Pub Date : 2021-12-15DOI: 10.3389/frans.2021.797520
Lucia Lazarowski, A. Simon, Sarah Krichbaum, C. Angle, Melissa Singletary, Paul Waggoner, Kelly Van Arsdale, Jason Barrow
Effective explosives detection requires dogs to generalize their response to untrained variations of targets that are related to those with which they were trained. Previous research suggests that dogs tend to be highly specific to their trained odors, and are sensitive to alterations in odor profiles. Triacetone triperoxide (TATP) is an increasingly popular homemade explosive due to the widespread accessibility of starting materials. The large variety of reagent sources and production approaches yields high variability in deployed formulations. Whether dogs trained with pure forms of TATP generalize to other variations is unknown, representing a potentially significant security gap. In the current study, we tested dogs (n = 11) previously trained to detect pure TATP with four variants: diacetone diperoxide (DADP), a homologue often created as a TATP byproduct, and three different clandestine TATP formulations designed to emulate those used by terrorists or insurgents. On average, dogs detected each untrained variant at rates equivalent to the trained TATP (ps > 0.07), with individual variability in first-trial alerts for some of the variants. Chemical analyses paralleled the canine results, showing distinct similarities and differences. For the TATP samples, the laboratory-grade was the purest sample tested and did not contain DADP or the TATP homologue that the three clandestine versions showed in their respective headspace profiles. The headspace results showed that each sample could be clearly identified as TATP, yet they showed recognizable differences due to their individual syntheses. These findings suggest that training on pure TATP may be effective for generalization to untrained variants. Further research is necessary to identify factors that influence individual variation in generalization between dogs, as well as other explosives.
{"title":"Generalization Across Acetone Peroxide Homemade Explosives by Detection Dogs","authors":"Lucia Lazarowski, A. Simon, Sarah Krichbaum, C. Angle, Melissa Singletary, Paul Waggoner, Kelly Van Arsdale, Jason Barrow","doi":"10.3389/frans.2021.797520","DOIUrl":"https://doi.org/10.3389/frans.2021.797520","url":null,"abstract":"Effective explosives detection requires dogs to generalize their response to untrained variations of targets that are related to those with which they were trained. Previous research suggests that dogs tend to be highly specific to their trained odors, and are sensitive to alterations in odor profiles. Triacetone triperoxide (TATP) is an increasingly popular homemade explosive due to the widespread accessibility of starting materials. The large variety of reagent sources and production approaches yields high variability in deployed formulations. Whether dogs trained with pure forms of TATP generalize to other variations is unknown, representing a potentially significant security gap. In the current study, we tested dogs (n = 11) previously trained to detect pure TATP with four variants: diacetone diperoxide (DADP), a homologue often created as a TATP byproduct, and three different clandestine TATP formulations designed to emulate those used by terrorists or insurgents. On average, dogs detected each untrained variant at rates equivalent to the trained TATP (ps > 0.07), with individual variability in first-trial alerts for some of the variants. Chemical analyses paralleled the canine results, showing distinct similarities and differences. For the TATP samples, the laboratory-grade was the purest sample tested and did not contain DADP or the TATP homologue that the three clandestine versions showed in their respective headspace profiles. The headspace results showed that each sample could be clearly identified as TATP, yet they showed recognizable differences due to their individual syntheses. These findings suggest that training on pure TATP may be effective for generalization to untrained variants. Further research is necessary to identify factors that influence individual variation in generalization between dogs, as well as other explosives.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48987045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.3389/frans.2021.709589
C. Bae, Yoori Im, Jong-hwan Lee, Choong-Shik Park, Miyoung Kim, H. Kwon, Boseon Kim, Hye-Ri Park, Chunggak Lee, I. Kim, Jeonghoon Kim
In this work, we used the health check-up data of more than 111,000 subjects for analysis, using only the data with all 35 variables entered. For the prediction of biological age, traditional statistical methods and four AI techniques (RF, XGB, SVR, and DNN), which are widely used recently, were simultaneously used to compare the predictive power. This study showed that AI models produced about 1.6 times stronger linear relationship on average than statistical models. In addition, the regression analysis on the predicted BA and CA revealed similar differences in terms of both the correlation coefficients (linear model: 0.831, polynomial model: 0.996, XGB model: 0.66, RF model: 0.927, SVR model: 0.787, DNN model: 0.998) and R 2 values. Through this work, we confirmed that AI techniques such as the DNN model outperformed traditional statistical methods in predicting biological age.
