cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c52tg
title: CCDC 2179635: Experimental Crystal Structure Determination

description: Related Article: Han Chen, Zesheng Li, Peng Shao, Haosen Yuan, Si-Cong Chen, Tuoping Luo|2022|J.Am.Chem.Soc.|144|15462|doi:10.1021/jacs.2c06934
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.21069271
title: Sebastiscus marmoratus data

description: <em>Sebastiscus marmoratus mitochondrion</em>
DOI: 10.6084/m9.figshare.21069271.v1
title: Sebastiscus marmoratus data

description: <em>Sebastiscus marmoratus mitochondrion</em>
DOI: 10.6084/m9.figshare.21069329.v1
title: Additional file 1 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 1: Table S1 Results of the functional enrichment analysis of genes in differentially expressed genes (DEGs) as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069332
title: Additional file 2 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 2: Table S2 Results of the functional enrichment analysis of genes in the blue module identified in WGCNA analysis as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069332.v1
title: Additional file 2 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 2: Table S2 Results of the functional enrichment analysis of genes in the blue module identified in WGCNA analysis as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069335
title: Additional file 3 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 3: Table S3 Results of the functional enrichment analysis of genes in the red module identified in lmQCM analysis as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069335.v1
title: Additional file 3 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 3: Table S3 Results of the functional enrichment analysis of genes in the red module identified in lmQCM analysis as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069338.v1
title: Additional file 4 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 4: Table S4 Results of the functional enrichment analysis of the 497 TED-associated coexpression genes as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069341
title: Additional file 5 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 5: Table S5 Detailed information of predicted transcription factors.
DOI: 10.6084/m9.figshare.21069341.v1
title: Additional file 5 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 5: Table S5 Detailed information of predicted transcription factors.
DOI: 10.6084/m9.figshare.21069338
title: Additional file 4 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 4: Table S4 Results of the functional enrichment analysis of the 497 TED-associated coexpression genes as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069329
title: Additional file 1 of Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

