411 Works

Additional file 2 of Total Sedentary Time and Cognitive Function in Middle-Aged and Older Adults: A Systematic Review and Meta-analysis

Kirsten Dillon, Anisa Morava, Harry Prapavessis, Lily Grigsby-Duffy, Adam Novic & Paul A. Gardiner
Additional file 2: The definition and acceptable cognitive tests for eachdomain.

Additional file 2 of Identification of SSBP1 as a ferroptosis-related biomarker of glioblastoma based on a novel mitochondria-related gene risk model and in vitro experiments

Jun Su, Yue Li, Qing Liu, Gang Peng, Chaoying Qin & Yang Li
Additional file 2: Fig. S2. The predictivity of 12 DE-MRGs model for other cancers. A The univariate cox analysis showed that the risk score, based on our 12 DE-MRGs, significantly associated with other 12 types of TCGA cancers. B Kaplan–Meier curves for OS in the other 12 types of TCGA cancers. The patients were divided in to high- and low-risk groups based on the median value of risk score.

Additional file 5 of Identification of SSBP1 as a ferroptosis-related biomarker of glioblastoma based on a novel mitochondria-related gene risk model and in vitro experiments

Jun Su, Yue Li, Qing Liu, Gang Peng, Chaoying Qin & Yang Li
Additional file 5: Fig. S5. Validation of the association between risk score and immune cell infiltration. A Scatter plot showed the positive correlation between the risk score and ImmuneScore (Spearman’s rank correlation coefficient) in the GSE16011 GBM cohort. B Scatter plot showed the positive correlation between the risk score and StromalScore (Spearman’s rank correlation coefficient) in the GSE16011 GBM cohort. C Scatter plot showed the positive correlation between the risk score and ESTAMEScore (Spearman’s rank...

Additional file 10 of Identification of SSBP1 as a ferroptosis-related biomarker of glioblastoma based on a novel mitochondria-related gene risk model and in vitro experiments

Jun Su, Yue Li, Qing Liu, Gang Peng, Chaoying Qin & Yang Li
Additional file 10: Table S5. GO enrichment analysis by using DAVID.

Additional file 1 of Engineering Yarrowia lipolytica for the sustainable production of β-farnesene from waste oil feedstock

Yinghang Liu, Jin Zhang, Qingbin Li, Zhaoxuan Wang, Zhiyong Cui, Tianyuan Su, Xuemei Lu, Qingsheng Qi & Jin Hou
Additional file 1: Figure S1. Screening β-farnesene synthase from different plants. A The evolutionary tree of β-farnesene synthase from different plants. B β-Farnesene production and biomass of strains containing different β-farnesene synthase. Data represent the mean ± SD of biological triplicate. Figure S2. The SDS-PAGE electrophoresis analysis of purified protein AanFSK197T/F180H and AanFS. Figure S3. The copy number and relative expression of AanFSK197T/F180H in Q6 and Q7 strains. Data represent the mean ± SD of...

Additional file 1 of Engineering Yarrowia lipolytica for the sustainable production of β-farnesene from waste oil feedstock

Yinghang Liu, Jin Zhang, Qingbin Li, Zhaoxuan Wang, Zhiyong Cui, Tianyuan Su, Xuemei Lu, Qingsheng Qi & Jin Hou
Additional file 1: Figure S1. Screening β-farnesene synthase from different plants. A The evolutionary tree of β-farnesene synthase from different plants. B β-Farnesene production and biomass of strains containing different β-farnesene synthase. Data represent the mean ± SD of biological triplicate. Figure S2. The SDS-PAGE electrophoresis analysis of purified protein AanFSK197T/F180H and AanFS. Figure S3. The copy number and relative expression of AanFSK197T/F180H in Q6 and Q7 strains. Data represent the mean ± SD of...

Additional file 6 of Identification of SSBP1 as a ferroptosis-related biomarker of glioblastoma based on a novel mitochondria-related gene risk model and in vitro experiments

Jun Su, Yue Li, Qing Liu, Gang Peng, Chaoying Qin & Yang Li
Additional file 6: Table S1. The mitochondria-related genes extracted from the uniprot database.