{"title":"Comparison of Biological Age Prediction Models Using Clinical Biomarkers Commonly Measured in Clinical Practice Settings: AI Techniques Vs. Traditional Statistical Methods","authors":"C. Bae, Yoori Im, Jong-hwan Lee, Choong-Shik Park, Miyoung Kim, H. Kwon, Boseon Kim, Hye-Ri Park, Chunggak Lee, I. Kim, Jeonghoon Kim","doi":"10.3389/frans.2021.709589","DOIUrl":"https://doi.org/10.3389/frans.2021.709589","url":null,"abstract":"In this work, we used the health check-up data of more than 111,000 subjects for analysis, using only the data with all 35 variables entered. For the prediction of biological age, traditional statistical methods and four AI techniques (RF, XGB, SVR, and DNN), which are widely used recently, were simultaneously used to compare the predictive power. This study showed that AI models produced about 1.6 times stronger linear relationship on average than statistical models. In addition, the regression analysis on the predicted BA and CA revealed similar differences in terms of both the correlation coefficients (linear model: 0.831, polynomial model: 0.996, XGB model: 0.66, RF model: 0.927, SVR model: 0.787, DNN model: 0.998) and R 2 values. Through this work, we confirmed that AI techniques such as the DNN model outperformed traditional statistical methods in predicting biological age.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43785690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-09DOI: 10.3389/frans.2021.822819
{"title":"Erratum: Grand Challenges in Analytical Science","authors":"","doi":"10.3389/frans.2021.822819","DOIUrl":"https://doi.org/10.3389/frans.2021.822819","url":null,"abstract":"","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45520447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-08DOI: 10.3389/frans.2021.729732
Adéline Paris, C. Duchesne, É. Poulin
Increasing raw material variability is challenging for many industries since it adversely impacts final product quality. Establishing multivariate specification regions for selecting incoming lot of raw materials is a key solution to mitigate this issue. Two data-driven approaches emerge from the literature for defining these specifications in the latent space of Projection to Latent Structure (PLS) models. The first is based on a direct mapping of good quality final product and associated lots of raw materials in the latent space, followed by selection of boundaries that minimize or best balance type I and II errors. The second rather defines specification regions by inverting the PLS model for each point lying on final product acceptance limits. The objective of this paper is to compare both methods to determine their advantages and drawbacks, and to assess their classification performance in presence of different levels of correlation between the quality attributes. The comparative analysis is performed using simulated raw materials and product quality data generated under multiple scenarios where product quality attributes have different degrees of collinearity. First, a simple case is proposed using one quality attribute to illustrate the methods. Then, the impact of collinearity is studied. It is shown that in most cases, correlation between the quality variable does not seem to influence classification performance except when the variables are highly correlated. A summary of the main advantages and disadvantages of both approaches is provided to guide the selection of the most appropriate approach for establishing multivariate specification regions for a given application.
