*3.1. Inorganic Soil Nitrogen Production*

For all landscape patches, median whole core net mineralization and nitrification were highest in the Agr soils, with median rates of 1.1 mg and 0.7 mg N kg/d, respectively (Figure 1; Table S2, Supplementary Materials). Whole core net mineralization and nitrification were next highest in the PP soils (median 0.8 and 0.5 mg N kg/d, respectively). The relatively high inorganic N production rate results for the PP patch could be related to the ability of perennial peanut to fix atmospheric N in the soil in the presence of rhizobia bacteria [25]. In the process of N fixation, atmospheric dinitrogen is cleaved and enzymatically catalyzed to produce two molecules of ammonia, leading to subsidies of NH4 + to the soil. When soil NH4 + levels are initially low, net mineralization is slow because inorganic N is preferentially incorporated into microbial biomass [26]. On the other hand, increasing levels of soil NH4 + have been shown to increase N mineralization rates in soils [26]. In this way, it is likely that N fixation by the perennial peanut led to high initial levels of soil NH4 +, which in turn promoted the high N mineralization rates seen in the PP soil. We are aware of no studies on the fate of fixed N when perennial peanut is used as a turfgrass alternative in urban landscapes. However, as discussed below, exchangeable NO3 − was lower in the PP soils than all other landscape patches, indicating that even though the PP soils had high levels of NO3 − production (nitrification), they were not maintaining high NO3 − levels. Potential fates of the produced NO3 − to explain its loss in the PP soils include denitrification, plant uptake, and leaching. The well-oxygenated and low organic matter conditions of our sandy soils make denitrification unlikely, since denitrification requires oxygen-limited environments and an organic carbon source [27]. We recommend future studies to investigate whether the inorganic N produced in soils under perennial peanut is readily lost via leaching, especially in sandy soils where NO3 − mobility would be high. While perennial peanut offers water-saving advantages over traditional turfgrass [28], it may come with the tradeoff of greater inorganic N leaching potential, though this has not been studied yet for urban soils.

**Figure 1.** Net N mineralization (**a**) and nitrification (**b**) rates in landscape patches. Values based on whole-core basis. Agr; remnant agricultural field, TfGr; turfgrass, PP; perennial peanut, OMx; patches with a mixture of ornamental grasses, OGa; patch with gamma grass, MulP; patch with pine needle mulch, MulC; patch with pine bark nuggets.

After Agr and PP, the TfGr soils had the third highest median rates of net N mineralization and nitrification (Figure 1; Table S2). There were likely fertilizer subsidies of inorganic N in both the Agr and TfGr patches. As with the PP soils, relatively high initial levels of inorganic N for Agr and TfGr probably led to higher inorganic N production rates during the soil incubations when compared to non-perennial peanut turfgrass alternatives (OMx, Oga, MulP, and MulC patches) [26]. For all landscape patches except the mulched areas (MulP and MulC), regression analysis revealed a significant linear relationship between nitrification and net mineralization on a whole-core basis (Figure 2). This is not surprising since nitrifying bacteria need ammonia as an energy source thus that nitrification generally increases with increasing ammonia levels in soils [27]. Identifying the underlying mechanism responsible for the lack of this relationship under the mulched landscape areas was beyond the scope of this paper, but factors that may inhibit nitrification include soil C:N ratios above ~22, acid soil pH conditions, and low oxygen availability [15]. We observed that the MulP landscape patch had the highest C:N among all landscape types (~22–23, Table S1), possibly indicating suppressed nitrification and a relatively less active N cycling microbial community in the MulP soils.

**Figure 2.** Regressions of nitrification against net N mineralization in the landscape patches, on a whole-core basis. Agr; remnant agricultural field, TfGr; turfgrass, PP; perennial peanut, OMx; patch with a mixture of ornamental grasses, OGa; patch with gamma grass, MulP; patch with pine needle mulch, MulC; patch with pine bark nuggets.

#### *3.2. Exchangeable Inorganic Soil Nitrogen*

Among the non-agricultural soils (all patches except Agr), the PP soils contained the highest median KCl-exchangeable NH4 + (1.4 mg/kg) but the lowest KCl-exchangeable NO3 − (0.4 mg/kg) on a whole-core basis (Figure 3). As discussed above, this relatively low exchangeable NO3 − pool for the PP soils indicates that N losses via leaching may be taking place. While the use of perennial peanut for urban landscapes has not been studied in terms of its potential for increased N leaching, several studies have looked at this for agricultural settings where perennial peanut is used for forage crops or as a cover crop. Woodard et al. [29] observed that a forage system with perennial peanut leached more N than a comparable one with bermudagrass. In that study, the authors concluded that perennial peanut might pose threats to groundwater quality under agricultural systems because of its apparent role in subsidizing high NO3 − levels in the soil.

**Figure 3.** Exchangeable soil ammonium (**a**) and nitrate (**b**) in the landscape patches. Values are on a whole-core basis. Agr; remnant agricultural field, TfGr; turfgrass, PP; perennial peanut, OMx; patch with a mixture of ornamental grasses, OGa; patch with gamma grass, MulP; patches with pine needle mulch, MulC; patch with pine bark nuggets.

The mulched landscape patches (MulP and MulC), for which vegetation was absent, contained higher mean pools of exchangeable NH4 + and NO3 − than the turfgrass and ornamental patches, though there was considerable spread in the data (Figure 3). It is likely that the mulched landscapes had higher mean exchangeable inorganic N due to the lack of a plant sink to remove inorganic N from the soils [18]. It is also likely that the mulches contained their own pools of N, which leached from the mulch and added to soil N pools [30]. In a study of several urban landscape alternatives, Loper et al. [30] observed that organic soil amendments such as composts could increase inorganic N leaching when compared to soils without amendments. In that study, the authors recommended that landowners did not apply nutrients to mulched areas and that landscape managers recognize that different patches of the same landscape may have different management needs.

In general, exchangeable NH4 + was highest in the surface 0–15 cm for all soils, while NO3 − was generally higher with soil depth, perhaps indicating a tendency for NO3 − to move downward in the soil profiles that are predominantly sand (Figure 4). At the 30–52 cm soil depth, exchangeable NO3 − was higher in 4 out of 6 non-agricultural soils than it was in the Agr soil, indicating that the urban landscape patches may pose a greater threat of NO3 − leaching than the agricultural soil (Figure 4). Turfgrass N fertilization has been found to contribute to the leaching of NO3 − in Florida's sandy soils. For example, Shaddox and Surtain [31] found that applied N in Bermuda grass turf leached between 8% and 12% on a 10% slope. Notably, the NO3 − concentrations in the 30–52 cm depth for the mixed ornamental (OMx) and the gamma grass patch (OGa) were in sharp contrast to each other, with much more NO3 − at depth for the mixed ornamental patch than for the gamma grass patch. Gamma grass is one of the most deeply rooted ornamental grasses, with root depth up to 1.8 m [32], and its deep rooting may be contributing to N uptake deep in the soil profile.

**Figure 4.** Exchangeable soil ammonium (**a**) and nitrate (**b**) in each landscape patch by soil depth. Agr; remnant agricultural field, TfGr; turfgrass, PP; perennial peanut, OMx; patch with a mixture of ornamental grasses, OGa; patches with gamma grass, MulP; patch with pine needle mulch, MulC; patch with pine bark nuggets.

#### **4. Management Implications and Future Research Needs**

It is beyond the scope of this paper to make recommendations about which landscape patch is preferable from a N availability or groundwater protection standpoint, as our main intent was to only investigate fine-scale variability in N production in a mixedvegetation urban landscape. However, some key takeaways are manifest in our results. The first of these is that inorganic N availability and production were, in fact, highly variable among landscape patches at scales of a few meters, in support of our hypothesis. This finding supports the habitat structure framework of urban soil ecology presented by Bryne [19], who argued that the high spatial heterogeneity of human activities in urban landscapes leads to a large range of soil properties at often small scales. The presence of soil amendments such as mulches or plants such as perennial peanuts that can subsidize soil N pools is just two ways that humans influence urban soil properties. We have shown here that inorganic N production is variable in a single urban landscape and have presented some discussion as to why this may be the case for our specific landscape patch types. At a minimum, we argue that urban landscapes should not be broadly managed but that landscape managers should recognize that the nutrient needs of patches within a single landscape may be different and that the nutrient fates in soil under those patches may also be different. This has important implications for how urban soil properties are included in models that aim to predict nutrient transport from urban landscapes. Very likely, we need spatially finer-scale investigations of nutrient fate and transport in urban landscapes, especially those with mixed vegetation or heterogeneous human influences. Too often, urban landscapes are assumed to be covered with turfgrass only, and other possible land covers need to be more fully considered by those who aim to quantify the effects of urbanization on the environment.

