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Research Article
The current study describes a fine motor behavior test for examining motor deficits in rodent models, including the TgF344-AD rat, using machine learning.
Motor dysfunction is a critical, yet often underappreciated, component of neurodegenerative diseases such as Alzheimer's disease (AD) and related dementias. New research has shown that tasks involving fine motor behavior, visuospatial ability, and executive function in humans may have potential in early disease detection. However, little is known about the exact mechanisms by which these tasks predict cognitive and functional decline associated with AD. In the current method, a fine motor task, Kinematic Motor Ability Task (KINEMAT), was created to examine changes in motor ability across disease progression in preclinical models of AD relative to other neurodegenerative diseases and normal aging using free, open-source machine learning software. Here, proof-of-concept was established with the design, testing, and implementation of the fine motor task in a pilot study of Fisher 344 CDF rats with and without Harmaline-induced motor impairments. Additional validation, with an Alzheimer's disease rat model (TgF344-AD), further demonstrated model feasibility and task sensitivity. Rats were trained to reach through a small slot at the front of the chamber to retrieve a sugar pellet from one of three bowls with varying difficulty. Pellet retrieval was scored using one of three classifications: success (successfully grabbing and eating the sugar pellet), drop (grabs but drops the treat upon grasp), or failure (reach in which no pellet is procured, or pellets are knocked out of the bowl when attempting to grasp). Significant differences in motor ability were observed between control and Harmaline-treated rats, demonstrated by reduced total reaches and increased paw pose variability. TgF344-AD rats also showed reduced total reaches, as well as reduced successes across all bowls. In validating this as a fine motor task in rodents, it has future potential to study normal aging and preclinical models of neurodegenerative diseases, including AD, as pathology and symptoms emerge over time.
Motor function has been shown to be impaired in people with dementia due to Alzheimer's disease (AD) or other subtypes (e.g., dementia with Lewy bodies). Grip strength and gait speed can distinguish between cognitively impaired and unimpaired older adults1,2, but cannot necessarily differentiate between different subtypes of dementias (i.e., low specificity)1,2. Thus, there continues to be a need for new diagnostic tools for early dementia, possibly paired with machine learning, for evaluating complex patterns and relationships between behavior and disease state3,4. Animal models are valuable for investigating the progression of neurodegenerative diseases to offer insights into early disease onset, behavioral correlates, and integrating machine learning into analysis pipelines to distinguish between severity and presentation5,6,7.
New behavioral methods in humans have identified tasks that involve fine motor skill along with cognitive processing, like visuospatial ability and executive function8,9,10. For example, a functional upper-extremity task can track patterns of neurodegeneration and can distinguish mild cognitive impairment (MCI) from AD. In this task, humans move raw kidney beans (two at a time) from a central home cup to a target cup using a spoon. The phase of the task in which beans are acquired with the spoon shows stronger associations with cognition (specifically, visuospatial memory) than the phase in which the beans are transported to the target cup11,12. As the spoon enters the cup, it may perturb the beans in the cup, meaning the participant's movements may make the task more difficult. Further, as the trial progresses and the number of beans in the home cup decreases, the task becomes more difficult (i.e., time to acquire the beans with the spoon increases)13. This task has been shown to correlate with AD-specific pathology (amyloid)9,14 and neurodegeneration (hippocampal atrophy)10,15, suggesting its performance may depend on neural circuitry and behavioral processes that are affected early in the disease process. It is known that movement initiation, planning, execution, and correction of movement are controlled by a diverse network traversing the brain, including the motor cortex, with cell bodies of upper motor neurons located in the cerebral cortex synapsing on lower motor neurons in the spinal cord16. These lower motor neurons directly communicate with muscles to control movement at the neuromuscular junction. Ascending tracts carry sensory information from the periphery to the spinal cord, while descending tracts within the brain communicate directional information regarding how and where to move back to muscles17,18. Understanding how these pathways are selectively affected by AD pathology is critical to understanding disease progression, particularly since genetic animal models, including the 3xTg-AD mouse and TgF344-AD rat, have been previously used to study how AD genes impact locomotion, coordination, and other motor impairments19,20,21. Typically, these tasks focus on gross motor impairments relating to whole-body movement, but motor deficits in humans are much more variable, which can be difficult to identify in rodents22. New tasks are therefore needed to bridge the gap between human and animal models to capture this variability.