{"title":"Establishing Multivariate Specification Regions for Incoming Raw Materials Using Projection to Latent Structure Models: Comparison Between Direct Mapping and Model Inversion","authors":"Adéline Paris, C. Duchesne, É. Poulin","doi":"10.3389/frans.2021.729732","DOIUrl":"https://doi.org/10.3389/frans.2021.729732","url":null,"abstract":"Increasing raw material variability is challenging for many industries since it adversely impacts final product quality. Establishing multivariate specification regions for selecting incoming lot of raw materials is a key solution to mitigate this issue. Two data-driven approaches emerge from the literature for defining these specifications in the latent space of Projection to Latent Structure (PLS) models. The first is based on a direct mapping of good quality final product and associated lots of raw materials in the latent space, followed by selection of boundaries that minimize or best balance type I and II errors. The second rather defines specification regions by inverting the PLS model for each point lying on final product acceptance limits. The objective of this paper is to compare both methods to determine their advantages and drawbacks, and to assess their classification performance in presence of different levels of correlation between the quality attributes. The comparative analysis is performed using simulated raw materials and product quality data generated under multiple scenarios where product quality attributes have different degrees of collinearity. First, a simple case is proposed using one quality attribute to illustrate the methods. Then, the impact of collinearity is studied. It is shown that in most cases, correlation between the quality variable does not seem to influence classification performance except when the variables are highly correlated. A summary of the main advantages and disadvantages of both approaches is provided to guide the selection of the most appropriate approach for establishing multivariate specification regions for a given application.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42562814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-18DOI: 10.3389/frans.2021.754447
D. Rutledge, J. Roger, M. Lesnoff
A tricky aspect in the use of all multivariate analysis methods is the choice of the number of Latent Variables to use in the model, whether in the case of exploratory methods such as Principal Components Analysis (PCA) or predictive methods such as Principal Components Regression (PCR), Partial Least Squares regression (PLS). For exploratory methods, we want to know which Latent Variables deserve to be selected for interpretation and which contain only noise. For predictive methods, we want to ensure that we include all the variability of interest for the prediction, without introducing variability that would lead to a reduction in the quality of the predictions for samples other than those used to create the multivariate model.
{"title":"Different Methods for Determining the Dimensionality of Multivariate Models","authors":"D. Rutledge, J. Roger, M. Lesnoff","doi":"10.3389/frans.2021.754447","DOIUrl":"https://doi.org/10.3389/frans.2021.754447","url":null,"abstract":"A tricky aspect in the use of all multivariate analysis methods is the choice of the number of Latent Variables to use in the model, whether in the case of exploratory methods such as Principal Components Analysis (PCA) or predictive methods such as Principal Components Regression (PCR), Partial Least Squares regression (PLS). For exploratory methods, we want to know which Latent Variables deserve to be selected for interpretation and which contain only noise. For predictive methods, we want to ensure that we include all the variability of interest for the prediction, without introducing variability that would lead to a reduction in the quality of the predictions for samples other than those used to create the multivariate model.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46036618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-24DOI: 10.3389/frans.2021.709748
E. Psillakis
In 2015, the United Nations (UN) adopted the 2030 Agenda with 17 Sustainable Development Goals (SDGs) to improve people’s lives and the natural world by 2030 (United Nations, 2015). The outbreak of Coronavirus disease 2019 (COVID-19) occurred in 2020, right at the beginning of the “decade of actions” aiming to cover implementation gaps on delivering SDGs. The pandemic affected the core focus of the SDGs by triggering an economic crisis of large proportions and restricting mobility and migration (Shulla et al., 2021). COVID-19 and the ensuing implications have demonstrated the fragility of SDGs and their interconnections. As the world recovers from this pandemic, the importance of environmental health and resilience as a critical complement to public health is underscored. Rebuilding requires countries to succeed in transitioning to green economies and protection against future disruption from global stressors. From an environmental perspective, the pandemic created both problems and opportunities, each time emphasizing the importance of delivering the SDGs. They also highlighted the need for a multidisciplinary system-thinking approach to explore interconnections between the environment, wildlife, and humans. The COVID-19 pandemic and the 2030 Agenda for Sustainable Development are a dual grand challenge that can only be addressed by everyone as part of a transition to an inclusive and sustainable future. On this basis, environmental analysis is critical in understanding and mitigating current environmental changes at a global level. In a less physically-connected world and an unstable setting where new types of pollution rise fast, environmental analysis must find the pace and tackle this dual grand challenge. Admittedly, this unprecedented time represents an opportunity for environmental analysis to examine the impact of human activities on the natural world and gather information that will protect human health, biodiversity efforts, and help towards combating climate change.
