The primary objective was to develop an educational video to teach patients with rheumatoid arthritis (RA) self-assessment of their disease activity. Secondary objectives were to validate the video, identify the challenges in producing it, and the responses to these challenges.Rheumatologists from 7 Middle Eastern Arab countries (MEAC) discussed unmet needs in the education of patients with RA. They reviewed pre-existing educational audiovisual material and drafted the script for a new video in Arabic. The video was produced in collaboration with a technical team, then validated by patients using a standardized interview. At each step of production, challenges were identified.Twenty-three rheumatologists from MEAC identified unmet needs in patients’ education. A video was produced, explaining the concepts of treat-to-target and showing a patient performing self-assessment using DAS-28. Sixty-two patients were interviewed for validation and found the video to be useful and easy to understand, albeit not replacing the physician’s visit. Most common challenges encountered included acceptance of patient empowerment, agreement on DAS-28 as composite measure, production of a comprehensible written Arabic text, and addressing the population cultural mix.Despite challenges, the video was well accepted among patients and can be used for clinical and research purposes. It is particularly useful in pandemic periods where social distancing is recommended. Untreated, RA may lead to severe progressive joint destruction, functional disability, and premature mortality.5 First published in 2010, the main pillars of the T2T recommendations are the definition of a treatment target, the assessment of disease activity using composite measures, the modification of treatment if the target is not achieved within a specific timeframe, and the consideration of individual patient characteristics and shared decision-making.7Over the last decade, the concept of treat-to-target (T2T) revolutionized RA management and improved disease prognosis.9 and their use in daily practice is recommended by major rheumatology associations such as the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR).11 Such measures include the widely used Disease Activity Score (DAS-28), or the more recent scales, the Clinical Disease Activity Index (CDAI), and the Simplified Disease Activity Index (SDAI).13 All three correlate almost linearly with each other and with impairment in physical function.15The use of composite disease measures are critical to the success of T2T,16Although helpful, collecting these indices regularly in daily practice by the healthcare providers is time-consuming. Thus, involving the patient in the self-assessment of their joint count may expedite the evaluation process during their clinic visits. Moreover, empowering the patient, through education, improves the perception of disease activity in-between visits and enhances treatment adherence.22With the help of new technologies, several self-assessment tools were recently developed and are available through interactive e-health platforms, web applications, and smartwatches. They allow the monitoring of patients between consultations and assist in making more informed treatment decisions. They are becoming a cornerstone of the emerging telemedicine movement, especially during times where social distancing is recommended, such as the COVID-19 pandemic.23 Video-assisted patient education has also shown efficacy in modifying patient behavior, especially when using a model patient enacting a behavior.23 The video format is time-efficient, round-the-clock accessible, and allows the learners to proceed at their own pace.24However, the optimal format for teaching self-assessment to patients is still debated. Using audiovisual material, such as videos, is one effective method to convey complicated medical concepts in a simple and accessible manner to a layperson.26 The videos are around 20 minutes long and feature a healthcare provider explaining the T2T concept, followed by a patient performing self-assessment of 28 joints. To our knowledge, no such material was developed and adapted in the Arabic language.The National Rheumatoid Arthritis Society (NRAS) and the Cochin hospital’s team developed each an educational video in English and French, respectively, to teach RA patients self-assessment of their disease activity using DAS-28.The objective of our study is to develop an educational video, in Arabic, to teach patients with RA self-assessment of their disease activity using DAS-28. Secondary objectives are to validate the video, identify challenges in producing it, and to discuss responses to these challenges.Rheumatologists (“study team”) from seven Middle Eastern Arab countries  were invited through emails to participate in the project. The study team met in-person during a regional rheumatology meeting to identify the unmet needs in patient education in the Middle East and to delineate the ideal formats to address these needs. The development of a culturally appropriate educational video was identified as a priority, and a roadmap was created for its production.Table 1) was used to develop the video.35 Pre-existing educational audiovisual material was identified through a search of the literature, of patients’ associations’ web-sites and social media platforms. Two videos addressing self-assessment using DAS-28 were found and reviewed by the study team. The first is published online in English by the NRAS, and the second in French by the Cochin hospital’s team, under the auspices of the French Society of Rheumatology.26 The strengths and weaknesses of these videos, as well