description: Additional file 1: Table S1 Results of the functional enrichment analysis of genes in differentially expressed genes (DEGs) as the output of cluster profiler analysis.
DOI: 10.6084/m9.figshare.21069360
title: Additional file 2 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 2. Features extracted from the datasets for the single/multi-domain classifier. This file includes the extracted features for the single/multi-domain classifier from the datasets ASTRAL95, ASTRAL40, Benchmark_1, Benchmark_2, Benchmark_3, Islam and Jones. For each protein structure, 78 features were extracted. The last column indicates class label. Refer to Additional file 1for description.
DOI: 10.6084/m9.figshare.21069360.v1
title: Additional file 2 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 2. Features extracted from the datasets for the single/multi-domain classifier. This file includes the extracted features for the single/multi-domain classifier from the datasets ASTRAL95, ASTRAL40, Benchmark_1, Benchmark_2, Benchmark_3, Islam and Jones. For each protein structure, 78 features were extracted. The last column indicates class label. Refer to Additional file 1for description.
DOI: 10.6084/m9.figshare.21069366
title: Additional file 3 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 3. Results and performance evaluation of KluDo over the multi-domain structures of ASTRAL40, assuming all structures as multi-domain. This file contains KluDo results as well as accuracy and mean and standard deviation of the ARI score over the multi-domain structures of ASTRAL40, assuming all structures as multi-domain. The accuracy is calculated based on the OL and ARI scores considering the thresholds from 5% to 100% with the interval 5%. From the ASTRAL40 dataset, 2208 out of 11958 protein chains that were recognized as multi-domain by both SCOP and CATH were considered. All these structures were assumed as multi-domain by KluDo (the single/multi-domain classifier was not employed). The results and performance for all kernel function-clustering method combinations with the default parameter values are provided. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069366.v1
title: Additional file 3 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 3. Results and performance evaluation of KluDo over the multi-domain structures of ASTRAL40, assuming all structures as multi-domain. This file contains KluDo results as well as accuracy and mean and standard deviation of the ARI score over the multi-domain structures of ASTRAL40, assuming all structures as multi-domain. The accuracy is calculated based on the OL and ARI scores considering the thresholds from 5% to 100% with the interval 5%. From the ASTRAL40 dataset, 2208 out of 11958 protein chains that were recognized as multi-domain by both SCOP and CATH were considered. All these structures were assumed as multi-domain by KluDo (the single/multi-domain classifier was not employed). The results and performance for all kernel function-clustering method combinations with the default parameter values are provided. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069369
title: Additional file 4 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 4. Results of KluDo along with the other methods over the test datasets. This file encompasses the results of KluDo along with the other methods (DomainParser, PDP, SWORD and DDomain)over the datasets Benchmark_1, Benchmark_2, Benchmark_3, Islam, Jones and ASTRAL40. KluDo’s results are presented for all combinations of kernel functions and clustering methods using the default parameter values. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069369.v1
title: Additional file 4 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 4. Results of KluDo along with the other methods over the test datasets. This file encompasses the results of KluDo along with the other methods (DomainParser, PDP, SWORD and DDomain)over the datasets Benchmark_1, Benchmark_2, Benchmark_3, Islam, Jones and ASTRAL40. KluDo’s results are presented for all combinations of kernel functions and clustering methods using the default parameter values. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069372
title: Additional file 5 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 5. KluDo's performance along with the other methods in terms of accuracy over the test datasets. This file comprises the accuracies over the results in Additional file 4. Accuracy is calculated based on both of the OL and ARI scores considering the thresholds from 5% to 100% with the interval 5%. In the cases of no agreement in the number of domains between predicted and target assignments, the OL score conventionally was set to -1. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069372.v1
title: Additional file 5 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 5. KluDo's performance along with the other methods in terms of accuracy over the test datasets. This file comprises the accuracies over the results in Additional file 4. Accuracy is calculated based on both of the OL and ARI scores considering the thresholds from 5% to 100% with the interval 5%. In the cases of no agreement in the number of domains between predicted and target assignments, the OL score conventionally was set to -1. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069375
title: Additional file 6 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 6. Kludo's performance along with the other methods in terms of ARI mean and standard deviation over the test datasets. This file includes mean and standard deviation of the ARI score over the results in Additional file 4 with respect to SCOP, CATH and the mean score based on SCOP and CATH. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069375.v1
title: Additional file 6 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 6. Kludo's performance along with the other methods in terms of ARI mean and standard deviation over the test datasets. This file includes mean and standard deviation of the ARI score over the results in Additional file 4 with respect to SCOP, CATH and the mean score based on SCOP and CATH. KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069378
title: Additional file 7 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 7. KluDo's performance based on the number of domains over ASTRAL40. This file consists of the accuracies over the results in Additional file 4 separated by the number of domains. Based on each of the SCOP and CATH databases, four subsets were extracted from ASTRAL40: 1-domain, 2-domain, 3-domain, and 4-domain structures. For each subset, the percent of the correct assignments (the cases of compliance with SCOP or CATH based on the OL score using an 85% threshold), overcuts (the cases of assigning a higher number of domains than both SCOP and CATH), undercuts (the cases of fewer domains than both SCOP and CATH), boundary inconsistencies (the cases of incorrect assignment where the number of domains complies with SCOP or CATH) and other cases were measured. CA, OC, UC, and BI represent correct assignments, overcuts, undercuts and boundary inconsistencies, respectively. Also, KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.21069378.v1
title: Additional file 7 of Assignment of structural domains in proteins using diffusion kernels on graphs

description: Additional file 7. KluDo's performance based on the number of domains over ASTRAL40. This file consists of the accuracies over the results in Additional file 4 separated by the number of domains. Based on each of the SCOP and CATH databases, four subsets were extracted from ASTRAL40: 1-domain, 2-domain, 3-domain, and 4-domain structures. For each subset, the percent of the correct assignments (the cases of compliance with SCOP or CATH based on the OL score using an 85% threshold), overcuts (the cases of assigning a higher number of domains than both SCOP and CATH), undercuts (the cases of fewer domains than both SCOP and CATH), boundary inconsistencies (the cases of incorrect assignment where the number of domains complies with SCOP or CATH) and other cases were measured. CA, OC, UC, and BI represent correct assignments, overcuts, undercuts and boundary inconsistencies, respectively. Also, KK and SP stand for kernel k-means and spectral clustering, respectively.
DOI: 10.6084/m9.figshare.20439051
title: Supplementary Material for: Monitoring minimal residual disease in RUNX1-mutated acute myeloid leukemia