One brain region that is commonly implicated in motor impairments and tremors is the cerebellum23. The cerebellum is a significant regulator of precise and coordinated movements and acts as a processor of sensory information to provide feedback through descending pathways to correct and adapt this voluntary movement24,25. It is important to note that cerebellar atrophy has been recognized in individuals with AD, but, contrary to other regions like the hippocampus, increased amyloid deposition in the cerebellum does not correlate with increased atrophy26. While the progression of AD pathology is different in the cerebellum, there are multi-synaptic bidirectional connections between the cerebellum and hippocampus that are likely disrupted in AD27,28,29. Notably, there are functional connections between the cerebellum and hippocampus, as memory deficits have been documented to occur after cerebellar tumor resection30,31,32,33,34,35,36,37. In rodents, hippocampal place cells are disrupted with a cerebellar perturbation38. These overlapping deficits in memory, sociability, and motor control highlight the importance of uncovering potential early changes in motor pathways that occur before cognitive decline30,31,38,39. With the implementation of fine motor tasks in early AD research, we may begin to reshape the traditional understanding of AD as simply a cognitive disorder and redirect diagnostic efforts to early motor changes that may predict disease progression and may be more greatly distinguishable between cognitively impaired and unimpaired aging individuals.
To understand how fine motor behavior can be indicative of changes in brain structure, a rodent version of the human task described above was created and tested. This rodent task has been named KINEMAT (KINEmatic Motor Ability Task), and it deviates from previous reaching tasks that require the retrieval of a single pellet from a pedestal or staircase and the more simplistic scoring as a result of consistent pellet placement at all stages of the task40,41,42,43,44,45,46,47,48. This fine motor reaching task includes new scoring methods, bowls with various difficulties, and self-perturbing features to mimic the human counterpart, with pellet movement and subsequent participant strategy adjustments central to the design. This task employs the machine learning tools, Social LEAP Estimates Animal Poses (SLEAP) and keypoint-MoSeq49,50, allowing an unbiased and quantitative measurement of fine motor ability and reaching behaviors. While other notable computational models have been developed for skilled reaching tasks in rodents44,45,51, keypoint-MoSeq eliminates noise to allow for precise quantification of movement and grasping poses50. Task performance and motor learning strategy were reconstructed using a high-performance machine vision camera in typical and motor-perturbed rats using Harmaline52. Harmaline hyperactivates the inferior olivary nucleus and disrupts cerebellar function through glutamatergic climbing fibers53,54. In addition, fine motor ability was examined in a subset of TgF344-AD rats. Thus, it was expected that the fine motor task would find measurable impairments in Harmaline-treated and TgF344-AD rats relative to their typical counterparts as a proof-of-concept. This protocol describes constructing the fine motor task, training and testing animals, and quantifying reaching behavior using SLEAP and keypoint-MoSeq.
All methods described here have been approved by the Institutional Animal Care and Use Committee (IACUC) of Arizona State University.
1. Design and construction of the chamber
2. Computer and camera hardware
Figure 1: Schematic of the fine motor task. (A) Cartoon representation of the fine motor task whereby a rat reaches through a slot to obtain a sugar pellet. Reaches are recorded for machine learning analysis. There are three separate bowls used: a (bottom left) training bowl, (bottom middle) Plain/Less bowl, and the (bottom right) Plinko bowl with obstructions circled in red. (B) Schematics of each bowl design, including (top) training bowl, (middle) Plain/Less bowl, and (bottom) Plinko bowl. Please click here to view a larger version of this figure.
3. Animal preparation
4. Habituation (2 days)
5. Shaping (10 days)
6. Prepare for testing:
7. Testing (9 days)
Figure 2: Training and testing of rats performing the fine motor task. (A) Timeline of experiment. Rats were habituated to the apparatus for two days then underwent shaping procedures for 10 days. Animals were tested for fine motor ability across all three bowls for three days each bowl. Then, animals were injected with Harmaline and retested on each bowl across three days. (B) Rats (n = 6) underwent two different bowls (training bowl and Plain) during shaping procedures, and the bowl type was switched on day 5 to improve reaches. There was a significant increase in shaping across 10 days to reach the criteria (F(9, 45) = 13.5, p < 0.001). The grey bar designates the switch between the original and the current elongated tube training bowl. (C) For testing all three bowls, there was a significant difference in total reaches across the 9 days of testing (F(8, 32) = 3.74, p = 0.003). Note: Rat 2 was removed after shaping. The Plinko bowl resulted in significant differences between the Less (p = 0.001) and Plain bowl (p = 0.027). Please click here to view a larger version of this figure.
