The study was approved by the ethics committee of the SWPS University of Social Science and Humanities in Warsaw (number 46/2016).
1. Choosing an experimental site for logger deployment
<ol>
	<li>Choose an indoor experimental site that will allow mounting the logger in close proximity to the light source (for adequate light changes detection) as well as to gather the data on the behavior regarding the room occupancy status (for adequate movement detection) of individual participants (i.e., one at a time).</li>
	<li>Establish the intended use of the room and its designated users (males, females or co-ed). 
	NOTE: An example of an experimental site could be a public single-stall restroom due to the fact that this type of room is frequently and individually visited by its users. Furthermore, in most instances, it is possible to specify if the room is visited by males or females, based on its designation.</li>
	<li>Visit a chosen site and note the type / number of functioning light sources along with their light switches. Check whether multiple light sources are controlled by one or multiple light switches.</li>
	<li>Check the possibilities of mounting the logger next to the light source. Ensure that the place of logger mounting is not in proximity to any kind of heating sources (e.g., heaters, windows or mirrors) to ensure that only the body heat of the room users will be recorded.</li>
	<li>Acquire any necessary written permissions from the site owner for installation of the logger and conducting the experiment. Provide the site owner with the details of the experiment, loggers&#39; type and its application in written form.</li>
</ol>
2. Logger configuration before deployment
<ol>
	<li>Download and install the dedicated software (see Table of Materials) available for Windows/Mac platforms for launching, reading out, and plotting data from data loggers. 
	NOTE: Additionally a detailed description with basic system requirements and the software manual can be found on the manufacturer&#39;s website (see Table of Materials).</li>
	<li>Connect the logger via USB cable to the computer (plug the larger end of the USB interface cable into a USB port on the computer and the smaller end of the USB interface cable into the port on the device).</li>
	<li>Launch the software.</li>
	<li>Click the Launch icon on the toolbar (or select Launch command from the device menu) which opens loggers&#39; setup window. 
	NOTE: This option will be unavailable when the logger is not connected to the computer. The Launch Logger window is divided into the following three sections: 1) logger Information which presents model, serial number, deployment number, and current battery level of the selected logger; 2) list of the sensors available for the logger; and 3) deployment configuration. From this interface, one can set specific features that will configure the logger before deployment, such as those previously mentioned: sensor configuration, configuration of data display filters, start/stop logging, and display of the LCD screen.</li>
	<li>Enter a name for the launch which will be used as the default file name during read out and saving the data recorded by the logger.</li>
	<li>Select the Light sensor. Set the measurement to log State from the drop-down list, and choose the state description off/on from the drop-down list.</li>
	<li>Select the Occupancy sensor. Set the measurement to log State from the drop-down list, and choose the state description unoccupied/occupied from the drop-down list. 
	NOTE: Occupancy and light sensor channels can be configured to log state changes or runtime. On the state change setting, the work of the logger is event-dependent. While checking every second for a state change, the logger will only record a time-stamped value (how long an event lasts, date and time) when the state change occurs. On the other hand, on the runtime configuration setting, the logger checks and records the state of the sensor status once every second.</li>
	<li>Click the Filters button to enable automatic calculation of additional values (e.g., maximum, minimum, average, or total). 
	NOTE: Step 2.8 is optional and serves for filtering data for each series during loggers&#39; readout.
	<ol>
		<li>Select the sensor type of choice. Select the type of filter and the interval to use.</li>
		<li>Edit the Name and click Create New Series. Click Done.</li>
	</ol>
	</li>
	<li>Click the Advanced button to access the sensor properties.
	<ol>
		<li>Select the Light sensor. Select Set to maximum sensitivity for calibration and click the Save button. 