as the appropriateness for the Middle Eastern cultural setting, were discussed by the study team, a rheumatology nurse, and a medical student. Based on the discussion, a script for the video was drafted. The physical exam section was transcribed from the French language video, translated into Arabic by the study team, and then reviewed by a professional translator for clarity of the language. A patient with RA was then invited to participate in to be the model/ actress in the video. Then, with the help of a professional technical team , the video scenario was planned. This included choosing the video setting, the movie cuts sequence, and the patient’s clothing. On the day of filming, a rheumatologist (NZ) and a nurse (NS) were present on site. The video was edited in a professional studio in the presence of a rheumatologist (NZ) and a medical student (AD).A list of evidence-based guiding principles .First, the video was reviewed for accuracy and clarity by the study team through multiple email rounds. Then, a sample of consecutive Arabic speaking patients with RA from 6 MEAC was invited by their rheumatologist to participate in evaluating the video. The rheumatologist explained the purpose of the video and obtained consent from the patients. In a separate room, the patients viewed the video and were interviewed by a trained rheumatology nurse who collected the patients’ feedback about the clarity of the video, acceptance, ability, and willingness to self-assess  the patient’s difficulty in understanding the chronic nature of rheumatic diseases, and 2) the low adherence to prescribed regimens, especially after the symptoms have improved. Those gaps were deemed as a priority as they are tightly related to disease prognosis. Moreover, the study team agreed that, although the use of objective composite measures for assessment of disease activity in RA was desirable and can improve patients’ outcomes, their implementation can be highly time-consuming in daily practice. Therefore, developing appropriate educational material that involves patients in their disease management strategy was considered a priority by the study team.37The study team then discussed and resolved several challenges before proceeding into the stage of video development. The first was to decide on the choice of the disease activity measure to be used in the video. The following measures were discussed: DAS-28, CDAI, and SDAI. Although DAS-28 has some limitations, it was strongly recommended by the majority of the study team as it is the most widely used and includes joint counts, inflammatory markers, and patient’s global assessment.The second challenge was accepting the idea of patient empowerment and participation in their disease management. Several rheumatologists, especially those working in the private healthcare sector, raised their concern about whether promoting patient self-assessment could lead to a decrease in the frequency of patients visits and a decline in their income. This could be a valid concern, yet the purpose of promoting self -assessment is to complement the physician’s evaluation, decrease the visit time, and motivate the patient’s adherence to the therapeutic strategy.The third challenge was the skepticism of some members of the study team about patients’ adherence to the skills taught in the video. It was agreed that the rheumatologist should use their judgment in selecting the suitable candidate-patient to whom self-assessment would be offered.The two videos from NRAS and Cochin had detailed content and were used as reference documents. Both NRAS and Cochin team provided permission to use the videos for information. However, the study team felt that both videos were too long (around 20 minutes), that some sections used technical terms that may be difficult to understand, and that the choice of clothing of the model may not be acceptable by all cultures in the region. Also, both videos were relatively difficult to access, requiring an internet connection or a DVD player. Thus, the study team decided to create a new video in Arabic that would be shorter in duration, uses less technical terms, and better aligns with the local culture. Moreover, the video was designed in a user-friendly format and accessible without the need for an Internet connection.The study team wrote the script for the video. The script was then reviewed by a professional translator for accuracy. To simplify the medical terms used in the video and make them understandable by the majority of the patients who speak the different Arabic dialects, the study team, using euphonic nomenclatures, translated these terms based on their dialect. Then, they reached a consensus on which translation should be included in the video.The choice of the actor’s gender and clothing was also a matter of debate by the study team. Taking into consideration that the target audience is middle-aged women with diverse cultural and religious beliefs, a middle-aged female RA patient, with previous experience in television advertising, was selected for the role. The patient/actor wore neutral, conservative, and dark clothing for the filming. Prior to filming, she received a detailed briefing by her rheumatologist on self-assessment of disease activity. The video was filmed in a traditional home setting in the presence of a professional technical team, a rheumatologist, and a nurse. Then, the best video cuts were selected by the technical team. The cuts went through editing, voice cover, musical cover, and subtitling phases. The duration of the video was seven minutes, which was significantly shorter than the reference videos. English subtitles were prepared by the