Due to the complex circuitry and plethora of cell types involved in somatosensation, it is becoming increasingly important to be able to observe cellular activity at the population level. In addition, since cells rely...Due to the complex circuitry and plethora of cell types involved in somatosensation, it is becoming increasingly important to be able to observe cellular activity at the population level. In addition, since cells rely on an intricate variety of extracellular factors, it is important to strive to maintain the physiological environment. Many electrophysiological techniques require the implementation of artificially-produced physiological environments and it can be difficult to assess the activity of many cells simultane- ously. Moreover, imaging Ca^2＋ transients using Ca^2＋- sensitive dyes often requires in vitro preparations or in vivo injections, which can lead to variable expression levels. With the development of more sensitive geneticallyencoded Ca^2＋ indicators （GECIs） it is now possible to observe changes in Ca^2＋ transients in large populations of cells at the same time. Recently, groups have used a GECI called GCaMP to address fundamental questions in somatosensation. Researchers can now induce GCaMP expression in the mouse genome using viral or gene knock- in approaches and observe the activity of populations of cells in the pain pathway such as dorsal root ganglia （DRG）, spinal neurons, or glia. This approach can be used in vivo and thus maintains the organism＇s biological integrity. The implementation of GCaMP imaging has led to many advances in our understanding of somatosensation. Here, we review the current findings in pain research using GCaMP imaging as well as discussing potential method- ological considerations.展开更多
Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic.Most of the current studies utilize travel volume per day as the critical indicato...Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic.Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders,which however may not accurately present the actual impacted dates.The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns.Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order,the methodology in this study investigates the within-day timedependent travel speed as time series,and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group.Using the state-wide travel speed data in Alabama,these study measures dissimilarities among within-day travel speed time series.By incorporating the dissimilarities/distance matrix,various agglomerative hierarchical clustering(AHC)methods(average,complete,Ward’s)are tested to conduct proper unsupervised classification.The Ward’s AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order.The results further show that a new travel speed pattern appears at the end of stay-at-home order,which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern,which leads to a conclusion that a’new normal’within-day travel pattern emerges.展开更多
基金supported by grants from the National Institutes of Health to X.D.(RO1DE022750 and ROINS054791)
文摘Due to the complex circuitry and plethora of cell types involved in somatosensation, it is becoming increasingly important to be able to observe cellular activity at the population level. In addition, since cells rely on an intricate variety of extracellular factors, it is important to strive to maintain the physiological environment. Many electrophysiological techniques require the implementation of artificially-produced physiological environments and it can be difficult to assess the activity of many cells simultane- ously. Moreover, imaging Ca^2＋ transients using Ca^2＋- sensitive dyes often requires in vitro preparations or in vivo injections, which can lead to variable expression levels. With the development of more sensitive geneticallyencoded Ca^2＋ indicators （GECIs） it is now possible to observe changes in Ca^2＋ transients in large populations of cells at the same time. Recently, groups have used a GECI called GCaMP to address fundamental questions in somatosensation. Researchers can now induce GCaMP expression in the mouse genome using viral or gene knock- in approaches and observe the activity of populations of cells in the pain pathway such as dorsal root ganglia （DRG）, spinal neurons, or glia. This approach can be used in vivo and thus maintains the organism＇s biological integrity. The implementation of GCaMP imaging has led to many advances in our understanding of somatosensation. Here, we review the current findings in pain research using GCaMP imaging as well as discussing potential method- ological considerations.
基金supported by New Faculty Award from UAH’s Office of the Vice President for Research and Economic Development.
文摘Recent comparative studies on mobility patterns are emerging to describe the changes in mobility patterns due to the COVID-19 pandemic.Most of the current studies utilize travel volume per day as the critical indicator and identify the impacted period by the dates of governmental lockdown or stay-at-home orders,which however may not accurately present the actual impacted dates.The objective of this study is to provide an alternative perspective to identify the normal and pandemic-influenced daily traffic patterns.Instead of only using traffic volumes per day or assuming the impacted travel pattern began with the stay-at-home order,the methodology in this study investigates the within-day timedependent travel speed as time series,and then applies dynamic time warping algorithm and hierarchical clustering unsupervised classification methods to classify days into various groups without assuming a start date for any group.Using the state-wide travel speed data in Alabama,these study measures dissimilarities among within-day travel speed time series.By incorporating the dissimilarities/distance matrix,various agglomerative hierarchical clustering(AHC)methods(average,complete,Ward’s)are tested to conduct proper unsupervised classification.The Ward’s AHC classification results show that within-day travel speed pattern in Alabama shifted more than two weeks before the issuance of the State stay-at-home order.The results further show that a new travel speed pattern appears at the end of stay-at-home order,which is different from either the normal pattern before the pandemic or the initial pandemic-influenced pattern,which leads to a conclusion that a’new normal’within-day travel pattern emerges.