		NOTE: By default, the light sensor can be auto-calibrated at the location where the logger will be deployed using the control button located on the top panel. By simply pressing the calibration button, while on the site of deployment, the loggers&#39; LCD screen will display the signal strength of the light being monitored (use this option when light levels in the experimental site are unknown prior to deployment). The sensors&#39; sensitivity can also be adjusted via option &#34;Set to Maximum/Minimum Sensitivity&#34; - if the light levels in the place of deployment are known in advance. These forms of calibration ensure an accurate readout of light changes between ON and OFF states.</li>
		<li>Select the Occupancy sensor. Select a preset timeout value (i.e., 10 s; 30 s; 1 min; 2 min; 5 min) or select Custom and enter a value in minutes and seconds if needed. Click the Save button. 
		NOTE: The timeout value specifies the period of inactivity required for the sensor to consider the area unoccupied. By default, this attribute is set to 1 min.</li>
	</ol>
	</li>
	<li>Select when to launch the logger, depending on the experimental plan: 1) immediately; 2) at intervals (available when logging runtime); 3) on a specified date/time; or 4) by manually using the start button.</li>
	<li>Select when logger should stop logging: 1) when memory fills; 2) stop at a specified date/time; 3) stop manually or 4) never stop&#8212;resulting in the newest data overwriting the oldest.</li>
	<li>Click the Start button upon finishing the configuration. Unplug the logger from the computer.</li>
</ol>
3. Deploying the logger in the field settings
<ol>
	<li>Visit the experimental site before the time the logger will start recording the data.</li>
	<li>Equip the logger with an additional fiber optic light pipe (see Table of Materials) by connecting it to the back of the logger, in order to filter out any ambient light (coming from windows or mirror reflections) and ensure the most accurate readings. 
	NOTE: The light pipe is 30.48 cm long and can be bent to gain access to hard-to-reach areas, which can be also useful in hiding the logger from the sight of any room user.</li>
	<li>Mount the logger with the light pipe next to the designated light source with the use of: 1) four built-in magnets on the back of the logger that can attach it to a magnetic surface; 2) adhesive strip that can be attached to the back of the logger to mount it on walls or other flat surfaces; 3) any double-sided tape to stick the logger to a surface; or 4) the hook-and-loop strap which can be used through the mounting loops on both sides of the logger to mount it to a curved surface. 
	NOTE: The choice of the mounting method depends on the type of surface to which the logger will be mounted.</li>
	<li>Leave the experimental site for the time of data logging set or planned.</li>
	<li>After finishing recording, revisit the experimental site and remove the logger for the purpose of data readout.</li>
</ol>
4. Data readout
<ol>
	<li>Connect the logger via USB cable to the computer and launch the analysis software package dedicated to the data logger (see Table of Materials).</li>
	<li>Click the Readout device button from the control panel or select Readout from the device menu, which will enable the logger to unload the gathered data.</li>
	<li>Choose a location and a filename or accept the default location and name to save the data. Click Save and select the sensors and/or events to display in a graph and click Plot.</li>
	<li>Select the series to view on the table data and plot. Click the All or None button to select or deselect all series, or click the checkboxes to select or deselect individual series. 
	NOTE: The table data is presented numerically using added filters that were set before the deployment. Each column corresponds to the type of data gathered. For example, the column labeled &#34;light&#34; presents the occurrences of light-switching, whereas the column labeled &#34;occupancy&#34; presents the information about the presence of movement in the field where the logger was deployed. In each column, the state changes are presented dichotomously (the number &#34;0&#34; represents the light status of off in the &#34;light&#34; column and a lack of movement in the &#34;occupancy&#34; column).</li>
	<li>Select Export table data from the control panel. Choose destination folder for the export. 
	NOTE: It is possible to perform a data readout and export it to text, comma-separated values, or spreadsheet files. Other options, such as data plotting, are also available; however, due to the fact that most researchers work on exported data and use statistical packages, we decided to present the most basic data readout. For more information refer to the loggers manual19.</li>
</ol>