translator and reviewed and approved by the study team through multiple email rounds.https://www.youtube.com/results?search_query=autodas+meac.The new video was made easily transferable to the patient’s cellular phone and accessible without the need for an internet connection. The video can be accessed at this link: After completion of the video and in collaboration with a graphic designer, a supplementary cartoon version, depicting a male character, was also created as an alternative to be used for male patients and patients from more conservative populations.Table 2). First, it was challenging for patients to feel confident in the accuracy of their assessment. They were reassured that their self-assessment will not replace the rheumatologist’s assessment but rather than help them make more informed therapeutic decisions.After the study team approved the video, a sample of 62 consecutive RA patients attending the rheumatology clinics of the study team participated in the validation of the video. All patients were keen to be educated about their disease management and to understand the treatment decision algorithm followed by their physicians. However, several were reluctant to accept that they can accurately self-assess, and most stated that it would not replace the doctor’s visit. Ninety-six percent found the section of the video explaining the self-joint examination was easy to understand. Forty-one percent of the patients, however, found the scoring to be challenging and needed practice and support from the physician or the nurse. Most of the patients (76%) reported that self-assessment would not totally replace the doctor’s visit. Several challenges were identified during the development of the video and its validation (Second, several patients found the scoring to be challenging. This was facilitated by providing several practice sessions by the physician or study nurse that helped improve their skills and confidence.Third, it was challenging to find an unbiased source of funding. Thus, to complete the project, all centers graciously volunteered to participate in the study without financial compensation. All study group meetings were aligned with local, regional, or international conferences.The production of an educational video in Arabic for self-assessment of disease activity for patients with RA was successfully implemented.38 Additional important enablers were adhering to the evidence-based guiding principles for developing educational videos28 and identifying the unmet needs for RA patient education by the study group.40 Moreover, the availability of two previous videos26 allowed recognizing a priori strengths and weaknesses in each video, which helped improve the current video. Finally, the inclusion of patients in the validation phase added to its pertinence and applicability. A major advantage of the produced video was the collaboration between a highly experienced team of rheumatologists and a professional technical team. This collaboration lead to a script that was understandable in most Arab countries and visuals that are culturally appropriate to the Middle East region. The English subtitles made the video also accessible to non-Arab speaking expatriate patients living in Arab countries. Moreover, having a real patient acting in the video facilitated the acceptance by the target patients’ population.28This was primarily facilitated by the collaboration between rheumatologists from several Arab countries, as witnessed in previous RA projects.41 and cervical cancer health literacy,42 was a clear impact on changing patients’ behaviors positively. Also, the above videos, as well as the NRAS and Cochin RA videos,44 were well received by the patients who appreciated the education offered to them.Furthermore, as compared to the Cochin and NRAS video, the video was relatively short in duration, downloadable on a smartphone, and accessible without the need of an internet connection, making it easier to watch repeatedly. Finally, if needed at a later stage, the video can be easily modified and adapted for use by other populations. The outcome of educational videos on several different topics, such as education about pneumococcal vaccination24The video format is relatively expensive to produce, and its creation is time-consuming. However, it remains an accessible source of education to most of the patients and allows them to watch it repetitively at their own pace, from the comfort of their home.45 DAS-28 is strongly associated with both inflammation and patient-reported outcomes.36 However, the subjectivity related to the patient global assessment and the possible variability of the biological inflammatory markers, which may be elevated due to causes other than RA, may have an impact on the stability of the final score. Moreover, DAS-28 calculation may be complicated for the layperson and needs a special calculator. In case patients were resistant to calculating a composite score, the concordance of more intuitive scores, such as CDAI and SDAI, may be used as alternatives in the future. The video may be easily adapted to editing the final section on scoring.The study also has several limitations. The choice of DAS-28 as the outcome measure may be questioned.Another limitation is the generalisability of the video to different patient populations. Patients with low levels of education may find the video difficult to use and, therefore, may not fully benefit from this educational intervention. Finally, patients who may be known to be non-compliant to treatment may use the self-assessment as an excuse to skip important and irreplaceable visits to the rheumatologist. Although this was not