cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.qvr2gg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Poecilia latipinna",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029751-250811113504898/datasets/export for details.
DOI: 10.17632/4rh2jv4rm4
title: Impact of plasma storage duration and SERS signal consistency on Raman spectroscopy-based Tuberculosis prediction models

description: The plasma sample for this study was retrieved from our previous study (attached). In brief, a thousand of participants were recruited from community and hospital networks across eight provinces in northeastern Thailand. Of these, 500 cases were positive to interferon-gamma release assay (IGRA) or IGRA-positive and considered latent tuberculosis cases, while the other 500 individuals were IGRA-negative and served as healthy controls. The participants included 258 males and 742 females, aged 5 to 99 years. The study was approved by the Khon Kaen University Ethics Committee (Ethics number HE651163). All experiments were performed in accordance with relevant guidelines and regulations. 
Protein was extracted from human plasma by published protocol. Then, the SERS measurements were conducted using a confocal Raman microscopic system (inVia Reflex confocal Raman microscope, Renishaw plc., UK). Each dried protein sample was individually mixed with sterile water to achieve a final concentration of 0.25 g/l. Subsequently, 2.0 µl of protein sample was carefully placed onto the center of silver-based SERS sensors and allowed to air-dry for 10 min. A Raman spectrum was collected from a central region on each SERS sensor using a 785 nm wavelength laser through a 50× objective lens. Each spectral acquisition was performed with 25 mW laser power and a 10-second exposure time. For each sample, spectral data were collected from 49 points covering a grid area of 7×7 at 15 µm steps.
DOI: 10.17632/4rh2jv4rm4.1
title: Impact of plasma storage duration and SERS signal consistency on Raman spectroscopy-based Tuberculosis prediction models

description: The plasma sample for this study was retrieved from our previous study (attached). In brief, a thousand of participants were recruited from community and hospital networks across eight provinces in northeastern Thailand. Of these, 500 cases were positive to interferon-gamma release assay (IGRA) or IGRA-positive and considered latent tuberculosis cases, while the other 500 individuals were IGRA-negative and served as healthy controls. The participants included 258 males and 742 females, aged 5 to 99 years. The study was approved by the Khon Kaen University Ethics Committee (Ethics number HE651163). All experiments were performed in accordance with relevant guidelines and regulations. 
Protein was extracted from human plasma by published protocol. Then, the SERS measurements were conducted using a confocal Raman microscopic system (inVia Reflex confocal Raman microscope, Renishaw plc., UK). Each dried protein sample was individually mixed with sterile water to achieve a final concentration of 0.25 g/l. Subsequently, 2.0 µl of protein sample was carefully placed onto the center of silver-based SERS sensors and allowed to air-dry for 10 min. A Raman spectrum was collected from a central region on each SERS sensor using a 785 nm wavelength laser through a 50× objective lens. Each spectral acquisition was performed with 25 mW laser power and a 10-second exposure time. For each sample, spectral data were collected from 49 points covering a grid area of 7×7 at 15 µm steps.
DOI: 10.15468/dl.nmdd2p
title: Occurrence Download

description: A dataset containing 31492675 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 31492675 records from 2953 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029712-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17587924
title: Immune cell senescence drives responsiveness to immunotherapy in melanoma

description: Immunotherapy has significantly improved cancer treatment. However, it is not effective in all cancer patients, rendering the need to further delineate the differences among responders and non-responders at the molecular and cellular level. Unresponsiveness to immunotherapy has been attributed to dysfunctional immune cell states such as T-cell exhaustion and anergy, whereas the contribution of cellular senescence remains elusive. Herein, we have investigated the role of immune cell senescence in the response to checkpoint inhibitors in melanomas where these immunotherapies are applied as a first line treatment.

Two senescence detecting complementary approaches were utilized in a case control study we conducted. First, we implemented a senescence molecular signature we developed, termed "SeneVick", retrospectively in a single cell RNA-seq dataset from melanoma patients who received immunotherapy. Prior to this analysis, the signature was extensively validated in a variety of cell/tissue contexts, senescence types and species. Second, cellular senescence was assessed via an established experimental algorithmic approach in circulating immune cells of an analogous melanoma clinical cohort.

