cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6864239
title: Additional file 4: of CIGene: a literature-based online resource for cancer initiation genes

description: Table S4. Functional enrichment analysis for the 81 CIGs without identified tumor suppressor or oncogenic roles. (XLS 100Â kb)
DOI: 10.25582/data.2021-12.2390402/2285597
title: Data for EMSL Project 51834 from December 2021
DOI: 10.6084/m9.figshare.6864239.v1
title: Additional file 4: of CIGene: a literature-based online resource for cancer initiation genes

description: Table S4. Functional enrichment analysis for the 81 CIGs without identified tumor suppressor or oncogenic roles. (XLS 100Â kb)
DOI: 10.25582/data.2021-10.2372853/2285598
title: Data for EMSL Project 51834 from October 2021
DOI: 10.6084/m9.figshare.6864245
title: Additional file 5: of CIGene: a literature-based online resource for cancer initiation genes

description: Table S5. Empirical P-values of survival significance of CIGs in 21 TCGA cancer types. (XLS 33Â kb)
DOI: 10.6084/m9.figshare.6864245.v1
title: Additional file 5: of CIGene: a literature-based online resource for cancer initiation genes

description: Table S5. Empirical P-values of survival significance of CIGs in 21 TCGA cancer types. (XLS 33Â kb)
DOI: 10.25582/data.2022-10.2623264/2285599
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2021-10.2368804/2285600
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-12.2390061/2285601
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2022-09.2609413/2285602
title: Data for EMSL Project 51834 from September 2022
DOI: 10.6084/m9.figshare.14728552
title: Additional file 1 of Transcriptional dynamics of transposable elements when converting fibroblast cells of Macaca mulatta to neuroepithelial stem cells

description: Additional file 1 Supplementary Table S1. The expression levels of 1627 dynamically expressed genes in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S2 to S17. The enriched GO terms of genes in Cluster G0 to G15 of Figure 1A, respectively. Supplementary Table S18. The expression levels of 495 dynamically expressed TEs in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S19. The expression levels of 98 dynamically expressed LTRs in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S20. The 13 MacERV3 LTRs that were selected for PCR verification. Supplementary Table S21. The primers used in this study.
DOI: 10.6084/m9.figshare.14728552.v1
title: Additional file 1 of Transcriptional dynamics of transposable elements when converting fibroblast cells of Macaca mulatta to neuroepithelial stem cells

description: Additional file 1 Supplementary Table S1. The expression levels of 1627 dynamically expressed genes in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S2 to S17. The enriched GO terms of genes in Cluster G0 to G15 of Figure 1A, respectively. Supplementary Table S18. The expression levels of 495 dynamically expressed TEs in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S19. The expression levels of 98 dynamically expressed LTRs in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S20. The 13 MacERV3 LTRs that were selected for PCR verification. Supplementary Table S21. The primers used in this study.
DOI: 10.25582/data.2021-08.2305327/2285603
title: Data for EMSL Project 51834 from August 2021
DOI: 10.25582/data.2021-09.2330069/2285604
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-07.2289542/2285605
title: Data for EMSL Project 51834 from July 2021
DOI: 10.25582/data.2021-11.2385077/2285606
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-09.2320763/2285607
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-11.2389148/2285608
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-01.2417412/2285609
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2022-09.2607814/2285610
title: Data for EMSL Project 51834 from September 2022
DOI: 10.6084/m9.figshare.22592951
title: RNA methylation-related genes of m6A, m5C, and m1A predict prognosis and immunotherapy response in cervical cancer

description: To investigate the prognostic value of N6-methyladenosine (m6A)-, 5-methylcytosine (m5C)-, and N1-methyladenosine (m1A)-related genes in cervical cancer (CESC) and predicting immunotherapy response. We downloaded cervical cancer mRNA expression profiles, clinical data, and m6A, m5C, m1A-related genes from public databases, and subjected them to serial bioinformatics analysis and clinical sample validation. Differential analysis revealed 106 methylation-related differential genes (MEDs), including 44 differentially downregulated and 62 upregulated genes. We then obtained methylation models containing 10 genes by univariate and multifactorial COX analysis. High risk genes with HR &gt; 1 include IQGAP3, PTBP1, STAC3, CUX1, SLC2A1, and CA2, and low risk genes with HR &lt; 1 include IGBP1, DUOX1, CHAF1A, and STAC3. We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729). K-M analysis showed shorter survival times in the High-risk group, and Immunocytic infiltration analysis showed model genes closely associated with six immune cells. The high-risk group may benefit more effectively from immunosuppressive therapy, especially anti-CTLA-4 therapy (<i>p</i> &lt; .05). We also screened nine drugs for potential treatment and verified the expression of three key genes SLC2A1, CUX1, and CA2 using immunohistochemistry and RT-qPCR experiments with clinical samples. We identified a prognostic model using m6A/m5C/m1A-related genes in cervical cancer, which can predict survival time and correlate with immune cell infiltration. Additionally, anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer.KEY MESSAGESCervical cancer still has a high mortality rate, we aim to establish a strong prognostic index and new treatment goals for improving patient survival.The role of three types of RNA methylation modifications, m6A, m5C, and m1A, in cervical cancer, remains unknown. Therefore, it is essential to explore the potential molecular mechanisms of m6A, m5C, and m1A methylation regulation in cervical cancer.We also screened nine drugs for potential treatment and anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. We verified the expression of three key genes SLC2A1, CUX1, and CA2 Cervical cancer still has a high mortality rate, we aim to establish a strong prognostic index and new treatment goals for improving patient survival. The role of three types of RNA methylation modifications, m6A, m5C, and m1A, in cervical cancer, remains unknown. Therefore, it is essential to explore the potential molecular mechanisms of m6A, m5C, and m1A methylation regulation in cervical cancer. We also screened nine drugs for potential treatment and anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. We verified the expression of three key genes SLC2A1, CUX1, and CA2
DOI: 10.6084/m9.figshare.22592951.v1
title: RNA methylation-related genes of m6A, m5C, and m1A predict prognosis and immunotherapy response in cervical cancer