reflected in the patient interview, the clinical judgment of the rheumatologist should guide the selection of patients to whom the video will be offered and how it will be used. Empowering patients to self assess their disease activity might be viewed as a double-edged sword to some physicians. They might fear some loss of income by losing the visits of patients who can self-assess their disease. In our opinion, and in the setting of a busy practice, the precious time saved can provide additional consultation time slots to offer care for more patients in more need to see the rheumatologist, potentially increasing both income and patient satisfaction.The collaboration between several countries sharing the same language, close cultural backgrounds, and comparable unmet needs was possible and able to produce an educational material in lay language aiming at self-assessment of patients with RA. Many challenges were faced at every step of the production and were resolved through consensus and teamwork. The video may serve for future research and clinical purposes, according to the rheumatologist’s clinical judgment.


Aeronautical ad hoc network (AANET) has been considered a promising candidate to complete the vision of “Internet in the sky” by supporting high-speed broadband connections on airplanes for 6G networks. However, the specific characteristics of AANET restrict the applicability of conventional topology and routing management algorithms. Here, these conventional methodologies reduce the packet delivery success of AANET with higher transfer delay. At that point, the artificial intelligence (AI)-driven solutions have been adapted to AANET to provide intelligent frameworks and architectures to cope with the high complexity. The AI-driven AANET can provide intelligent topology formation, sustainability, and routing management decisions in an automated fashion by considering its specific characteristics during the learning operations. More clearly, AI-driven AANETs support intelligent management architectures, overcoming conventional methodologies’ drawbacks. Although AI-based management approaches are widely used in terrestrial networks, there is a lack of a comprehensive study that supports AI-driven solutions for AANETs. To this end, this article explores the possible utilization of primary AI methodologies on the road to AI-driven AANET. Specifically, the article addresses unsupervised, supervised, and reinforcement learning as primary AI methodologies to enable intelligent AANET topology formation, sustainability, and routing management. Here, we identify the challenges and opportunities of these primary AI methodologies during the execution of AANET management. Furthermore, we discuss the critical issue of security in AANET before providing open issues. The satellite and air-to-ground (ATG) connectivity are the two pivotal methods to enable in-flight connectivity (IFC) for the next generation (6G) of mobile wireless networks . HoweverThe AANET operation is to form a topology and sustain it to enable packet routing through A2A links during IFC. However, at this point, the use of existing topology formation, sustainability, and routing management algorithms is challenging due to the specific characteristics of AANETs . AccordiThere are various literature works proposing clustering algorithms for AANET topology formation. A honeycomb division-based clustering algorithm to solve the scalability, cluster head bottleneck, and overlap problems presented in . Here, tIn addition to the above, few algorithms enable the sustainability of AANET in literature. Some works promote this sustainability through handover management similar to the cellular networks. The dual connectivity-based handover management algorithm is proposed with a software-defined networking controller on VHF and mobile user objective links in . They aiMoreover, numerous studies propose routing management algorithms for AANETs in the literature. A geographic routing protocol AeroRP is presented to enable multihop routing for highly dynamic aeronautical networks in . The autAs explained in the above section, there are various works in the literature proposing AANET topology formation, sustainability, and routing management algorithms. On the other hand, these algorithms cannot handle the AANET-specific characteristics simultaneously. Therefore, to overcome the AANET management challenges caused by its specific characteristics, we require intelligent frameworks and architectures specially designed for topology formation, sustainability, and routing procedures. This intelligence could be achieved by embedding artificial intelligence (AI)-based solutions to the design of AANETs. The embedding of AI to wireless networks is well accomplished ,24. HoweExamination of AI for AANET management: The utilization of AI-based methodologies for AANET management is still an unexplored area. This is the first work to investigate possible AI methodologies for AANET management.Investigation of 6G and AANET relation: This is the first work to highlight the importance of the relationship between core 6G supports and AANET-specific characteristics.Discovery of AANET’s specific characteristics: We describe AANET’s specific characteristics to highlight the necessity of AI for AANETs. These specific characteristics also become the starting point in AANET management problems.Discussion about AANET management problems: We discuss three main management problems for AANET caused by its specific characteristics.Matching of AI methodologies and AANET management problems: We give our specific attention to proposing AI-based methodologies in three main AANET