Additional file 9 of Identification of SSBP1 as a ferroptosis-related biomarker of glioblastoma based on a novel mitochondria-related gene risk model and in vitro experiments

Jun Su, Yue Li, Qing Liu, Gang Peng, Chaoying Qin & Yang Li
Additional file 9: Table S4. The 21 prognositc DE-MRGs based on the TCGA GBM cohort.

Additional file 1 of Attention to visual motion suppresses neuronal and behavioral sensitivity in nearby feature space

Sang-Ah Yoo, Julio C. Martinez-Trujillo, Stefan Treue, John K. Tsotsos & Mazyar Fallah
Additional file 1: Table S1. Distribution of all neurons’ minimum responses. Figure S1. Histogram of Table S1. Table S2. Distribution of minimum responses of the neurons where the sum of two Gaussians model fits better. Figure S2. Histogram of Table S2. Table S3. Distribution of minimum responses of the neurons with the center-surround profile. Figure S3. Histogram of Table S3.

Attention to visual motion suppresses neuronal and behavioral sensitivity in nearby feature space

Sang-Ah Yoo, Julio C. Martinez-Trujillo, Stefan Treue, John K. Tsotsos & Mazyar Fallah
Abstract Background Feature-based attention prioritizes the processing of the attended feature while strongly suppressing the processing of nearby ones. This creates a non-linearity or “attentional suppressive surround” predicted by the Selective Tuning model of visual attention. However, previously reported effects of feature-based attention on neuronal responses are linear, e.g., feature-similarity gain. Here, we investigated this apparent contradiction by neurophysiological and psychophysical approaches. Results Responses of motion direction-selective neurons in area MT/MST of monkeys were recorded...

Additional file 5 of LARRPM restricts lung adenocarcinoma progression and M2 macrophage polarization through epigenetically regulating LINC00240 and CSF1

Yue Li, Chen Chen, Hai-lin Liu, Zhen-fa Zhang & Chang-li Wang
Additional file 5: Figure S4. The correlation between LARRPM expression, 5hmC level at CSF1 promoter, CpG82 methylation level and CSF1 expression in LUAD tissues. A Correlation between CSF1 and LARRPM expression analysed using the TCGA LUAD data. r = − 0.3069, P < 0.0001 by Spearman correlation analysis. B Correlation between CSF1 and LARRPM expression analysed in our LUAD cohort. r = − 0.5774, P < 0.0001 by Spearman correlation analysis. C 5hmC levels of...

Additional file 3 of LARRPM restricts lung adenocarcinoma progression and M2 macrophage polarization through epigenetically regulating LINC00240 and CSF1

Yue Li, Chen Chen, Hai-lin Liu, Zhen-fa Zhang & Chang-li Wang
Additional file 3: Figure S2. The correlation between LARRPM expression, 5hmC level at LINC00240 promoter, CpG43 methylation level and LINC00240 expression in LUAD tissues. A Correlation between LINC00240 and LARRPM expression analysed using the TCGA LUAD data. r = 0.6164, P < 0.0001 by Spearman correlation analysis. B Correlation between LINC00240 and LARRPM expression analyzed in our LUAD cohort. r = 0.5521, P < 0.0001 by Spearman correlation analysis. C 5hmC levels of CpG43 from...

Additional file 1 of Comparison of international guidelines for diagnosis of hepatocellular carcinoma and implications for transplant allocation in liver transplantation candidates with gadoxetic acid enhanced liver MRI versus contrast enhanced CT: a prospective study with liver explant histopathological correlation

Devang Odedra, Ali Babaei Jandaghi, Rajesh Bhayana, Khaled Y. Elbanna, Osvaldo Espin-Garcia, Sandra E. Fischer, Anand Ghanekar, Gonzalo Sapisochin & Kartik S. Jhaveri
Additional file 1: Supplementary Table 1. CECT quadriphasic liver protocol (Aquilion 64). Supplementary Table 2. Protocol for gadoxetic acid-enhanced liver MRI (Gd-EOB-MRI). Supplementary Table 3. LI-RADS v2018 – Major and Ancillary Features with EOB-MRI. Supplementary Table 4. Differences between sensitivities for scoring guidelines for EOB-MRI for lesions > 1 cm (numbers represent p values, all lesions seen on histopathology/ imaging-visible lesions only). SupplementaryTable 5. Differences between sensitivities for scoring guidelines systems forEOB-MRI for lesions of...