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The authors have nothing to disclose.

When planning to use more than one site (for logger deployment) at the same time, it should be ensured that each site has an identical architectural layout in order to exclude the possibility of occurrence of different behavioral patterns from participants (i.e., resulting from occupancy times and light-switching possibilities). A suitable site should be equipped with one or more light sources with only one corresponding light switch, visible to the occupant. If otherwise, one should plann to use one logger fir each ligh...

One of the most commonly used measures of pro-environmental behavior in psychology are self-reports in the form of surveys, interviews, or questionnaires1. Among the reasons indicated for this trend is simply the difficulty in conducting field experiments, which usually require a fair amount of resources and precise operationalization2,3. However, the tradeoff is worth the effort since it is well established that relying on self-reporting measures can be misleading in the prediction of objective behavior4,5,6.
While trying to avoid this problem, researchers that are focused on studying energy conservation behavior generally use observational (nominal categorization of observed events, e.g., turning on/off lights) or residual (quantifiable evidence of a past behavior, e.g., energy consumption in kWh) data as measurements of dependent variables7. Although both types of measurements are valuable, observational data is most commonly used in field experiments2,3,8, particularly when their dependent variables concern light-switching behavior.
Before obtaining observational data, researchers should consider several methodological issues, which are: 1) sample representativeness; 2) the number of observers in order to exclude possible human errors; 3) inter-observer agreement in order to exclude experimenter bias; 4) observer location, which should be concealed in order to reduce the possibility of being spotted by participants; 5) clearly and specifically defined observation coding; 6) pretest of observational measures; 7) observer training; and 8) establishing systematic timing of observation9. Even though most of the mentioned issues were already addressed&#8212;for example those that concern reliability analysis10 or coding observational data11&#8212;it seems that not all of them receive much attention in articles that describe experiments on light-switching behavior.
An analysis of four studies12,13,14,15 that were chosen for their similarity in experimental context (all of them concerned light-switching behavior in public bathrooms/restrooms) showed that even though the location details in each of the studies were precise, the observation measurement details varied. Since each study employed naturalistic observation, gathering information about the behavior of participants that were the opposite sex of observers was not always possible14 due to possible interference or violation of social norms (e.g., if a male experimenter were to enter a women&#39;s restroom or vice versa). In some instances, the precise data of the participants&#39; genders were not provided15. This seems to be a limitation when taking into consideration that gender can be an important factor in predicting pro-environmental behavior16.
The biggest differences, however, emerged in the description of observers and measurement times. Even though these descriptions will naturally differ on the basis of experimental location, the precise number of observers was not always provided14. Furthermore, the exact location of observers was not explicit12,14,15 which makes it hard to conduct possible replications and ensure that participants are unaware of being observed. Across four analyzed articles, only one provided a detailed description of the observer&#39;s location13.
Moreover, the exact times of observation intervals were provided only by one study12 whereas other studies either described overall study times (with a general description of how many times on each study day the observation took place)13,15 or did not describe it at all14. This can again impede replicating and establishing whether the observation timing was systematic and sufficient for the purposes of the study aims.
The limitations of these experiments are presented as guidelines and important points that should be taken into consideration in future research. In no case it was intended to undermine the importance of these studies. The indicated areas should be considered for maximizing study operationalization in order to facilitate replications, which play an important role in psychology17,18, and simplify the conduction of field experiments. However, it is questionable whether all of the mentioned issues can be dealt with by improving observation methods that ultimately rely on human observers.
For these reasons, the occupancy and light data logger (see Table of Materials) is a valuable tool that can be effectively used to gather information about a particular type of energy conservation behaviors, light-switching, without the limitations of using observers or ethical restrictions (the logger does not gather the audio-visual data). Overall, the aim of this article is to present the technical description and possibilities of one model of the occupancy and light data logger. To the authors&#39; knowledge, this is the first attempt to present this tool thoroughly in the context of its use in field experiments in psychology.
Loggers&#39; technical description 
The model of occupancy/light data logger (see Table of Materials) that was used for this article was equipped with standard memory capacity of 128 kB. The logger weights 30 g and its size is 3.66 cm &#215; 8.48 cm &#215; 2.36 cm. Additional details and the product manual can be found on the manufacturer&#39;s website19.
Control buttons, the light sensor and the battery tray are located on the top panel. The front panel consists of the occupancy sensor and an LCD screen, whereas the back panel is equipped with mounting magnets and loops (Figure 1). The USB 2.0 port is located on the bottom panel, to allow the connection of the logger to the computer with a USB cable in order to enable set-up before deployment and to later obtain readouts using analysis software package dedicated to this data logger.
The integrated light sensor (photocell) threshold is greater than 65 lx, which works with different light types (LED, CFL, fluorescent, HID, incandescent, natural) that can be found in most public spaces. Overall, the logger interprets light status changes (ON/OFF) depending on the strength of the light signal, more precisely, whether it drops below or rises above levels of the calibration threshold. It should also be noted that the sensor is secured from false detection of ON and OFF states by a built-in hysteresis level of approximately &#177;12.5%19.
A motion sensor determines whether the room is occupied or unoccupied. With the use of a pyroelectric infrared (PIR) sensor, it detects the motion of people by their body temperature (which differs from the temperature of the surroundings). The detection range of the discussed logger has a maximum of 5 m and the extended version of the logger has a range of 12 m. Horizontal detection performance works up to 94&#176; (&#177;47&#176;), and vertical up to 82&#176; (&#177;41&#176;).
The described model of occupancy/light data logger has been validated alongside Open Source Building Science Sensors and appears to provide a reliable measurement of light intensity and occupancy frequency21. Furthermore, these models of loggers have been shown useful in built-environment research, precisely in lighting applications22,23,24.

