cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7270/q20g3jkt
title: BindingDB Entry 50014128: Binding of an imidazopyridoindole at imidazoline I2 receptors.

description: Small molecule binding data for protein Imidazoline receptor 1 (I1R), Uniprot Q9Y2I1
DOI: 10.7270/q21z43xf
title: BindingDB Entry 50015781: Synthesis of half-mustard combi-molecules with fluorescence properties: correlation with EGFR status.

description: Small molecule binding data for protein Epidermal growth factor receptor, Uniprot P00533
DOI: 10.7270/q22f7mrp
title: BindingDB Entry 50012116: Pyrrolidine-5,5-trans-lactams as novel mechanism-based inhibitors of human cytomegalovirus protease. Part 3: potency and plasma stability.

description: Small molecule binding data for protein Human herpes virus 5 capsid protein P40 and Human rhinovirus A protease, Uniprot P16753
DOI: 10.7270/q23b5zgr
title: BindingDB Entry 50012092: Pyrrolo[2,3-d]pyrimidines containing diverse N-7 substituents as potent inhibitors of Lck.

description: Small molecule binding data for protein Cyclin-dependent kinase 1, Hepatocyte growth factor receptor, Insulin receptor, PDGFR-beta/Platelet-derived growth factor receptor alpha, Protein kinase C (PKC), SRC, Tyrosine-protein kinase Blk, Tyrosine-protein kinase CSK, Tyrosine-protein kinase ZAP-70, VEGF-receptor 2 and tyrosine-protein kinase SRC, Vascular endothelial growth factor receptor 2, and Vascular endothelial growth factor receptor 2 and tyrosine-protein kinase TIE-2 (KDR and TIE2), Uniprot O94806, P05129, P05771, P06213, P06239, P06241, P06493, P07948, P08581, P09619, P12931, P16234, P17252, P24723, P35968, P41240, P41743, P43403, P51451, Q02156, Q02763, Q04759, Q05513, Q05655, and Q15139
DOI: 10.7270/q23f4p7z
title: BindingDB Entry 50017670: Flexible synthesis and biological evaluation of novel 5-deoxyadenophorine analogues.

description: Small molecule binding data for protein β-galactosidase, Alpha-galactosidase, and Beta-glucosidase, Uniprot Q58D55 and Q5DUH8
DOI: 10.7270/q247499j
title: BindingDB Entry 50014126: Modulation of selective serotonin reuptake inhibitor and 5-HT1A antagonist activity in 8-aza-bicyclo[3.2.1]octane derivatives of 2,3-dihydro-1,4-benzodioxane.

description: Small molecule binding data for protein 5-hydroxytryptamine receptor 1A and Serotonin and norepinephrine transporters (SERT/NET), Uniprot P08908 and P31645
DOI: 10.7270/q25q4vkw
title: BindingDB Entry 50015776: Reversal of anticancer drug resistance by COTC based on intracellular glutathione and glyoxalase I.

description: Small molecule binding data for protein Glyoxalase 1 (GLO1), Uniprot Q04760
DOI: 10.7270/q2736q6g
title: BindingDB Entry 50012091: Focused library approach for identification of new N-acylphenylalanines as VCAM/VLA-4 antagonists.

description: Small molecule binding data for protein Fibronectin receptor beta/Integrin alpha-4, Uniprot P05556 and P13612
DOI: 10.7270/q2765dxt
title: BindingDB Entry 50017669: 4-(Aminoalkylamino)-3-benzimidazole-quinolinones as potent CHK-1 inhibitors.

description: Small molecule binding data for protein Serine/threonine-protein kinase Chk1, Uniprot O14757
DOI: 10.7270/q280522q
title: BindingDB Entry 50014124: Polar substitutions in the benzenesulfonamide ring of celecoxib afford a potent 1,5-diarylpyrazole class of COX-2 inhibitors.

description: Small molecule binding data for protein Cyclooxygenase and Prostaglandin G/H synthase (cyclooxygenase), Uniprot P05979 and P35354
DOI: 10.7270/q29c6wzs
title: BindingDB Entry 50015775: Human immunodeficiency virus-reverse transcriptase inhibition and hepatitis C virus RNA-dependent RNA polymerase inhibition activities of fullerene derivatives.