A second key takeaway of this work is the implication related to potential N losses by leaching from certain landscape patches. The high nitrification potential of perennial peanut seen here suggests that this turfgrass alternative may produce NO3 − that can be readily leached from sandy soils. However, our sample size was small, and similar work should be conducted in the future to expand the temporal and spatial scope of this work. In particular, we recommend studying N leaching under perennial peanut when it is used as an urban groundcover. We also recommend future work to better understand the mechanisms that drive the differences in potential N losses we observed among landscape patches. Some of the potential drivers include irrigation status, soil pH, moisture content, organic matter content, and variations in the soil microbial community. Finally, because urban landscapes are tied to human activities, we recognize that sociocultural variables may drive differences in soil properties as well [33,34]. These sociocultural variables may include the choice of plants that are used in ornamental beds. For example, we observed

that the two ornamental patches had very different levels of inorganic N at the greatest soil depth, likely due to greater uptake of N at depth by the deeply rooted gamma grass. This human choice as to which plants to include in a landscape may therefore impact N losses by leaching through the soil profile.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/nitrogen3010009/s1, Table S1, Basic soil physical/chemical properties for landscape patches used in this study; Table S2, Raw data showing nitrification, mineralization, and exchangeable inorganic N for all landscape patches and soil depths.

**Author Contributions:** Conceptualization, M.G.L. and J.B.; methodology, M.G.L.; formal analysis, J.B. and M.G.L.; investigation, J.B.; resources, M.G.L.; data curation, J.B.; writing—original draft preparation, J.B.; writing—review and editing, M.G.L.; visualization, J.B.; supervision, M.G.L.; project administration, M.G.L.; funding acquisition, M.G.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded in part by the Center for Land Use Efficiency at the University of Florida.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data used in this manuscript is available in Table S2.

**Conflicts of Interest:** The authors declare no conflict of interest.


Preface

The colored pigments of berries, blackcurrants, black carrot, red cabbage, purple potato, and other types of colored foods are potential functional foods that have been consumed as chemopreventives and as strong dietary antioxidants for many diseases. Flavonoids are the most commonly and abundantly available natural coloring compounds in many foods, herbs, and plants.

This book discusses the nature and role of these compounds by studying the molecular mechanism of flavonoids using fluorescence spectroscopy and computational tools. This book also addresses natural vs. synthetic colors from both chemical and biological points of view. More importantly, several chapters are designed to explain in full detail the usefulness of these natural coloring properties that provide a safe, efficient, and economic therapy and/or prophylaxis for many health problems, e.g. obesity and cardiovascular disorders.

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Several controversial issues regarding these interesting compounds with fascinating pharmacological and therapeutic effects are addressed in three sections. Each chapter has been reviewed and revised, and new authors have provided up-to-date research to make the book more informative, illustrative, and easy to read.

**Section 1:** Natural Sources and Traditional Uses of Anthocyanins and Flavonoids:

**Section 2:** Molecular Mechanism of Flavonoids:

**Section 3:** New Trends in Therapeutic and Industrial Applications of Anthocyanins and Flavonoids:

We hope this book will be useful to a wide range of people, from students first learning about natural coloring compounds, to advanced clinicians, nutrition specialists, and researchers who are looking for a review of current treatments and conceptualizations for various conditions.


**Author details**

Babu Rao Gudipudi Department of Civil Engineering, Narasaraopeta Engineering College (Autonomous), Narasaraopet, Guntur, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**5. Conclusions**

Pulsed laser deposition was used for the synthesis of biological-derived hydroxyapatite doped with lithium carbonate (Li-C) and phosphate (Li-P) coatings. The Li-C and Li-P structures were investigated in vivo, as coatings onto 3D metallic implants which were inserted in rabbits' femoral condyles, for 4 and 9 weeks, respectively. The bone density measurements of the functionalized Ti implants, performed either at 4 or 9 weeks, showed superior values in comparison to simple (control) Ti ones. The inferred detachment force values of the functionalized Ti implants were ~2 times higher than those registered for the corresponding control ones. When referring to longer implantation time periods (i.e., 9 weeks), the extraction test results indicated improved bonding strength values (~5 times higher) of the functionalized Ti implants as compared to the same structures, but corresponding to an implantation time period of 4 weeks. Therefore, the mechanical testing is indicated as a promising tool to investigate the early phase of 3D Ti implants attachment to bone.

The demonstrated improvement of in vivo mechanical characteristics of the Li-C and Li-P synthesized coatings (as compared to control, commercial Ti implants), along with the fact that the materials used in this study were fabricated from natural, renewable sources, could stand for a pioneering contribution to the progress of medical devices. These important aspects should be taken into consideration when advancing this type of implant modification as an adequate choice to bare, non-deposited Ti implants for the manufacturing of a new generation of orthopedic implants, which will integrate better and much faster into the living body, corroborated with a substantially improved life-time.


FIGURE 9 | Simultaneous voltage and calcium imaging of Purkinje neuron dendrites and electric recording from the soma in the awake mouse. (A) A linescan at 2 kHz was taken along the Purkinje neuron dendrite (left) to measure a voltage spatio-temporal map in an awake mouse. Note red vertical lines (black triangles) indicating activation of the full dendritic tree during a complex spike, and background pattern with red 'hotspots' indicating localized subthreshold electrical activity. The spatially averaged dendritic voltage trace shows dendritic complex spikes at high SNR. (B) The corresponding dendritic calcium spatio-temporal map shows large transients for every dendritic complex spike. Note the spatial variability of the peak amplitude of calcium transients within single dendritic complex spikes and the amplitude variability between different dendritic complex spikes. (C) The access port allowed simultaneous extracellular electrical recordings from the soma while imaging voltage and calcium transients from the dendrites. Simple spikes (somatic Na+ spikes) result in a current sink at the soma, while complex spikes (dendritic Ca2+ spikes) result in a dominant current source signal at the soma. (D) Different parts of the dendritic tree show a different number of spikelets during the same complex spike event. The number of spikelets correlate with the amplitude of the calcium transients in each part of the dendritic tree. Open arrowheads indicate spatially localized low activity, filled arrowheads high activity. Spatially localized dendritic spikelets during complex spikes correlate with a local boost in the dendritic calcium transient (small arrowheads). Note that the depolarization is caused by the influx of Ca2+. Therefore, the voltage and calcium signal should have the same onset. The observed delay in onset of the calcium signal is the result of the dynamics of the calcium indicator GCaMP6f (Roome and Kuhn, 2018).

decay of the spectral tail considers only one vibrational mode of the chromophore and is therefore the steepest possible decay. In more realistic models, additional vibrational modes must be taken into account which would flatten the spectral tail. If this spectral tail is now shifted due to an external electric field, the steepest spectral tail will give the highest voltage sensitivity. Therefore, this simple model of the spectral Boltzmann tail will give us a theoretical thermodynamic limit for voltage sensitivity for a given dye. For ANNINE-6 this limit is −0.77%/mV for extracellular application of the dye and a positive voltage change of the cell (Kuhn and Fromherz, 2003; Kuhn et al., 2004). The highest measured sensitivity for ANNINE-6 is −0.52%/mV (with two-photon microscopy at an excitation wavelength of 1040 nm, corresponding to 520 nm with one-photon excitation) (Kuhn et al., 2004). For comparison, the theoretical limit of voltage sensitivity for di-4- ANEPBS and RH-421 is 0.43%/mV and 0.24%/mV, respectively (Kuhn and Fromherz, 2003; Kuhn et al., 2004).

Excitation at the red spectral edge of absorption has additional advantages which might be interesting for imaging applications other than voltage imaging: (1) The amount of energy converted into heat during the relaxation process after excitation is minimized. (2) The probability for intersystem crossing is minimized, as the energy levels with high probability of crossing might not be excited any more. Both effects reduce phototoxicity and contribute to an extended imaging time and/or increased SNR.

#### APPLICATIONS OF TWO-PHOTON VOLTAGE IMAGING WITH ANNINE DYES

With the above described mechanistic understanding and optimizations of two-photon voltage imaging with ANNINE dyes, a wide variety of applications are possible. For example, ANNINE-6 can be bolus loaded in vivo and the average membrane potential can be measured (**Figure 8**) (Kuhn et al., 2008). Sensory responses or brain oscillations up to at least 15Hz can be observed.

#### REFERENCES

To overcome labeling of all cell surfaces and a resulting mixture of voltage signals, ANNINE-6plus can be internally loaded into single cells in vivo (**Figure 9**) (Roome and Kuhn, 2018).  Suprathreshold as well as localized subthreshold events can be detected in dendrites of Purkinje neurons in awake animals with high spatial and temporal resolution. In this case, the labeling after a single electroporation lasts for at least 2 weeks and therefore allows for long-term single neuron imaging experiments. The access port also allows electrical recording, and virus, dye and drug application in combination with the voltage imaging technique. Additionally, voltage imaging with ANNINE dyes can be easily combined with green calcium indicators, specifically GCaMP6 (Roome and Kuhn, 2018). All these experiments can be done without bleaching and with neglectable phototoxicity for many minutes of repetitive laser excitation.