Melanoma patients who did not respond to immunotherapy exhibited increased cellular senescence in the CD8 + T-cell, CD4 + T-cell, B-cell (CD19 + /CD20 +) and NK cell compartments compared to responders. This phenomenon was independent of patients’ clinical features (age, sex, melanoma type, stage) and not an outcome of immunotherapy, in contrast to conventional anti-cancer treatments. Interestingly, alterations of cell-to-cell interactions among the immune sub-populations in non-responders compared to responders were identified supporting, along with cytotoxicity assays, that senescent immune cells display immunosuppressive properties driving defective immune responses and treatment failure.

Overall, our findings provide evidence that cellular senescence within the immune cell compartment of the tumor micro-environment is a potent determinant of the response to immunotherapy and pave the way for strategies targeting it, as promising approaches to improve the outcome of such interventions.
DOI: 10.5281/zenodo.17587925
title: Immune cell senescence drives responsiveness to immunotherapy in melanoma

description: Immunotherapy has significantly improved cancer treatment. However, it is not effective in all cancer patients, rendering the need to further delineate the differences among responders and non-responders at the molecular and cellular level. Unresponsiveness to immunotherapy has been attributed to dysfunctional immune cell states such as T-cell exhaustion and anergy, whereas the contribution of cellular senescence remains elusive. Herein, we have investigated the role of immune cell senescence in the response to checkpoint inhibitors in melanomas where these immunotherapies are applied as a first line treatment.

Two senescence detecting complementary approaches were utilized in a case control study we conducted. First, we implemented a senescence molecular signature we developed, termed "SeneVick", retrospectively in a single cell RNA-seq dataset from melanoma patients who received immunotherapy. Prior to this analysis, the signature was extensively validated in a variety of cell/tissue contexts, senescence types and species. Second, cellular senescence was assessed via an established experimental algorithmic approach in circulating immune cells of an analogous melanoma clinical cohort.

Melanoma patients who did not respond to immunotherapy exhibited increased cellular senescence in the CD8 + T-cell, CD4 + T-cell, B-cell (CD19 + /CD20 +) and NK cell compartments compared to responders. This phenomenon was independent of patients’ clinical features (age, sex, melanoma type, stage) and not an outcome of immunotherapy, in contrast to conventional anti-cancer treatments. Interestingly, alterations of cell-to-cell interactions among the immune sub-populations in non-responders compared to responders were identified supporting, along with cytotoxicity assays, that senescent immune cells display immunosuppressive properties driving defective immune responses and treatment failure.

Overall, our findings provide evidence that cellular senescence within the immune cell compartment of the tumor micro-environment is a potent determinant of the response to immunotherapy and pave the way for strategies targeting it, as promising approaches to improve the outcome of such interventions.
DOI: 10.5281/zenodo.10522646
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.10604746
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.11065418
title: Wasserstoffatlas (Hydrogen Map) Dataset

description: Input files required for Hydrogen Map workflow
DOI: 10.5281/zenodo.7665475
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.7708757
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8013998
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8135781
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8189432
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8341076
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8341248
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.10522945
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.10727969
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.10965260
title: Wasserstoffatlas (Hydrogen Map) Dataset

description: Input files required for Hydrogen Map workflow
DOI: 10.5281/zenodo.11093427
title: Wasserstoffatlas (Hydrogen Map) Dataset

description: Input files required for Hydrogen Map workflow
DOI: 10.5281/zenodo.13960465
title: Wasserstoffatlas Deutschland (Hydrogen Map) Dataset

description: Input files required for Hydrogen Map workflow in the project "Wasserstoffatlas Deutschland", funded by the BMBF.
DOI: 10.5281/zenodo.7665474
title: Wasserstoffatlas Deutschland (Hydrogen Map) Dataset

description: Input files required for Hydrogen Map workflow in the project "Wasserstoffatlas Deutschland", funded by the BMBF.
DOI: 10.5281/zenodo.7684772
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.7708772
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8135714
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8210603
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.
DOI: 10.5281/zenodo.8341523
title: Wasserstoffatlas (Hydrogen Map) input dataset

description: Input files required for Hydrogen Map workflow.