Supplemental Material - The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning

Supplemental Material for The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning by Shuyang Zhang, Nianxiong Liu, Beini Ma and Shurui Yan in Environment and Planning B: Urban Analytics and City Science</p

Supplemental Material - The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning

Supplemental Material for The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning by Shuyang Zhang, Nianxiong Liu, Beini Ma and Shurui Yan in Environment and Planning B: Urban Analytics and City Science</p

Stereolithography 3D Printing of Lignin-Reinforced Composites with Enhanced Mechanical Properties

Due to the availability, biodegradability, and biological effects, lignin has emerged as an interesting alternative to petroleum-based compounds for developing sustainable chemicals, materials, and composites. In this study, lignin at various concentrations was incorporated into methacrylate resin via solution blending to fabricate lignin-reinforced composites using stereolithography apparatus three-dimensional printing. Softwood kraft lignin in the amounts of 0.2, 0.4, 0.5, 0.8, and 1.0 wt % in the methacrylate resin was used as a printing ink, and the gel contents and relative contents of the residual resin in the printed samples with various lignin concentrations were measured. The effects of the lignin on the ultimate mechanical properties of the non-postcured and postcured printed composites were determined. The tensile testing results revealed that the incorporation of lignin in the composite increased the tensile strength by 46–64% and Young’s modulus by 13–37% for the postcured printed composites compared with that of the control sample (no lignin added). Employing a 0.4 wt % softwood kraft lignin, the tensile strength of the postcured printed composite reached the highest value of 49.0 MPa, which was a 60% increase in comparison to that of the control sample with 30.7 MPa. Scanning electron microscopy images of the fracture samples illustrated that the lignin-incorporated composites exhibited a rougher fracture surface that can presumably dissipate the stress, which could be a contributing factor for the mechanical enhancement

Table_1_Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis.XLSX

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.</p

Table_2_Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis.XLSX

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.</p

Additional file 2: of Whole exome sequencing identified a novel truncation mutation in the NHS gene associated with Nance-Horan syndrome

Detailed annotation of the retained 129 variants. ACMG standards were used for variant classification, and the OMIM database was used for priority analysis of the phenotype matching. (XLSX 47 kb

Additional file 1 of Epidemiological research on rare diseases using large-scale online search queries and reported case data

Supplementary Material

Additional file 3 of Epidemiological research on rare diseases using large-scale online search queries and reported case data

Supplementary Material

Additional file 4: of Whole exome sequencing identified a novel truncation mutation in the NHS gene associated with Nance-Horan syndrome

Variants in gene NHS accounted for pathogenic clinical conditions. Pathogenic mutation revealed in this study and those reported in ClinVar and Cat Map databases were reviewed in the table. (XLSX 14 kb

Table_3_Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis.XLSX

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.</p