CN115024244A - Black hamster sleep-wake detection system and method based on infrared open field and Python analysis and application - Google Patents
Black hamster sleep-wake detection system and method based on infrared open field and Python analysis and application Download PDFInfo
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Abstract
The invention discloses a black line hamster sleep-wake detection system, method and application based on infrared open field and Python analysis, comprising a data collection unit: monitoring hamster position coordinates every 0.2 second by an infrared open field testing system; the data processing unit: and (4) converting the coordinate data in the step (i) into speed data to count the sleeping time of the hamster and draw the sleep-wake rhythm of the hamster within 24 h. The system and the method are noninvasive for the cricetulus barnyard, do not need surgical intervention, and are more time-saving and labor-saving. And the requirement on equipment and software is lower, and the method is more suitable for large-scale popularization. Meanwhile, theoretical basis is provided for the next step of preparing the sleep-wake medicine, preparing animal models and the like.
Description
Technical Field
The invention relates to a small rodent behavior detection method, in particular to a sleep-wake behavior, and specifically relates to a black-line hamster sleep-wake detection system and method based on infrared open field and Python analysis, and application of the system and method.
Background
Sleep is the most common biological rhythm produced by a variety of brain structures and neurotransmitters. Accurate detection of sleep-arousal is critical to understanding intermediate information on animal health, intermediate information on cognitive and impairment recovery, and to preparing models for related aspects. The detection method of the human sleep cycle is mainly a non-invasive instrument, including electroencephalogram (EEG), Electromyogram (EMG), Electrooculogram (EOG), and the like. However, studies of rodent sleep-wake can rely on less signal, primarily traumatic EEG and EMG to the animal. The monitoring steps of these two methods are as follows: (a) surgery: implanting electrodes into the muscles of the head or limbs of the animal; (b) recovering the animal; (c) animals were attached to a recording device and acclimated for a period of time. However, the surgical operation of electrode implantation is detrimental to animal health, such as weight loss, and the like, to the welfare of the animal, and these necessary recovery and adaptation periods are time consuming and labor intensive, and are not conducive to large-scale screening of animal sleep patterns. In addition, monitoring EEG and EMG requires specialized surgical knowledge, knowledge of the location of electrode placement, and specialized equipment needed to collect sleep score data. Therefore, there is a need to develop a non-invasive system or method to detect sleep patterns in rodents.
At present, the established methods for monitoring the sleep-wake rhythm of animals have disadvantages. For example, Flores et al attach a pressure sensor to the bottom of the cage and identify periods of sleep and arousal based on signals received by the pressure sensor. The method has the principle that when an animal is in a sleep stage, the pressure sensor can accurately output the respiratory frequency, and the signal peak value is stable and small; the animal is in the wake phase, breathing is masked by other motion, and the signal output by the pressure sensor is a large peak. And deducing the activity state of the animal according to different signal peaks. Although this method allows large-scale monitoring of animal behavior, the need for special equipment and custom software may limit the application of this method. Furthermore, non-invasive video tracking systems have been widely used, i.e. after marking animals with fluorescent dyes or bleaches, recording behavioral videos using a camera, and analyzing the sleep-wake behavior of the animals using specific software. However, manual marking of animals is time consuming (re-marking after a time interval) and requires marking when the animal is unconscious, which may affect animal behaviour. Therefore, the video tracking method has more disadvantages.
Disclosure of Invention
Aiming at the current invasive electroencephalogram method, the pressure sensor method with more defects and the video tracking method, the invention provides a noninvasive system, a noninvasive method and application based on the combination of an infrared open field and computer assistance.
1. A black-line hamster sleep-wake detection system based on infrared open field and Python analysis, comprising:
a data collection unit: placing the hamsters in an infrared open field connected with a computer, monitoring hamster position coordinates every 0.2 seconds by using an infrared open field testing system, collecting position coordinates (x, y) of the hamsters under 24h, and introducing the position coordinates into an Excel table;
the data processing unit: the purpose of this unit is to convert the coordinate data in the data collection unit into velocity data to count the sleep-wake rhythm of the cricetulus barnacle. The 24h position coordinate data is first divided equally into 48 half-hour position coordinate data (9000 points per table). Then using a program 'sleep-wake' written in Python language according to the formula (
) And converting the coordinate data into speed data, counting the time of the black-line hamster with the speed of 0 for more than 40 seconds every half hour, and summing up the time, so that the sleeping time of the hamster in every half hour can be calculated. Final use mapping software (example)As GRAPH PRISM 7.0.0), the sleep-wake rhythm of the cricetulus bardadae was plotted over 24 h.
2. The program "sleep-wake" for coordinate data processing and statistics of animal sleep duration.
The data processing unit processes the derived coordinate data through a user operation program sleep-wake, namely, the coordinate data of the hamster in 24h can be converted into speed data. The time that the continuous more than 40 seconds of the hamsters with the black line are 0 is counted every half hour and summed, so that the sleeping time of the hamsters in every half hour can be calculated.