description: To investigate the prognostic value of N6-methyladenosine (m6A)-, 5-methylcytosine (m5C)-, and N1-methyladenosine (m1A)-related genes in cervical cancer (CESC) and predicting immunotherapy response. We downloaded cervical cancer mRNA expression profiles, clinical data, and m6A, m5C, m1A-related genes from public databases, and subjected them to serial bioinformatics analysis and clinical sample validation. Differential analysis revealed 106 methylation-related differential genes (MEDs), including 44 differentially downregulated and 62 upregulated genes. We then obtained methylation models containing 10 genes by univariate and multifactorial COX analysis. High risk genes with HR &gt; 1 include IQGAP3, PTBP1, STAC3, CUX1, SLC2A1, and CA2, and low risk genes with HR &lt; 1 include IGBP1, DUOX1, CHAF1A, and STAC3. We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729). K-M analysis showed shorter survival times in the High-risk group, and Immunocytic infiltration analysis showed model genes closely associated with six immune cells. The high-risk group may benefit more effectively from immunosuppressive therapy, especially anti-CTLA-4 therapy (<i>p</i> &lt; .05). We also screened nine drugs for potential treatment and verified the expression of three key genes SLC2A1, CUX1, and CA2 using immunohistochemistry and RT-qPCR experiments with clinical samples. We identified a prognostic model using m6A/m5C/m1A-related genes in cervical cancer, which can predict survival time and correlate with immune cell infiltration. Additionally, anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer.KEY MESSAGESCervical cancer still has a high mortality rate, we aim to establish a strong prognostic index and new treatment goals for improving patient survival.The role of three types of RNA methylation modifications, m6A, m5C, and m1A, in cervical cancer, remains unknown. Therefore, it is essential to explore the potential molecular mechanisms of m6A, m5C, and m1A methylation regulation in cervical cancer.We also screened nine drugs for potential treatment and anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. We verified the expression of three key genes SLC2A1, CUX1, and CA2 Cervical cancer still has a high mortality rate, we aim to establish a strong prognostic index and new treatment goals for improving patient survival. The role of three types of RNA methylation modifications, m6A, m5C, and m1A, in cervical cancer, remains unknown. Therefore, it is essential to explore the potential molecular mechanisms of m6A, m5C, and m1A methylation regulation in cervical cancer. We also screened nine drugs for potential treatment and anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. We verified the expression of three key genes SLC2A1, CUX1, and CA2
DOI: 10.25582/data.2021-09.2322421/2285611
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-09.2322420/2285612
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-10.2361380/2285613
title: Data for EMSL Project 51834 from October 2021
DOI: 10.6084/m9.figshare.25152269
title: Data for "Nitrogen enrichment induces more plant species loss under drier conditions"

description: Nitrogen (N) deposition is a major driver of plant species loss worldwide. However, what regulates N-driven species loss remains unclear. Based on a 7-year field experiment on the Qinghai-Tibetan Plateau, we found that the impact of N addition on plant species richness strongly depended on precipitation. During experimental years with lower precipitation, N addition induced more species loss. The main underlying mechanism was that lower precipitation stimulated soil inorganic N accumulation under N addition, resulting in stronger competitive exclusion and ammonium toxicity in plant communities. These site observations were complemented by a global synthesis derived from 45 N addition experiments, showing N-induced more species loss in dry than in wet ecosystems. Given the importance of plant species richness in supporting ecosystem functioning and stability, our findings suggest that ecosystems during drought periods or in arid areas are particularly sensitive to N deposition, having important implications for their management and conservation.
DOI: 10.6084/m9.figshare.25152269.v1
title: Data for "Nitrogen enrichment induces more plant species loss under drier conditions"

