Small mammal detection in agro-ecosystems
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB49250 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR9078560_1.fastq.gz | Fastqsanger.gz | |||
| ERR9078560_2.fastq.gz | Fastqsanger.gz | |||
| ERR9078570_1.fastq.gz | Fastqsanger.gz | |||
| ERR9078570_2.fastq.gz | Fastqsanger.gz | |||
| ERR9078580_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 4156 |
Similar Datasets
Project description:The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1,070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8,887 TCGA, and 1,261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n=1,261) including our lung cancer cohort (n=497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.
2024-02-14 | GSE218989 | GEO
Project description:BCM-HGSC Marine Mammal Genome Projects
| PRJNA170427 | ENA
Project description:Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a new machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially-resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally-defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell state co-occurrence patterns, we discovered 10 clinically-distinct multicellular communities with unexpectedly strong conservation, including four with unique myeloid and stromal elements, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue (https://ecotyper.stanford.edu).
2021-08-12 | GSE181817 | GEO
Project description:A comparative analysis of the gut microbiota in 10 European small mammal species
| PRJEB30121 | ENA
Project description:SOIL SAMPLES FROM AGRO-ECOSYSTEMS Raw sequence reads
| PRJNA308378 | ENA
Project description:Attempts to increase food crop yields by intensifying agricultural systems using high inputs of nonrenewable resources and chemicals frequently lead to de-gradation of natural resources, whereas most technological innovations are not accessible for smallholders that represent the majority of farmers world wide. Alternatively, cocultures consisting of assemblages of plant and animal species can support ecological processes of nutrient cycling and pest control, which may lead to increasing yields and declining susceptibility to extreme weather conditions with increasing complexity of the systems. Here we show that enhancing the complexity of a rice production system by adding combinations of compost, azolla, ducks, and fish resulted in strongly increased grain yields and revenues in a season with extremely adverse weather conditions on East Java, Indonesia. We found that azolla, duck, and fish increased plant nutrient content, tillering and leaf area expansion, and strongly reduced the density of six different pests. In the most complex system comprising all components the highest grain yield was obtained. The net revenues of this system from sales of rice grain, fish, and ducks, after correction for extra costs, were 114% higher than rice cultivation with only compost as fertilizer. These results provide more insight in the agro-ecological processes and demonstrate how complex agricultural systems can contribute to food security in a changing climate. If smallholders can be trained to manage these systems and are supported for initial investments by credits, their livelihoods can be improved while producing in an ecologically benign way.
| S-EPMC3434917 | biostudies-literature
Project description:Functional profiles predicted based on taxonomic affiliations differed from those obtained by GeoChip microarray analysis, which separated community functional capacity based on plant location. The identified metabolic pathways provided insight regarding microbial strategies for colonization and survival in these ecosystems. Sixteen samples analyzed.
2015-07-09 | E-GEOD-70539 | biostudies-arrayexpress
Project description:Provisioning ecosystem services play a vital role in sustaining human well-being. Agro-ecosystems contribute a significant share of these services, besides food and fodder and also fuel and fibre as well as regulating and cultural ecosystem services. Until now, the indication of provisioning ecosystem services of agro-ecosystems has been based almost only on yield numbers of agricultural products. Such an indication is problematic due to several reasons which include a disregard of the role of significant anthropogenic contributions to ecosystem service co-generation, external environmental effects and strong dependence on site conditions. We argue for an enhanced indication of provisioning ecosystem services that considers multiple aspects of their delivery. The conceptual base for such an indication has been made by prior publications which have been reviewed. Relevant points were taken up in this article and condensed into a conceptual model in order to develop a more holistic and expanded set of indictors, which was then exemplarily applied and tested in three case studies in Germany. The case studies represent different natural conditions, and the indicator set application showed that ecosystem services (ES) flow-in terms of output alone-does not characterise agro-ecosystems sufficiently. The proposed aspects of provisioning ecosystem services can give a fuller picture, for example, by input-output relationships, as it is possible by just using single indicators. Uncertainties as well as pros and cons of such an approach are elaborated. Finally, recommendations for an enhanced indication of provisioning ecosystem services in agro-ecosystems that can help to integrate agricultural principles with ideas of sustainability and site-specific land use are derived.
| S-EPMC8121745 | biostudies-literature
Project description:Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at unprecedented resolution and identify opportunities for therapeutic targeting (https://ecotyper-stanford-edu.stanford.idm.oclc.org/lymphoma).
2021-09-30 | GSE182435 | GEO
Project description:Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell-state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at unprecedented resolution and identify opportunities for therapeutic targeting (https://ecotyper-stanford-edu.stanford.idm.oclc.org/lymphoma).
2021-09-30 | GSE182434 | GEO