2015年，联合国通过了《2030年议程》，其中包含17项可持续发展目标，旨在到2030年改善人们的生活和自然世界（联合国，2015年）。2019冠状病毒病（新冠肺炎）的爆发发生在2020年，当时正值旨在弥补实现可持续发展目标的实施缺口的“行动十年”的开始。疫情引发了大规模的经济危机，限制了流动和移民，从而影响了可持续发展目标的核心焦点（Shulla et al.，2021）。新冠肺炎及其随之而来的影响表明了可持续发展目标及其相互关联的脆弱性。随着世界从这场疫情中复苏，环境健康和复原力作为公共卫生的关键补充的重要性得到了强调。重建需要各国成功地向绿色经济转型，并保护各国免受未来全球压力的干扰。从环境角度来看，疫情既带来了问题，也带来了机遇，每次都强调了实现可持续发展目标的重要性。他们还强调，需要采用多学科的系统思维方法来探索环境、野生动物和人类之间的相互联系。新冠肺炎疫情和2030年可持续发展议程是一个双重的巨大挑战，只有作为向包容性和可持续未来过渡的一部分，每个人才能应对。在此基础上，环境分析对于理解和缓解当前全球环境变化至关重要。在一个物理联系较少的世界和一个新类型污染快速上升的不稳定环境中，环境分析必须找到速度并应对这一双重挑战。诚然，这一前所未有的时刻为环境分析提供了一个机会，以研究人类活动对自然世界的影响，并收集信息，保护人类健康、生物多样性努力，并有助于应对气候变化。
{"title":"Environmental Analysis and the Dual Grand Challenge of COVID-19 and Sustainable Development","authors":"E. Psillakis","doi":"10.3389/frans.2021.709748","DOIUrl":"https://doi.org/10.3389/frans.2021.709748","url":null,"abstract":"In 2015, the United Nations (UN) adopted the 2030 Agenda with 17 Sustainable Development Goals (SDGs) to improve people’s lives and the natural world by 2030 (United Nations, 2015). The outbreak of Coronavirus disease 2019 (COVID-19) occurred in 2020, right at the beginning of the “decade of actions” aiming to cover implementation gaps on delivering SDGs. The pandemic affected the core focus of the SDGs by triggering an economic crisis of large proportions and restricting mobility and migration (Shulla et al., 2021). COVID-19 and the ensuing implications have demonstrated the fragility of SDGs and their interconnections. As the world recovers from this pandemic, the importance of environmental health and resilience as a critical complement to public health is underscored. Rebuilding requires countries to succeed in transitioning to green economies and protection against future disruption from global stressors. From an environmental perspective, the pandemic created both problems and opportunities, each time emphasizing the importance of delivering the SDGs. They also highlighted the need for a multidisciplinary system-thinking approach to explore interconnections between the environment, wildlife, and humans. The COVID-19 pandemic and the 2030 Agenda for Sustainable Development are a dual grand challenge that can only be addressed by everyone as part of a transition to an inclusive and sustainable future. On this basis, environmental analysis is critical in understanding and mitigating current environmental changes at a global level. In a less physically-connected world and an unstable setting where new types of pollution rise fast, environmental analysis must find the pace and tackle this dual grand challenge. Admittedly, this unprecedented time represents an opportunity for environmental analysis to examine the impact of human activities on the natural world and gather information that will protect human health, biodiversity efforts, and help towards combating climate change.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46115393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-23DOI: 10.3389/frans.2021.721657
Tim Offermans, Lynn Hendriks, Geert H. van Kollenburg, Ewa Szymańska, L. Buydens, J. Jansen
Understanding how different units of an industrial production plant are operationally related is key to improving production quality and sustainability. Data science has proven indispensable in obtaining such understanding from vast amounts of historical process data. Path modelling is a valuable statistical tool to obtain such information from historical production data. Investigating how relationships within a process are affected by multiple production conditions and their interactions can however provide an even deeper understanding of the plant’s daily operation. We therefore propose conditional path modelling as an approach to obtain such improved understanding, demonstrated for a milk protein powder production plant. For this plant we studied how the relationships between different production units and steps are dependent on factors like production line, different seasons and product quality range. We show how the interaction of such factors can be quantified and interpreted in context of daily plant operation. This analysis revealed an augmented insight into the process that can be readily placed in the context of the plant’s structure and behavior. Such insights can be vital to identify and improve upon shortcomings in current plant-wide monitoring and control routines.
