column L] Distance to nearest RefSeq TSS [Column M] Nearest TSS: Native ID of annotation file [Column N] Nearest TSS: Entrez Gene ID [Column O] Nearest TSS: Unigene ID [Column P] Nearest TSS: RefSeq ID [Column Q] Nearest TSS: Ensembl ID [Column R] Nearest TSS: Gene Symbol [Column S] Nearest TSS: Gene Aliases [Column T] Nearest TSS: Gene description [Column U] Nearest TSS: Gene type

These data are linked to our paper "Chromosomal translocation formation is sufficient to produce fusion circular RNAs specific to patient tumor cells" from Babin et al; iScience 2018 Each figure raw data sets are available.

We created this dataset and scripts with the research aim to analyze the influence of Integrated Assessment Modelling (IAM) research on Intergovernmental Panel of Climate Change (IPCC) reports. We analyzed the following research questions (Table 1):1. Which IAM researchers have been cited in IPCC WG3 reports?2. To what extent are IPCC WG3 reports based on IAM research?2.1. What share of IPCC WG3 references are authored by any IAM researcher individually?2.2. How much of the evidence base of IPCC WG3 reports is IAM research?2.2.1. What share of IPCC WG3 references are authored by all IAM researchers together?2.2.2. How sensitive are the results to different sample sizes of IAM researchers?2.3. How many IAM researchers are responsible for the majority of IAM research in IPCC reports?2.4. What share of IPCC WG3 references in each chapter are authored by IAM researchers?3. To what extent are IPCC WG3 SPM statements based on IAM research?4. How diverse is the sample of IAM research covered in IPCC WG3 reports? RQs that can be answered using the same dataset:· Who are the most cited researchers by IPCC WG3 reports? (Section 1)· Which chapters act as knowledge bridges in IPCC reports? Which chapters are most commonly co-cited in the SPM? Which chapters are not well covered in the SPM? (Section 3)· What share of evidence in IPCC WG3 reports is attributable to the top 30 most cited researchers? (Section 1 & 2.3)· What is the distribution of affiliated institutes for IAM researchers who have been cited in IPCC WG3 reports? (Section 4) RQs that can be answered by modifying the script/files:· Who are the most cited researchers in other reports (e.g. UNEP reports)? (Section 1)· To what extent has research from another research field (e.g. nuclear energy research) been cited in IPCC WG3 reports? (Section 2.1, 2.2 & 2.4)· To what extent has IAM research been covered in other reports (e.g. UNEP reports)? (Section 2.1, 2.2 & 2.4)· How diverse is the IAM research covered in IPCC WG3 reports regarding ethnicity and academic experience level? (Section 4) Check the documentation file for more details.

We created this dataset and scripts with the research aim to analyze the influence of Integrated Assessment Modelling (IAM) research on Intergovernmental Panel of Climate Change (IPCC) reports. We analyzed the following research questions (Table 1):1. Which IAM researchers have been cited in IPCC WG3 reports?2. To what extent are IPCC WG3 reports based on IAM research?2.1. What share of IPCC WG3 references are authored by any IAM researcher individually?2.2. How much of the evidence base of IPCC WG3 reports is IAM research?2.2.1. What share of IPCC WG3 references are authored by all IAM researchers together?2.2.2. How sensitive are the results to different sample sizes of IAM researchers?2.3. How many IAM researchers are responsible for the majority of IAM research in IPCC reports?2.4. What share of IPCC WG3 references in each chapter are authored by IAM researchers?3. To what extent are IPCC WG3 SPM statements based on IAM research?4. How diverse is the sample of IAM research covered in IPCC WG3 reports? RQs that can be answered using the same dataset:· Who are the most cited researchers by IPCC WG3 reports? (Section 1)· Which chapters act as knowledge bridges in IPCC reports? Which chapters are most commonly co-cited in the SPM? Which chapters are not well covered in the SPM? (Section 3)· What share of evidence in IPCC WG3 reports is attributable to the top 30 most cited researchers? (Section 1 & 2.3)· What is the distribution of affiliated institutes for IAM researchers who have been cited in IPCC WG3 reports? (Section 4) RQs that can be answered by modifying the script/files:· Who are the most cited researchers in other reports (e.g. UNEP reports)? (Section 1)· To what extent has research from another research field (e.g. nuclear energy research) been cited in IPCC WG3 reports? (Section 2.1, 2.2 & 2.4)· To what extent has IAM research been covered in other reports (e.g. UNEP reports)? (Section 2.1, 2.2 & 2.4)· How diverse is the IAM research covered in IPCC WG3 reports regarding ethnicity and academic experience level? (Section 4) Check the documentation file for more details.

This contains 33 ensembles of 50 DT-LEMBAS models each. The models were trained on 33 cell line transcriptomic data from the L1000 dataset.

Prostate cancer (PCa) frequently presents as a multifocal disease within a single gland. Herein, the transcriptome-wide profiles of glandular epithelial (GE) cells of four PCa tissues with various Gleason scores (GSs) are analyzed with Visium spatial transcriptomics (ST). The transcriptomic classifications across PCa section sites generally matched the spatial patterns of histological structures with different GSs. Average inferred copy number variation (inferCNV) values gradually increased during GS development. Developing trajectories during GS upgrading were assessed, and differentially expressed genes (DEGs) during GS progression were analyzed which exhibited heterogeneity among individual PCa patients. Several crucial genes, such as NANS, PABPC1L, PILRB, PPFIA2, and SESN3, were associated with GS upgrading. Enrichment analysis showed that biological functions, such as cadherin binding, Golgi vesicle transport, protein folding, and cell adhesion molecules were related to GS progression. In conclusion, this study provides insight into ST-based transcriptome-wide expression patterns during GS progression.

IsSupplementTo: Diomedi, S., Vaccari, F. E., Filippini, M., Fattori, P., Galletti, C. (2020). Mixed Selectivity in Macaque Medial Parietal Cortex during Eye-Hand Reaching. IScience, 23(10), 101616. (https://doi.org/10.1016/j.isci.2020.101616)

Human activities increasingly challenge wild animal populations by disrupting ecological connectivity and population persistence. Yet, human-modified habitats can provide resources, resulting in selection of disturbed areas by generalist species. To investigate spatial and temporal responses of a generalist carnivore to human disturbance, we investigated habitat selection and diel activity patterns in caracals (Caracal caracal). We GPS-collared 25 adults and subadults in urban and wildland-dominated subregions in Cape Town, South Africa. Selection responses for landscape variables were dependent on subregion, animal age class, and diel period. Contrary to expectations, caracals did not become more nocturnal in urban areas. Caracals increased their selection for proximity to urban areas as the proportion of urban area increased. Differences in habitat selection between urban and wildland caracals suggest that individuals of this generalist species exhibit high behavioral flexibility in response to anthropogenic disturbances that emerge as a function of habitat context.

This repository contains data described in this study: Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia. Ennis S et. al., iScience, 2023. DOI: 10.1016/j.isci.2023.106943. Contents: bone_marrow.h5ad - AnnData file with the integrated dataset ref_model_final.tar.gz - A zipped folder containing the scVI model for the integrated dataset Supplemental_material.tar.gz - A zipped folder containing the supplemental figures and tables from the publication