cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bc5c8
title: CCDC 2155689: Experimental Crystal Structure Determination

description: Related Article: Natalia Skrzypczak, Krystian Pyta, Wiktor Bohusz, Aleksandra Leśniewska, Maria Gdaniec, Piotr Ruszkowski, Wojciech Schilf, Franz Bartl, Piotr Przybylski|2023|J.Org.Chem.|88|9469|doi:10.1021/acs.joc.3c00493
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bc5d9
title: CCDC 2155690: Experimental Crystal Structure Determination

description: Related Article: Natalia Skrzypczak, Krystian Pyta, Wiktor Bohusz, Aleksandra Leśniewska, Maria Gdaniec, Piotr Ruszkowski, Wojciech Schilf, Franz Bartl, Piotr Przybylski|2023|J.Org.Chem.|88|9469|doi:10.1021/acs.joc.3c00493
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bc5fb
title: CCDC 2155691: Experimental Crystal Structure Determination

description: Related Article: Natalia Skrzypczak, Krystian Pyta, Wiktor Bohusz, Aleksandra Leśniewska, Maria Gdaniec, Piotr Ruszkowski, Wojciech Schilf, Franz Bartl, Piotr Przybylski|2023|J.Org.Chem.|88|9469|doi:10.1021/acs.joc.3c00493
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bc5gc
title: CCDC 2155692: Experimental Crystal Structure Determination

description: Related Article: Natalia Skrzypczak, Krystian Pyta, Wiktor Bohusz, Aleksandra Leśniewska, Maria Gdaniec, Piotr Ruszkowski, Wojciech Schilf, Franz Bartl, Piotr Przybylski|2023|J.Org.Chem.|88|9469|doi:10.1021/acs.joc.3c00493
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bc5hd
title: CCDC 2155693: Experimental Crystal Structure Determination

description: Related Article: Natalia Skrzypczak, Krystian Pyta, Wiktor Bohusz, Aleksandra Leśniewska, Maria Gdaniec, Piotr Ruszkowski, Wojciech Schilf, Franz Bartl, Piotr Przybylski|2023|J.Org.Chem.|88|9469|doi:10.1021/acs.joc.3c00493
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bc5jf
title: CCDC 2155694: Experimental Crystal Structure Determination

description: Related Article: Natalia Skrzypczak, Krystian Pyta, Wiktor Bohusz, Aleksandra Leśniewska, Maria Gdaniec, Piotr Ruszkowski, Wojciech Schilf, Franz Bartl, Piotr Przybylski|2023|J.Org.Chem.|88|9469|doi:10.1021/acs.joc.3c00493
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjv6z
title: CCDC 2190931: Experimental Crystal Structure Determination

description: Related Article: Agnieszka Brzyska, Sebastian Majewski, Łukasz Ponikiewski, Monika Zubik-Duda, Agnieszka Lipke, Agnieszka Gadysz-Paska, Sylwia Sowa|2023|J.Org.Chem.|88|7901|doi:10.1021/acs.joc.2c02355
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjv70
title: CCDC 2190932: Experimental Crystal Structure Determination

description: Related Article: Agnieszka Brzyska, Sebastian Majewski, Łukasz Ponikiewski, Monika Zubik-Duda, Agnieszka Lipke, Agnieszka Gadysz-Paska, Sylwia Sowa|2023|J.Org.Chem.|88|7901|doi:10.1021/acs.joc.2c02355
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cjztq
title: CCDC 2191074: Experimental Crystal Structure Determination

description: Related Article: Tatiana A. Shestimerova, Ivan A. Andreev, Nina K. Ratmanova, Igor V. Trushkov, Alexey N. Kuznetsov, Andrei V. Shevelkov |2023|Struct.Chem.|34|1557|doi:10.1007/s11224-022-02097-6
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2djj4m
title: CCDC 2220410: Experimental Crystal Structure Determination

description: Related Article: Savio Cardoza, Manoj Kumar Shrivash, Laura Riva, Arnab K. Chatterjee, Ajay Mandal, Vibha Tandon|2023|J.Org.Chem.|88|9105|doi:10.1021/acs.joc.3c00754
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dp6y8
title: CCDC 2224930: Experimental Crystal Structure Determination

description: Related Article: Jia-Tian Lu, Yingxiao Zong, Xiaodong Yue, Junke Wang|2023|J.Org.Chem.|88|8761|doi:10.1021/acs.joc.3c00614
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f7ps5
title: CCDC 2241727: Experimental Crystal Structure Determination

description: Related Article: Shih-Yun Chen, Rong Chang, Zhong-Xin Lin, Chien-Wen Lin, Li-Ching Shen, Andrew C.-H. Sue, Mei-Chun Tseng, Jean-Ho Chu|2023|J.Org.Chem.|88|8441|doi:10.1021/acs.joc.3c00429
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f7pt6
title: CCDC 2241728: Experimental Crystal Structure Determination