<table><tbody><tr><td>HOBO Occupancy/Light (5m Range) Data Logger</td><td>ONSET</td><td>UX90-005</td><td>As advertised by Onset - The HOBO UX90-005 Room Occupancy/Light Data Logger is available in a standard 128 KB memory model (UX90-005) capable of 84,650 measurements and an expanded 512KB memory version (UX90-005M) capable of over 346,795 measurements. For details and other products visit: &lt;a href=&quot;https://www.onsetcomp.com/products/data-loggers/ux90-005&quot; target=&quot;_blank&quot;&gt;https://www.onsetcomp.com/products/data-loggers/ux90-005&lt;/a&gt;</td></tr><tr><td>HOBO Light Pipe</td><td>ONSET</td><td>UX90-LIGHT-PIPE-1</td><td>An optional fiber optic attachment or light pipe that eliminates effects of ambient light to ensure the most accurate readings. For details visit: &lt;a href=&quot;https://www.onsetcomp.com/support/manuals/17522-using-ux90-light-pipe-1&quot; target=&quot;_blank&quot;&gt;https://www.onsetcomp.com/support/manuals/17522-using-ux90-light-pipe-1&lt;/a&gt;</td></tr><tr><td>HOBOware</td><td>ONSET</td><td>-</td><td>Setup, graphing and analysis software for Windows and Mac. There are two versions of HOBOware: HOBOware (available for free) and HOBOware Pro (paid version which allows for additional analysis with different loggers). Each of them are dedicated to HOBO loggers. For details visit: &lt;a href=&quot;https://www.onsetcomp.com/products/software/hoboware&quot; target=&quot;_blank&quot;&gt;https://www.onsetcomp.com/products/software/hoboware&lt;/a&gt;</td></tr></tbody></table>

measuring light-switching behavior using an occupancy and light data logger

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. Although direct behavioral observation may increase the external validity and generalizability of a study, it may be time-consuming and be subject to experimenter or observer bias. To address these issues, the use of data loggers as an alternative to natural observation can allow researchers to conduct broad studies without interrupting the participants&#39; naturally occurring behaviors. This article describes one of such tools&#8212;the occupancy and light data logger&#8212;with its technical description, deployment protocol, and information about its possible applications in psychological experiments. The results of testing the reliability of the logger in comparison to human observation is provided alongside an example of the gathered data during a 15-day measurement in public restroom (N = 1,148) that includes: 1) room occupancy changes; 2) indoor light changes; and 3) room occupancy time.