description: Small molecule binding data for protein Hepatitis C virus NS5B RNA-dependent RNA polymerase and Human immunodeficiency virus type 1 reverse transcriptase, Uniprot Q8JXU8
DOI: 10.7270/q2bz65n7
title: BindingDB Entry 50017667: Design and structure-activity relationship of 3-benzimidazol-2-yl-1H-indazoles as inhibitors of receptor tyrosine kinases.

description: Small molecule binding data for protein Fibroblast growth factor receptor 1, Platelet-derived growth factor receptor, Vascular endothelial growth factor receptor, and Vascular endothelial growth factor receptor 2, Uniprot L7RSL3, P05622, P11362, P17948, and P35918
DOI: 10.7270/q2cr5sts
title: BindingDB Entry 50014122: Fentanyl derivatives bearing aliphatic alkaneguanidinium moieties: a new series of hybrid molecules with significant binding affinity for mu-opioid receptors and I2-imidazoline binding sites.

description: Small molecule binding data for protein Imidazoline I2-receptor and Mu-type opioid receptor, Uniprot P21397, P27338, and P35372
DOI: 10.7270/q2f47nn2
title: BindingDB Entry 50015774: Structure-based design of potent and selective inhibitors of collagenase-3 (MMP-13).

description: Small molecule binding data for protein ADAM17, Collagenase, Matrix metalloproteinase-1 (MMP1), and Stromelysin-1, Uniprot P03956, P08253, P08254, P14780, P45452, and P78536
DOI: 10.7270/q2fn15jt
title: BindingDB Entry 50012106: Synthesis and evaluation of a novel series of 2-chloro-5-((1-methyl-2-(S)-pyrrolidinyl)methoxy)-3-(2-(4-pyridinyl)vinyl)pyridine analogues as potential positron emission tomography imaging agents for nicotinic acetylcholine receptors.

description: Small molecule binding data for protein Neuronal acetylcholine receptor protein alpha-4/beta-2 subunit, Uniprot P09483 and P12390
DOI: 10.7270/q2gq6xck
title: BindingDB Entry 50017659: 2,5-Disubstituted pyrrolidine carboxylates as potent, orally active sphingosine-1-phosphate (S1P) receptor agonists.

description: Small molecule binding data for protein Sphingosine 1-Phosphate Receptor 2, Sphingosine 1-Phosphate Receptor 4, Sphingosine 1-Phosphate Receptor 5, Sphingosine 1-phosphate receptor, and Sphingosine 1-phosphate receptor 3, Uniprot O95136, O95977, P21453, Q99500, and Q9H228
DOI: 10.7270/q2jw8dcg
title: BindingDB Entry 50015773: Synthesis and evaluation of novel peripherally restricted kappa-opioid receptor agonists.

description: Small molecule binding data for protein Kappa-type opioid receptor, Uniprot P41145
DOI: 10.7270/q2kd1x85
title: BindingDB Entry 50012105: Structural features of azidopyridinyl neonicotinoid probes conferring high affinity and selectivity for mammalian alpha4beta2 and Drosophila nicotinic receptors.

description: Small molecule binding data for protein Neuronal acetylcholine receptor protein alpha-4/beta-2 subunit, Uniprot P17787 and P43681
DOI: 10.7270/q2n58ksm
title: BindingDB Entry 50014115: 1,2-Diaryl-1-ethanone and pyrazolo [4,3-c] quinoline-4-one as novel selective cyclooxygenase-2 inhibitors.

description: Small molecule binding data for protein Cyclooxygenase and Prostaglandin G/H synthase (cyclooxygenase), Uniprot P05979 and P35354
DOI: 10.7270/q2pn954n
title: BindingDB Entry 50015772: 5-Lipoxygenase inhibition by N-hydroxycarbamates in dual-function compounds.

description: Small molecule binding data for protein 5-lipoxygenase/FLAP and Histamine H1 receptor, Uniprot P09917 and P35367
DOI: 10.7270/q2q52p0w
title: BindingDB Entry 50012101: Synthesis and structure-affinity-activity relationships of novel benzofuran derivatives as MT(2) melatonin receptor selective ligands.