management problems.In this article, we investigate the possible AI methodologies to ease the management of AANETs. More specifically, as shown in The rest of the paper is organized as follows: In Unlike other ad hoc networks, the AANETs have specific characteristics, which are the root causes for different management-level challenges. Before giving the details of these challenges, we first investigate the specific characteristics of AANETs by matching them with AI-based methodologies to underline the necessity of AI-based solutions.High Aircraft Density in 3D Space: One of the main characteristics of AANET is the movement of a large number of aircraft in the 3D environment, which produces a massive amount of data on heterogeneous characteristics . Accordingly, this enormous amount of heterogeneous data overwhelms the management procedures of AANETs. AI has become the leading solution to learn and analyze these data for AANET management. Therefore, we can make estimations, create clusters, and recognize patterns in AANET by analyzing the collected information through AI.Unstructured Ad Hoc Topology: As the name suggests, the AANETs have an ad hoc characteristic that is independent of central guidance. Airplanes in the network can directly communicate with each other, and during this communication, each aircraft behaves as a router. Coupled with the ultra-dynamic network characteristic, this ad hoc infrastructure makes topology formation a challenging issue. Here, we can handle the topology formation of AANET through AI. At this point, we propose to utilize unsupervised learning due to its unlabeled data supporting character. Clearly, we can form a topology by trying to search for meaningful structures in an unstructured topology through unsupervised learning, as shown in Ultra-Dynamic Network: The ultra-dynamism is one of the essential AANET characteristics resulting from airplanes’ high speed. This over-speed makes the formed network ultra-dynamic by increasing the breakage rate of A2A links between airplanes. Accordingly, the AANET topology with aircraft nodes and A2A links changes continuously. However, continuous topology creation becomes time-consuming in this highly dynamic network. Instead, the initially formed topology could be utilized through supervised learning, as shown in Packet Transfer through A2A Link: The packets of the source aircraft are transferred through A2A links until reaching the destination to enable Internet connectivity. However, due to the ad hoc behavior and ultra-dynamic network characteristics, the determination of the next plane becomes challenging . More spThese specific characteristics become the origin of management level challenges, as illustrated in Moreover, in literature, various works propose AANET algorithms without utilizing AI i.e., ,27,28). ,28. 27,2One of the crucial needs of 6G is to enable seamless connectivity for harsh environments, and Internet connectivity should be easily sustained in all circumstances and conditions. With this connectivity support, 6G can gain an advantage over other generations. At that point, airplanes are one of the main harsh areas of Internet connectivity. Although the satellite and air-to-ground networks enable Internet connectivity to airplanes, their high cost and low coverage problems reduce efficiency. Here, the AANETs can enable Internet connectivity to airplanes by extending coverage areas with cost reduction. For this reason, the AANETs will be very important for the next-generation 6G concept. Moreover, the core features of 6G support the AANET by enabling support to its specific characteristics, as given in As summarized in In the previous section, we explained the necessity of AI for AANETs by matching the critical AI methodologies with AANET-specific characteristics. In what follows, we investigate the details of three management challenges caused by AANET’s specific characteristics. We aim to handle each of these management challenges with AI-based methodologies. At that point, the AI-driven methodologies enable fast adaptation of airplanes to the dynamic conditions of AANETs . More spAs explained in Unsupervised learning is the most relevant AI concept for AANET topology formation management. Here, the main aim of unsupervised learning is to find previously unknown patterns in data. Accordingly, it aims to explore the data to find meaningful structures and define classes. If we think about AANETs, we have independently located airplanes with unstructured characteristics before forming the topology. They could be considered as the unlabeled training data for unsupervised learning. Accordingly, our main aim is to group the airplanes with similar features under one cluster to form the AANET topology . TherefoO(n2).Prototype-based Clustering: It assigns the instances to the nearest prototypes, and the K-means clustering constitutes one of the primary examples. It can easily be implemented and effectively work with large data sets. This enables high-speed performance for the ultra-dense and 3D AANET environment. However, the cluster number should be assigned before the algorithm runs, which contradicts the ultra-dynamic characteristic of AANET as summarized in O(kn2), as presented in n represents the clustered elements while k becomes the number of clusters.Hierarchical Clustering: The hierarchical clustering aims to create clusters based on the previously established ones. During this creation, two different algorithms, agglomerative and divisive, could be utilized. The divisive approach starts with one initial cluster and then splits this into child clusters. Conversely, the agglomerative clustering starts from