description: Related Article: Shih-Yun Chen, Rong Chang, Zhong-Xin Lin, Chien-Wen Lin, Li-Ching Shen, Andrew C.-H. Sue, Mei-Chun Tseng, Jean-Ho Chu|2023|J.Org.Chem.|88|8441|doi:10.1021/acs.joc.3c00429
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fcrcy
title: CCDC 2245620: Experimental Crystal Structure Determination

description: Related Article: Natalia Skrzypczak, Krystian Pyta, Wiktor Bohusz, Aleksandra Leśniewska, Maria Gdaniec, Piotr Ruszkowski, Wojciech Schilf, Franz Bartl, Piotr Przybylski|2023|J.Org.Chem.|88|9469|doi:10.1021/acs.joc.3c00493
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fg514
title: CCDC 2247935: Experimental Crystal Structure Determination

description: Related Article: Agnieszka Brzyska, Sebastian Majewski, Łukasz Ponikiewski, Monika Zubik-Duda, Agnieszka Lipke, Agnieszka Gadysz-Paska, Sylwia Sowa|2023|J.Org.Chem.|88|7901|doi:10.1021/acs.joc.2c02355
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22109041
title: Recurrent neural network for complex survival problems

description: Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional, and machine learning models and our model demonstrates better performance across all datasets.
DOI: 10.6084/m9.figshare.22109041.v2
title: Recurrent neural network for complex survival problems

description: Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional, and machine learning models and our model demonstrates better performance across all datasets.
DOI: 10.6084/m9.figshare.23730654
title: Supplemental Table S1. H9c2 Tug1KD vs Ctrl all genes.xlsx

description: Table S1. Comparison of gene expression in Tug1 KD vs control H9c2 cardiomyocytes by RNA-seq.
DOI: 10.6084/m9.figshare.23730654.v1
title: Supplemental Table S1. H9c2 Tug1KD vs Ctrl all genes.xlsx

description: Table S1. Comparison of gene expression in Tug1 KD vs control H9c2 cardiomyocytes by RNA-seq.
DOI: 10.15485/1991624
title: Yakima River Basin Temporal Study: Sensor and Sample Data from Wenas Creek following the Evans Canyon Fire in Washington, USA