description: Small molecule binding data for protein Melatonin receptor, Uniprot P48039 and P49286
DOI: 10.7270/q2qz29dn
title: BindingDB Entry 50014130: Affinity and intrinsic efficacy (IE) of 5'-carbamoyl adenosine analogues for the A1 adenosine receptor--efforts towards the discovery of a chronic ventricular rate control agent for the treatment of atrial fibrillation (AF).

description: Small molecule binding data for protein Adenosine receptor A1, Uniprot P25099 and P30542
DOI: 10.25582/data.2023-01.2721425/2292893
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25386/genetics.12161718.v1
title: Supplemental Material for Zhou et al., 2020

description: Figure S1 contains verification of related deletion strains and
complementary strain.Figure S2 contains verification of related GFP-labeling strains.Figure S3 contains the phenotypes of <i>paxB</i> and <i>acnA </i>deletion strains.Figure S4 contains the localization of α-actinin and PaxB.Figure S5 contains the
detection of the mRNA expression levels of <i>acnA </i>and <i>paxB.</i>Figure S6 contains Co-IP assay for α-actinin and PaxB.Figure S7 contains bioinformatic analysis of the potential
interacting proteins of α-actinin
AcnA, PaxB and Actin ActA.<i></i><sub></sub><sup></sup><b></b><br>Table S1 contains GFP-α-actinin pulled-down protein list. Table S2 contains PaxB-Flag pulled-down protein list. Table S3 contains putative common-shared interacted proteins by α-actinin and PaxB.
DOI: 10.6084/m9.figshare.16880165
title: Additional file 3 of A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data

description: Additional file 3. A complete list of differentially expressed genes inferred by the t-test, Wilcoxon test, MAST,and their corresponding MRF models.
DOI: 10.25582/data.2023-01.2721405/2292894
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880171.v1
title: Additional file 4 of A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data

description: Additional file 4. A complete list of pathway enrichment results using IPA for all models.
DOI: 10.25582/data.2023-01.2721559/2292895
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721147/2292896
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.15831776
title: MicroRNA-624-mediated ARRDC3/YAP/HIF1α axis enhances esophageal squamous cell carcinoma cell resistance to cisplatin and paclitaxel

description: Development of chemoresistance remains a major challenge in treating patients suffering from esophageal squamous cell carcinoma (ESCC), despite treatment advances. MicroRNAs (miRNAs) have been shown to play critical roles in the regulation of ESCC cell chemoresistance. Here, we aimed to investigate the role of miR-624 in ESCC and its molecular mechanism in mediating the resistance of ESCC cells to two common chemotherapeutic drugs, cisplatin (CIS) and paclitaxel (PT). Expression patterns of miR-624, arrestin domain-containing 3 (ARRDC3), Yes-associated protein (YAP), and hypoxia-inducible factor-1α (HIF1α) in ESCC tissues and cell lines were identified using RT-qPCR and Western blot analysis. The binding affinities with the miR-624/ARRDC3/YAP/HIF1α axis were characterized. The chemotherapy-sensitive cell line KYSE150 and chemotherapy-resistant cell line KYSE410 were transfected with an overexpression plasmid or shRNA to study the effect of miR-624/ARRDC3/YAP/HIF1α axis on ESCC cell resistance to CIS and PT. Their <i>in vivo</i> effects on resistance to PT were assessed in tumor-bearing nude mice. High expression of miR-624, YAP and HIF1α, and low expression of ARRDC3 were observed in ESCC tissues and cell lines. miR-624 presented with higher expression in KYSE410 than in KYSE150 cells. miR-624 downregulated ARRDC3 to increase YAP and HIF1α expression so as to enhance ESCC cell resistance to CIS and PT <i>in vitro</i> and <i>in vivo</i>. Taken together, these data indicate an important role for miR-624 in promoting the chemoresistance of ESCC cells, highlighting a potential strategy to overcome drug resistance in ESCC treatment. miR-624 targets ARRDC3 to inhibit its expression, and consequently upregulates YAP expression by inhibiting degradation of YAP. By this mechanism, HIF1α expression is upregulated and the HIF1α signaling pathway is activated. ESCC cell chemotherapy resistance is eventually increased.
DOI: 10.25582/data.2023-01.2721371/2292897
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721068/2292898
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721605/2292899
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721112/2292900
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721065/2292901
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721607/2292902
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721296/2292903
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.7445363
title: Calibration Concordance for Astronomical Instruments via Multiplicative Shrinkage

