cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7483/opendata.cms.dcw0.f237
title: /WminusToLNuWminusTo2JJJ_EWK_LO_mjj100_pTj10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.6084/m9.figshare.28218647
title: Intermediate results for the ICPE 2025 research paper artifact related to "Quantifying Data Leakage in Failure Prediction Tasks"

description: Extract the results of the different experiment stages to the logs folder as explained in the readme file in the project repository.
DOI: 10.7483/opendata.cms.jwt7.x6uv
title: /WminusToLNuWminusTo2JJJ_EWK_LO_mjj100_pTj10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.48597/8ydw-fyrx
title: Heavy metals at Aluksne

description: Measurement of Heavy metals at Aluksne
DOI: 10.7483/opendata.cms.4gkp.7ijn
title: /WminusToLNuWminusTo2JJJ_QCD_LO_SM_MJJ100PTJ10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.48597/9833-9sjx
title: Measurement of Heavy metals at Aluksne

description: Measurement of Heavy metals at Aluksne
DOI: 10.48597/dxhu-quuw
title: Inorganics in precipitation at Aluksne

description: Measurement of Inorganics in precipitation at Aluksne
DOI: 10.48597/9cjt-trjg
title: Measurement of Inorganics in precipitation at Aluksne

description: Measurement of Inorganics in precipitation at Aluksne
DOI: 10.7483/opendata.cms.sija.qj74
title: /WminusToLNuWminusTo2JJJ_QCD_LO_SM_MJJ100PTJ10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.48597/24cy-482z
title: Measurement of Inorganics in precipitation at Aluksne

description: Measurement of Inorganics in precipitation at Aluksne
DOI: 10.7483/opendata.cms.lsgn.4uy8
title: /WminusToLNuZTo2JJJ_dipoleRecoil_EWK_LO_SM_MJJ100PTJ10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.48597/u2bf-78ru
title: Measurement of Heavy metals at Aluksne

description: Measurement of Heavy metals at Aluksne
DOI: 10.7483/opendata.cms.s50h.acoj
title: /WminusToLNuZTo2JJJ_dipoleRecoil_EWK_LO_SM_MJJ100PTJ10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.iq41.qcv1
title: /WminusToLNuZTo2JJJ_EWK_LO_mjj100_pTj10_aQGC_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qc2c.7x9e
title: /WminusToLNuZTo2JJJ_EWK_LO_mjj100_pTj10_aQGC_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.6084/m9.figshare.28218782
title: <b>Platelet-Based Biomarkers: A New Frontier in Inflammation Monitoring for Respiratory Infections</b>

description: Platelets are essential for haemostasis and have emerging roles in inflammation, particularly in respiratory infections like pulmonary tuberculosis (PTB) and pneumonia. The objective of this study was to assess alterations in platelet counts and indices and their association with ESR in PTB and pneumonia patients compared to healthy controls, in evaluating their diagnostic and prognostic potential. A retrospective case-control study was conducted at a tertiary care center involving 450 participants: 158 PTB patients, 142 pneumonia patients, and 150 healthy controls. Laboratory data including platelet counts, MPV, Pct, PDW, and ESR were retrieved from the hospital’s Laboratory Information System. Receiver Operating Characteristic (ROC) curve analysis using SPSS to determine diagnostic efficacy. PTB and pneumonia patients had significantly higher platelet counts and Pct, and lower MPV compared to controls (p&lt;0.001). In PTB patients, platelet count positively correlated with Pct and ESR, and negatively with MPV and PDW (p&lt;0.001). Similar patterns were observed in pneumonia patients. ROC analysis identified platelet count and Pct cutoff values of 312,500/µl and 0.21%, respectively, with moderate sensitivity and specificity for differentiating PTB from pneumonia. ESR cutoff was 66.5 mm/h with comparable diagnostic performance. Altered platelet counts and indices in PTB and pneumonia patients correlate with ESR levels, suggesting their utility as non-invasive diagnostic and prognostic markers in respiratory infections. Further prospective studies are needed to validate these findings and integrate platelet indices into clinical diagnostics.
DOI: 10.7483/opendata.cms.0l77.te5l
title: /WminusToLNuZTo2JJJ_EWK_LO_mjj100_pTj10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.zk93.w8la
title: /WminusToLNuZTo2JJJ_EWK_LO_mjj100_pTj10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.p5vh.dyr0
title: /WminusToLNuZTo2JJJ_QCD_LO_SM_MJJ100PTJ10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.0eek.qf3z
title: /WminusToLNuZTo2JJJ_QCD_LO_SM_MJJ100PTJ10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.fse4.ogyi
title: /WminusToLNuZToJJJJ_mjj100_pTj10_QCD_LO_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.16nb.ndjx
title: /WminusToLNuZToJJJJ_mjj100_pTj10_QCD_LO_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.dxc4.4k05
title: /WminusToZpTo3MuNu_M-10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.el2s.ep9o
title: /WminusToZpTo3MuNu_M-10_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.xcau.8eut
title: /WminusToZpTo3MuNu_M-15_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.5281/zenodo.14671569
title: Navigating Decision-Making in Serverless Migration: A Socio-Technical Grounded Theory Approach