description: Nitrogen (N) deposition is a major driver of plant species loss worldwide. However, what regulates N-driven species loss remains unclear. Based on a 7-year field experiment on the Qinghai-Tibetan Plateau, we found that the impact of N addition on plant species richness strongly depended on precipitation. During experimental years with lower precipitation, N addition induced more species loss. The main underlying mechanism was that lower precipitation stimulated soil inorganic N accumulation under N addition, resulting in stronger competitive exclusion and ammonium toxicity in plant communities. These site observations were complemented by a global synthesis derived from 45 N addition experiments, showing N-induced more species loss in dry than in wet ecosystems. Given the importance of plant species richness in supporting ecosystem functioning and stability, our findings suggest that ecosystems during drought periods or in arid areas are particularly sensitive to N deposition, having important implications for their management and conservation.
DOI: 10.25582/data.2021-09.2314894/2285614
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-10.2361845/2285615
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2022-09.2609649/2285616
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-10.2347998/2285617
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2022-09.2609655/2285618
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-09.2322368/2285619
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-12.2399490/2285620
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2021-09.2329138/2285621
title: Data for EMSL Project 51834 from September 2021
DOI: 10.17028/rd.lboro.12631517
title: Supplementary Information files for: Electronic coupling strategy to boost water oxidation efficiency based on the modelling of Trimetallic Hydroxides Ni1-x-yFexCry(OH)2: from theory to experiment

description: Supplementary Information files for: Electronic coupling strategy to boost water oxidation efficiency based on the modelling of Trimetallic Hydroxides Ni1-x-yFexCry(OH)2: from theory to experiment. Developing low-cost yet highly efficient earth-abundant electrocatalysts for oxygen evolution reaction (OER) is of great significance for industrial scale water splitting for clean hydrogen production, as well as for rechargeable metal-air batteries. In searching for advanced catalysts, it is equally important to fundamentally understand working mechanism and be able to rationally design and manipulate catalytic sites. Starting from density functional theory (DFT) calculations as a guidance, our theoretical model revealed that chromium substitution in nickel-iron hydroxides (Ni1-xFex(OH)2) not only accelerated the charge transfer but also regulated the adsorption energy of OER intermediates such to achieve optimal binding strength. Experimentally, chromium was doped into the laminate of Ni1- xFex(OH)2, resulting in enhanced catalytic performance for oxygen evolution reaction, which confirmed the predictions from the theoretical data. The porous and ultra-thin ternary Ni1-xyFexCry(OH)2 electrocatalysts were grown directly on a nickel foam (NF) substrate, with an optimum composition Ni0.66Fe0.27Cr0.07(OH)2/NF identified, which exhibited a superior OER performance, i.e., achieving a significant current density of 10 mA cm-2 at a low overpotential of 231 mV, a small Tafel slope (31 mV dec-1) and an excellent stability at a highly oxidative potential of 1.68 V vs RHE in alkaline electrolyte. The comprehensive study involving both theoretical and experimental results in this work provides an insightful guidance in designing efficient OER catalysts for chemical and electrical energy conversion and storage.
DOI: 10.25582/data.2021-09.2329155/2285622
title: Data for EMSL Project 51834 from September 2021
DOI: 10.17028/rd.lboro.12631517.v1
title: Supplementary Information files for: Electronic coupling strategy to boost water oxidation efficiency based on the modelling of Trimetallic Hydroxides Ni1-x-yFexCry(OH)2: from theory to experiment

description: Supplementary Information files for: Electronic coupling strategy to boost water oxidation efficiency based on the modelling of Trimetallic Hydroxides Ni1-x-yFexCry(OH)2: from theory to experiment. Developing low-cost yet highly efficient earth-abundant electrocatalysts for oxygen evolution reaction (OER) is of great significance for industrial scale water splitting for clean hydrogen production, as well as for rechargeable metal-air batteries. In searching for advanced catalysts, it is equally important to fundamentally understand working mechanism and be able to rationally design and manipulate catalytic sites. Starting from density functional theory (DFT) calculations as a guidance, our theoretical model revealed that chromium substitution in nickel-iron hydroxides (Ni1-xFex(OH)2) not only accelerated the charge transfer but also regulated the adsorption energy of OER intermediates such to achieve optimal binding strength. Experimentally, chromium was doped into the laminate of Ni1- xFex(OH)2, resulting in enhanced catalytic performance for oxygen evolution reaction, which confirmed the predictions from the theoretical data. The porous and ultra-thin ternary Ni1-xyFexCry(OH)2 electrocatalysts were grown directly on a nickel foam (NF) substrate, with an optimum composition Ni0.66Fe0.27Cr0.07(OH)2/NF identified, which exhibited a superior OER performance, i.e., achieving a significant current density of 10 mA cm-2 at a low overpotential of 231 mV, a small Tafel slope (31 mV dec-1) and an excellent stability at a highly oxidative potential of 1.68 V vs RHE in alkaline electrolyte. The comprehensive study involving both theoretical and experimental results in this work provides an insightful guidance in designing efficient OER catalysts for chemical and electrical energy conversion and storage.
DOI: 10.25582/data.2021-09.2329635/2285623
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-12.2410398/2285624
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2021-09.2330050/2285625
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-08.2305314/2285626
title: Data for EMSL Project 51834 from August 2021
DOI: 10.25582/data.2022-10.2623285/2285627
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2021-12.2390394/2285628
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2021-09.2322381/2285629
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-12.2410575/2285630
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2022-01.2417448/2285631
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2022-01.2417373/2285632
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2022-10.2622429/2285633
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-04.2478891/2285634
title: Data for EMSL Project 51834 from April 2022
DOI: 10.6084/m9.figshare.25108130
title: <b>Assessing influential factors and refining GEDI L2A data for large-scale building height retrieval</b>