description: Calibration data are often obtained by observing several well-understood objects simultaneously with multiple instruments, such as satellites for measuring astronomical sources. Analyzing such data and obtaining proper concordance among the instruments is challenging when the physical source models are not well understood, when there are uncertainties in “known” physical quantities, or when data quality varies in ways that cannot be fully quantified. Furthermore, the number of model parameters increases with both the number of instruments and the number of sources. Thus, concordance of the instruments requires careful modeling of the mean signals, the intrinsic source differences, and measurement errors. In this article, we propose a log-Normal model and a more general log-<i>t</i> model that respect the multiplicative nature of the mean signals via a half-variance adjustment, yet permit imperfections in the mean modeling to be absorbed by residual variances. We present analytical solutions in the form of power shrinkage in special cases and develop reliable Markov chain Monte Carlo algorithms for general cases, both of which are available in the Python module <i>CalConcordance</i>. We apply our method to several datasets including a combination of observations of <i>active galactic nuclei</i> (AGN) and spectral line emission from the <i>supernova remnant</i> E0102, obtained with a variety of X-ray telescopes such as <i>Chandra</i>, XMM- <i>Newton</i>, <i>Suzaku</i>, and <i>Swift</i>. The data are compiled by the <i>International Astronomical Consortium for High Energy Calibration</i>. We demonstrate that our method provides helpful and practical guidance for astrophysicists when adjusting for disagreements among instruments. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7445363.v2
title: Calibration Concordance for Astronomical Instruments via Multiplicative Shrinkage

description: Calibration data are often obtained by observing several well-understood objects simultaneously with multiple instruments, such as satellites for measuring astronomical sources. Analyzing such data and obtaining proper concordance among the instruments is challenging when the physical source models are not well understood, when there are uncertainties in “known” physical quantities, or when data quality varies in ways that cannot be fully quantified. Furthermore, the number of model parameters increases with both the number of instruments and the number of sources. Thus, concordance of the instruments requires careful modeling of the mean signals, the intrinsic source differences, and measurement errors. In this article, we propose a log-Normal model and a more general log-<i>t</i> model that respect the multiplicative nature of the mean signals via a half-variance adjustment, yet permit imperfections in the mean modeling to be absorbed by residual variances. We present analytical solutions in the form of power shrinkage in special cases and develop reliable Markov chain Monte Carlo algorithms for general cases, both of which are available in the Python module <i>CalConcordance</i>. We apply our method to several datasets including a combination of observations of <i>active galactic nuclei</i> (AGN) and spectral line emission from the <i>supernova remnant</i> E0102, obtained with a variety of X-ray telescopes such as <i>Chandra</i>, XMM- <i>Newton</i>, <i>Suzaku</i>, and <i>Swift</i>. The data are compiled by the <i>International Astronomical Consortium for High Energy Calibration</i>. We demonstrate that our method provides helpful and practical guidance for astrophysicists when adjusting for disagreements among instruments. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7445363.v1
title: Calibration Concordance for Astronomical Instruments via Multiplicative Shrinkage