smaller groups to form larger clusters according to the bottom-up approach. In this clustering, we do not specify the number of clusters initially, which is the main advantage of the dynamic environment of AANETs, as summarized in O(nlogn).Density-based Clustering: Density-based clustering can discover non-linear clusters with arbitrary shapes. This feature increases the clustering performance in the unstructured topology of AANETs. Similar to hierarchical clustering, this method also does not require the defined cluster number . HoweverThe main aim of the clustering-enabled AANET topology formation is to create different aircraft groups according to their similarities . In AANEClustering is mainly utilized to form the AANET topology, as explained above. However, other unsupervised learning methodologies could also be used for AANET topology formation. Here, we introduce the Self-Organizing Maps (SOMs), an artificial neural network based on unsupervised learning. The SOMs use competitive learning to cluster the distinct patterns from the input data into output sets. SOMs can handle the previously unknown inputs, and this strongly matches the ultra-dynamic characteristic of the AANETs, as summarized in O(n2), as noted in n represents the size of the samples.After explaining the main reasons for selecting the SOMs for topology formation, we aim to adapt it for AANETs. During this adaptation, each aircraft in AANET corresponds to the input vector in SOMs. These input nodes are directly connected to the output without any hidden layer, and here, each connection is associated with a weight value. In the output layer, the AANET clusters become the output vectors, and the number of these represents the maximum cluster number of the AANET . These cThe ultra-dynamic network constitutes one of the main characteristics of the AANET caused by the high speed of airplanes. As detailed in Supervised learning to ensure the sustainability of AANET based on the initial topology. More specifically, we can use the initially formed topology as a training set to determine its upcoming states due to supervised learning can produce output by utilizing previous experience [At that point, we can take advantage of perience ,37. MoreThe learning vector quantization (LVQ) could be considered the supervised version of the SOM. Similarly, the LVQ classifies the patterns into output units that denote classes. More clearly, the initially given network and output classes are utilized to classify an input vector . This wopre-classified training data in multidimensional space of AANET. Here, the weight vectors are the airplanes in the formed AANET cluster. Additionally, the aircraft with a broken A2A link corresponds to the input vector. Accordingly, each known aircraft cluster becomes output. We aim to sustain the AANET topology instead of continuous topology formation by utilizing LVQ. Although LVQ is critical for enabling topology sustainability, it can achieve lower success on high-dimensional data. This situation contradicts the high aircraft density characteristic of AANETs in 3D, as summarized in Therefore, we can utilize the LVQ for topology sustainability management in AANETs. Accordingly, our main aim is to find the best matching cluster for an aircraft observing A2A link breakage . Here, wAs explained in Unlike the routing algorithms given in Environment of Q-learning, and each aircraft performing packet transfer through A2A link in this environment becomes an Agent, as shown in (RReward ). Therefore, a reward is obtained according to the result of each action. The obtained rewards are used to fill the Reward Table. Here, each cluster in an AANET is represented with a reward table. This table shows the airplanes in that cluster with rows and columns. Accordingly, the rewards for the corresponding links are placed in the cells of this table. After obtaining rewards and filling the reward table, we can observe the new states, as shown in (sState ) represents the status of an aircraft according to its packet transfers. These determined states and rewards help us to update the Q-Table that enables aircraft to learn the environment as a result of experiences. Then, each aircraft can determine the actions in the next step. At this point, the Bellman Equation, as given with Equation  is a reward value obtained by taking action a in state s. The Q(s) equals the value of being in a particular state, and the s′ is the state coming after s by taking action a. Finally, γ is defined as a discount factor. Additionally, the complexity of this algorithm is the O(n2), as summarized in Deep Q-Learning could be utilized to solve the performance defects caused by the complex action, and state-space [According to the Q-learning concept, aircraft can choose the path that maximizes Equation  by consite-space ,46. In dte-space .There are various works based on these three management-level issues, and only some results among them take advantage of the AI-driven management in a limited way, as shown in In addition to the AANET A2A links with the above-explained characteristics, different connection types could be used for various use cases. We summarize the important connection types and the related use cases in Security is hugely related to the mode in which the networks operate. With ad hoc features, the security needs to be swift and flexible. In the case of AANETs, transmission security becomes an ultra problematic area as an attack on the AANETs system can be devastating, leading to huge losses. Moving towards the pillars of confidentiality, integrity, authentication, and availability, security will become complex given the topology