Additional file 1 of A high-quality Buxus austro-yunnanensis (Buxales) genome provides new insights into karyotype evolution in early eudicots

Zhenyue Wang, Ying Li, Pengchuan Sun, Mingjia Zhu, Dandan Wang, Zhiqiang Lu, Hongyin Hu, Renping Xu, Jin Zhang, Jianxiang Ma, Jianquan Liu & Yongzhi Yang
Additional file 1: Fig. S1. The previously reported topologies within eudicots. Fig. S2. 19-Kmer-based analysis to estimate the genome size of Buxus austro-yunnanensis. Fig. S3. Interaction frequency distribution of Hi-C links among chromosomes. Fig. S4. GC contents of five early-diverging eudicot species. Fig. S5. BUSCO results for six eudicots. Fig. S6. LTR insertion time of Buxus austro-yunnanensis. Fig. S7. Gene structures of Aquilegia, Buxus, Nelumbo, Trochodendron and Tetracentron. Fig. S8. The phylogenetic trees of the...

Additional file 1 of A high-quality Buxus austro-yunnanensis (Buxales) genome provides new insights into karyotype evolution in early eudicots

Zhenyue Wang, Ying Li, Pengchuan Sun, Mingjia Zhu, Dandan Wang, Zhiqiang Lu, Hongyin Hu, Renping Xu, Jin Zhang, Jianxiang Ma, Jianquan Liu & Yongzhi Yang
Additional file 1: Fig. S1. The previously reported topologies within eudicots. Fig. S2. 19-Kmer-based analysis to estimate the genome size of Buxus austro-yunnanensis. Fig. S3. Interaction frequency distribution of Hi-C links among chromosomes. Fig. S4. GC contents of five early-diverging eudicot species. Fig. S5. BUSCO results for six eudicots. Fig. S6. LTR insertion time of Buxus austro-yunnanensis. Fig. S7. Gene structures of Aquilegia, Buxus, Nelumbo, Trochodendron and Tetracentron. Fig. S8. The phylogenetic trees of the...

Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE

Ahmed A. Al-Jaishi, Monica Taljaard, Melissa D. Al-Jaishi, Sheikh S. Abdullah, Lehana Thabane, P. J. Devereaux, Stephanie N. Dixon & Amit X. Garg
Abstract Background Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieval from bibliographic databases difficult. Machine learning algorithms may improve their identification and retrieval. Therefore, we aimed to develop machine learning algorithms that accurately determine whether a bibliographic citation is a CRT report. Methods We trained, internally validated, and externally...

Additional file 2 of Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE

Ahmed A. Al-Jaishi, Monica Taljaard, Melissa D. Al-Jaishi, Sheikh S. Abdullah, Lehana Thabane, P. J. Devereaux, Stephanie N. Dixon & Amit X. Garg
Additional file 2. Additional details for the external dataset.

Additional file 4 of Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE

Ahmed A. Al-Jaishi, Monica Taljaard, Melissa D. Al-Jaishi, Sheikh S. Abdullah, Lehana Thabane, P. J. Devereaux, Stephanie N. Dixon & Amit X. Garg
Additional file 4: Description of the gamma and c parameters. Fig. S2. This figure shows the decision boundaries and support vectors for different settings of the regularization parameter (C) and Kernel coefficient (gamma). We used the mglearn package to create this figure [27].

Targeted OUM1/PTPRZ1 silencing and synergetic CDT/enhanced chemical therapy toward uveal melanoma based on a dual-modal imaging-guided manganese metal–organic framework nanoparticles

Yue Li, Fang Li, Hui Pan, Xiaolin Huang, Jie Yu, Xueru Liu, Qinghao Zhang, Caiwen Xiao, He Zhang & Leilei Zhang
Abstract Metastasis and chemical resistance are the most serious problems in the treatment of highly aggressive uveal melanoma (UM). The newly identified lncRNA OUM1 is overexpressed in UM, functions as a catalyst and regulates protein tyrosine phosphatase (PTP) activity by binding to PTP receptor type Z1 (PTPRZ1), which plays an important role in cell proliferation, metastasis and chemotherapy resistance in the UM microenvironment. Hence, siRNAs that selectively knocking down the lncRNA OUM1 (siOUM1) and its...