description: Calibration data are often obtained by observing several well-understood objects simultaneously with multiple instruments, such as satellites for measuring astronomical sources. Analyzing such data and obtaining proper concordance among the instruments is challenging when the physical source models are not well understood, when there are uncertainties in “known” physical quantities, or when data quality varies in ways that cannot be fully quantified. Furthermore, the number of model parameters increases with both the number of instruments and the number of sources. Thus, concordance of the instruments requires careful modeling of the mean signals, the intrinsic source differences, and measurement errors. In this article, we propose a log-Normal model and a more general log-<i>t</i> model that respect the multiplicative nature of the mean signals via a half-variance adjustment, yet permit imperfections in the mean modeling to be absorbed by residual variances. We present analytical solutions in the form of power shrinkage in special cases and develop reliable Markov chain Monte Carlo algorithms for general cases, both of which are available in the Python module <i>CalConcordance</i>. We apply our method to several datasets including a combination of observations of <i>active galactic nuclei</i> (AGN) and spectral line emission from the <i>supernova remnant</i> E0102, obtained with a variety of X-ray telescopes such as <i>Chandra</i>, XMM- <i>Newton</i>, <i>Suzaku</i>, and <i>Swift</i>. The data are compiled by the <i>International Astronomical Consortium for High Energy Calibration</i>. We demonstrate that our method provides helpful and practical guidance for astrophysicists when adjusting for disagreements among instruments. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.25582/data.2023-01.2721326/2292904
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721232/2292905
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721414/2292906
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721075/2292907
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721367/2292908
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721629/2292909
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721299/2292910
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721540/2292911
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721445/2292912
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721316/2292913
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721634/2292914
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880272
title: Additional file 1 of Parkinsonism and dysautonomia with anti-CV2/CRMP5 associated paraneoplastic neurological syndromes mimicking multiple system atrophy: a case report

description: Additional file 1: Table 1. Clinical characteristics of anti-CV-2 related PNS presenting with Parkinsonism.
DOI: 10.25582/data.2023-01.2721651/2292915
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880272.v1
title: Additional file 1 of Parkinsonism and dysautonomia with anti-CV2/CRMP5 associated paraneoplastic neurological syndromes mimicking multiple system atrophy: a case report

description: Additional file 1: Table 1. Clinical characteristics of anti-CV-2 related PNS presenting with Parkinsonism.
DOI: 10.25582/data.2023-01.2721893/2292916
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721332/2292917
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721361/2292918
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721409/2292919
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721344/2292920
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721257/2292921
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721199/2292922
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721082/2292923
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721138/2292924
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721092/2292925
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721111/2292926
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721595/2292927
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721542/2292928
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721083/2292929
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880266.v1
title: Additional file 7 of Validating biomarkers and models for epigenetic inference of alcohol consumption from blood

description: Additional file 7: Tables S21. Providing the coefficients for our two new models including four-categories for each marker set.
DOI: 10.25582/data.2023-01.2721401/2292930
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880251
title: Additional file 2 of Validating biomarkers and models for epigenetic inference of alcohol consumption from blood

description: Additional file 2: Table S1. Which shows the replication results for the candidate-CpG association study on alcohol consumption.
DOI: 10.25582/data.2023-01.2721056/2292931
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880251.v1
title: Additional file 2 of Validating biomarkers and models for epigenetic inference of alcohol consumption from blood

description: Additional file 2: Table S1. Which shows the replication results for the candidate-CpG association study on alcohol consumption.
DOI: 10.25582/data.2023-01.2721516/2292932
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721289/2292933
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880254
title: Additional file 3 of Validating biomarkers and models for epigenetic inference of alcohol consumption from blood

description: Additional file 3: Table S2. Presenting the CpGs incorporated in the four prediction marker sets, together with the mean DNA methylation residual β-value per alcohol consumption category per CpG in the model building dataset, and in the four external validation datasets.
DOI: 10.25582/data.2023-01.2721307/2292934
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721331/2292935
title: Data for EMSL Project 60066 from January 2023
DOI: 10.6084/m9.figshare.16880254.v1
title: Additional file 3 of Validating biomarkers and models for epigenetic inference of alcohol consumption from blood

description: Additional file 3: Table S2. Presenting the CpGs incorporated in the four prediction marker sets, together with the mean DNA methylation residual β-value per alcohol consumption category per CpG in the model building dataset, and in the four external validation datasets.
DOI: 10.25582/data.2023-01.2721446/2292936
title: Data for EMSL Project 60066 from January 2023
DOI: 10.25582/data.2023-01.2721100/2292937
title: Data for EMSL Project 60066 from January 2023