Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Intrinsically disordered Prosystemin discloses biologically active repeat motifs

Dataset referred to "Intrinsically disordered Prosystemin discloses biologically active repeat motifs". Abstract: The in-depth studies over the years on the defence barriers by tomato plants have shown that the Systemin peptide controls the response to a wealth of environmental stress agents. This multifaceted stress reaction seems to be related to the intrinsic disorder of its precursor protein, Prosystemin (ProSys). Since latest findings show that ProSys has biological functions besides Systemin sequence, here we wanted to assess if this precursor includes peptide motifs able to trigger stress-related pathways. Candidate peptides were identified in silico and synthesized to test their capacity to trigger defence responses in tomato plants against different biotic stressors. Our results demonstrated that ProSys harbours several repeat motifs which triggered plant immune reactions against pathogens and pest insects. Three of these peptides were detected by mass spectrometry in plants expressing ProSys, demonstrating their effective presence in vivo. These experimental data shed light on unrecognized functions of ProSys, mediated by multiple biologically active sequences which may partly account for the capacity of ProSys to induce defense responses to different stress agents.

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data : 1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data: cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field. Data:  MDAlab_cDC1_maturation.tar : Docker image used for the analysis

Compound activity data sets for 15 biological targets compiled from the ChEMBL and PubChem databases.

Compound activity data sets for the 15 biological targets are deposited, along with structure-activity relationship matrices IDs. Active compounds were extracted from the ChEMBL database and inactive were from the PubChem database. Details of the data sets are described in the original publication. and the summary of the data sets is given in the readme.txt file.

opencc-by-4.0Oct 2017View details →

Dataset of "Tracking Urban Human Activity from Mobile Phone Calling Patterns" PLOS Computational Biology paper

This are the dataset file for "Tracking Urban Human Activity from Mobile Phone Calling Patterns", to be published in PLOS Computational Biology. The files contain probability distributions of finding a first, last, or any call as a function of time, derived from anonymized call detail records for a 12 months period in the year 2007 from a mobile phone service provider in a European country. The first data file contains the data obtained fom 30 different cities. the second for the six most populated cities, splitting the data into different age and gender groups. Details in README files.

opencc-by-4.0Apr 2024View details →

DESIGN, SYNTHESIS AND STUDY OF BIOLOGICAL ACTIVITY OF PEPTIDES WITH NEUROPROTECTIVE PROPERTIES

The main purpose of this project is to search for novel compounds that will protect neurons against oxidative stress (6-hydroxydopamine, 6-OHDA) and glucocorticoids (corticosterone, CORT) in a human neuroblastoma cell line (SH-SY5Y) and elucidate the mechanism by which attenuated the physiological changes induced by these neurotoxins. We will explore whether this protective role of tested compounds will be involved also in the regulation of apoptosis, maintaining oxidoreductive balance, and stabilization of mitochondrial membrane potential. Furthermore, the involvement of the BDNF/TrkB-ERK-CREB/mTOR signalling pathway in neuronal survival will be examined.

opencc-by-4.0Jun 2024View details →

Data and Script used in "Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest"

The R code and data provided in this repository allow to reproduce the data carpentry, analysis and visualization of “Effects of canopy gaps on microclimate, soil biological activity and their relationship in a European mixed floodplain forest” (https://doi.org/10.1016/j.scitotenv.2024.173572).   Folder structure   Data abstracts: Data_abstract_climate.pdf Data_abstract_soil_biotics.pdf Data_abstract_soil_abiotics_openness.pdf   Data: Climate_data.xlsx Soil_biotics.xlsx Soil_abiotics_openness.xlsx   R Scripts: 00-preamble.R loads all required packages 01-data-carpentry.R loads all datasets and prepares the analysis of all experimental periods. 02-data-analyses-microclimate.R compares understorey air and soil microclimate between forest types and treatments, presents diurnal and seasonal variations and tests the relationship of under- and overstorey openness on microclimate. 03-data-analyses-decomposition.R compares decomposition rates and feeding activity between forest types and treatments and models the dependencies of soil biological activity on microclimate and soil abiotic factors.   Information of related software and package versions used in the script: R version 4.3.2 (2023-10-31 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19045) Matrix products: default   Contact Please contact me at annalena.lenk@uni-leipzig.de if you have further questions.

opencc-by-4.0Jul 2024View details →

Figure 2. Energy dispersive X in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi

Figure 2. Energy dispersive X-ray spectroscopy of α - and γ-Al2O3NPs.

opencc-by-4.0Jul 2024View details →

Figure 1 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi

Figure 1. Field emission scanning electron microscope (FESEM) of α- and γ-Al2O3NPs.

opencc-by-4.0Jul 2024View details →

Figure 3 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi

Figure 3. UV-Vis and direct optical band gap spectra of (A) α-Al2O3NPs and (B) γ-Al2O3NPs.

opencc-by-4.0Aug 2021View details →

Dataset for manuscript entitled "The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm"

The following datasets were used for the 16s rRNA analysis in the manuscript entitled " The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm". BZ2 files were obtained from next generation sequencing with the Illumina Mi-Seq. Mothur was used to analyze the BZ2 files, creating the listed excel documents.

opencc-by-4.0Nov 2022View details →

Activation patterns of afferent synapses on layer 5 tufted pyramidal cells in a biologically detailed simulation

The data set is based on a biologically detailed simulation of a circuit of cortical neurons. The circuit was activated by thalamo-cortical inputs every 1 s for 500 ms. We report for a number of exemplary tufted pyramidal cells in layer 5 the pattern activation of their afferent synapses. Specifically, we report for all afferent excitatory synapses the pairwise path distances along the dendrite / soma (note that the soma was simplified to a point for the purpose of calculating path distances, but not during the simulation), and the times of activation of each of these synapses. The simulation is based on the model described in <a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144v1">this preprint</a>. The model can also be found <a href="https://zenodo.org/record/6906785">here on Zenodo</a>. For more details on the simulation, contact the author. For more details about the format of the data, refer to the included jupyter notebook.

closedIPD-NOFeb 2026View details →

Study to Assess the Safety and Biological Activity of AMX0035 for the Treatment of Alzheimer's Disease

ClinicalTrials.gov study NCT03533257. IPD Sharing: NO. Countries: 1. Publications: 2.

controlledIPD-YESFeb 2026View details →

A Study of the Efficacy and Safety of Upadacitinib in Participants With Moderately to Severely Active Crohn's Disease Who Have Inadequately Responded to or Are Intolerant to Conventional and/or Biolog

ClinicalTrials.gov study NCT03345849. IPD Sharing: YES. Countries: 49. Publications: 7.

restrictedIPD-UNDECIDEDFeb 2026View details →

A Study of Subcutaneous RoActemra/Actemra (Tocilizumab) as Monotherapy or in Combination With Methotrexate or Other Non-Biologic DMARDs in Patients With Active Rheumatoid Arthritis

ClinicalTrials.gov study NCT01995201. IPD Sharing: Not stated. Countries: 3. Publications: 3.

closedIPD-NOFeb 2026View details →

Mediterranean Diet and Disease Activity in Axial Spondyloarthritis Receiving Biologic Therapy

ClinicalTrials.gov study NCT07170384. IPD Sharing: NO. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

Study of Abatacept (BMS-188667) in Subjects With Active Rheumatoid Arthritis on Background Non-biologic DMARDS (Disease Modifying Antirheumatic Drugs) Who Have an Inadequate Response to Anti-TNF Thera

ClinicalTrials.gov study NCT00124982. IPD Sharing: Not stated. Countries: 10. Publications: 5.