description: Introduction: Mutated RUNX1 is considered a poor prognostic factor and usually is mutually exclusive with NPM1 mutations. Monitoring of molecular markers for minimal residual disease provides a powerful tool to assess remission and guide clinical decisions. Methods: Newly diagnosed RUNX1-mutated AML patients, designated to intensive chemotherapy-based treatment or non-intensive regimens, were monitored for mutated RUNX1 transcript levels by qPCR with patient-specific primers. Samples were obtained along the treatment course and follow-up. Results: A clear correlation was observed between mutated RUNX1 levels and response to treatment as observed by flow-cytometry and STR-based assessment. Conclusion: We demonstrate the feasibility of RUNX1-based MRD to correlate with the clinicopathological status of leukemia. We further suggest how RUNX1 qPCR monitoring can influence clinical decision-making and contribute to improved personalized patient care.
DOI: 10.6084/m9.figshare.20439051.v1
title: Supplementary Material for: Monitoring minimal residual disease in RUNX1-mutated acute myeloid leukemia

description: Introduction: Mutated RUNX1 is considered a poor prognostic factor and usually is mutually exclusive with NPM1 mutations. Monitoring of molecular markers for minimal residual disease provides a powerful tool to assess remission and guide clinical decisions. Methods: Newly diagnosed RUNX1-mutated AML patients, designated to intensive chemotherapy-based treatment or non-intensive regimens, were monitored for mutated RUNX1 transcript levels by qPCR with patient-specific primers. Samples were obtained along the treatment course and follow-up. Results: A clear correlation was observed between mutated RUNX1 levels and response to treatment as observed by flow-cytometry and STR-based assessment. Conclusion: We demonstrate the feasibility of RUNX1-based MRD to correlate with the clinicopathological status of leukemia. We further suggest how RUNX1 qPCR monitoring can influence clinical decision-making and contribute to improved personalized patient care.
DOI: 10.5281/zenodo.7059955
title: BembaSpeech: A Speech Recognition Corpus for the Bemba Language

description: We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30% of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we explored different approaches; supervised pre-training (training from scratch), cross-lingual transfer learning from a monolingual English pre-trained model using DeepSpeech on the portion of the dataset and fine-tuning large scale self-supervised Wav2Vec2.0 based multilingual pre-trained models on the complete BembaSpeech corpus. From our experiments, the 1 billion XLS-R parameter model gives the best results. The model achieves a word error rate (WER) of 32.91%, results demonstrating that model capacity significantly improves performance and that multilingual pre-trained models transfers cross-lingual acoustic representation better than monolingual pre-trained English model on the BembaSpeech for the Bemba ASR. Lastly, results also show that the corpus can be used for building ASR systems for Bemba language
DOI: 10.5281/zenodo.7059954
title: BembaSpeech: A Speech Recognition Corpus for the Bemba Language

description: We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30% of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we explored different approaches; supervised pre-training (training from scratch), cross-lingual transfer learning from a monolingual English pre-trained model using DeepSpeech on the portion of the dataset and fine-tuning large scale self-supervised Wav2Vec2.0 based multilingual pre-trained models on the complete BembaSpeech corpus. From our experiments, the 1 billion XLS-R parameter model gives the best results. The model achieves a word error rate (WER) of 32.91%, results demonstrating that model capacity significantly improves performance and that multilingual pre-trained models transfers cross-lingual acoustic representation better than monolingual pre-trained English model on the BembaSpeech for the Bemba ASR. Lastly, results also show that the corpus can be used for building ASR systems for Bemba language
DOI: 10.6084/m9.figshare.20338191
title: Supplementary Material for: Estimating the effect of tafamidis on cardiovascular-related hospitalization in NYHA class III patients with transthyretin amyloid cardiomyopathy in the presence of death