Detailed protocols for these experiments will be available in Multiphoton Microscopy, Springer Nature Neuromethods (Kuhn and Roome, 2019). We hope, that in combination with this paper, it will be possible to adapt the experimental conditions for a wide range of voltage imaging applications on different experimental setups and with different cell types.

#### AUTHOR CONTRIBUTIONS

BK and CJR wrote the manuscript.

#### ACKNOWLEDGMENTS

The authors thank Espen Hartveit, Steven D. Aird, Neil Dalphin, Ray X. Lee, Mohamed M. Eltabbal, Alisher Duspayev, Claudia Cecchetto, and Leonidas Georgiou for valuable feedback on the manuscript and the Okinawa Institute of Science and Technology Graduate University for internal funding.

Jameson, D. M. (2014). Introduction to Fluorescence. Boca Raton, FL: CRC Press.

function. J. Neurosci. 30, 2871–2879. doi: 10.1523/JNEUROSCI.5607-09. 2010

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Kuhn and Roome. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Using Genetically Encoded Voltage Indicators (GEVIs) to Study the Input-Output Transformation of the Mammalian Olfactory Bulb

Douglas A. Storace1,2 \*, Lawrence B. Cohen2,3 \* and Yunsook Choi2,3

1 Department of Biological Science, Florida State University, Tallahassee, FL, United States, 2 Department of Cellular and Molecular Physiology, Yale School of Medicine, Yale University, New Haven, CT, United States, 3 Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, South Korea

#### Edited by:

Srdjan D. Antic, University of Connecticut Health Center, United States

#### Reviewed by:

Diego Restrepo, University of Colorado Denver, United States Bernd Kuhn, Okinawa Institute of Science and Technology Graduate University, Japan

#### \*Correspondence:

Douglas A. Storace<EMAIL_ADDRESS>Lawrence B. Cohen<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cellular Neurophysiology, a section of the journal Frontiers in Cellular Neuroscience

> Received: 26 March 2019 Accepted: 11 July 2019 Published: 31 July 2019

#### Citation:

Storace DA, Cohen LB and Choi Y (2019) Using Genetically Encoded Voltage Indicators (GEVIs) to Study the Input-Output Transformation of the Mammalian Olfactory Bulb. Front. Cell. Neurosci. 13:342. doi: 10.3389/fncel.2019.00342 Genetically encoded voltage indicators (GEVIs) are fluorescent protein reporters of membrane potential. These tools can, in principle, be used to monitor the neural activity of genetically distinct cell types in the brain. Although introduced in 1997, they have been a challenge to use to study intact neural circuits due to a combination of small signal-tonoise ratio, slow kinetics, and poor membrane expression. New strategies have yielded novel GEVIs such as ArcLight, which have improved properties. Here, we compare the in vivo properties of ArcLight with Genetically Encoded Calcium Indicators (GECIs) in the mouse olfactory bulb. We show how voltage imaging can be combined with organic calcium sensitive dyes to measure the input-output transformation of the olfactory bulb. Finally, we demonstrate that ArcLight can be targeted to olfactory bulb interneurons. The olfactory bulb contributes substantially to the perception of the concentration invariance of odor recognition.

#### Keywords: ArcLight, odors, olfactory bulb, mouse, perception, GEVIs, GECIs

## INTRODUCTION

Traditional optical imaging techniques using intrinsic signals (Blasdel and Salama, 1986; Grinvald et al., 1986) or organic dyes for measuring voltage (Davila et al., 1973) and calcium (Brown et al., 1975; Tsien, 1980), have limited ability to distinguish the cell types contributing to the signal, except in a few special cases (Tsau et al., 1996; Wenner et al., 1996; Friedrich and Korsching, 1997; Wachowiak and Cohen, 2001). In contrast, protein sensors of neural activity can be genetically targeted to different cell types. Genetically encoded voltage indicators (GEVI) optically report membrane potential in targeted cell types.

 ArcLight is a GEVI that can detect action potentials in cultured mammalian neurons (Jin et al., 2012), in vivo in Caenorhabditis elegans (Wooltorton et al., 2013) and Drosophila (Cao et al., 2013), and population voltage signals in mice (Storace et al., 2015, 2016; Borden et al., 2017; Storace and Cohen, 2017). Bando et al. (2019) compared ArcLight to other GEVIs and reported that ArcLight had the largest signal-to-noise ratio in both 1 and 2-photon imaging from the in vivo mouse brain.

In the first section of the paper we show that both the GEVIs and the voltage processes that they follow are faster than the GECIs and the calcium concentration changes that they follow.

In the second section of the paper we describe the use of GEVIs and organic calcium dyes to determine the input-output transformation of the mammalian olfactory bulb. Comparing the input and output of a brain region defines the function(s) carried out by that region. In the third section of the paper we demonstrate that ArcLight can be targeted to an olfactory bulb interneuron. Measurements from different bulb cell types may help determine the mechanisms that shape the input-output transformations.

Typically an odorant is perceived as the same over a substantial range of odorant concentrations (Gross-Isseroff and Lancet, 1988; Krone et al., 2001; Uchida and Mainen, 2007; Homma et al., 2009). This perceptual concentration invariance is essential for animals, including mammals, that use odor to find food, or mates, or family members, or avoid predators, or to make the correct food aversion associations. The input from the nose to the olfactory bulb is a confound of odor quality and odor concentration (Rubin and Katz, 1999; Wachowiak and Cohen, 2001; Bozza et al., 2004). It was not known where in the olfactory sensory system this confound is disambiguated. Each individual olfactory bulb output mitral cell has a very large axon that reaches 12 different olfactory brain regions (Igarashi et al., 2012). Presumably it is in the olfactory bulb and/or in one or more of these cortical areas that this confound is unraveled.

In principle, the input-output comparison can be made using many single cell electrical or optical recordings but this process typically provides only a limited sampling from each animal. An alternative, used in the experiments described below, is to make population recordings with glomerular instead of cellular resolution. In the mouse each glomerulus receives the axon terminals from ∼1,000 olfactory receptor cells in the nose and ∼25 dendritic tufts from output mitral/tufted cells. Simultaneous in vivo sampling the odorant responses from 20 glomeruli thus includes the activity of ∼20,000 input cells and ∼500 output cells thereby reducing the sampling noise that occurs when recording from only a few neurons in each preparation. Using glomerular resolution gives up individual cell information in favor of the average response of the populations of the input and output neurons. Measuring population signals typically involves lower temporal resolution recordings and together with the averaging of responses from many neurons results in larger signal-tonoise ratios.

Optical measurements can simultaneously record the input and output in the same glomerulus by using indicators with different excitation or emission spectra, one in the input neurons and the other in the output cells.

Individual neuron recordings with GECIs are well established while single neuron recordings with GEVIs are just beginning. These are described elsewhere (Gong et al., 2015; Lou et al., 2016).

This paper emphasizes population GEVI measurements from the in vivo mouse olfactory bulb using ArcLight.

#### MATERIALS AND METHODS

#### Surgical and Imaging Procedures

All experiments were performed in accordance with relevant guidelines and regulations, including a protocol approved by the Institutional Animal Care and Use Committees of Yale University. For all surgical procedures, male or female adult (40–100 days old) mice were anesthetized with a mixture of ketamine (90 mg kg−1 ) and xylazine (10 mg kg−1 ). Anesthesia was supplemented as needed to maintain areflexia, and anesthetic depth was monitored periodically via the pedal reflex. Animal body temperature was maintained at approximately 37.5◦C using a heating pad placed underneath the animal. For recovery manipulations, animals were maintained on the heating pad until awakening. Local anesthetic (1% bupivacaine, McKesson Medical) was applied to all incisions. Respiration was recorded with a piezoelectric sensor.

For virus injections, a small (<1 mm) craniotomy was performed and AAV1 expressing ArcLight or GCaMP (injection volumes between 0.2 and 2 µl) was injected slowly over 10–15 min using a glass capillary (tip diameter 8–15 µm) ∼500 µm below the surface of the bulb. The ArcLight virus was generated at the Penn Vector Core and had a titer of 4.6e12 genome copies (GC) ml−1 . The GCaMP3 and GCaMP6f virus were acquired from the Penn Vector Core and had titers of 4.7e13 GC ml−1 and 4.3e13 GC ml−1 , respectively. We waited at least 10 days for expression of the virus prior to performing optical measurements.

