title: Nanostructure of Gasification Charcoal (Biochar).

authors: Martin, Jacob W.

subjects: Carbon

abstract: In this work, we investigate the molecular composition and nanostructure of gasification charcoal (biochar) by comparing it with heat-treated fullerene arc-soot. Using ultrahigh resolution Fourier transform ion-cyclotron resonance and laser desorption ionization time-of-flight mass spectrometry, Raman spectroscopy, and high resolution transmission electron microscopy we analyzed charcoal of low tar content obtained from gasification. Mass spectrometry revealed no magic number fullerenes such as C60 or C70 in the charcoal. The positive molecular ion m/ z 701, previously considered a graphitic part of the nanostructure, was found to be a breakdown product of pyrolysis and not part of the nanostructure. A higher mass distribution of ions similar to that found in thermally treated fullerene soot indicates that they share a nanostructure. Recent insights into the formation of all carbon fullerenes reveal that conditions in charcoal formation are not optimal for the formation of fullerenes, but instead, curved carbon structures coalesce into fulleroid-like structures. Microscopy and spectroscopy support such a stacked, fulleroid-like nanostructure, which was explored using reactive molecular dynamics simulations.

text: Nanostructure of Gasification Charcoal (Biochar). In this work, we investigate the molecular composition and nanostructure of gasification charcoal (biochar) by comparing it with heat-treated fullerene arc-soot. Using ultrahigh resolution Fourier transform ion-cyclotron resonance and laser desorption ionization time-of-flight mass spectrometry, Raman spectroscopy, and high resolution transmission electron microscopy we analyzed charcoal of low tar content obtained from gasification. Mass spectrometry revealed no magic number fullerenes such as C60 or C70 in the charcoal. The positive molecular ion m/ z 701, previously considered a graphitic part of the nanostructure, was found to be a breakdown product of pyrolysis and not part of the nanostructure. A higher mass distribution of ions similar to that found in thermally treated fullerene soot indicates that they share a nanostructure. Recent insights into the formation of all carbon fullerenes reveal that conditions in charcoal formation are not optimal for the formation of fullerenes, but instead, curved carbon structures coalesce into fulleroid-like structures. Microscopy and spectroscopy support such a stacked, fulleroid-like nanostructure, which was explored using reactive molecular dynamics simulations.

year: 2019

title: Understanding the causes and consequences of variability in infant ERP editing practices.

authors: Monroy, Claire

subjects: ERP methodology

abstract: The current study examined the effects of variability on infant event-related potential (ERP) data editing methods. A widespread approach for analyzing infant ERPs is through a trial-by-trial editing process. Researchers identify electroencephalogram (EEG) channels containing artifacts and reject trials that are judged to contain excessive noise. This process can be performed manually by experienced researchers, partially automated by specialized software, or completely automated using an artifact-detection algorithm. Here, we compared the editing process from four different editors-three human experts and an automated algorithm-on the final ERP from an existing infant EEG dataset. Findings reveal that agreement between editors was low, for both the numbers of included trials and of interpolated channels. Critically, variability resulted in differences in the final ERP morphology and in the statistical results of the target ERP that each editor obtained. We also analyzed sources of disagreement by estimating the EEG characteristics that each human editor considered for accepting an ERP trial. In sum, our study reveals significant variability in ERP data editing pipelines, which has important consequences for the final ERP results. These findings represent an important step toward developing best practices for ERP editing methods in infancy research.

text: Understanding the causes and consequences of variability in infant ERP editing practices. The current study examined the effects of variability on infant event-related potential (ERP) data editing methods. A widespread approach for analyzing infant ERPs is through a trial-by-trial editing process. Researchers identify electroencephalogram (EEG) channels containing artifacts and reject trials that are judged to contain excessive noise. This process can be performed manually by experienced researchers, partially automated by specialized software, or completely automated using an artifact-detection algorithm. Here, we compared the editing process from four different editors-three human experts and an automated algorithm-on the final ERP from an existing infant EEG dataset. Findings reveal that agreement between editors was low, for both the numbers of included trials and of interpolated channels. Critically, variability resulted in differences in the final ERP morphology and in the statistical results of the target ERP that each editor obtained. We also analyzed sources of disagreement by estimating the EEG characteristics that each human editor considered for accepting an ERP trial. In sum, our study reveals significant variability in ERP data editing pipelines, which has important consequences for the final ERP results. These findings represent an important step toward developing best practices for ERP editing methods in infancy research.