description: Background: The Tafamidis in Transthyretin Cardiomyopathy Clinical Trial (ATTR-ACT) demonstrated the effectiveness of tafamidis for the treatment of patients with transthyretin amyloid cardiomyopathy (ATTR-CM). Tafamidis reduced mortality in all subgroups of patients studied. Tafamidis also reduced observed frequency of cardiovascular (CV)-related hospitalizations in all subgroups except those who were New York Heart Association (NYHA) class III at baseline who, paradoxically, had a higher frequency of CV-related hospitalizations than placebo. Given the greater mortality rate with placebo, this analysis assessed the impact of the confounding effect of death on the frequency of CV-related hospitalization in ATTR-ACT. Methods: In ATTR-ACT, patients with ATTR-CM were randomized to tafamidis (n = 264) or placebo (n = 177) for 30 months. Post hoc analyses first defined and compared the effect of tafamidis treatment in the subset of NYHA class III patients from each treatment arm alive at month 30. The impact of a potential survivor bias was then adjusted for using principal stratification, estimating the frequency of CV-related hospitalization in NYHA class III patients who would have survived regardless of assigned treatment group (defined as the survivor average causal effect [SACE]). Results: In the subset of NYHA class III patients alive at month 30, tafamidis reduced the relative risk of CV-related hospitalization versus placebo (relative risk: 0.95 [95% CI: 0.55–1.65]). In the principal stratification analyses of those patients who would survive to 30 months regardless of treatment, tafamidis treatment was associated with a 24% lower risk of CV-related hospitalization (relative risk: 0.76 [95% CI: 0.45–1.24). Similarly, there was a larger reduction in CV-related hospitalization frequency with tafamidis in NYHA class I or II patients in the SACE than was initially observed in ATTR-ACT. Conclusions: Initial data from ATTR-ACT likely underestimated the effect of tafamidis on CV-related hospitalizations due to the confounding effect of death. When SACE was used to adjust for survivor bias, there was a 24% reduction in the frequency of CV-related hospitalization in NYHA class III patients treated with tafamidis.
DOI: 10.6084/m9.figshare.20338191.v1
title: Supplementary Material for: Estimating the effect of tafamidis on cardiovascular-related hospitalization in NYHA class III patients with transthyretin amyloid cardiomyopathy in the presence of death

description: Background: The Tafamidis in Transthyretin Cardiomyopathy Clinical Trial (ATTR-ACT) demonstrated the effectiveness of tafamidis for the treatment of patients with transthyretin amyloid cardiomyopathy (ATTR-CM). Tafamidis reduced mortality in all subgroups of patients studied. Tafamidis also reduced observed frequency of cardiovascular (CV)-related hospitalizations in all subgroups except those who were New York Heart Association (NYHA) class III at baseline who, paradoxically, had a higher frequency of CV-related hospitalizations than placebo. Given the greater mortality rate with placebo, this analysis assessed the impact of the confounding effect of death on the frequency of CV-related hospitalization in ATTR-ACT. Methods: In ATTR-ACT, patients with ATTR-CM were randomized to tafamidis (n = 264) or placebo (n = 177) for 30 months. Post hoc analyses first defined and compared the effect of tafamidis treatment in the subset of NYHA class III patients from each treatment arm alive at month 30. The impact of a potential survivor bias was then adjusted for using principal stratification, estimating the frequency of CV-related hospitalization in NYHA class III patients who would have survived regardless of assigned treatment group (defined as the survivor average causal effect [SACE]). Results: In the subset of NYHA class III patients alive at month 30, tafamidis reduced the relative risk of CV-related hospitalization versus placebo (relative risk: 0.95 [95% CI: 0.55–1.65]). In the principal stratification analyses of those patients who would survive to 30 months regardless of treatment, tafamidis treatment was associated with a 24% lower risk of CV-related hospitalization (relative risk: 0.76 [95% CI: 0.45–1.24). Similarly, there was a larger reduction in CV-related hospitalization frequency with tafamidis in NYHA class I or II patients in the SACE than was initially observed in ATTR-ACT. Conclusions: Initial data from ATTR-ACT likely underestimated the effect of tafamidis on CV-related hospitalizations due to the confounding effect of death. When SACE was used to adjust for survivor bias, there was a 24% reduction in the frequency of CV-related hospitalization in NYHA class III patients treated with tafamidis.
DOI: 10.6084/m9.figshare.21069468
title: Additional file 10 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 10: Table S7. List of GO terms represented between early and late PD cases in the frontal cortex. Biological processes, molecular functions and cellular components are split into different columns. Top 30 GO terms that overlap between both sets of brains compared to controls are given as well as those only found in early PD and only found in late PD.