The venerable, but not very reliable, method first described by Wachowiak and Cohen (2001) was used to load calcium dye into the olfactory sensory neurons. Mice were anesthetized, placed on their back, and an 8 µl mixture of 8/0.2% calcium dye/Triton-X was drawn into a flexible plastic syringe, which was inserted ∼10 mm into the nasal cavity. Using a Hamilton syringe, four injections of 2 µl of the dye/triton mixture was infused into the nose every 3 min. Mice were allowed to recover for at least 4 days prior to optical measurements. The organic calcium dye Fura dextran (F-3029) was from Thermo Fisher Scientific, Waltham, MA, United States. Cal-590 dextran is from AAT Bioquest (Sunnyvale, CA). The excitation spectra of these calcium dyes are well separated from that of many GEVIs and GECIs.

For epifluorescence or 2-photon imaging fluorescence measurements, mice were anesthetized, and the bone above both of the hemi-bulbs was either thinned or removed. The exposure was covered with agarose and then sealed with a glass coverslip.

Epifluorescence imaging was performed by illuminating the dorsal surface of the bulb with 150 W Xenon arc lamp (Opti Quip) on a custom antique Leitz Ortholux II microscope. The light was reflected by a 515 nm long-pass dichroic mirror before being delivered to the sample via an objective lens (various,

typically a 4× 0.16NA or 10× 0.4NA lens). The fluorescence emission above 530 nm was recorded with a NeuroCCD-SM256 camera (RedShirtImaging, Decatur, GA, United States) with 2 × 2 binning at 40 or 125 Hz.

Two-photon imaging was performed with a modified MOM two-photon laser-scanning microscope (Sutter Instruments). 2 photon excitation was achieved using a Coherent Discovery laser (wavelengths between 940 and 980 nm), and laser scanning was performed with an 8 kHz resonant scanner (Cambridge Technology). Fluorescence was passed through a 510/84 nm bandpass filter and detected with a GaAsP PMT (Hamamatsu, Japan). Power delivered to the sample ranged from 75 to 140 mW as determined using a power meter (Thorlabs) placed underneath the microscope objective.

#### AAV Vector

ArcLight A242-2A-nls-mCherry was constructed by fusing ArcLight A242 (Jin et al., 2012) with the self cleaving 2A peptide sequence followed by nuclear localized mCherry (Kim et al., 2012). To allow constitutive expression of the voltage sensor under the CAG promoter in wild type (C57BL/6) mice, ArcLight A242-2A-nls-mCherry was inserted into the aavCAG Jx vector (Gene bank JN898959) in a forward orientation. An adeno-associated virus serotype 1 (AAV1) of the ArcLight construct was produced at the Penn Vector Core at the University of Pennsylvania. For experiments with GCaMP3 and GCaMP6f, we used AAV1s expressing the gene under the human synapsin 1 (**Figure 1B**) promoter (#AV-1-PV1627 and AV-1- PV2822 purchased from the Penn Vector Core). For the TH-Cre targeting experiments, a Cre-dependent ArcLight virus was used (AV1.EF1a.ArcLight.DIO, Penn Vector Core).

#### Transgenic Animals

Protocadherin 21 (Pcdh21) (Nagai et al., 2005) embryos were acquired from RIKEN BioResource Center (No. RBRC02189) and recovered by the Yale Genome Editing Center. OMP-GCaMP3 transgenic mice were a gift from T. Bozza. TH-Cre transgenic line were a gift from Matthew McGinley (Jax

Stock #008601). Thy1-GCaMP6f GP5.11 transgenic mice were acquired from Jax (Stock #024339). Tbx21-Cre transgenic mice were acquired from Jax (Stock #024507) and crossed to a floxed GCaMP6f reporter transgenic mouse (Jax Stock #024105). Animals used in the study were confirmed to express the sensor via genotyping by Transnetyx (Cordova, TN, United States), and by visual inspection (in vivo and histologically).

#### Data Analysis

For all experiments, odorant-evoked signals were collected in consecutive (1–20) trials separated by a minimum of 45 s. Individual trials were manually inspected and were discarded if they exhibited obvious movement artifact. Trials were averaged after being aligned to the time of the first inspiration following the odorant presentation. Individual glomeruli were visually identified via a frame subtraction analysis that identified stimulus activated glomeruli. The activation maps were generated by subtracting the temporal average of the 1–2 s preceding the stimulus from a 1 s temporal average at the signal peak using frame subtraction in NeuroPlex (RedShirtImaging). Spatial filtering is indicated in the figure legend. The response maps were depixelated for display. Response amplitudes were calculated as the difference between the peak of the response, and the 1–2 s period preceding the stimulus. Fluorescence signals were converted to 1F/F by dividing the spatial average of each glomerulus by resting fluorescence (the average of the time period prior to the odor presentation).

The input and output measurements in **Figure 5** were performed alternatively using Fura dextran and ArcLight. The 0.04% odor concentration did not evoke any detectable input glomerular response, and was subtracted from the input for the traces and values in (C) and (D). The evoked signal size for each concentration was normalized to the signal evoked at 11% of saturated vapor. To illustrate the similarity of the output maps, in **Figure 5** each output response map was scaled so that all five maps had the same maximum and minimum intensity values. The input map for 11% saturated odor was scaled to have the same maximum and minimum brightness values as the five output maps. The input maps for the lower odorant concentrations were first scaled by the same scaling factors that were used for the output maps and are shown using the same maximum and minimum brightness scale as the 11% odorant map.

The signal-to-noise analysis was performed by measuring the amplitude of the ArcLight response to the first sniff of the odorant vs. the response immediately preceding the odorant. This analysis was performed in 10 preparations in averaged trials that were aligned to the first sniff following the odorant. An ROI that evoked the largest signal for that trial was selected for each preparation.

For the 2-photon input and output measurements in **Figure 7**, glomerular responses were normalized to the signal evoked at the highest odorant concentration (6% of saturated vapor). The responses from all identified individual glomeruli were

measured (as in **Figure 7B**), and averaged together to form the values plotted in **Figure 7C**. Statistical significance for **Figure 7** was measured using a Wilcoxon rank sum test comparing the average of input vs. output preparations at each concentration.

For the ArcLight measurements from TH expressing interneurons in **Figure 8**, functional signals were measured in 5 preparations, of which histology was processed and examined to confirm expression in the expected locations in 4 of them. The activity maps in **Figure 8B** are scaled to the minimum and maximum values evoked by the highest odorant concentration. The data in **Figure 8D** come from concentration series performed in 3 of the preparations. For a given odor, all of the activated glomeruli

were identified, measured and then averaged together for each odor concentration to give each data point. The 1F/F values were normalized to the highest odor concentration used for that preparation (11% of saturated vapor). Responses to 3 odorants were measured in one preparation, only one odorant was used in the other two preparations. The input and output data in **Figure 8D** (red and black lines) are taken from Storace and Cohen (2017). The statistical comparison of the data in **Figure 8D** was performed using a Wilcoxon rank sum test, and similar results were obtained whether the comparisons were performed across preparations (N = 3 independent TH measurements), across odor presentations (5 independent TH measurements), or when collapsing all normalized values

FIGURE 4 | Approach to measure both the input and output of the same glomeruli in the mouse olfactory bulb. (A) Experimental approach: Left, the olfactory receptor neuron nerve terminal input is labeled via nasal infusion of an organic calcium sensitive dextran dye together with a low concentration of Triton-X. In the same preparation, GEVIs or GECIs were targeted to mitral and tufted output cells using cre-dependent AAVs in a transgenic mouse that expressed cre recombinase in those cells. Right, input and output can be measured independently from the same glomerulus by using activity sensors with substantially different excitation or emission spectra. The signals from the two cell types can be distinguished by changing the excitation or emission wavelengths. (B) A doubled labeled histological section showing targeting to input vs. output. In this section, Cal-590 dextran was anatomically targeted to the olfactory receptor nerve terminal input (left) and ArcLight was genetically targeted via AAV injection to the mitral and tufted cell output (middle). The merged image is shown on the right. Both sensors are present in each glomerulus. onl, olfactory nerve layer; gl, glomerular layer; epl, external plexiform layer; mcl, mitral cell layer. Scale bar in panel (B), 50 µm. Modified from Storace and Cohen (2017).

to the lower odor concentrations (yielding 9 independent TH measurements).

#### Odorant Stimuli and Delivery

Odorants (Sigma-Aldrich) were diluted from saturated vapor with cleaned air using a flow dilution olfactometer (Vucinic et al., 2006). The olfactometer provided a constant flow of air blown over the nares. Through a separate set of tubing, odorants were constantly injected into the olfactometer, but sucked away via a vacuum that was switched off during odorant presentation. Different odorants were delivered using separate Teflon tubing. Odorants were delivered at different concentrations (between 0.1 and 6% of saturated vapor). Ethyl tiglate, methyl valerate, 2-heptanone, a mixture of the 3 odorants, and n-butyl acetate were used in these experiments. The time course and relative concentrations of odorants presented by the olfactometer was confirmed using a photo-ionization detector (Aurora Scientific Inc., Aurora, ON, Canada) at the beginning of experiments.