8. Modeling fine motor disruption using Harmaline
NOTE: To identify the effects of motor disturbances on fine motor ability and performance in the fine motor task, rats were injected with Harmaline (10 mg/kg), a β-carboline alkaloid that is a reversible inhibitor of monoamine oxidase-A and targets the inferior olive52,55,56,57. Harmaline in rodent models affects posture and balance, limits fine motor coordination, and induces motor disruption in the upper limbs.
9. Video analysis using machine learning
NOTE: Following data collection and video recording, reaches were counted by hand and scored as follows: Success (retrieve and consume pellet), Drops (retrieves the pellet but drops the pellet prior to consumption), Failure (unable to retrieve pellet). This hand-scored data was used as ground-truth data for the machine learning analysis.
Figure 3: Changes in reaching across bowls and treatment. (A) For baseline (n = 5) testing, across all 3 bowls, there was a significant difference in total reaches (H(df) = 8.48, p = 0.014, Kruskal-Wallis), with more reaches occurring with the Plinko bowl type (p = 0.011, Post-hoc Dunn's test with Bonferroni correction). (B) Harmaline (n = 5) perturbation did not result in differences between bowl types. Errors are reported as S.E.M. *p < 0.05. Please click here to view a larger version of this figure.
Figure 4: Comparison of performance between conditions and across bowl types. (A) There was a significant bowl (F(2) = 5.78, p = 0.007) and outcome difference (F(2) = 3.92, p = 0.029) during baseline testing. Reach attempts increased as bowl type became more difficult, although reaches were reduced from baseline across all bowls in the harmaline condition. (B) No differences were found in Harmaline-treated rats for reach counts across bowls. (C) There was a significant outcome difference (F(2) = 2.07, p < 0.001) during baseline testing when analyzed by performance. (D) No differences were found in Harmaline-treated rats for bowl or outcome performance. Errors are reported as S.E.M. Please click here to view a larger version of this figure.
Figure 5: Comparison of baseline and Harmaline conditions. (A) There were no differences in performance success between conditions. (B) For failure counts, there was a significant difference between Baseline and Harmaline conditions (H(df) = 4.85, p = 0.028, Kruskal-Wallis) with a significant effect found in the Harmaline condition (p = 0.028, Post-hoc Dunn's test with Bonferroni correction). Errors are reported as S.E.M. Please click here to view a larger version of this figure.
Figure 6: Learning and reaching rates. (A) Harmaline animals had a perturbed learning rate compared to the baseline condition (t-stat = 2.833, p = 0.022). (B) For total reaches per minute, there was a significant interaction between bowl (less) and condition (harmaline) (F(1,5) = 9.83, p = 0.002, mixed linear model) and bowl (plinko) and condition (harmaline) (F(1,5) = 22.67, p < 0.001, mixed linear model). Harmaline reduced the total reaches per minute across all bowl types (Less: p = 0.01, Plinko: p < 0.001) compared to baseline. Errors are reported as S.E.M. *p < 0.05, ***p < 0.001. Please click here to view a larger version of this figure.
Figure 7: Machine learning identifies unique poses and task strategy. (A) Node locations on the rat paw labeled using SLEAP. (B) Examples of syllables identified by keypoint-MoSeq during reaching. (C) Matrix of bigram transition frequencies between consecutive syllables. Transition probabilities are normalized by the total bigram count. (D) A transition graph comparing syllable transition differences between Harmaline and Baseline. Nodes of the graph represent syllables, and the edges indicate transitions between them. More prominent edges indicate syllable transitions with higher frequencies. Red connections indicate an up-regulated transition, while a blue connection indicates a down-regulated transition in Harmaline-treated animals. Red circles indicate up-regulated usage, while a blue circle indicates a down-regulated usage of the indicated syllable in Harmaline-treated animals. Pairs of syllables with frequencies less than 0.005 were excluded. Please click here to view a larger version of this figure.