adjustment because of the increasing density of aircraft. The attackers may exploit the sensitive equipment on board, which may severely impact flight monitoring, fabricated path generations, navigation, and data sharing. One of the aims of the AANETs is to provide distributed services across the passengers through high-speed channel facilitation, which would require addressing traditional security issues associated with ad hoc formations such as synchronization, exposed terminals, resource reservation, and adaptive rate control. It would be an additional challenge to the security to facilitate location-dependent contention when data is transmitted across airplanes. An incorrectly configured security protocol can increase route acquisition delays, making the shared channel prone to errors. Adversaries can compromise the lookup tables to initiate loops in data transmission and impact the scalability of operations. Fast, flexible, and adaptable encryption is required when dealing with security provisioning in a highly dynamic setup, such as AANETs. Physical layer security and throughout maximization are challenges to be resolved in AANETs that can be handled through multiple access schemes . As suchAnother challenge is that commercial flights are traceable, which means that it also reveals the coordinates, and each flight in itself is a terminal. Hence, location-based attacks would be prominent that need careful addressing and resolution. A loop attack on the lookup tables would cause a denial of service, further affected by the age of information, as the topology is expected to change rapidly, and information loss would be very high. When dealing with AI for AANETs, securely configuring the algorithms would be a challenge as it will have a tradeoff of performance security associated with it, which must not be compromised towards performance without balancing the security. Here, deep learning could be a go-to area for maximizing the secrecy rate . AdditioIn this article, we have shown that the AANET’s specific characteristics mainly cause the management challenges of AANETs, and these challenges could be solved by utilizing different AI methodologies. During these investigations, we considered four AANET-specific characteristics as the origin of management challenges. However, the other characteristics of AANETs caused by altitudes, such as oxygen absorption, rain attenuation, and cloud attenuation, could be considered since they can lead to various management challenges by decreasing the ATA link qualities.In addition to these management-level challenges, the AANET can experience different problems in terms of the data link and medium access control (MAC) layers due to its specific characteristics. Generally, these problems gather around link establishment, stability, and connectivity issues. The direction, free space loss, transmission range, communication range, and distance could be the main parameters for these issues. We can also utilize different AI methodologies to adjust these parameters automatically.The AANET will be of great importance for 6G networks because it can enable seamless connectivity to one of the main harsh environments. In this article, we propose using AI-based methodologies to overcome the AANET management challenges caused by its specific characteristics. The article investigates these management challenges in three main aspects: topology formation, sustainability, and routing management. Accordingly, the AI-based methodologies enable us to obtain intelligent frameworks and architectures for these management issues. For this aim, we first underline the necessity of AI by matching its primary methodologies with AANET-specific characteristics. Here, we considered unsupervised, supervised, and reinforcement learning as primary methodologies for these management challenges on the road ahead to AI-driven AANET. We reviewed the challenges and opportunities of different approaches from these methodologies to automatically make topology formation, sustainability, and routing management decisions on AANET. Correspondingly, we investigated clustering and self-organized maps as unsupervised learning methodologies, while the learning vector quantization was analyzed under supervised learning. Finally, we evaluated the reinforcement learning to make routing decisions on AANETs. As a result of these, we aim to provide an insight into the road ahead towards AI-based AANET management.


Behavioral changes are one of the mechanisms for broilers to adjust their body temperature under heat stress conditions. However, the behavioral responses of heavy broilers to environmental changes have not yet been studied well. Therefore, this research investigated the behavioral changes of broilers under two dynamic air velocity treatments (high and low) under summer conditions. Video data collected from a heat stress experiment conducted on broilers aged 42–54 days were used to investigate variations in the number of chickens feeding, drinking, standing, walking, sitting, wing flapping, and leg stretching. The results indicated that the high air velocity treatments increased the number of chickens feeding, standing, and walking. In addition, age significantly affected the number of birds feeding, drinking, panting, and sitting, while the time of the day also affected the number of chickens drinking and panting. This study reveals the thermal stress of heavy broilers from their behavior under summer conditions to help manage the performance and welfare of birds under environmental stress.Broiler chickens exposed to heat stress adapt to various behavioral changes to regulate their comfortable body