description: This replication package includes all resources necessary to replicate the study detailed in the paper. The study explores the decision-making processes involved in migrating to serverless architecture, combining technical and organizational perspectives through a grounded theory methodology.

Package Contents:





Interview Script: A structured interview script used to guide discussions with industry practitioners about their experiences with serverless migration, covering:




Data Files: A detailed spreadsheet containing qualitative and quantitative data from the study, including:



Participant demographics

Key decision points during migration

Identified socio-technical factors influencing serverless adoption




Purpose:

This package is designed to:



Support reproducibility of the study

Provide valuable resources for researchers studying serverless adoption

Facilitate comparative studies and further exploration of socio-technical aspects in cloud computing
DOI: 10.5281/zenodo.14671570
title: Navigating Decision-Making in Serverless Migration: A Socio-Technical Grounded Theory Approach

description: This replication package includes all resources necessary to replicate the study detailed in the paper. The study explores the decision-making processes involved in migrating to serverless architecture, combining technical and organizational perspectives through a grounded theory methodology.

Package Contents:





Interview Script: A structured interview script used to guide discussions with industry practitioners about their experiences with serverless migration, covering:




Data Files: A detailed spreadsheet containing qualitative and quantitative data from the study, including:



Participant demographics

Key decision points during migration

Identified socio-technical factors influencing serverless adoption




Purpose:

This package is designed to:



Support reproducibility of the study

Provide valuable resources for researchers studying serverless adoption

Facilitate comparative studies and further exploration of socio-technical aspects in cloud computing
DOI: 10.7483/opendata.cms.gx7g.2zc3
title: /WminusToZpTo3MuNu_M-15_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.fyom.hsm5
title: /WminusToZpTo3MuNu_M-1_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.8qlb.60n6
title: /WminusToZpTo3MuNu_M-1_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.b4zg.nwus
title: /WminusToZpTo3MuNu_M-20_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.0oh8.1tw3
title: /WminusToZpTo3MuNu_M-20_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.wf86.ku0c
title: /WminusToZpTo3MuNu_M-25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.nfan.vebz
title: /WminusToZpTo3MuNu_M-25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.xqel.hn8h
title: /WminusToZpTo3MuNu_M-2_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.34740/kaggle/dsv/10486108
title: World Stock Prices ( Daily Updating )
DOI: 10.7483/opendata.cms.4kbd.lbpf
title: /WminusToZpTo3MuNu_M-2_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.6asz.nj4r
title: /WminusToZpTo3MuNu_M-30_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.tcby.yeec
title: /WminusToZpTo3MuNu_M-30_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.yyb6.6xny
title: /WminusToZpTo3MuNu_M-35_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ix7p.oy6k
title: /WminusToZpTo3MuNu_M-35_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.4s0d.q7ek
title: /WminusToZpTo3MuNu_M-3_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.mzb7.5j33
title: /WminusToZpTo3MuNu_M-3_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.oyz6.aq8k
title: /WminusToZpTo3MuNu_M-40_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.q4lf.2jax
title: /WminusToZpTo3MuNu_M-40_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.cysd.c5nb
title: /WminusToZpTo3MuNu_M-45_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.pjqx.d0dz
title: /WminusToZpTo3MuNu_M-45_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.6gau.35a3
title: /WminusToZpTo3MuNu_M-4_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.wixj.4cy0
title: /WminusToZpTo3MuNu_M-4_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.3jx7.7dz7
title: /WminusToZpTo3MuNu_M-50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ji8z.4px6
title: /WminusToZpTo3MuNu_M-50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.33qj.5p8m
title: /WminusToZpTo3MuNu_M-55_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.afga.agjq
title: /WminusToZpTo3MuNu_M-55_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.rgc9.e41w
title: /WminusToZpTo3MuNu_M-5_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.p769.aqfa
title: /WminusToZpTo3MuNu_M-5_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.14651082
title: Dataset for the evaluation of Supercritical Fluid Chromatography Non-Polar Stationary Phases: HSS C18 SB, CSH PFP, PGC