The execution steps are as follows:
(1) positional coordinate data of a black hamster was derived and equally divided into 48 pieces, and each table possessed 9000 positional coordinate points.
(2) Converting the position coordinate data into speed data and recording the speed data by using a program' sleep-wake
Speed 1[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
Speed 2[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
Velocity 3[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
…………………………………，
Speed 48[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
(3) Acquiring a speed value at every 0.2 second, and summing the continuous more than 200 time periods corresponding to 0 in adjacent data in each speed, namely the statistical time of the speed of 0 continuously for more than 40 seconds to obtain the sleep time of the black-line hamster within half an hour, wherein other time is the awakening time.
The invention discloses a method for detecting sleep-wake law of a cricetulus barnacle by using a detection system.
The invention discloses application of a black hamster sleep-wake detection system based on infrared open field and Python analysis in the aspects of preparing medicines in the aspect of sleep-wake, preparing animal models and the like.
Advantageous effects
Compared with the prior art, the system and the method have the following remarkable advantages:
1. the Python language programming program is used for processing the data, all the data can be obtained in a short time, and the experimental period is greatly shortened.
2. The coordinate data was used in conjunction with the Python program to analyze and define the sleep-wake rhythm of the black-line hamster.
3. Compared with electroencephalogram, the method is noninvasive, does not need surgical intervention, and is more time-saving and labor-saving.
4. Compared with a piezoelectric sensor method, the method has lower requirements on equipment and software and is more suitable for large-scale popularization.
5. Compared with a video tracking system, the system does not need to mark animals and has no influence on the behaviors of the animals.
Drawings
FIG. 1 is a schematic diagram of the experimental setup and animal operation required by the present invention. A denotes an infrared open field detection program (ACTITRACK program; Panlab, Harvard Apparatus, Spain), which directly detects the position coordinates of an animal every 0.2 seconds by connecting to a computer B and an infrared open field C (IR Meter; Panlab, Harvard Apparatus, Spain) and stores them on the computer B. D represents a black-line hamster. After 1 hour of acclimation in an open field, hamsters were opened to a and B simultaneously and monitored for sleep-wake rhythm for 24 hours.
Figure 2 shows a "sleep-wake" code diagram of the Python program of the present invention. A represents a "sleep-wake" code, and the position coordinates (x, y) table B (. txt, 48 tables per animal) of the hamsters in the infrared open field in every half hour are imported into the C software PyCharm, and the coordinate points can be converted into speed data by using the program a, so that the sleeping time of the hamsters in the half hour can be obtained.
FIG. 3 is a graph showing the results of the present invention. A represents a single female hamster randomly picked, counted every half hour, for a period of sleep over 24 h. B represents the average of the sleep duration over 24h for six female hamsters tested, counted every half hour.
Fig. 4 is a schematic system flow diagram according to an embodiment of the present invention.
Detailed Description
In order to understand the details and nature of the present invention, the following detailed description of specific embodiments is provided.
Example 1
6 (No. 1-6) female hamsters from the field of Jiuxian mountain, Qufukan, Shandong, Jinning, Shandong, Jining, were harvested from the field using the iron cage live trap method. The hamsters were then raised in single cages in standard polypropylene rearing boxes (32 cm. times.21 cm. times.16 cm) and were freely fed and drunk at a photoperiod of 12L:12D (illumination time 08:00-20:00, illumination intensity 300 lux) and a temperature of 22. + -. 2 ℃. We chose hamsters of similar age (5-7 months of age), weight (20-25 g) and similar character to examine their sleep-wake rhythm. By applying the system, the sleep-wake law is detected by the data collection unit and the data processing unit.
We placed hamster # 1 in an infrared open field C for 1 h. An infrared open field detection program A (ACTITRACK program) in a computer B is started, and a computer screen is closed after the detection time length is adjusted (24 h) (so that the light in a dark environment is prevented from influencing the behavior of hamsters). After the test was completed, the hamsters were returned to their cages. The other 5 hamsters were then tested sequentially for sleep-wake rhythm over 24h using the same method. Finally, the position coordinate (x, y) data of 6 hamsters in computer B in 24h is exported to a usb disk, and format modification and renaming (e.g., coordinate No. 1-coordinate No. 6. csv) are performed for further analysis.
We take as an example the position coordinate data of hamster No. 1 in the infrared open field and transform it into a visualization result chart (fig. 3A) using Python program. The data of "coordinate No. 1. txt" was first divided equally into 48 parts (9000 position coordinate points of each table, respectively designated as "coordinate No. 1 0-0.5h, coordinate No. 1 0.5-1h, coordinate No. 1-1.5h, … …, coordinate No. 1.5-24 h"). Using fig. 2A with PyCharm on (program "sleep-wake"), the time periods of over 40 s (greater than 200 data) in each table at speed 0 were calculated and summed to count the length of time that hamsters had been sleeping for half an hour.
The execution steps are as follows:
(1) positional coordinate data of a black hamster was derived and equally divided into 48 pieces, and each table possessed 9000 positional coordinate points.
(2) Converting the position coordinate data into speed data and recording the speed data by using a program' sleep-wake
Speed 1[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
Speed 2[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
Speed 3[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
…………………………………，
Speed 48[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
(3) Acquiring a speed value at every 0.2 second, and summing the continuous more than 200 time periods corresponding to 0 in adjacent data in each speed, namely the statistical time of the speed of 0 continuously for more than 40 seconds to obtain the sleep time of the black-line hamster within half an hour, wherein other time is the awakening time.
Finally, the sleep-wake results were visualized using GRAPH PRISM 7.0.0 software, as shown in FIG. 3A. The results of the sleep duration data for 6 hamsters shown using means ± SEM are shown in fig. 3B.
Our results are similar to those of Fisher et al (2012) using electroencephalography to detect sleep-wake in mice, demonstrating the accuracy of our method.
The system and the method detect the sleep-wake result of the mouse, and provide theoretical basis for preparing the medicament in the aspect of sleep-wake, preparing animal models and the like.
Claims (5)
1. A black-line hamster sleep-wake detection system based on infrared open field and Python analysis, comprising:
a data collection unit: placing the hamsters in an infrared open field connected with a computer, monitoring hamster position coordinates every 0.2 seconds by using an infrared open field testing system, collecting position coordinates (x, y) of the hamsters under 24h, and introducing the position coordinates into an Excel table;
the data processing unit: the 24h speed data is divided equally into 48 half-hour speed data (9000 position coordinates per table); based on Python, the coordinate data is converted into speed data, the time that the speed of the hamster in each table is 0 for more than 40 seconds is counted and summed, and then the sleep time and the wake time of the hamster in each half hour can be calculated, so that the sleep-wake rhythm characteristics of the hamster are obtained.
2. The sleep-wake detection system for the hamsters on black lines based on the infrared open field and Python analysis as claimed in claim 1, wherein the step of counting the time of the black line hamsters continuously for more than 40 seconds with the speed of 0 every half hour is performed by the following steps:
(1) the velocity group is noted as:
speed 1[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
Speed 2[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
Speed 3[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
…………………………………，
Speed 48[ v ] 1 ，v 2 ，v 3 ……，v 9000 ],
And acquiring speed values at every 0.2 second, and summing corresponding time periods of more than 200 continuous 0 in adjacent data in each speed to obtain the sleep time length of the black hamster in half an hour, wherein other time is the awakening time length.
3. The sleep-wake detection system for the hamsters on black lines based on the infrared open field and Python analysis as claimed in claim 1, wherein the method for converting the coordinate data in the data processing unit into the velocity data comprises the following steps:
。
4. a method for detecting the sleep-wake law of cricetulus bardadae by using the infrared open field and Python analysis-based cricetulus bardadae sleep-wake detection system of any one of claims 1 to 3.
5. Use of the cricetulus barnyard sleep-wake test system based on infrared open field and Python analysis of any one of claims 1-3 in the preparation of a medicament for sleep-wake and in the preparation of an animal model.
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Citations (6)
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| US6477408B1 (en) * | 2000-03-07 | 2002-11-05 | Northwestern University | Analysis of muscular activity in neonatal animals to screen for mutations and/or drugs that alter sleep and wake states |
| CN105875423A (en) * | 2016-04-29 | 2016-08-24 | 湖北佳境生物科技有限责任公司 | Mouse sleep function check device and mouse sleep function check method |
| WO2017073694A1 (en) * | 2015-10-28 | 2017-05-04 | 国立大学法人筑波大学 | Computer program and sleep state determination device for determining sleep state of object animal |
| US20200337272A1 (en) * | 2017-08-07 | 2020-10-29 | The Jackson Laboratory | Long-term and continuous animal behavioral monitoring |
| CN112868548A (en) * | 2021-02-05 | 2021-06-01 | 安徽医科大学 | Mouse sleep monitoring case based on behaviourology |
| CN114549371A (en) * | 2022-04-26 | 2022-05-27 | 中国科学技术大学 | Image analysis method and device |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6477408B1 (en) * | 2000-03-07 | 2002-11-05 | Northwestern University | Analysis of muscular activity in neonatal animals to screen for mutations and/or drugs that alter sleep and wake states |
| WO2017073694A1 (en) * | 2015-10-28 | 2017-05-04 | 国立大学法人筑波大学 | Computer program and sleep state determination device for determining sleep state of object animal |
| CN105875423A (en) * | 2016-04-29 | 2016-08-24 | 湖北佳境生物科技有限责任公司 | Mouse sleep function check device and mouse sleep function check method |
| US20200337272A1 (en) * | 2017-08-07 | 2020-10-29 | The Jackson Laboratory | Long-term and continuous animal behavioral monitoring |
| CN112868548A (en) * | 2021-02-05 | 2021-06-01 | 安徽医科大学 | Mouse sleep monitoring case based on behaviourology |
| CN114549371A (en) * | 2022-04-26 | 2022-05-27 | 中国科学技术大学 | Image analysis method and device |
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