Loggers&#39; reliability test in comparison to human observation 
In order to test the reliability of the logger in comparison to human observation, a 4 h field test was conducted in a single-stall male restroom located on the University campus. Two male observers waited outside the restroom (approximately 5 m away from the front door) and independently recorded the visitors&#39; behavior in terms of occupancy rates/times and light switching (lights left ON or OFF upon exiting). Simultaneously, two ...

Watch this Scientific Journal Video about Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger at JoVE.com

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

This article describes a procedure for using and deploying an occupancy and light data logger which allows collecting data about light-switching behavior of participants in field settings.

Due to discrepancies between self-reported and observed pro-environmental behavior, researchers suggest the use of more direct measures of behavior. ...

measuring-light-switching-behavior-using-an-occupancy-light-data

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Behavior

Unit 1

Summarizing and Visualizing Data

SCIENTISTS IN ACTION

5.8K Views.  Maria Curie-Skłodowska University. De bezetting en lichte datalogger kan worden gebruikt in het veld instellingen om informatie te verzamelen over het optreden van een bepaald type van energiebesparing gedrag, dat is licht-switching. In tegenstelling tot natuurlijke observatie, met behulp van datalogger zorgt voor een snelle, betrouwbare en ononderbroken meting van het gedrag van de deelnemer, evenals biedt een expliciete operationalisering van een bepaald veld experiment. Om te beginnen sluit u de logger via usb-kabel aan op ...

Video: Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

Light/dark Transition Test for Mice

Although all of the mouse genome sequences have been determined, we do not yet know the functions of most of these genes. Gene-targeting techniques, however, can be used to delete or manipulate a specific gene in mice. The influence of a given gene on a specific behavior can then be determined by conducting behavioral analyses of the mutant mice. As a test for behavioral phenotyping of mutant mice, the light/dark transition test is one of the most widely used tests to measure anxiety-like behavior in mice. The test is based on the natural aversion of mice to brightly illuminated areas and on their spontaneous exploratory behavior in novel environments. The test is sensitive to anxiolytic drug treatment. The apparatus consists of a dark chamber and a brightly illuminated chamber. Mice are allowed to move freely between the two chambers. The number of entries into the bright chamber and the duration of time spent there are indices of bright-space anxiety in mice. To obtain phenotyping results of a strain of mutant mice that can be readily reproduced and compared with those of other mutants, the behavioral test methods should be as identical as possible between laboratories. The procedural differences that exist between laboratories, however, make it difficult to replicate or compare the results among laboratories. Here, we present our protocol for the light/dark transition test as a movie so that the details of the protocol can be demonstrated. In our laboratory, we have assessed more than 60 strains of mutant mice using the protocol shown in the movie. Those data will be disclosed as a part of a public database that we are now constructing.

Visualization of the protocol will facilitate understanding of the details of the entire experimental procedure, allowing for standardization of the protocols used across laboratories and comparisons of the behavioral phenotypes of various strains of mutant mice assessed using this test.

The light/dark transition test is one of the most widely used tests to measure anxiety-like behavior in mice. Here, we present a movie that shows detailed procedures on how we conduct the test.

Although all of the mouse genome sequences have been determined, we do not yet know the functions of most of these genes. Gene-targeting techniques, ...

Biology

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

We present a protocol related to a video-tracking technique based on the background subtraction and image thresholding that makes it possible to individually track cohoused animals. We tested the tracking routine with four cohoused Norway lobsters (Nephrops norvegicus) under light-darkness conditions for 5 days. The lobsters had been individually tagged. The experimental setup and the tracking techniques used are entirely based on the open source software. The comparison of the tracking output with a manual detection indicates that the lobsters were correctly detected 69% of the times. Among the correctly detected lobsters, their individual tags were correctly identified 89.5% of the times. Considering the frame rate used in the protocol and the movement rate of lobsters, the performance of the video tracking has a good quality, and the representative results support the validity of the protocol in producing valuable data for research needs (individual space occupancy or locomotor activity patterns). The protocol presented here can be easily customized and is, hence, transferable to other species where the individual tracking of specimens in a group can be valuable for answering research questions.