## RESULTS AND DISCUSSION

### Comparing in vivo GEVI and GECI Signals From AAV Transfected Mouse Mitral/Tufted Cells

ArcLight was used to record the population voltage signals from mitral and tufted cell dendritic tufts in each glomerulus. ArcLight was expressed using an adeno-associated virus serotype 1 (AAV1) that co-expressed nuclear localized mCherry to facilitate identification of the transduced neurons (**Figure 1A**). GECIs GCaMP3 and GCaMP6f were similarly expressed via AAV transduction. Sensor expression was examined by comparing the fluorescence of the GEVIs and GECIs histologically. The ArcLight (**Figure 1D**) and GCaMP3 (**Figure 1E**) vectors had similar expression patterns in the external bulb layers, and were both largely selective for mitral and tufted neurons (**Figures 1D,E**). We presume that the labeled glomeruli are the result of sensor expression in the mitral/tufted neuron primary dendritic tufts. These AAV1 vectors were selective for restricted cell populations in the bulb: this specificity may be due to a combination of the AAV serotype and the promoter selectivity.

Different odorants evoke distinct spatial and temporal patterns of glomerular activation in the mouse olfactory bulb (Rubin and Katz, 1999; Wachowiak and Cohen, 2001; Spors and Grinvald, 2002). AAV vectors carrying the genes for ArcLight and GCaMP3 or GCaMP6f (**Figures 1A,B**) were injected into separate bulb hemispheres (**Figure 1C**) between 10 and 50 days prior to optical measurements. Expression tended to be widespread in the injected hemispheres.

The ArcLight voltage signals were compared with those from the GECIs GCaMP3 (Tian et al., 2009) and GCaMP6f (Chen et al., 2013) using simultaneous recordings from opposite olfactory bulb hemispheres. Odor-evoked responses could be detected in the glomerular layer in single trials from all three sensors. ArcLight's temporal kinetics were substantially faster than both GCaMPs but the signal-to-noise ratios were lower.

Odorants presented for 0.3 s often coincided with a single inhalation, which resulted in ArcLight and GCaMP3 (**Figure 2A**, red vs. green trace) or ArcLight and GCaMP6f (**Figure 2B**, red vs. blue trace) fluorescence signals that were easily detected in single trials. Depolarizations measured with ArcLight and increases in calcium measured with the GCaMPs are shown as upward signals.

The ArcLight and GCaMP signals had different amplitudes, time courses and signal-to-noise ratios. While ArcLight had a much smaller fractional fluorescence change (1F/F) than either GCaMP, ArcLight had a faster onset, rise time, and decay (Storace et al., 2015). With a longer odorant pulse that elicits two odorant inhalations (**Figures 2C,D**) the ArcLight signal returns almost all the way to the baseline between the two inhalations while the GCaMP signals either have only an inflection (**Figure 2C**) or a modest return toward the baseline (**Figure 2D**).

While the signal-to-noise ratios of the ArcLight signals are relatively large, the ArcLight signals in **Figure 2** appear noisier than those of the GCaMPs. We attempted to quantify

FIGURE 5 | Comparing the input and output from ten glomeruli in the same bulb using Fura dextran (input) and the GEVI ArcLight (output). (A) Frame subtraction activity maps at five methyl valerate concentrations for input (left) and output (right). The output maps are much more similar to each other in comparison with the input maps. To illustrate the similarity of the output maps, each output response map was scaled so that all five maps had the same maximum and minimum brightness values. The input map for 11% saturated odor was scaled to have the same maximum and minimum brightness values as the five output maps. The input maps for the lower odorant concentrations were first scaled by the same scaling factors that were used for the output maps and are shown using the same maximum and minimum brightness scale as the 11% odorant map. The input map's signals declined much more rapidly with decreasing odorant concentration. The selected glomeruli used for the time course results in panel (C) are indicated in the bottom panel (ROIs from 1.83%). (B) Spatial correlation of the 1.83% frame subtraction map with the four other odor concentrations. (C) Input and output traces of fluorescence vs. time for the ten glomeruli. The input signals decrease more dramatically than those of the output. The traces are low-pass filtered at 1 Hz. Odor time course (measured simultaneously with a photo-ionization detector), odor command pulse and respiration are shown under glomerulus 10. (D) Normalized peak fluorescence change vs. odorant concentration for the glomeruli in panel (C) (black, input; red, output). The activity maps in panel (A) and the traces in panel (C) are the average of 4–20 individual trials aligned to the first sniff following odor onset; ∗∗p < 0.001. ant, anterior; lat, lateral. Modified from Storace and Cohen (2017).

anterior; lat, lateral. Modified from Storace and Cohen (2017).

the ArcLight signal-to-noise ratio by measuring the difference between the odor-evoked change, and the pre-odor oscillations. On average the largest signal evoked by an odor was 1.83% (±0.17), and the baseline oscillation was 0.47% (±0.05). The fold-change of the odor vs. baseline was 4.07 (±0.28). However some of this baseline "noise" is actually a real respiratory signal which is emphasized more in the ArcLight signals because of the faster ArcLight response (**Figures 2B,D**). We compared the timing of baseline ArcLight and GCaMP signals. Consistent with the notion that the baseline signals are physiological and not movement artifacts, the ArcLight signals preceded the GCaMP signals.

The results from repeated imaging trials were relatively consistent (Storace et al., 2015), which demonstrates that substantial photo-bleaching or photodynamic effects were not present with either ArcLight or the GCaMPs at the incident light intensities which we used. Our comparison of intrinsic signal activity from injected and control hemispheres did not detect a pharmacological effect of ArcLight on normal bulb function. However, continued attention to the possibility of photodynamic and phototoxic effect is desirable. The ArcLight signals were sufficiently large and fast, which made them clearly distinct from the intrinsic light scattering and flavoprotein autofluorescence signals.

### Comparison of ArcLight and GCaMP Sensor Response Speed

The differences in time course and respiration coupling that we found between ArcLight and the two GCaMPs are likely due to a combination of differences in the time courses of the cellular voltage and calcium changes as well as the response speeds of the different sensors. To further understand the differences in signal time courses, we compared the temporal kinetics of the three kinds of protein sensors.

ArcLight had onset and offset time constants of 20 and 30 m s, respectively, for 80 mV depolarizing steps in HEK293 cells (**Figure 3A**). GCaMP3 has onset time constants of 1100 and 250 m s for calcium steps to 250 and 490 nM (**Figure 3B**, green line, time constants of 1100 and 250 m s) (Akerboom et al., 2012). GCaMP6f is about 50% faster for calcium concentration steps to ∼ 510 and ∼ 940 nM (**Figure 3B**, blue line). **Figures 3C– E** compares the temporal responses for ArcLight (C), GCaMP3 (D), and GCaMP6f (E) for selected steps of voltage and calcium.

The GCaMP3 and GCaMP6f decay constants are reported to be ∼150 m s (Sun et al., 2013) and 71 m s (Kim et al., personal communication), respectively. These observations were done when starting at a high calcium concentration, however the effect of different calcium steps on these GECI decay times has not been reported. Clearly, ArcLight has faster kinetics than either of the two GCaMPs, which may explain some of its improved ability to detect individual inspiratory responses.

The ArcLight signal likely reflects the average of all of the mitral/tufted dendritic processes in the glomerular region of interest. Electrode measurements have shown that individual mitral and tufted cells can produce action potentials that are tightly linked to the respiration cycle (Dhawale et al., 2010; Carey and Wachowiak, 2011; Shusterman et al., 2011). Thus, our ArcLight measurements appear to accurately reflect the respiration driven envelope of mitral/tufted cell spiking activity (Rojas-Libano et al., 2014).

The onset and decay kinetics of the GCaMP sensor responses to step changes in calcium result from the calcium binding and unbinding rates. The mechanism generating the ArcLight signal results from a dimerization of the super ecliptic pHluorin of two ArcLight molecules which is then affected by the movement of S4 in the voltage sensitive domain (Kang and Baker, 2016). A pH fluorescent protein like super ecliptic pHluorin is necessary but the ArcLight signal is not the result of a change in pH (Han et al., 2014; Kang and Baker, 2016).

#### Determining the Olfactory Bulb Input-Output Transformation

An approach was developed to measure both the input and output of the same glomeruli in the mouse olfactory bulb. Spectrally distinct sensors of neural activity were used; one in the olfactory receptor neurons (input), the other in the mitral and tufted cells (output). This approach employed both anatomical and genetic targeting. Olfactory receptor neurons (input) were labeled via nasal infusion with an organic calcium sensitive dye (**Figure 4A**; Friedrich and Korsching, 1997; Wachowiak and Cohen, 2001). In the same preparation, GEVIs or GECIs were targeted to mitral and tufted (output) cells using credependent AAVs in a transgenic mouse (Protocadherin 21) that expresses cre recombinase in the mitral and tufted cells (**Figure 4A**; Nagai et al., 2005). Appropriate targeting of the sensors in the expected locations was confirmed via histological examination (**Figure 4B**).