After rats reached a 90% body weight food restriction, they were habituated to the chamber for 2 days, then underwent shaping procedures for 1-2 weeks. The number of successful, dropped, and failed reaches were scored by a trained observer blind to each condition. Over the course of 9 days (Baseline), rats were tested for fine motor ability on 3 different bowl configurations (Plain, Less, and Plinko). Then, the same rats (n = 5 per group, 1 removed after training bowl shaping due to illness) were injected with Harmaline (10 mg/kg, i.p.) once a day across 3 days to determine changes in reach ability, strategy, and performance (Figure 2A). In the design and construction phases, the preliminary training bowl failed to increase the reaching ability (Days 1-4). This resulted in a redesign of the training bowl with an elongated feature, and animals began to reach consistently for a pellet (Days 5-10), reaching training criteria by Day 10 (Figure 2B). Across all bowls, individual total reaches varied, with increased reaches occurring as the task became more difficult, demonstrating persistence (Figure 2C and Figure 3A). Inducing motor impairment via Harmaline disrupted normal reaching and reduced total reaches (Figure 3B). The number of reaches increased with bowl difficulty during Baseline (Figure 4A), but did not change in Harmaline-treated rats (Figure 4B). Performance, measured by percent success, dropped, and failures, were mostly successes and drops during Baseline testing until rats reached the Plinko bowl, which increased failures (Figure 4C). Harmaline disrupted typical reach performance (Figure 4D), and while success performance did not differ between conditions, suggesting possible adaptation (Figure 5A), failures remained consistent across bowls in the Harmaline condition compared to Baseline (Figure 5B). Prior to Harmaline treatment, failures increased with bowl difficulty, as seen in the Baseline group (Figure 5B). Harmaline reduced the learning rate of the task (Figure 6A) and total reaches per minute across all bowl types (Figure 6B). Analysis of reaches using machine learning revealed unique poses and task strategy between Baseline and Harmaline conditions and within each bowl type (Figure 7A,B) using SLEAP and keypoint-MoSeq. Applying these software tools to this fine motor task, the software found unique transitions between syllables, which are defined as a stereotyped behavior or movement that the algorithm identifies and segments, and the use of specific syllables within each bowl type (Figure 7C) and between conditions (Figure 7D). Interestingly, syllables revealed less variation in grasping motions as bowls increased in difficulty, suggesting an improvement in task strategy and that unique poses are required for task success. Between conditions, Harmaline-treated rats were found to engage in different reaching strategies to match similar task performance to Baseline to possibly compensate for the induced acute neural impairment. Lastly, the fine motor task was implemented for a preclinical model of Alzheimer's disease, the TgF344-AD rat (n = 12 per genotype). Testing was done at 6 months of age to examine whether the fine motor task could detect motor deficits in Tg rats prior to the onset of neural loss and widespread accumulation of amyloid. Compared to wildtype littermates, transgenic (Tg) rats had reduced total reaches (Figure 8A). WT rats increased reaches as bowls became more challenging (Figure 8B), which was not seen in the Tg rats (Figure 8C). While performance type (success, dropped, failure) was variable within each group (Figure 8D,E), Tg rats had significantly reduced success performance compared to WT (Figure 8F). As bowls became more challenging, the number of failures increased in both WT and Tg rats (Figure 8G).
Figure 8: The fine motor task was used to examine a preclinical model of Alzheimer's disease at 6 months of age. (A) For total reaches there was a significant difference between the genotypes (H(df) = 17.6, p < 0.001, Kruskal-Wallis) with Tg rats (n = 12) having significantly less reaches than WT (n = 12) (p < 0.001, Post-hoc Dunn's test with Bonferroni correction). (B) WT rats demonstrated increased reach attempts across bowl types (F 2,33) = 4.422, p = 0.02, ANOVA). (C) No significant differences were found in reaching for Tg rats. No significant differences were found in (D) WT or (E) Tg performance across bowls. (F) There was a significant effect of genotype between WT and Tg rats for success performance (H(df) = 7.63, p = 0.006, Kruskal-Wallis). (G) For failure counts, there was a significant bowl difference, but no differences in genotype (H(df) = 25.41, p < 0.001, Kruskal-Wallis). Errors are reported as S.E.M. *p < 0.05, **p < 0.01, ***p < 0.001. Please click here to view a larger version of this figure.