A multi-country study on the impact of sex and age on oral features of COVID-19 infection in adolescents and young adults

Heba Jafar Sabbagh, Wafaa Abdelaziz, Maryam Quritum, Rana Abdullah Alamoudi, Nada Abu Bakr AlKhateeb, Joud Abourdan, Nafeesa Qureshi, Shabnum Qureshi, Ahmed H. N. Hamoud, Nada Mahmoud, Ruba Odeh, Nuraldeen Maher Al-Khanati, Rawiah Jaber, Abdulrahman Loaie Balkhoyor, Mohammed Shabi, Morenike Oluwatoyin Folayan, Omolola Alade, Noha Gomaa, Raqiya Alnahdi, Nawal A. Mahmoud, Hanane El Wazziki, Manal Alnaas, Bahia Samodien, Rawa A. Mahmoud, Nour Abu Assab … & Maha El Tantawi
Abstract Background Oral diseases are features of COVID-19 infection. There is, however, little known about oral diseases associated with COVID-19 in adolescents and young adults (AYA). Therefore, the aim of this study was to assess oral lesions’ association with COVID-19 infection in AYA; and to identify if sex and age will modify these associations. Methodology Data was collected for this cross-sectional study between August 2020 and January 2021 from 11-to-23 years old participants in 43-countries...

Data from: Selection bias in mutation accumulation

Lindi Wahl & Deepa Agashe
Mutation accumulation (MA) experiments, in which de novo mutations are sampled and subsequently characterized, are an essential tool in understanding the processes underlying evolution. In microbial populations, MA protocols typically involve a period of population growth between severe bottlenecks, such that a single individual can form a visible colony. While it has long been appreciated that the action of positive selection during this growth phase cannot be eliminated, it is typically assumed to be negligible....

sj-docx-1-cjk-10.1177_20543581221129442 – Supplemental material for The Living Kidney Donor Safety Study: Protocol of a Prospective Cohort Study

Amit X. Garg, Jennifer B. Arnold, Meaghan Cuerden, Christine Dipchand, Liane S. Feldman, John S. Gill, Martin Karpinski, Scott Klarenbach, Greg A. Knoll, Charmaine Lok, Matthew Miller, Mauricio Monroy-Cuadros, Christopher Nguan, G. V. Ramesh Prasad, Jessica M. Sontrop, Leroy Storsley & Neil Boudville
Supplemental material, sj-docx-1-cjk-10.1177_20543581221129442 for The Living Kidney Donor Safety Study: Protocol of a Prospective Cohort Study by Amit X. Garg, Jennifer B. Arnold, Meaghan Cuerden, Christine Dipchand, Liane S. Feldman, John S. Gill, Martin Karpinski, Scott Klarenbach, Greg A. Knoll, Charmaine Lok, Matthew Miller, Mauricio Monroy-Cuadros, Christopher Nguan, G. V. Ramesh Prasad, Jessica M. Sontrop, Leroy Storsley and Neil Boudville in Canadian Journal of Kidney Health and Disease

Additional file 1 of Comparative transcriptomic analysis provides insights into the molecular basis underlying pre-harvest sprouting in rice

Dong Liu, Mingyang Zeng, Yan Wu, Yanli Du, Jianming Liu, Shaoqiang Luo & Yongjun Zeng
Additional file 1: Fig. S1. Heatmap showing the results of pairwise correlation analyses between different samples.

Additional file 2 of Comparative transcriptomic analysis provides insights into the molecular basis underlying pre-harvest sprouting in rice

Dong Liu, Mingyang Zeng, Yan Wu, Yanli Du, Jianming Liu, Shaoqiang Luo & Yongjun Zeng
Additional file 2: Table S1. The list of annotated DEGs identified in JXZ-T vs. JXZ-C.

Additional file 6 of Comparative transcriptomic analysis provides insights into the molecular basis underlying pre-harvest sprouting in rice

Dong Liu, Mingyang Zeng, Yan Wu, Yanli Du, Jianming Liu, Shaoqiang Luo & Yongjun Zeng
Additional file 6: Table S5. The results of KEGG enrichment of DEGs in JXZ-T vs. JXZ-C.

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