FIGURE 8 | Odor-evoked in vivo recordings of the GEVI voltage indicator ArcLight from tyrosine hydroxylase (TH) expressing olfactory bulb juxtaglomerular interneurons. (A) Histology of cells expressing ArcLight. (B) Activity maps evoked by three different odorants at three concentrations. Maps are from an average of 2–4 individual trials that were aligned to the onset of the signal, and were generated by subtracting the ∼0.5–1 s during the odor stimulus from the 2 s prior to the stimulus onset. The images were sharpened using high-pass Gaussian filter with a kernel size of 61 × 61 pixels. Activity maps are scaled to the minimum and maximum values evoked by the highest odor concentration. The maximum 1F/F value for each odor is included in the bottom right of the highest odor concentration map. (C) Fluorescence time courses from the 11 glomeruli identified in the middle concentration methyl valerate image in panel (B). The trials are aligned to the onset of the response. (D) Normalized response amplitude vs. odor concentration for populations of glomeruli measured from 3 different preparations. TH-ArcLight measurements are from 5 different odorant trials across three preparations. Input and output measurements (red and black lines) are adapted from Storace and Cohen (2017). ∗∗p < 0.01 and ∗p < 0.05.

Because the input and output sensors had substantially different excitation spectra, input vs. output signals could be measured in the same glomeruli by changing the excitation wavelength. Odor-evoked activity was measured using epi-fluorescence imaging across ∼2 log units of odorant concentration in freely breathing anesthetized mice. Input and output measurements were made from the same hemi-bulb using the calcium dye Fura dextran as the input sensor, and

the protein voltage sensor ArcLight as the output sensor. The Fura dextran input signal was imaged using 380 nm excitation light. The ArcLight output signals were measured with an excitation light of 480 nm. Fluorescence time courses were recorded from regions of interest corresponding to activated glomeruli. These time courses reflect the population average of the olfactory receptor neuron axon input activity or the population average of the mitral and tufted neuron output activity from individual glomeruli.

In the experiment illustrated in **Figure 5**, signal measurements were made from 10 glomeruli at five odorant concentrations. The output frame subtraction maps at the five odorant concentrations are more similar to each other than are the input maps (**Figure 5A**). The similarity of the input vs. output maps at different odor concentrations was quantified by performing a spatial correlation of each map with one another (**Figure 5B**). The output maps had a significantly higher spatial correlation than the input maps (p < 0.05, two-sample t-test). The signals from the 10 input and output glomeruli (**Figure 5C**, ROIs) also demonstrate that the input glomeruli have a considerably steeper function of odor concentration than the output glomeruli. The input exhibited no detectable signal at the lowest odorant concentration (0.04% saturated vapor, not shown), and very few had a signal at 0.12% saturated vapor. In contrast, an output signal was detected for all glomeruli at an odorant concentration of 0.04%.

The amplitudes of the odor-evoked input and output signals for each glomerulus were normalized to the response evoked by the highest odorant concentration (11% of saturated vapor). Responses from individual glomeruli are plotted as the thin lines in **Figure 5D**, and the mean evoked signals are shown in the thick lines (red for output; black for input). Reducing the concentration of the odor presentation from 11 to 0.12% reduced the input amplitude to decrease by more than 95%, while the output decreased by only 40% (p < 0.001). This result was similarly consistent in a population average across 13 preparations (Storace and Cohen, 2017). Thus both the output activity maps and the output signal size are much less concentration dependent than are the input maps and signals. The olfactory bulb contributes to the perception of concentration invariance of odor recognition.

In another mouse we measured the concentration dependence of the output maps using two different odorants, methyl valerate and 2-heptanone. **Figure 6A** again shows that for each odorant the output maps do not change markedly over a substantial range of odorant concentration. Nonetheless the output maps are markedly different for the two odorants. **Figures 6B,C** show that the map correlations for the individual odorants are relatively large while the correlations across odorants are very small. Thus the bulb contributes to the perception of concentration invariance while maintaining an odorant specific output.

One concern about the results illustrated in **Figures 5C,D** (and in similar figures in Storace and Cohen, 2017) is the possible contribution of the out-of-focus mitral/tufted lateral dendrite signals to the measurements. Accordingly, we have repeated the measurements of input and output concentration dependence using 2-photon microscopy where the depth-of-field is <10 microns instead of the >100 microns of wide-field microscopy. These experiments were carried out using different animals for each input and output measurement. **Figure 7A** illustrates the results from one glomerulus from two different animals. The input signals decline markedly as a function of concentration while the output signal is much less concentration dependent. **Figure 7B** shows a plot of signal size vs. odorant concentration for all of the identified glomeruli in the two preparations used in (a). The normalized response of the output was significantly higher than the input for all tested concentration (p < 0.05; Input: 31 glomeruli; Output: 37 glomeruli). **Figure 7C** shows that across a population the average input signal (N = 5 preparations; thick black line) declines much more rapidly than the average output signal (N = 7 preparations; thick red line) (**Figure 7C**) (p < 0.05 using a Wilcoxon rank sum test for all comparisons). The results in **Figure 7** are very similar to those published earlier (Storace and Cohen, 2017) using wide-field microscopy and thus they alleviate the concern that the wide-field conclusion might have been in error because of contamination from mitral/tufted lateral dendrite signals.

We conclude that the olfactory bulb contributes to the perception that the quality of an odorant is considered the same over a range of concentrations. It has been hypothesized that the combination of odorant receptors that are activated by an odorant determines odor identity.

However, previous studies have found that the olfactory bulb glomerular input maps were a confound of odorant identity and concentration (Wachowiak and Cohen, 2001; Bozza et al., 2004). While the activated receptors are critical for odor identification, our results show that odorant identity is determined by the olfactory bulb output. The intensity information may be normalized in the olfactory bulb, and thus the bulb in part removes the qualitative effect of odorant concentration so that odor identity is represented in the output maps. However, the mitral and tufted cell population average still maintains some concentration dependence so that intensity information is not entirely discarded.

The signal-to-noise ratio in the Fura dextran input recordings has a threshold that is ∼5% of the largest signal. If the largest signal represents the activation of ∼1000 olfactory receptor neurons, then the smallest detectable signal would reflect the average activity of less than 50 receptor neurons. The activity of <50 receptor neurons is enough to generate substantial output signals and enough to enable odorant recognition (Homma et al., 2009). However, this argument assumes linearity of the sensor signal with calcium concentration, while calcium signals are typically nonlinear.

The possibility that the olfactory bulb could be involved in normalizing the well-described concentration dependence of the olfactory sensory neurons was proposed by Thomas Cleland (Cleland et al., 2007, 2011). Since that suggestion there is a growing body of supporting evidence across multiple species including Drosophila (Asahina et al., 2009), Zebrafish (Niessing and Friedrich, 2010), and mouse (Banerjee et al., 2015; Sirotin et al., 2015; Storace and Cohen, 2017; this paper). A strength of the results in Storace and Cohen (2017) is that the input and output of the olfactory bulb were measured in the same preparation (in some cases simultaneously) allowing a direct comparison to be made. Candidate mechanisms include different

types of interneurons with distinct anatomical connections that could facilitate communication across glomeruli, and the ability of some cell types to bidirectionally modulate circuits (Yaksi and Wilson, 2010; Zhu et al., 2013; Banerjee et al., 2015). The results presented here and in other reports (Banerjee et al., 2015; Storace and Cohen, 2017; Bolding and Franks, 2018) also demonstrate that not all concentration information is discarded in the olfactory bulb. It seems likely that the olfactory bulb is performing computations that are critical for downstream targets (e.g., in the Piriform Cortex) to perform invariant object identification. Recent evidence has shown that while the olfactory bulb mitral/tufted cells are relatively concentration invariant, piriform cortex cells are even less sensitivity to concentration, a function that involves feedback from the olfactory cortex to the olfactory bulb (Bolding and Franks, 2018). Thus, concentration invariant odorant perception likely involves dynamic processing across multiple brain regions. Nonetheless, it is clear that the olfactory bulb is involved in a critical processing step to normalize the highly concentration dependent olfactory bulb input.

### In vivo ArcLight Signals From a Tyrosine Hydroxylase-Cre Transgenic Mouse

Measuring the contribution of different olfactory bulb cell types may help reveal the mechanisms that shape the transformations occurring in the olfactory bulb. Here, we targeted ArcLight to bulb tyrosine hydroxylase (TH) interneurons with an AAV1 cre dependent ArcLight vector. The TH interneurons are restricted to the glomerular layer and include more than one interneuron type (Pignatelli et al., 2005; Pignatelli and Belluzzi, 2017). **Figure 8A** illustrates the DAPI and ArcLight expression. The ArcLight expression is restricted to cells and processes in the juxtaglomerular layer consistent with the possibility that the expression is restricted to TH periglomerular interneurons.