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Here we present a protocol to individually track animals over a long period of time. It uses computer vision methods to identify a set of manually constructed tags by using a group of lobsters as case study, simultaneously providing information on how to house, manipulate, and mark the lobsters.

We present a protocol related to a video-tracking technique based on the background subtraction and image thresholding that makes it possible to ...

Environment

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

Recording and Analysis of Circadian Rhythms in Running-wheel Activity in Rodents

When rodents have free access to a running wheel in their home cage, voluntary use of this wheel will depend on the time of day1-5. Nocturnal rodents, including rats, hamsters, and mice, are active during the night and relatively inactive during the day. Many other behavioral and physiological measures also exhibit daily rhythms, but in rodents, running-wheel activity serves as a particularly reliable and convenient measure of the output of the master circadian clock, the suprachiasmatic nucleus (SCN) of the hypothalamus. In general, through a process called entrainment, the daily pattern of running-wheel activity will naturally align with the environmental light-dark cycle (LD cycle; e.g. 12 hr-light:12 hr-dark). However circadian rhythms are endogenously generated patterns in behavior that exhibit a ~24 hr period, and persist in constant darkness. Thus, in the absence of an LD cycle, the recording and analysis of running-wheel activity can be used to determine the subjective time-of-day. Because these rhythms are directed by the circadian clock the subjective time-of-day is referred to as the circadian time (CT). In contrast, when an LD cycle is present, the time-of-day that is determined by the environmental LD cycle is called the zeitgeber time (ZT).
Although circadian rhythms in running-wheel activity are typically linked to the SCN clock6-8, circadian oscillators in many other regions of the brain and body9-14 could also be involved in the regulation of daily activity rhythms. For instance, daily rhythms in food-anticipatory activity do not require the SCN15,16 and instead, are correlated with changes in the activity of extra-SCN oscillators17-20. Thus, running-wheel activity recordings can provide important behavioral information not only about the output of the master SCN clock, but also on the activity of extra-SCN oscillators. Below we describe the equipment and methods used to record, analyze and display circadian locomotor activity rhythms in laboratory rodents.

Circadian rhythms in voluntary wheel-running activity in mammals are tightly coupled to the molecular oscillations of a master clock in the brain. As such, these daily rhythms in behavior can be used to study the influence of genetic, pharmacological, and environmental factors on the functioning of this circadian clock.

When rodents have free access to a running wheel in their home cage, voluntary use of this wheel will depend on the time of day1-5. Nocturnal rodents, ...

Neuroscience

Light Preference Assay to Study Innate and Circadian Regulated Photobehavior in Drosophila Larvae

Light acts as environmental signal to control animal behavior at various levels. The Drosophila larval nervous system is used as a unique model to answer basic questions on how light information is processed and shared between rapid and circadian behaviors. Drosophila larvae display a stereotypical avoidance behavior when exposed to light. To investigate light dependent behaviors comparably simple light-dark preference tests can be applied. In vertebrates and arthropods the neural pathways involved in sensing and processing visual inputs partially overlap with those processing photic circadian information. The fascinating question of how the light sensing system and the circadian system interact to keep behavioral outputs coordinated remains largely unexplored. Drosophila is an impacting biological model to approach these questions, due to a small number of neurons in the brain and the availability of genetic tools for neuronal manipulation. The presented light-dark preference assay allows the investigation of a range of visual behaviors including circadian control of phototaxis.

Light Preference Assay to Study Innate and Circadian Regulated Photobehavior in Drosophila Larvae

Here we describe a light-dark preference test for Drosophila larva. This assay provides information about innate and circadian regulation of light sensing and processing photobehavior.

Light acts as environmental signal to control animal behavior at various levels. The Drosophila larval nervous system is used as a unique model to answer ...