description: Raw data used for the evaluation of supercritical fluid chromatography non-polar stationary phases published in article "AI-Enhanced Understanding of Retention Interactions in Supercritical Fluid Chromatography: Neural Network Insights into Retention on Selected Non-Polar Stationary Phases" in Analytical Chemistry, 2025. Data set contains: (i) chromatograms of 107 analytes measured on high strenght silica C18 (HSS C18 SB), charged surface hybrid pentafluoro phenyl (CSH PFP), and porous graphitic carbon (PGC) column using methanol, 10 mmol/L ammonium in methanol, and 2% water in methanol as organic modifiers in 8 points during 1 year (Empower project, Excel sheets of retention times and measured mixtures), (ii) 226 molecular descriptors calculated by CDK Descriptor Calculator (v.1.4.8) from 3D structures of the 107 analytes optimized by semi-empirical AM1 quantum mechanical calculations using the MOPAC application of Chem 3D Pro version 14.0 software (CambridgeSoft) (Excel sheet), (iii) weights assigned to each molecular descriptor at each chromatographic conditions by artificial neural network created using the neural network simulator in Matlab R2023a with the deep learning toolbox V.23.2 (The MathWorks, Inc., Massachusetts, USA) and a sigmoid activation function, a backpropagation learning algorithm with 500 learning cycles (Excel sheet).
DOI: 10.5281/zenodo.14651083
title: Dataset for the evaluation of Supercritical Fluid Chromatography Non-Polar Stationary Phases: HSS C18 SB, CSH PFP, PGC

description: Raw data used for the evaluation of supercritical fluid chromatography non-polar stationary phases published in article "AI-Enhanced Understanding of Retention Interactions in Supercritical Fluid Chromatography: Neural Network Insights into Retention on Selected Non-Polar Stationary Phases" in Analytical Chemistry, 2025. Data set contains: (i) chromatograms of 107 analytes measured on high strenght silica C18 (HSS C18 SB), charged surface hybrid pentafluoro phenyl (CSH PFP), and porous graphitic carbon (PGC) column using methanol, 10 mmol/L ammonium in methanol, and 2% water in methanol as organic modifiers in 8 points during 1 year (Empower project, Excel sheets of retention times and measured mixtures), (ii) 226 molecular descriptors calculated by CDK Descriptor Calculator (v.1.4.8) from 3D structures of the 107 analytes optimized by semi-empirical AM1 quantum mechanical calculations using the MOPAC application of Chem 3D Pro version 14.0 software (CambridgeSoft) (Excel sheet), (iii) weights assigned to each molecular descriptor at each chromatographic conditions by artificial neural network created using the neural network simulator in Matlab R2023a with the deep learning toolbox V.23.2 (The MathWorks, Inc., Massachusetts, USA) and a sigmoid activation function, a backpropagation learning algorithm with 500 learning cycles (Excel sheet).
DOI: 10.7483/opendata.cms.jzbs.1cp1
title: /WminusToZpTo3MuNu_M-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.r6ub.v3l1
title: /WminusToZpTo3MuNu_M-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.b5hv.nq32
title: /WminusToZpTo3MuNu_M-65_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.5281/zenodo.8199919
title: Modeling of TDP-43 proteinopathy by chronic oxidative stress identifies rapamycin as beneficial in ALS patient-derived 2D and 3D iPSC models

description: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterized by TDP-43 proteinopathy for which the hypothesized pathomechanisms are the loss of TDP-43 nuclear function and the concomitant toxic gain of function of cytoplasmic and phosphorylated TDP-43 (P-TDP-43) aggregates. Response to environmental insults and formation of stress granules (SGs) have been proposed as initiators of TDP-43 pathological aggregation. Aim of this study was to generate in vitro models of TDP-43 proteinopathy to be used for drug screening. We first induced a chronic oxidative insult in human SK-N-BE cells by exposure to low doses of sodium arsenite (ARS). Our data showed TDP-43 mislocalization into the cytoplasm, insoluble P-TDP-43 increase, P62 accumulation, and defective splicing activity of TDP-43 towards its target genes UNC13A and POLDIP3 as readouts of TDP-43 nuclear loss-of-function. As autophagy impairment plays a crucial role in ALS, we tested three drugs involved in promoting autophagy, namely rapamycin, lithium carbonate, and metformin: only rapamycin was able to rescue ARS-induced loss of TDP-43 splicing activity on UNC13A. We then tested rapamycin in ALS patient-derived primary fibroblasts, where it reduced P-TDP-43 aggregates and SGs formation. Rapamycin was also tested in motor neurons differentiated from induced pluripotent stem cells (iPSCs) derived from ALS patients, where we confirmed its efficacy in reducing P-TDP-43 aggregates and SGs and rescuing ARS-induced loss of TDP-43 splicing activity. Finally, we tested rapamycin in ALS iPSC-derived brain organoids exposed to chronic ARS, where it rescued loss of TDP-43 splicing activity. In conclusion, we established different human cell models of TDP-43 proteinopathy in which rapamycin was able to rescue chronic oxidative stress-induced alterations in TDP-43 splicing activity and cytoplasmic mislocalization by modulating autophagy. Human SK-N-BE and ALS patient-derived 2D and 3D iPSC models chronically treated with ARS can therefore be exploited as in vitro platforms for future drug screening approaches.
description: antonia.ratti@unimi.it
DOI: 10.5281/zenodo.8199920
title: Modeling of TDP-43 proteinopathy by chronic oxidative stress identifies rapamycin as beneficial in ALS patient-derived 2D and 3D iPSC models