{"title":"Improved Understanding of Industrial Process Relationships Through Conditional Path Modelling With Process PLS","authors":"Tim Offermans, Lynn Hendriks, Geert H. van Kollenburg, Ewa Szymańska, L. Buydens, J. Jansen","doi":"10.3389/frans.2021.721657","DOIUrl":"https://doi.org/10.3389/frans.2021.721657","url":null,"abstract":"Understanding how different units of an industrial production plant are operationally related is key to improving production quality and sustainability. Data science has proven indispensable in obtaining such understanding from vast amounts of historical process data. Path modelling is a valuable statistical tool to obtain such information from historical production data. Investigating how relationships within a process are affected by multiple production conditions and their interactions can however provide an even deeper understanding of the plant’s daily operation. We therefore propose conditional path modelling as an approach to obtain such improved understanding, demonstrated for a milk protein powder production plant. For this plant we studied how the relationships between different production units and steps are dependent on factors like production line, different seasons and product quality range. We show how the interaction of such factors can be quantified and interpreted in context of daily plant operation. This analysis revealed an augmented insight into the process that can be readily placed in the context of the plant’s structure and behavior. Such insights can be vital to identify and improve upon shortcomings in current plant-wide monitoring and control routines.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42635088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-11DOI: 10.3389/frans.2021.701891
Samineh Mesbah, Bonnie E. Legg Ditterline, Siqi Wang, Samuel Wu, J. Weir, J. Wecht, G. Forrest, S. Harkema, B. Ugiliweneza
Profound dysfunction of the cardiovascular system occurs after spinal cord injury (SCI), which is a leading cause of mortality in this population. Most individuals with chronic SCI experience transient episodes of hypotensive and hypertensive blood pressure in response to daily life activities. There are currently limited tools available to evaluate the stability of blood pressure with respect to a reference range. The aim of this study was to develop a clinimetric toolset for accurately quantifying stability of the blood pressure measurements and taking into consideration the complex dynamics of blood pressure variability among individuals with SCI. The proposed toolset is based on distribution of the blood pressure data points within and outside of the clinically recommended range. This toolset consists of six outcome measures including 1) total deviation of the 90% of the blood pressure data points from the center of the target range (115 mmHg); 2) The area under the cumulative distribution curve starting from the percentage of blood pressure measurements within the range, and the percentage of values within symmetrically expanded boundary ranges, above and below the target range; 3) the slope of the cumulative distribution curve that is calculated by fitting an exponential cumulative distribution function and the natural logarithm of its rate parameter; 4) its x- and 5) y-axis intercepts; and 6) the fitting error. These outcome measures were validated using blood pressure measurements recorded during cardiovascular perturbation tests and prolonged monitoring period from individuals with chronic SCI and non-injured controls. The statistical analysis based on the effect size and intra-class correlation coefficient, demonstrated that the proposed outcome measures fulfill reliability, responsiveness and discrimination criteria. The novel methodology proposed in this study is reliable and effective for evaluating the stability of continuous blood pressure in individuals with chronic spinal cord injury.