Reaching tasks for rodents are commonly restricted to single pellets and do not include obstructions or self-perturbing features, which are common struggles for patients with neurodegenerative pathology. The method presented here, using both a reimagined pellet-reaching task and machine learning, can detect both coordination and strategy through analysis of pose features and transitions, in addition to individual digit movements. The fine motor task created here detected coordination and strategy via analysis of pose and transitions between poses. A typical reaching task commonly involves a shelf upon which a single food reward is placed for the test subject to retrieve. With its reliance on a single, repetitive reaching strategy, the single-pellet iteration lacks metrics related to task difficulty, potential learning when used longitudinally, and challenges in strategy adjustment with pellet placement changes throughout testing, which are captured by this new method. This omission in the traditional single-pellet task introduces a ceiling effect in performance in which rodents plateau in both speed and accuracy due to mastery of a single, repetitive reach42. By varying bowl types that differ in pellet quantity and obstructions as shown here, rodents must continually update a strategy to navigate bowl variability and pellet movement as adjacent pellets are perturbed41,58. The self-perturbing features of this task may also more readily recruit a larger network of brain regions. Variability in the placement of pellets within the bowl requires the involvement of both sensation and movement-related systems, exemplified by greater cross-communication between the cerebellum, basal ganglia, and hippocampus. This is not substantially captured by a single-pellet task, which may recruit regions involved simply in the initiation and precision of movement once the task is learned46,59, forgoing a greater cognitive or learning component. This distinction is exemplified by the use of Harmaline. Harmaline had a notable impact on performance, specifically in learning and reaching rates from Baseline (Figure 6A). Despite this finding, successful performance remained constant, suggesting adaptation to the task. Furthermore, the performance of 6-month-old TgF344-AD rats was also impaired (Figure 8), showing additional proof-of-concept that the task may be sensitive to early motoric deficits in AD.
Preliminary iterations uncovered specific design elements that required further consideration and reconfiguration. In initial testing of bowl configurations, it was apparent that the bowl shape was crucial to the advancement of reaching behavior. Of note, reaching performance showed an upward trend following day 6 of shaping, which was the first day the rats were introduced to a revised bowl design (Figure 2B). As opposed to previous configurations that were simply circular dishes housing many pellets, this training bowl added an elongated extension, allowing for the retrieval of single pellets at a close distance during the shaping phase. The width of the extension was small enough to fit into the apparatus to allow for the movement of the bowl both farther into and farther out of the rodent's reach to promote reaching behavior and approach to the front of the apparatus. With the simple transition from a bowl with raised edges to a bowl with increased visibility and opportunity for early single-pellet reaching to learn the task, there were significant improvements in rodent behavior. Moreover, the shutter cutout at the front of the apparatus was also revised several times prior to testing. Rudimentary designs had a T-shaped cutout, allowing rodents to utilize both paws to procure pellets. Despite the added room to use both extremities, rodents were erratic in behavior, failing to grasp pellets in a manner that allowed for individual reach analysis and counting. Reaches appeared aimless until the introduction of the current shutter design, which limited the reaching space to a single, vertical opening. With the latter configuration, rodents are forced to reach one arm into the bowl, allowing for the quantification of individual reaches and the analysis of reaching pose using machine learning techniques. It is also important to take into consideration the size of the provided sugar pellets and their impact on proper analysis. The pellets utilized are manufactured in two available sizes, 25 mg and 45 mg. Both pellet sizes easily fit into the palm, but the larger size ensures that only singular pellets are retrieved. The 25 mg option allows rodents to grasp multiple pellets at once, often resulting in both a success and a drop within the same reaching bout. To eliminate this ambiguity in reach categorization, the larger 45 mg pellets are recommended to encourage rats to only grab one pellet at a time.
Additionally, it is essential that rodents are food-deprived throughout the habituation, shaping, and testing phases to maintain motivation to procure a food reward. Testing with the elimination of food deprivation yielded limited results, with rodents indifferent to approaching the front of the apparatus, unwilling to reach during the 10-min trials, and often preoccupied with grooming at the back of the apparatus, out of the camera's frame. As outlined in the protocol, rodents should be food-deprived a week prior to the start of habituation to encourage apparatus exploration and potentially limit the impact of the novel environment with the presentation of the food reward (i.e., sugar pellets) at the earliest stages. Rodents food-deprived prior to task initiation have been shown to express greater interaction with a food object along with greater exploratory behavior despite slight weight loss, without limiting locomotion60. To further facilitate the association between the apparatus and the presentation of a food reward, pellets should be placed within the apparatus for easy access at the rodent's first exposure to the apparatus.