description: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterized by TDP-43 proteinopathy for which the hypothesized pathomechanisms are the loss of TDP-43 nuclear function and the concomitant toxic gain of function of cytoplasmic and phosphorylated TDP-43 (P-TDP-43) aggregates. Response to environmental insults and formation of stress granules (SGs) have been proposed as initiators of TDP-43 pathological aggregation. Aim of this study was to generate in vitro models of TDP-43 proteinopathy to be used for drug screening. We first induced a chronic oxidative insult in human SK-N-BE cells by exposure to low doses of sodium arsenite (ARS). Our data showed TDP-43 mislocalization into the cytoplasm, insoluble P-TDP-43 increase, P62 accumulation, and defective splicing activity of TDP-43 towards its target genes UNC13A and POLDIP3 as readouts of TDP-43 nuclear loss-of-function. As autophagy impairment plays a crucial role in ALS, we tested three drugs involved in promoting autophagy, namely rapamycin, lithium carbonate, and metformin: only rapamycin was able to rescue ARS-induced loss of TDP-43 splicing activity on UNC13A. We then tested rapamycin in ALS patient-derived primary fibroblasts, where it reduced P-TDP-43 aggregates and SGs formation. Rapamycin was also tested in motor neurons differentiated from induced pluripotent stem cells (iPSCs) derived from ALS patients, where we confirmed its efficacy in reducing P-TDP-43 aggregates and SGs and rescuing ARS-induced loss of TDP-43 splicing activity. Finally, we tested rapamycin in ALS iPSC-derived brain organoids exposed to chronic ARS, where it rescued loss of TDP-43 splicing activity. In conclusion, we established different human cell models of TDP-43 proteinopathy in which rapamycin was able to rescue chronic oxidative stress-induced alterations in TDP-43 splicing activity and cytoplasmic mislocalization by modulating autophagy. Human SK-N-BE and ALS patient-derived 2D and 3D iPSC models chronically treated with ARS can therefore be exploited as in vitro platforms for future drug screening approaches.
description: antonia.ratti@unimi.it
DOI: 10.7483/opendata.cms.dvso.ny54
title: /WminusToZpTo3MuNu_M-65_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.lulh.wf9f
title: /WminusToZpTo3MuNu_M-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.jq1c.ws8n
title: /WminusToZpTo3MuNu_M-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ycpi.rzya
title: /WminusToZpTo3MuNu_M-75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.z9sjvw
title: Occurrence Download

description: A dataset containing 849 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Ichnotropis capensis (Smith, 1838)"
 ]
}

 The dataset includes 849 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072450-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.gy5z.01bz
title: /WminusToZpTo3MuNu_M-75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.lz3c.zfn0
title: /WminusToZpTo3MuNu_M-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.z2u1.ve7b
title: /WminusToZpTo3MuNu_M-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.vw3q.va2m
title: /WpH_HToUps1SG_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.p4sa.6ngz
title: /WpH_HToUps1SG_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.fqgs.vomx
title: /WpH_HToUps2SG_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.w6m6.xz0a
title: /WpH_HToUps2SG_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ih31.etxy
title: /WpH_HToUps3SG_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.t29v.v4ra
title: /WpH_HToUps3SG_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.yrvg.veym
title: /WpTo2JWmTo2JJJ_QCD_LO_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v3/MINIAODSIM
DOI: 10.7483/opendata.cms.uwmv.edaa
title: /WpTo2JWmTo2JJJ_QCD_LO_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.sqd1.iwiw
title: /WpToBpT_Wp2000_Bp170_Top170_Zbt_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.5xwd.bt7m
title: /WpToBpT_Wp2000_Bp170_Top170_Zbt_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM