58 research outputs found
Disagreement and Ambiguity in Biodiversity: A Digital Humanities Perspective
My talk has two aims. First, I will discuss the uncertainty surrounding concepts in biodiversity and taxonomy, along with approaches developed in the philosophy of biology to resolve or eliminate it. It has long been recognized that disagreement is rampant in taxonomy, and that this disagreement over species inventories has a direct (and a potentially damaging) effect on our understanding of biodiversity, with concomitant worries about practice in conservation biology. Philosophers have offered a number of diagnoses of this state of affairs, including various kinds of fatalism, proposals for standardization, and careful analyses of the roles of social and ethical values in conservation. Second, I will present preliminary work from my group using empirical analyses of the literature in taxonomy with the goal of better understanding the factors that shape and modulate taxonomic disagreement – and thus, we hope, better understanding where this disagreement might negatively affect conservation efforts
Additional file 3 of The risk of incident atrial fibrillation in patients with type 2 diabetes treated with sodium glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, and dipeptidyl peptidase-4 inhibitors: a nationwide cohort study
Additional file 3: Figure S2. Subgroup analysis of forest plot of HR for SGLT2i versus glucagon-like peptide-1 receptor agonist (GLP-1RA) among patients with T2D after PSM. Subgroup analysis revealed that use of SGLT2i was associated with a lower risk of new-onset AF compared with use of DPP4i across most subgroups. Use of SGLT2i was associated with greater reductions in new-onset AF events in subgroup including those without concomitant use of sulfonylurea when compared with GLP-1RA (P interaction < 0.01)
Additional file 3 of The risk of incident atrial fibrillation in patients with type 2 diabetes treated with sodium glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, and dipeptidyl peptidase-4 inhibitors: a nationwide cohort study
Additional file 3: Figure S2. Subgroup analysis of forest plot of HR for SGLT2i versus glucagon-like peptide-1 receptor agonist (GLP-1RA) among patients with T2D after PSM. Subgroup analysis revealed that use of SGLT2i was associated with a lower risk of new-onset AF compared with use of DPP4i across most subgroups. Use of SGLT2i was associated with greater reductions in new-onset AF events in subgroup including those without concomitant use of sulfonylurea when compared with GLP-1RA (P interaction < 0.01)
Additional file 2 of The risk of incident atrial fibrillation in patients with type 2 diabetes treated with sodium glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, and dipeptidyl peptidase-4 inhibitors: a nationwide cohort study
Additional file 2: Figure S1. Subgroup analysis of forest plot of hazard ratio (HR) for sodium-glucose cotransporter 2 inhibitors (SGLT2i) versus dipeptidyl peptidase-4 inhibitors (DPP4i) among patients with type 2 diabetes (T2D) after propensity score matching (PSM). Subgroup analysis revealed that use of SGLT2i was associated with a lower risk of new-onset AF compared with use of DPP4i across most subgroups. It is noted that dapagliflozin was specifically associated with a lower risk of new-onset AF compared with DPP4i (P interaction = 0.02)
Additional file 4 of The risk of incident atrial fibrillation in patients with type 2 diabetes treated with sodium glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, and dipeptidyl peptidase-4 inhibitors: a nationwide cohort study
Additional file 4: Figure S3. Subgroup analysis of forest plot of HR for GLP-1RA versus DPP4i among patients with T2D after PSM. There was no difference of the risk of incident AF between the GLP-1RA and DPP4i across all subgroups (P interaction > 0.05)
Modeling and simulation of a novel combined heat and power system with absorption heat pump based on solar thermal power tower plant
Core clinical competencies for dental graduates in Taiwan: Considering local and cultural issues
Age- and enthinity-specific brain templates and growth charts for children and adolescents at school age
Description: Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC.Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience Usage: The age-specific head brain templates can be used for pediatric neuroimaging studies to provide a standard reference on head brain spaces. Sample codes for such uses can be found on Github(https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts). The growth charts on various school-age children and adolescents can provide a normal growth standard on the brain development across school age, together with the normative modeling methods, they offer an analytic way of implementing individualized or personalized pediatrics. All the templates and growth charts are downloadable as NIFTI files. For a given NIFTI file in the dataset, IPCAS indicates the Chinese school age template,NKI named files indicate American template.Users can find the age-specific template in the name of (IPCAS/NKI)_age(X)_brain_template.nii.gz and the different tissue template are also provided by this dataset in the name of (IPCAS/NKI)_age(X)_brain_pve(_0/_1/_2/seg) in which 0 indicates CSF, 1 indicates gray matter, 2 indicates white matter, and seg indicates hard segmentatin
Age- and enthinity-specific brain templates and growth charts for children and adolescents at school age
Description: Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC.Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience Usage: The age-specific head brain templates can be used for pediatric neuroimaging studies to provide a standard reference on head brain spaces. Sample codes for such uses can be found on Github(https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts). The growth charts on various school-age children and adolescents can provide a normal growth standard on the brain development across school age, together with the normative modeling methods, they offer an analytic way of implementing individualized or personalized pediatrics. All the templates and growth charts are downloadable as NIFTI files. For a given NIFTI file in the dataset, IPCAS indicates the Chinese school age template,NKI named files indicate American template.Users can find the age-specific template in the name of (IPCAS/NKI)_age(X)_brain_template.nii.gz and the different tissue template are also provided by this dataset in the name of (IPCAS/NKI)_age(X)_brain_pve(_0/_1/_2/seg) in which 0 indicates CSF, 1 indicates gray matter, 2 indicates white matter, and seg indicates hard segmentatin
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