{"title":"Novel Clinimetric Toolset to Quantify the Stability of Blood Pressure and Its Application to Evaluate Cardiovascular Function After Spinal Cord Injury","authors":"Samineh Mesbah, Bonnie E. Legg Ditterline, Siqi Wang, Samuel Wu, J. Weir, J. Wecht, G. Forrest, S. Harkema, B. Ugiliweneza","doi":"10.3389/frans.2021.701891","DOIUrl":"https://doi.org/10.3389/frans.2021.701891","url":null,"abstract":"Profound dysfunction of the cardiovascular system occurs after spinal cord injury (SCI), which is a leading cause of mortality in this population. Most individuals with chronic SCI experience transient episodes of hypotensive and hypertensive blood pressure in response to daily life activities. There are currently limited tools available to evaluate the stability of blood pressure with respect to a reference range. The aim of this study was to develop a clinimetric toolset for accurately quantifying stability of the blood pressure measurements and taking into consideration the complex dynamics of blood pressure variability among individuals with SCI. The proposed toolset is based on distribution of the blood pressure data points within and outside of the clinically recommended range. This toolset consists of six outcome measures including 1) total deviation of the 90% of the blood pressure data points from the center of the target range (115 mmHg); 2) The area under the cumulative distribution curve starting from the percentage of blood pressure measurements within the range, and the percentage of values within symmetrically expanded boundary ranges, above and below the target range; 3) the slope of the cumulative distribution curve that is calculated by fitting an exponential cumulative distribution function and the natural logarithm of its rate parameter; 4) its x- and 5) y-axis intercepts; and 6) the fitting error. These outcome measures were validated using blood pressure measurements recorded during cardiovascular perturbation tests and prolonged monitoring period from individuals with chronic SCI and non-injured controls. The statistical analysis based on the effect size and intra-class correlation coefficient, demonstrated that the proposed outcome measures fulfill reliability, responsiveness and discrimination criteria. The novel methodology proposed in this study is reliable and effective for evaluating the stability of continuous blood pressure in individuals with chronic spinal cord injury.","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48151402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-08DOI: 10.3389/frans.2021.725070
Huan‐Tsung Chang
Analytical science is related to the development and application of techniques for detection of analytes, characterization of composites, analysis of samples, and monitoring of chemical and biochemical systems. It has played significant roles in the studies of physical, life, material, environmental, food, medical, and sustainability sciences. In the recent years, we have witnessed various techniques for single-cell analysis, screening of circulating tumor cells, viral diagnostics, detection of radioactive substances and explosive compounds, screening and identification of abused drugs, tracking contaminants and chemicals to ensure water quality and food safety, the study of omics, and characterization of synthetic polymers and nanomaterials. For example, various analytical technique, such as reverse transcription polymerase chain reaction (RT-qPCR), loopmediated amplification (LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR) assays have been applied for sensitive and specific detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19 disease (Huang et al., 2020; Wang et al., 2021). LAMP is attractive because there is no need for temperature cycling and it provides extremely high sensitivity (down to fM) with fluorescent, electrochemical or electroluminescent signal transduction. To minimize the threat of pandemics, vaccines against pathogens such as Zika virus and SARS-CoV-2 have been developed. For quality control and safety of vaccines, many analytical techniques such as sampling, purification, high performance liquid chromatography (HPLC), and gene expression profiling are needed. To meet the requirement of various studies and needs of society, analytical techniques must be in general sensitive, selective, fast, accurate, and simple. The instruments must be cost effective, easy in operation and maintenance, compact (portable ideally), suitable for the analysis of various samples, and available to provide wide dynamic ranges for quantitation of analytes. Analytical techniques are chosen mainly based on the purpose of the study, equipment available, properties of the analyte, and nature of the sample. For example, optical techniques provide high temporal and spatial resolution are commonly applied for