Activity maps for the odorant responses (**Figure 8B**) were made by subtracting imaging frames prior to the odorant stimulus from frames acquired during the response. Maps for three concentrations of three odorants are illustrated. These maps are very concentration dependent suggesting that they reflect the olfactory receptor cell input to the bulb rather that the mitral/tufted cell output (Banerjee et al., 2015). The time course of the ArcLight fluorescence changes at three concentrations from 11 glomeruli are shown in **Figure 8C** (single trials). **Figure 8D** is a plot of normalized response amplitude vs. odor concentration of the glomerular TH measurements, overlaid with the population input and output measurements from Storace and Cohen (2017).

The normalized TH concentration dependence was not statistically different than the input, and was significantly smaller than the output (p < 0.05; Wilcoxon rank sum). While these measurements are from a more limited dataset

#### REFERENCES

Akerboom, J., Chen, T. W., Wardill, T. J., Tian, L., Marvin, J. S., Mutlu, S., et al. (2012). Optimization of a GCaMP calcium indicator for neural activity imaging. J. Neurosci. 32, 13819–13840. doi: 10.1523/JNEUROSCI.2601- 12.2012

and performed in different animals, the result support other studies that have proposed that these cells carry the concentration dependent information from the bulb input. These signals may be transmitted throughout the bulb via long-range lateral connections that are found in many of these cells (Aungst et al., 2003; Zhu et al., 2013; Banerjee et al., 2015).

## CONCLUSION

In population measurements from mouse olfactory bulb glomeruli, the ArcLight signals provide important information about membrane potential changes and the perceptual computations carried out in the bulb. In the future we expect that the role of the olfactory bulb in other olfactory perceptions could be determined by using our approach to measure both the input and output. This strategy will help guide investigations into the function of the complex synaptic network of the olfactory bulb. Similar strategies as those used here could be used to understand the input-output transformation of other brain regions.

### DATA AVAILABILITY

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

## ETHICS STATEMENT

All experiments were performed in accordance with relevant guidelines and regulations, including a protocol approved by the Institutional Animal Care and Use Committees of Yale University.

### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

### FUNDING

This work was supported by the World Class Institute (WCI) Program of the National Research Foundation of Korea (NRF) (Grant No. WCI 2009-003), the Korea Institute of Science and Technology (KIST) Grants 2E26190 and 2E26170, and the United States NIH Grants DC005259, NS099691, DC012981, and DC016133.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Storace, Cohen and Choi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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*2.4. Randomization*

Participants were randomized in a 1:1 ratio to either TN-TC11M2 (arm 1) or TN-C200M2 (arm 2).

#### *2.5. Safety and Tolerability Assessments and Specimen Collection*

The visit schedule is depicted in Figure 1. Participants underwent a physical exam and occurrence of adverse events (AEs), use of concomitant medications and the presence of common symptoms associated with cannabinoids (including dizziness, nausea, headaches, appetite or mood changes) were assessed. Toxicity of TN-TC11M2 and TN-C200M2 was assessed using the World Health Organization (WHO) toxicity scale. All AEs, regardless of grade, were documented, and those possibly related to TN-TC11M2 and TN-C200M2 were managed by dose reduction. Cannabinoids were permanently discontinued when life-threatening AEs occurred.  Nasal swabs and stool specimens were collected at baseline and end of the treatment period for future microbiome analysis. Men had the option of donating a semen specimen collected at baseline and end of the treatment period for future HIV reservoir studies.

**Figure 1.** Schedule of visits and procedures. Screening: Up to 4 weeks prior to randomization, during the screening visit, study staff explained the study to the participants and obtained written informed consent prior to initiating any study procedures. Study staff assessed the participant's eligibility by assessing the inclusion and exclusion criteria. Study staff collected the medical history and concomitant medications of the study participants and they underwent a complete physical exam. Blood was collected for hematology, blood chemistry, HIV RNA load and CD4 and CD8 T cells counts. A urine pregnancy test was performed for female participants. Cannabis Use Disorder Identification Test-Revised (CUDIT-R), Drug Use Disorder Identification Test (DUDIT) and Alcohol Use Disorder Identification Test (AUDIT) questionnaires were administrated to the participants and they underwent testing for Hepatitis B and C and syphilis infections. They also underwent urine screen for cannabinoids use. Baseline 1: Up to 3 weeks before the randomization, study staff confirmed eligibility of the candidate and reviewed their medical history. Participants then underwent a second cannabinoids screening test, if his/her initial screen was positive, and answered the CUDIT-R questionnaire in order to identify any problematic cannabis use. The participants underwent a targeted physical exam and blood and semen (from male) were collected to quantify the HIV reservoir size in circulating PBMC from blood and in the semen. Nasal swab and stool specimens were collected from study participants. Antiretroviral Therapy (ART) compliance, alcohol intake and concomitant medication were reviewed by the study staff. Baseline 2 (week 0: Initiation of treatment): Participants confirmed their willingness to participate in the study and eligibility was confirmed, before participants were randomized to either arm 1 or arm 2. Blood was collected from participants. Participants underwent a targeted physical exam. Participants also completed the World Health Organization Quality of Life—HIV Brief Scale (WHOQOLHIV-BREF), Euro-Qol-5Dimension (EQ-5D) questionnaire, and Profile of MoodStates (POMS) questionnaires before receiving a one week supply of the study medication. Follow-up visits (visit 3–8; week 1 to 10): During the follow-up visits, participants underwent a physical examination, and blood was collected to assess the biological study measures. Study drug and ART compliance was assessed. Adverse effects (AEs) were recorded. Pregnancy test was performed on urine of female participants. The participants completed the WHOQOLHIV-BREF, EQ-5D, and POMS questionnaires (Visit 6) and received the study medication until their next visit. End of the treatment (Visit 9; week 12): At Visit 9, participants underwent a physical examination, and blood was collected to assess the biological study measures. Nasal swab and stool specimens were collected from all study participants and semen was collected from male participants. AEs were recorded. A pregnancy test was performed on urine of female participants. Participants then completed the WHOQOLHIV-BREF, EQ-5D, and POMS questionnaires. Final study visit (Visit 10; week 14): At the final visit, participants underwent a physical examination, and blood was collected to assess the biological study measures. AEs were recorded and ART compliance was assessed. A pregnancy test was performed on urine of female participants.

#### *2.6. Quality of Life and Mood Assessment*

WHO Quality of Life HIV Brief (WHOQOLHIV-BREF), Euro-Qol-5Dimension (EQ-5D) and Profile of Mood States (POMS) questionnaires were administered at baseline, midway through the study (Visit 6) and at the end of treatment (Visit 9) (Figure 1). WHOQOLHIV-BREF consists of 31 items that measure the following domains: physical health, psychological health, social relationships and environment [42]. EQ-5D is a descriptive questionnaire examining five dimensions: (1) mobility, (2) self-care, (3) usual activities, (4) pain/discomfort and (5) anxiety/depression [43]. Meanwhile, the POMS questionnaire measures the following six factors: (1) tension-anxiety, (2) anger-hostility, (3) fatigue-inertia, (4) depression-dejection, (5) vigour-activity and (6) confusion-bewilderment [44].

#### *2.7. Study Outcome Measures*

Endpoints consisted of (1) the proportion of participants in both groups without any sign of significant toxicity as determined by the WHO toxicity scale (i.e., number of participants with grades 0–2 scores on the WHO toxicity scale); (2) the proportions of participants who were able to complete the study and (3) changes in scores on the WHOQOLHIV-BREF scale, EQ-D5 and POMS questionnaires from week 0 to week 12. A description of WHO toxicity scale grades is presented in Supplementary Table S2.


**1. Introduction**

*Cryptosporidium* is a significant cause of diarrheal disease worldwide, with broad host ranges and the ability to infect all vertebrate groups, including humans [1]. As commonly seen, the transmission of enteric pathogens through contaminated surface water, such as *Cryptosporidium* spp. potentially cause large outbreaks of water- and food-borne infections in human populations [2]. Cryptosporidiosis is a global disease and is considered an important opportunistic disease in immunocompromised patients due to its high association with mortality in AIDS patients [3].

Characterization of pathogens at the species or genotype level is mandatory when assessing the potential sources of infection, pathogen load in animals, the environment, transmission routes in human populations, and public health relevance [1,4]. Currently, *Cryptosporidium* genotyping is mostly based on PCR and sequencing of the small subunit (SSU) rRNA gene, which has revealed no less

than 40 valid species and more than 70 genotypes of *Cryptosporidium* in humans and animals [1,5,6]. Humans infected by approximately 20 *Cryptosporidium* species and genotypes, with *C. hominis* and *C. parvum*, are responsible for the highest proportion (~90%) of human *Cryptosporidium* infections globally [7]; nevertheless, several primarily animal pathogens, such as *C. meleagridis*, *C. felis*, *C. canis*, and *C. cuniculus* are less commonly found in humans [7].