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on the triggering of lever and nose-poke sensors present inside the chambers. While this provides a detailed view of when and how animals perform certain responses, it cannot be used to evaluate behaviors that do not trigger any sensors. As such, assessing how animals position themselves and move inside the chamber is rarely possible. To obtain this information, researchers generally have to record and analyze videos. Manufacturers of operant conditioning chambers can typically supply their customers with high-quality camera setups. However, these can be very costly and do not necessarily fit chambers from other manufacturers or other behavioral test setups. The current protocol describes how to build an inexpensive and versatile video camera using hobby electronics components. It further describes how to use the image analysis software package DeepLabCut to track the status of a strong light signal, as well as the position of a rat, in videos gathered from an operant conditioning chamber. The former is a great aid when selecting short segments of interest in videos that cover entire test sessions, and the latter enables analysis of parameters that cannot be obtained from the data logs produced by the operant chambers.

This protocol describes how to build a small and versatile video camera, and how to use videos obtained from it to train a neural network to track the position of an animal inside operant conditioning chambers. This is a valuable complement to standard analyses of data logs obtained from operant conditioning tests.

Operant conditioning chambers are used to perform a wide range of behavioral tests in the field of neuroscience. The recorded data is typically based on ...

Investigating Migraine-Like Behavior Using Light Aversion in Mice

Migraine is a complex neurological disorder characterized by headache and sensory abnormalities, such as hypersensitivity to light, observed as photophobia. Whilst it is impossible to confirm that a mouse is experiencing migraine, light aversion can be used as a behavioral surrogate for the migraine symptom of photophobia. To test for light aversion, we utilize the light/dark assay to measure the time mice freely choose to spend in either a light or dark environment. The assay has been refined by introducing two critical modifications: pre-exposures to the chamber prior to running the test procedure and adjustable chamber lighting, permitting the use of a range of light intensities from 55 lux to 27,000 lux. Because the choice to spend more time in the dark is also indicative of anxiety, we also utilize a light-independent anxiety test, the open field assay, to distinguish anxiety from light-aversive behavior. Here, we describe a modified test paradigm for the light/dark and open field assays. The application of these assays is described for intraperitoneal injection of calcitonin gene-related peptide (CGRP) in two mouse strains and for optogenetic brain stimulation studies.

Rodents are not able to report migraine symptoms. Here, we describe a manageable test paradigm (light/dark and open field assays) to measure light aversion, one of the most common and bothersome symptoms in patients with migraines.

Migraine is a complex neurological disorder characterized by headache and sensory abnormalities, such as hypersensitivity to light, observed as ...

Measurement of Larval Activity in the Drosophila Activity Monitor

Drosophila larvae are used in many behavioral studies, yet a simple device for measuring basic parameters of larval activity has not been available. This protocol repurposes an instrument often used to measure adult activity, the TriKinetics Drosophila activity monitor (MB5 Multi-Beam Activity Monitor) to study larval activity. The instrument can monitor the movements of animals in 16 individual 8 cm glass assay tubes, using 17 infrared detection beams per tube. Logging software automatically saves data to a computer, recording parameters such as number of moves, times sensors were triggered, and animals&#8217; positions within the tubes. The data can then be analyzed to represent overall locomotion and/or position preference as well as other measurements. All data are easily accessible and compatible with basic graphing and data manipulation software. This protocol will discuss how to use the apparatus, how to operate the software and how to run a larval activity assay from start to finish.

Measurement of Larval Activity in the Drosophila Activity Monitor

This report describes a method for measuring Drosophila larval activity using the TriKinetics Drosophila Activity Monitor. The device employs infrared beams to detect movements of up to 16 individual animals. Data can be analyzed to represent motion parameters including rates and the positions of the animals within the assay chambers.

Drosophila larvae are used in many behavioral studies, yet a simple device for measuring basic parameters of larval activity has not been available. This ...