Several methodological limitations are pertinent to mention. With the implementation of machine learning, motor planning, learning, and pose throughout KINEMAT can be analyzed in an unbiased manner. However, the reach counts of each outcome (success, dropped, failure) were scored by several trained observers by hand, without the use of machine learning. Future experimenters can use this data as ground truth and create automated scoring methods. The reaching outcomes (i.e., success, drop, and failure) must be standardized across experimenters to ensure that reaches are properly classified. Reaching analysis could be further improved with additional cameras to capture top-down, opposite side angle, and body position throughout the task for 3D pose estimation. Further, the size of the rat, but not the strain, may pose a constraint. Animals larger than 800 g in body weight will not comfortably fit within the proposed apparatus, and those much smaller than 100 g cannot comfortably reach for the pellets with the elevated bowl without rearing. Thus, behavior may be restricted if these requirements cannot be met. Lastly, while animals were handled daily, rats did not receive injections of saline to control for injection-related impacts on behavior prior to Harmaline. Future experiments should include a saline or vehicle injection condition.
The strength of this fine motor task lies in its translational value, bridging preclinical rodent work, allowing for more invasive investigations of the neural networks imperative in disease progression and decline, with important preliminary clinical findings. Here, it is shown that the fine motor task is sensitive to changes in reaching and performance for both an acute cerebellar perturbation model and a preclinical Alzheimer's disease model, the TgF344-AD rat, specifically prior to the onset of cognitive impairments. The dynamic, self-perturbing design and innovative analytics have significant clinical value. The use of machine learning accommodates discrete types of analysis, allowing for the identification of minute fine motor differences between animals of the same strain, different conditions, and potentially different genotypes. Importantly, this task leverages prior work on rodent limb, head, and tail kinematics to study gait32,61,62,63 and other tasks analyzing endpoint precision kinematics44,64, particularly focusing on cerebellar deficits. With a myriad of bowl designs and the use of machine learning to examine additional features of animal pose and strategy, this task expands upon former work. With the use of 15+ nodes and principal component analysis, this task allows for both discrete and global metrics focused on single node movements as well as coordination between these nodes of interest. Future additions to this preliminary study include paw cohesion analysis and looking at the distance between digits (i.e., knuckle and fingertip separation), which has been found to be a significant indicator of localized brain atrophy in patients with AD65. Unlike traditional tasks that assess repetitive movements, this task attempts to mirror real-world challenges encountered by patients with neurodegenerative conditions that require continuous strategy adjustment as opposed to singular, repetitive movements. By requiring animals to adapt to shifting pellet locations and bowl configurations, this paradigm models adaptability deficits often seen in human patients, providing a more comprehensive assessment of motor learning and strategy formation.
The authors declare no competing financial interests.
This work was supported by the Institute for Mental Health Research, Institute for Social Science Research, Arizona Department of Health Sciences [ADHS14-052688], US Department of Health and Human Services, National Institutes of Health [P30AG019610], Arizona Alzheimer's Disease Research Center REC Fellows Program, Arizona Alzheimer's Consortium, and Nancy Eisenberg Junior Faculty Scholar Award. Figures were created using Biorender.com. We would like to thank Nelson Yamada, Akash Kuppravalli, and Jeanne Kamau for their design assistance.
| 3D printing material | Amazon (Overture) | B07PGY2JP1 | PLA Filament 1.75 mm PLA 3D Printer Filament, Dimensional Accuracy +/- 0.03 mm |
| Camera | Edmond Optics | BFS-U3-13Y3M-C | 1.3 MP, Mono, 170 FPS, ON Semi PYTHON 1300 x2 |
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| Camera lens | Thor labs | MVL5WA | 4.5 mm EFL, F/1.4, 1/2" Format Machine Vision Lens |
| Camera to tripod connection | Edmond Optics | 88-210 | Connection for camera to tripod |
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| Computer | Dell | Precision | Intel Core i7-13700 @ 2.1 GHz, NVIDIA T1000 8 GB, RAM 16 GB |
| IR light | Amazon (Serlium) | B0BJFDBT44 | Camera IR Light 48 LED IR Illuminators Lights Waterproof Infrared Night Visionss Light for Security CCTV Camera |
| Panel glue | Amazon (Sdintar) | B0B1DLRPNZ | Glass glue |
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| Python | Python Software Foundation | Python 3.7 | |
| Spinview | Teledyne Technologies | Version 3.1.0.79 | |
| Sugar pellets (45 mg) | Bioserv | F0023 | 45 mg, Unflavored 50,000/Box; Ingredients: Sucrose, Dextrose, Magnesium Stearate, Calcium Silicate, Mineral Oil |
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