In northwestern China, Bactrian camels (*Camelus bactrianus*) represent the major livestock species, especially in Xinjiang Uygur Autonomous Region (hereinafter referred to as Xinjiang), because they are well adapted to desert and semi-desert areas and provide milk, meat, and camel hair. *Cryptosporidium* infection of camel calves resulted in diarrhea and debility, while infected adult camels showed no symptoms [8]. Camels infected with *Cryptosporidium* have been reported in many countries, such as the United States, Australia, Czech Republic, Algeria, Iran, Egypt, and China [9–20]. However, compared with other livestock animals, information on prevalence, species, genotype, and zoonotic potential of *Cryptosporidium* spp. in Bactrian camels is still limited in China.

The main focus of the current study was to investigate the prevalence of *Cryptosporidium* and identify the species and subtypes of Bactrian camels in Xinjiang, China (Figure 1). The data will contribute to an improved understanding of *Cryptosporidium* spp. in Bactrian camels and assessment of their zoonotic potential.

**Figure 1.** Bactrian camels fecal sampling locations in Xinjiang, northwestern China. No copyright permission was required. The figure was designed with the software ArcGIS 10.2. The map has been originally modified and assembled according to permission and attribution guidelines of the National Geomatics Center of China (http://www.ngcc.cn).


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308 Real-Time Systems, Architecture, Scheduling, and Application

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Due to the great variety of biological protocols and exposure parameters (dose, duration, frequency and waveform of the EM signal), different exposure systems can be found in the literature, based on radiating (antennas), propagating (waveguides), and resonant (resonant cavities) structures. From an accurate review of them, it emerges that real-time systems, in almost all cases, were designed for *in vitro* investigations on the electrophysiological activity of excitable cells. Such systems generally require modifications of standard RF structures to allow the continuous monitoring of the sample while avoiding RF coupling and interference with the recording apparatus (Paffi et al., 2010). Different solutions can be found in the literature, almost all based on propagating structures. They are mostly open planar structures or semi-open and closed structures modified with holes to allow the access to the sample for the data acquisition. The proposed systems, tailored for the particular biological endpoint and protocol, often result from the trade-off between the two conflicting requirements: the easy access to the sample and the avoidance of interference with the laboratory equipment. Therefore, in these cases, guidelines to design the optimal and most

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**0**

**15**

*Poland*

**Real–Time Low–Latency Estimation of the**

Electrooculography biosignals (EOG) are very important for the eye orientation and eyelid movements (blinking) estimation. There are many applications of the EOG signals. Most important applications are related to the medical applications [Duchowski (2007)]. The EOG signal is used for the analysis of eye movement in the selected medical test of the eye related health problems. It is also important for the sleep analysis. The EOG signal has much higher level than the important EEG (electroencephalography) signals and should be removed from the EEG measurements [Duchowski (2007); Shayegh & Erfanian (2006)]. The reduction of the EOG artifacts from EEG is considered by many researchers and it is also important for the

The EOG and blinking signals are used in Human–Computer Interfaces in: the ergonomics, the advertisement analysis [Poole & Ball (2005)], the human–computer interaction (HCI) systems (e.g. a virtual keyboard [Usakli at al. (2010)], the vehicle control [Barea et al. (2002); Firoozabadi (2008)], the wearable computers [Bulling et al. (2009)]), and the video compression

Many alternative oculography techniques are available. The applications of the EOG signals for the HCI applications should be considered as one of the available techniques. The most important disadvantage is the long–time stability of the measurements and the influences of the other factors like light sources. The video–oculography (VOG) is interesting alternative, but the long–time influence of the infrared illuminators usually used on the eye has not been well tested. The infrared oculography (IROG) applies a small set of the illuminators (IR LEDs)

The recent application of the EOG signal is the computer animation. The estimated orientation and blinking signals are used for the control of eye and eyelid of the human–generated avatar [Deng et al. (2008)]. This is specific for the motion capture technique [Duchowski (2007); Krupi ´nski & Mazurek (2009)] that, for instance, was used successfully in Beowulf movie [Sony et al. (2006); Warner Brothers (2008)]. Such a motion capture technique is alternative to the

A measured biosignal has two important subsignals: electrooculography and blinking. Both of them should be separated and the interesting parameters should be estimated. The

practical applications of the EEG–based Human–Computer Interfaces.

driven by eye–interest [Khan & Komogortsev (2004)].

and IR sensors for the estimation of eye movements.

video–based motion capture systems fixed to the human head.

**1. Introduction**

**Blinking and EOG Signals**

Robert Krupi ´nski and Przemysław Mazurek

*West Pomeranian University of Technology, Szczecin*

*Department of Signal Processing and Multimedia Engineering*


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**12. AI and differently abled**

AI empowers the differently abled. The disability could be physical, restricting the access or mental, restricting the cognition. Smart devices provide support in the activities of daily living. Assistance to the disabled is showing promise. The AI is stepping into help communication and cognition. Assistive technologies are showing lot of promise and a positive outlook for the differently abled. AI is into the diagnosis of cognitive disabilities like autism and such disorders. However, there are issues that need consideration and scope for improvement [75–77]. ABIDE (Autism Brain Imaging Data Exchange) is one of the AI projects for Autism and it helps the diagnostic evaluation to be less time consuming, more efficient, and accurate. It even identifies certain phenotypes that respond better for therapeutic interventions [78].


Contents

#### **Preface XI**

Thomas B. Zanders and Stanislaw P. Stawicki

Chapter 8 **Transfusion Error in the Gynecology Patient: A Case Review with Analysis 125**

Carly Madison Hornis, R.S. Vigh, J.F. Zabo and E.L. Dierking

Chapter 9 **Patient Safety Issues in Pathology: From Mislabeled Specimens to Interpretation Errors 141** Derek Tang, Peter A. Dowbeus, Michael S. Firstenberg and Thomas J. Papadimos

Preface

Modern healthcare continues to evolve. With the shift away from primarily quantitative meas‐ ures of performance, the new landscape of quality and value-based metrics became the hall‐ mark of the ongoing paradigm shift. *The Vignettes in Patient Safety* book series is an attempt to highlight some of the prevailing healthcare trends, focusing on highlighting the increasingly complex matrix of multidisciplinary teams, rapidly evolving treatments, technological ad‐ vancements, regulatory requirements, and ever greater patient (consumer) expectations.

The third volume of *The Vignettes* is the most successful tome in the current series. This is both a testament to the importance of the topic and a reflection of the high quality of work published in the earlier volumes. The favorable response of our readership also corroborates the growing importance of patient safety as an essential component of the modern health‐ care landscape. In fact, the permanency of the "quality and safety" mindset is slowly becom‐

Despite many important advances in our collective understanding of patient safety, a tre‐ mendous amount of work remains before the ultimate goal of "zero incidence" is achieved across the entire spectrum of the so-called "never events" that continue to affect our health‐ care systems. Topics discussed in the current book include fundamental principles of the performance improvement process, the application of levels of scientific evidence in clinical‐ ly relevant contexts, assessment of patient safety culture in a primary care setting, patient

It is our goal as editors of *The Vignettes in Patient Safety* to introduce new concepts and clini‐ cal scenarios that will enrich the cumulative value of the entire book series. Volume 3 fol‐ lows this important principle as well, adding important information regarding teamwork and communication, the "Swiss cheese" model of medical error genesis, transfusion-related patient safety issues, errors involving pathology labeling and reporting, operating room

The editors of *Vignettes in Patient Safety* would like to thank all of the individuals involved in bringing this important work to fruition. We also want to thank our friends and family who supported our efforts for their patience and understanding during the entire process of book preparation, editing, and readying the content for publication. In addition, we formally ac‐ knowledge and express our appreciation to all of the authors that have committed their val‐ uable time and effort to making this third tome of *The Vignettes* a success. Their contributions, especially in the context of an open source publication model in which the authors support the expenses of a publication, clearly reflect their dedication to the primary objectives of this text—and the passion to share and promote this work's important mes‐ sage. Finally, we must recognize the important role of various departments and institutions

ing the long-sought reality throughout our clinics, hospitals, and operating rooms.

safety education, and a number of different patient safety scenarios.

fires, and various dangers associated with intrahospital patient transfers.

Chapter 10 **Avoiding Fire in the Operating Suite: An Intersection of Prevention and Common Sense 161** Maryam Saeed, Mamta Swaroop, Franz S. Yanagawa, Anita Buono and Stanislaw P. Stawicki