cell tracking. To improve reproducibility, efficiency, and accuracy of the cell studies, the sequential cell images are then subjected to computational object tracking to track cells events over time and to obtain signals from each object. When in-vivo monitoring of drug function is the aim, nonconstructive optical techniques allowing deep penetration from the surface is usually carried out. In this case, materials can absorb light and generate optical signals like fluorescence in the infrared (IR) or near IR (NIR) region are suitable. For environmental analysis and forensics, portable and low-cost on-field analytical instruments are ideal. To provide high specificity and sensitivity
分析科学涉及分析物检测、复合材料表征、样品分析以及化学和生化系统监测等技术的发展和应用。它在物理、生命、材料、环境、食品、医学和可持续性科学的研究中发挥了重要作用。近年来，我们见证了单细胞分析、循环肿瘤细胞筛选、病毒诊断、放射性物质和爆炸性化合物检测、滥用药物筛选和鉴定、追踪污染物和化学物质以确保水质和食品安全、组学研究、合成聚合物和纳米材料表征等各种技术的发展。例如，各种分析技术，如逆转录聚合酶链反应(RT-qPCR)、环介导扩增(LAMP)和聚集规律间隔短回文重复(CRISPR)检测已被用于敏感和特异性检测引起COVID-19疾病的严重急性呼吸综合征冠状病毒2 (SARS-CoV-2) (Huang et al.， 2020;Wang等人，2021)。LAMP之所以具有吸引力，是因为它不需要温度循环，而且它具有荧光、电化学或电致发光信号转导的极高灵敏度(低至fM)。为了尽量减少大流行的威胁，已经开发了针对寨卡病毒和SARS-CoV-2等病原体的疫苗。为了控制疫苗的质量和安全性，需要多种分析技术，如采样、纯化、高效液相色谱(HPLC)和基因表达谱。为了满足各种研究和社会的需要，分析技术必须具有灵敏、选择性、快速、准确和简单的特点。这些仪器必须具有成本效益，易于操作和维护，结构紧凑(理想情况下是便携式)，适合分析各种样品，并可为分析物的定量提供宽动态范围。分析技术的选择主要基于研究的目的、可用的设备、分析物的性质和样品的性质。例如，光学技术提供高时间和空间分辨率，通常用于细胞跟踪。为了提高细胞研究的可重复性、效率和准确性，然后对连续的细胞图像进行计算对象跟踪，以随时间跟踪细胞事件，并从每个对象获取信号。当体内监测药物功能为目的时，通常采用允许从表面深入渗透的非建设性光学技术。在这种情况下，可以吸收光并在红外(IR)或近红外(NIR)区域产生荧光等光信号的材料是合适的。对于环境分析和取证，便携式和低成本的现场分析仪器是理想的。为了为各种分析物的定量提供高特异性和灵敏度，具有高电化学活性和电导率的纳米材料在开发电化学传感系统中越来越受欢迎(Wongkaew等，2019)。许多基于纳米材料的功能电极在各个领域显示出了它们的潜力;例如，燃料电池，从受污染的水中去除污染物，以及空气中有毒化学物质的降解。纳米或微型设备在生物分析中越来越受欢迎，其具有样本量小、试剂和溶剂消耗极低、分辨率高的优点，编辑和评审:Elefteria Psillakis，克里特岛技术大学，希腊
{"title":"Grand Challenges in Analytical Science","authors":"Huan‐Tsung Chang","doi":"10.3389/frans.2021.725070","DOIUrl":"https://doi.org/10.3389/frans.2021.725070","url":null,"abstract":"Analytical science is related to the development and application of techniques for detection of analytes, characterization of composites, analysis of samples, and monitoring of chemical and biochemical systems. It has played significant roles in the studies of physical, life, material, environmental, food, medical, and sustainability sciences. In the recent years, we have witnessed various techniques for single-cell analysis, screening of circulating tumor cells, viral diagnostics, detection of radioactive substances and explosive compounds, screening and identification of abused drugs, tracking contaminants and chemicals to ensure water quality and food safety, the study of omics, and characterization of synthetic polymers and nanomaterials. For example, various analytical technique, such as reverse transcription polymerase chain reaction (RT-qPCR), loopmediated amplification (LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR) assays have been applied for sensitive and specific detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes COVID-19 disease (Huang et al., 2020; Wang et al., 2021). LAMP is attractive because there is no need for temperature cycling and it provides extremely high sensitivity (down to fM) with fluorescent, electrochemical or electroluminescent signal transduction. To minimize the threat of pandemics, vaccines against pathogens such as Zika virus and SARS-CoV-2 have been developed. For quality control and safety of vaccines, many analytical techniques such as sampling, purification, high performance liquid chromatography (HPLC), and gene expression profiling are needed. To meet the requirement of various studies and needs of society, analytical techniques must be in general sensitive, selective, fast, accurate, and simple. The instruments must be cost effective, easy in operation and maintenance, compact (portable ideally), suitable for the analysis of various samples, and available to provide wide dynamic ranges for quantitation of analytes. Analytical techniques are chosen mainly based on the purpose of the study, equipment available, properties of the analyte, and nature of the sample. For example, optical techniques provide high temporal and spatial resolution are commonly applied for cell tracking. To improve reproducibility, efficiency, and accuracy of the cell studies, the sequential cell images are then subjected to computational object tracking to track cells events over time and to obtain