description: This study provides a valuable reference for the efficient generation of high-quality GEDI footprint data for building height retrieval. Based on the proposed GEDI data filtering method, we downloaded and processed the GEDI L2A V2 data for China in the year 2020 to generate high-quality GEDI data for building height retrieval. Ultimately, 973,608 high-quality GEDI footprints were obtained for China's built-up areas, effectively illustrating building heights. GEDI footprints are significantly sparser in southern China than in the north. Among these footprints, the maximum RH98 value is 148.01 m, the minimum value is 1.08 m, and the mean value is 13.26 m. Two distinct peaks are evident at RH98 values of approximately 7 m and 20 m, respectively. Additionally, it can be observed that building heights are notably higher in central and southern China compared to the northern and northwestern regions. These high-quality GEDI data provide accurate building height information and may contribute to map large-scale building height.
DOI: 10.25582/data.2021-09.2330077/2285635
title: Data for EMSL Project 51834 from September 2021
DOI: 10.6084/m9.figshare.25108130.v1
title: Performance evaluation and effect factor analysis of GEDI L2A data for building height retrieval

description: This study provides a valuable reference for the efficient generation of high-quality GEDI footprint data for building height retrieval. Based on the proposed GEDI data filtering method, we downloaded and processed the GEDI L2A V2 data for China in the year 2020 to generate high-quality GEDI data for building height retrieval. The 30-m Forest and Buildings removed Copernicus DEM (FABDEM) (Hawker et al., 2022) was utilized to eliminate footprints that failed to detect ground elevation, while the 10-m World Settlement Footprint 2019 map (Marconcini et al., 2021) was employed to exclude GEDI footprints in non-building areas. Ultimately, 973,608 high-quality GEDI footprints were obtained for China's built-up areas, effectively illustrating building heights. GEDI footprints are significantly sparser in southern China than in the north. Among these footprints, the maximum RH98 value is 148.01 m, the minimum value is 1.08 m, and the mean value is 13.26 m. Two distinct peaks are evident at RH98 values of approximately 7 m and 20 m, respectively. Additionally, it can be observed that building heights are notably higher in central and southern China compared to the northern and northwestern regions. These high-quality GEDI data provide accurate building height information and may contribute to wall-to-wall building height mapping research.
DOI: 10.25582/data.2021-12.2399497/2285636
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2022-09.2607219/2285637
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-10.2363569/2285638
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-11.2389224/2285639
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2022-09.2608777/2285640
title: Data for EMSL Project 51834 from September 2022
DOI: 10.25582/data.2021-12.2390390/2285641
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2022-01.2417098/2285642
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2021-09.2330010/2285643
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2022-10.2617746/2285644
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-01.2417312/2285645
title: Data for EMSL Project 51834 from January 2022
DOI: 10.25582/data.2021-12.2391099/2285646
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2022-10.2613648/2285647
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-10.2617436/2285648
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-10.2621730/2285649
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-10.2614418/2285650
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2022-10.2623292/2285651
title: Data for EMSL Project 51834 from October 2022
DOI: 10.25582/data.2021-10.2368708/2285652
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-12.2410388/2285653
title: Data for EMSL Project 51834 from December 2021
DOI: 10.25582/data.2021-10.2368133/2285654
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-11.2387314/2285655
title: Data for EMSL Project 51834 from November 2021
DOI: 10.25582/data.2021-09.2318389/2285656
title: Data for EMSL Project 51834 from September 2021
DOI: 10.25582/data.2021-10.2361651/2285657
title: Data for EMSL Project 51834 from October 2021
DOI: 10.25582/data.2021-07.2287160/2285658
title: Data for EMSL Project 51834 from July 2021