description: The Evans Canyon Fire occurred in Washington in August 2020 and burned 76,000 acres of the semi-arid shrub-steppe landscape at low to moderate severities. This fire burned across the Wenas Creek watershed, allowing for a sampling design that included a portion of the stream within the burned area and a reference site upstream of the burn. Monthly data was collected at an unburned (W10) and burned (W20) site from November 2020 to August 2022 for in situ hydrological and water chemistry sensor data, handheld sensor water chemistry data, general environmental context photos, and field metadata.This dataset is comprised of one folder with field photos and one main data folder containing (1) sensor and sample field protocols; (2) file-level metadata (flmd); (3) data dictionary (dd); (4) international geo-sample number (IGSN) mapping file; (5) field metadata; (6) readme; (7) methods codes; (8) dissolved organic carbon (DOC; measured as non-purgeable organic carbon; NPOC); (9) total nitrogen (TN); (10) total suspended solids (TSS); (11) sensor data (specific conductance, pH, total dissolved solids, temperature, pressure, dissolved oxygen, and turbidity) averages; and (12) sensor installation methods. All files are .csv, .jpg, .jpeg, or .pdf.
DOI: 10.5519/qd.gqbgvnfm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (20 records)
DOI: 10.6084/m9.figshare.23730854
title: Additional file 1 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 1: Table S1. Curated patient data set. Variants, age of onset, and severity phenotypes are shown for MLD patients curated from the literature. Nomenclature for ARSA variants has been updated, and for papers using the previous annotation system a +2 amino acid or +6 nucleotide coordinate shift has been applied. Cases for which this coordinate shift has been used are labeled in the Comments column. ARSA, arylsulfatase A; HGVSc, Human Genome Variation Society coding variant; HGVSp, Human Genome Variation Society protein variant.
DOI: 10.6084/m9.figshare.23730854.v1
title: Additional file 1 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 1: Table S1. Curated patient data set. Variants, age of onset, and severity phenotypes are shown for MLD patients curated from the literature. Nomenclature for ARSA variants has been updated, and for papers using the previous annotation system a +2 amino acid or +6 nucleotide coordinate shift has been applied. Cases for which this coordinate shift has been used are labeled in the Comments column. ARSA, arylsulfatase A; HGVSc, Human Genome Variation Society coding variant; HGVSp, Human Genome Variation Society protein variant.
DOI: 10.6084/m9.figshare.23730857
title: Additional file 2 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 2: Table S2. Tabulated genotypes. Consolidation and summary of all genotypes from curated MLD patients. For each genotype, the breakdown of all associated phenotypes in the literature is provided. Entropy, a measure of phenotypic variability for a genotype, and the phenotype matrix’s accuracy in predicting phenotypes are also included. ARSA, arylsulfatase A; CDS, coding sequence.
DOI: 10.6084/m9.figshare.23730857.v1
title: Additional file 2 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 2: Table S2. Tabulated genotypes. Consolidation and summary of all genotypes from curated MLD patients. For each genotype, the breakdown of all associated phenotypes in the literature is provided. Entropy, a measure of phenotypic variability for a genotype, and the phenotype matrix’s accuracy in predicting phenotypes are also included. ARSA, arylsulfatase A; CDS, coding sequence.
DOI: 10.6084/m9.figshare.23730860
title: Additional file 3 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 3: Table S3. Comprehensive variants. Summary of all variants present in the curated patient data set, all variants from gnomAD, and novel variants reported by MLD Foundation or NBS. All columns are described in the table legend. CDS, coding sequence; GRCh37, Genome Reference Consortium Human Build 37; SNP, single nucleotide polymorphism; WT, wild-type.
DOI: 10.6084/m9.figshare.23730860.v1
title: Additional file 3 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 3: Table S3. Comprehensive variants. Summary of all variants present in the curated patient data set, all variants from gnomAD, and novel variants reported by MLD Foundation or NBS. All columns are described in the table legend. CDS, coding sequence; GRCh37, Genome Reference Consortium Human Build 37; SNP, single nucleotide polymorphism; WT, wild-type.
DOI: 10.6084/m9.figshare.23730866
title: Additional file 5 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 5: Table S4. Enzyme activity of ARSA variants in a CDS construct. Summary of all data used to calculate mean CDS ARSA activity. Variants with negative mean percentage of wild-type activity exhibited lower levels of sulfatide catabolism than the plate blank. ARSA, arylsulfatase A; BCA, bicinchoninic acid; BLA, beta-lactamase; CDS, coding sequence; HGVSc, Human Genome Variation Society coding variant; HGVSp, Human Genome Variation Society protein variant; WT, wild-type.
DOI: 10.6084/m9.figshare.23730866.v1
title: Additional file 5 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 5: Table S4. Enzyme activity of ARSA variants in a CDS construct. Summary of all data used to calculate mean CDS ARSA activity. Variants with negative mean percentage of wild-type activity exhibited lower levels of sulfatide catabolism than the plate blank. ARSA, arylsulfatase A; BCA, bicinchoninic acid; BLA, beta-lactamase; CDS, coding sequence; HGVSc, Human Genome Variation Society coding variant; HGVSp, Human Genome Variation Society protein variant; WT, wild-type.
DOI: 10.6084/m9.figshare.23730869
title: Additional file 6 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 6: Table S5. Enzyme activity of ARSA variants in a genomic construct. Summary of all data used to calculate mean genomic ARSA variant activity. Variants with negative mean percentage of wild-type activity had less sulfatide catabolism than the plate blank. While multiple VUS in gnomAD and the literature are implicated as splice variants, testing all mutants was cost-prohibitive. We triaged variants for testing using the criteria listed in ‘Rationale for testing’. These results provide proof-of-concept for screening in genomic constructs and highlight the technique’s utility in identifying cryptic splice variants. ARSA, arylsulfatase A; BCA, bicinchoninic acid; BLA, beta-lactamase; CDS, coding sequence; gnomAD, Genome Aggregation Database; HGVSc, Human Genome Variation Society coding variant; HGVSp, Human Genome Variation Society protein variant; VUS, variant of unknown significance; WT, wild-type.
DOI: 10.6084/m9.figshare.23730869.v1
title: Additional file 6 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 6: Table S5. Enzyme activity of ARSA variants in a genomic construct. Summary of all data used to calculate mean genomic ARSA variant activity. Variants with negative mean percentage of wild-type activity had less sulfatide catabolism than the plate blank. While multiple VUS in gnomAD and the literature are implicated as splice variants, testing all mutants was cost-prohibitive. We triaged variants for testing using the criteria listed in ‘Rationale for testing’. These results provide proof-of-concept for screening in genomic constructs and highlight the technique’s utility in identifying cryptic splice variants. ARSA, arylsulfatase A; BCA, bicinchoninic acid; BLA, beta-lactamase; CDS, coding sequence; gnomAD, Genome Aggregation Database; HGVSc, Human Genome Variation Society coding variant; HGVSp, Human Genome Variation Society protein variant; VUS, variant of unknown significance; WT, wild-type.
DOI: 10.6084/m9.figshare.23730875
title: Additional file 7 of Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

description: Additional file 7: Table S6. Percent reduction of genotypes of unknown significance (GUS) with activity-based severity data. Burdens for each MLD subtype were predicted using allele frequencies from gnomAD and our phenotype matrix. Frequencies of unknown genotypes are reported across various populations. Calculations were performed using patient-based severity, as defined by our variant-severity criteria, and repeated using activity-based severity determined by MS/MS. Both one-in incidences and allele frequencies are provided, and the percent reduction in GUS when activity-based severity data are used instead of patient-based severity is shown. Classifying VUS by activity-based severity greatly reduces the number of genotypes with indeterminant phenotypes. gnomAD, Genome Aggregation Database; GUS, genotypes of unknown significance; MS/MS, tandem mass spectrometry; NFE, non-Finnish European; VUS, variants of unknown significance.