signals from each object. When in-vivo monitoring of drug function is the aim, nonconstructive optical techniques allowing deep penetration from the surface is usually carried out. In this case, materials can absorb light and generate optical signals like fluorescence in the infrared (IR) or near IR (NIR) region are suitable. For environmental analysis and forensics, portable and low-cost on-field analytical instruments are ideal. To provide high specificity and sensitivity","PeriodicalId":73063,"journal":{"name":"Frontiers in analytical science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41681798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-02DOI: 10.3389/frans.2021.700386
Q. Cheng
The importance of analytical sciences and biosensing to medical diagnosis has been well recognized by those involved in the field; the recent global pandemic due to severe acute respiratory syndrome-associated coronavirus 2 (SARS-CoV-2) has further elevated the topic to paramount worldwide prominence and urgency (Lippi et al., 2020). While the pandemic may be contained in the near future due to the heroic efforts of medical staff and biotechnologists around the world, the research interest in analytical sciences toward more efficient medical diagnosis will undoubtedly remain for the foreseeable future. Aside from innovative schemes that offer new angles for detection and quantification, evaluations and reevaluations of the state and efficacy of analytical sensing are also required when applied to medical samples. For a broader conversation of the directions of research, it is important to assess the state-of-the-art and significant trends across the field. There have been exciting technical developments in recent years that push forward the accuracy and sensitivity of techniques, expand the scope of analyses beyond simple biomarkers, and improve the accessibility and applicability of analytical methods. In addition, clinical data of increasing depth and complexity are gathered at an extraordinary pace in recent years due to “Big Data” movement in healthcare. Therefore, one of the most prominent trends in analytical science appears to be the application of artificial intelligence and machine learning models to correlate sensed or imaged markers from patients to diagnosis (Rajkomar et al., 2019). Recent examples include an artificial intelligence system that outperformed doctors by 11% in diagnosing breast cancers (McKinney et al., 2020), and a study of machine learning models that used imaging biomarkers and predictive models for rapid diagnosis of COVID-19 (Wynants et al., 2020). This dense, complex approach toward information accumulation also requires a scale-up in the sophistication of models by which the information is treated so that relevant outcomes and knowledge can be obtained. Clearly, the need for technical advances in medical diagnosis is ever-present, and this is manifested in the current pandemic. Mature technologies such as PCR and immunoassays continue to provide reliable tests for the rapidly spreading disease, while in the meantime we have seen a wave of new approaches rolling out of unconventional sectors that are shaping the course of diagnostic development (mass spectrometry, 3D printing, and CRISPR-Cas12, to name a few). The challenges in this field also suggest a range of opportunities, which we aim to describe in this Article. In the interest of brevity, we will organize the discussion into analysis targets, technological developments, and data processing.
分析科学和生物传感对医学诊断的重要性已得到该领域相关人员的充分认识;最近由严重急性呼吸综合征相关冠状病毒2 (SARS-CoV-2)引起的全球大流行进一步将这一主题提升到全球最重要的地位和紧迫性(Lippi等，2020)。虽然由于世界各地医务人员和生物技术专家的英勇努力，疫情可能在不久的将来得到控制，但在可预见的未来，对更有效的医疗诊断的分析科学的研究兴趣无疑将继续存在。除了为检测和量化提供新角度的创新方案外，在应用于医学样品时，还需要对分析传感的状态和功效进行评估和重新评估。为了更广泛地讨论研究方向，评估整个领域的最新技术和重要趋势是很重要的。近年来，令人兴奋的技术发展推动了技术的准确性和敏感性，扩大了分析范围，超越了简单的生物标志物，提高了分析方法的可及性和适用性。此外，近年来，由于医疗保健领域的“大数据”运动，临床数据的深度和复杂性都在以惊人的速度增长。因此，分析科学中最突出的趋势之一似乎是应用人工智能和机器学习模型将患者的感知或成像标记与诊断相关联(Rajkomar et al.， 2019)。最近的例子包括人工智能系统在诊断乳腺癌方面的表现比医生高出11% (McKinney等人，2020)，以及一项使用成像生物标志物和预测模型快速诊断COVID-19的机器学习模型研究(Wynants等人，2020)。这种密集、复杂的信息积累方法还需要在处理信息的模型的复杂程度上扩大规模，以便获得相关的结果和知识。显然，医疗诊断技术进步的必要性始终存在，这在当前的大流行病中得到了体现。PCR和免疫测定等成熟技术继续为这种快速传播的疾病提供可靠的检测，与此同时，我们看到一波新方法从非常规领域推出，这些新方法正在塑造诊断发展的进程(质谱法、3D打印和CRISPR-Cas12等)。这一领域的挑战也暗示着一系列的机遇，我们将在本文中描述这些机遇。为了简洁起见，我们将把讨论组织成分析目标、技术发展和数据处理。
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