year: 2021

title: Prevalence of sociodemographic factors in a cohort of diabetes mellitus: A retrospective study

authors: Erandathi, Madurapperumage A.

subjects: complications of diabetes

abstract: Exploring the sociodemographic factors of a cohort is a vital phase in revealing significant aspects of the societal health status. The health care sector utilises the results of exploratory analysis of the sociodemographic nature to fulfil various purposes such as constructing health care policies, allocating adequate resources, imposing necessary medications and many more. A large and growing body of evidence shows that understanding the pervasiveness of sociodemographic factors: age, ethnicity, gender, reveal crucial information. Therefore, this study aims to disclose the knowledge through analysing the sociodemographic details of a New Zealand diabetes cohort. Diabetes mellitus is a chronic fatal disease that occurs due to the inability to control proper blood sugar levels, which causes multitudinous acute and chronic complications. Diabetes became a high prevalence disease in the region of Waikato. Analysing the cohort of diabetes patients associated with complications of diabetes illustrate the prevalence of complications of diabetes among the patients. The dataset of the study has been collected from the Waikato district health board. This study intends to report the initial scanning of the dataset profile with visualising the resulting patterns of sociodemographic details from the samples and their association with complications of diabetes. The Sankey diagrams use to visualise the results of exploratory data analysis. The resulted graphs of the data screening descriptively illustrate the characteristics of the cohort associated with demographic factors. Maori population shows higher percentage (0.68) of diabetes patients than the other ethnicities, while having narrower age expansion (13-95) with early onset age, compared to others (20-103). Males (0.61%) are more vulnerable to diabetes than females (0.55%). Additionally, hypertension and cardiovascular diseases are common among the diabetes patients’ in the Waikato region. Maori male population is highly vulnerable to diabetes. This study will be beneficial in constructing and analysing the demographical categories of the cohort to comparatively study the pervasiveness of the diseases among resulting classes.

text: Prevalence of sociodemographic factors in a cohort of diabetes mellitus: A retrospective study Exploring the sociodemographic factors of a cohort is a vital phase in revealing significant aspects of the societal health status. The health care sector utilises the results of exploratory analysis of the sociodemographic nature to fulfil various purposes such as constructing health care policies, allocating adequate resources, imposing necessary medications and many more. A large and growing body of evidence shows that understanding the pervasiveness of sociodemographic factors: age, ethnicity, gender, reveal crucial information. Therefore, this study aims to disclose the knowledge through analysing the sociodemographic details of a New Zealand diabetes cohort. Diabetes mellitus is a chronic fatal disease that occurs due to the inability to control proper blood sugar levels, which causes multitudinous acute and chronic complications. Diabetes became a high prevalence disease in the region of Waikato. Analysing the cohort of diabetes patients associated with complications of diabetes illustrate the prevalence of complications of diabetes among the patients. The dataset of the study has been collected from the Waikato district health board. This study intends to report the initial scanning of the dataset profile with visualising the resulting patterns of sociodemographic details from the samples and their association with complications of diabetes. The Sankey diagrams use to visualise the results of exploratory data analysis. The resulted graphs of the data screening descriptively illustrate the characteristics of the cohort associated with demographic factors. Maori population shows higher percentage (0.68) of diabetes patients than the other ethnicities, while having narrower age expansion (13-95) with early onset age, compared to others (20-103). Males (0.61%) are more vulnerable to diabetes than females (0.55%). Additionally, hypertension and cardiovascular diseases are common among the diabetes patients’ in the Waikato region. Maori male population is highly vulnerable to diabetes. This study will be beneficial in constructing and analysing the demographical categories of the cohort to comparatively study the pervasiveness of the diseases among resulting classes.

year: 2022