Flexible Measurement of Bioluminescent Reporters Using an Automated Longitudinal Luciferase Imaging Gas- and Temperature-optimized Recorder (ALLIGATOR)

Luciferase-based reporters of cellular gene expression are in widespread use for both longitudinal and end-point assays of biological activity. In circadian rhythms research, for example, clock gene fusions with firefly luciferase give rise to robust rhythms in cellular bioluminescence that persist over many days. Technical limitations associated with photomultiplier tubes (PMT) or conventional microscopy-based methods for bioluminescence quantification have typically demanded that cells and tissues be maintained under quite non-physiological conditions during recording, with a trade-off between sensitivity and throughput. Here, we report a refinement of prior methods that allows long-term bioluminescence imaging with high sensitivity and throughput which supports a broad range of culture conditions, including variable gas and humidity control, and that accepts many different tissue culture plates and dishes. This automated longitudinal luciferase imaging gas- and temperature-optimized recorder (ALLIGATOR) also allows the observation of spatial variations in luciferase expression across a cell monolayer or tissue, which cannot readily be observed by traditional methods. We highlight how the ALLIGATOR provides vastly increased flexibility for the detection of luciferase activity when compared with existing methods.

Flexible Measurement of Bioluminescent Reporters Using an Automated Longitudinal Luciferase Imaging Gas- and Temperature-optimized Recorder ALLIGATOR

Genetically encoded luciferase is a popular non-invasive reporter of gene expression. Use of an automated longitudinal luciferase imaging gas- and temperature-optimized recorder (ALLIGATOR) enables longitudinal recording from bioluminescent cells under a wide range of conditions. Here we show how ALLIGATOR may be used in the context of circadian rhythms research.

Luciferase-based reporters of cellular gene expression are in widespread use for both longitudinal and end-point assays of biological activity. In ...

Operation of Laboratory Photobioreactors with Online Growth Measurements and Customizable Light Regimes

The laboratory study of microalgae can be experimentally challenging. In addition to the cultivation requirements of non-photosynthetic microorganisms, phototrophs also require illumination. Routinely, researchers seek to provide custom light supplies, i.e., vary the light intensity and time over which it is delivered. Such flexibility is difficult with standard benchtop lights. Usually, cultivation studies also require growth comparisons between experimental treatments. Frequently, growth is assessed over an extended duration, e.g., multiple times a day over a week-long trial. Manual measurements can be time-consuming and lack data resolution. Therefore, photobioreactors (PBRs) with automatic growth monitoring and customizable light supply are useful for replicated experiments with multiple treatments. The current work presents the design, construction, and operation of laboratory PBRs. The materials are easily sourced and relatively inexpensive. The design could be duplicated with moderate skill. Each structure has a footprint of ~40 cm2 and hosts three 1 L glass bottles for triplicate replication. Bottles rest upon platforms containing magnetic stirrers and are arranged vertically within a 1 m high and 15 cm diameter polyvinyl chloride (PVC) pipe. The pipe interior is lined with light-emitting diodes (LEDs). These LEDs produce continuous light intensities from 0&#8211;2400 &#181;mol photons m-2 s-1 of photosynthetically active radiation (PAR). Users design a custom lighting program. The light intensity can be adjusted each second or held constant for longer durations. Oxygen produced from photosynthesis exits each bottle via a one-way volumetric gas sensor. Software is used to record gas sensor data. The amount of oxygen produced can be correlated to biomass growth. If biomass samples are required, a syringe can be used to extract culture. The method is suited for microalgae grown with bicarbonate as the carbon source. These PBRs are valuable to a laboratory that requires replicated experiments, light regime flexibility, and continuous high-resolution growth data.

This publication describes the design of laboratory photobioreactors (PBRs) with customizable light regimes. The growth of cyanobacteria or microalgae, using bicarbonate as their carbon source, is monitored continuously by measuring volumetric oxygen production. These PBRs facilitate rapid, replicated laboratory growth comparisons with little user intervention during experiments.

The laboratory study of microalgae can be experimentally challenging. In addition to the cultivation requirements of non-photosynthetic microorganisms, ...

Measuring Light-Switching Behavior Using an Occupancy and Light Data Logger

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