temperature, which is critical to ensure their performance and welfare. Hence, assessing various behavioral responses in birds when they are subjected to environmental changes can be essential for assessing their welfare under heat-stressed conditions. This study aimed to evaluate the effect of two air velocity (AV) treatments on heavy broilers’ behavioral changes from 43 to 54 days under summer conditions. Two AV treatments (high and low) were applied in six poultry growth chambers with three chambers per treatment and 44 COBB broilers per chamber from 28 to 61 days in the summer of 2019. Three video cameras placed inside each chamber (2.44 m × 2.44 m × 2.44 m in dimension) were used to record the behavior of different undisturbed birds, such as feeding, drinking, resting, standing, walking, panting, etc. The results indicate that the number of chickens feeding, drinking, standing, walking, sitting, wing flapping, and leg stretching changed under AV treatments. High AV increased the number of chickens feeding, standing, and walking. Moreover, a two-way interaction with age and the time of day can affect drinking and panting. This study provides insights into heavy broilers’ behavioral changes under heat-stressed conditions and AV treatments, which will help guide management practices to improve birds’ performance and welfare under commercial conditions in the future. Broiler chickens are now bred to reach their market size weight of about 2.3 to 4.5 kg at the age of 42 to 63 days due to the demand for deboned meat compared to the whole bird ,2. FasteChickens adapt to heat stress by adopting several behavioral changes to maintain their homeostasis ,11. For Several studies have been conducted to help understand broiler chickens’ behavioral changes under different environmental conditions and other management strategies, such as through the use of dietary manipulation or the addition of supplements ,15,16. AControlling the inside environment of broiler grow-out houses is being recommended by several researchers ,19 to reThe experiment was conducted in the poultry engineering laboratory (PEL) of North Carolina State University (NCSU) in the summer of 2019 from 29 May to 1 July. The PEL has six simulated poultry chamber systems with core chambers with dimensions of 2.44 m × 2.44 m × 2.44 m for the birds’ stay . All the2, following the animal welfare guideline.A total of 400 male COBB 500 broilers were hatched and raised in the floor pens under similar conditions at the NCSU poultry unit. Then, 264 birds without leg defects were randomly selected to be placed in the six experimental chambers, with 44 birds per chamber after 28 days. They remained in the chamber until reaching the age of 61 days. The final stocking density was ≤40 kg/mEach chamber’s air temperatures (Ta) were monitored with a thermocouple, and a HOBO Pro v2 External T/RH Data Logger, Model U23-002 , was placed at the airflow inlet and outlet of each chamber at the birds’ height. Calibrated thermocouples  recorded temperature data continuously at 1 min intervals, while the HOBO logged both Ta and RH at 10 min intervals. The hourly average Ta values from both sensors were then averaged to obtain the hourly average temperature at the inlet.Two sets of dynamic AV treatments (high AV and low AV) were designed depending on inlet Ta and bird age. The AV treatment design criteria are detailed in . As showThe AV treatments began on the birds aged 35 days after the birds moved into the chamber for a week. As shown in Three video cameras  were installed in each chamber on the left- and right-side walls and on the celling to capture videos of undisturbed bird activity. The video recordings were saved in an external hard drive connected to each camera. Unfortunately, the hard drive that stored all the recordings of the cameras in chamber 2 and the ceiling cameras of all six chambers was destroyed; hence, only the two side wall video recordings of five chambers were available. Segmented videos were selected based on video quality, availability from both side cameras for the same period, and undisturbed bird appearance, as in The number of chickens feeding, drinking, walking, and standing , panting, resting, stretching legs, or wing flapping was counted manually by an observer for each individual video. The ethogram in No specific chickens were marked or colored for observation; hence, only the number of chickens engaging in any behavior listed in p < 0.05. If any factor had main effects, the Tukey HSD test was performed to check the differences at the level of that variable. The replicated chambers were considered blocking factors and the number of chickens was considered the experimental unit.The data were analyzed using Rstudio (version 1.0.143) . The number of birds having different behavior was the average from the replicated chambers under the two treatment AVs. A two-way ANOVA test was used to analyze the effect of treatment, age, time of day, and interactions on the number of chickens engaging in various behaviors. The main effects and the interactions were considered significant at The hourly averages of Ta and RH varied by time of day . There wThe experiment was designed to obtain the behavioral changes of broiler chickens due to AV treatments under heat stress conditions. p < 0.05) than low AV treatment (Treatment and age significantly affected the number of chickens engaging in feeding behavior . The numreatment . Althougreatment .p < 0.05) (The number of chickens drinking did not vary under AV treatment . Althoug < 0.05) . The num < 0.05) .The numbers of chickens standing and walking were analyzed together, since it was difficult to distinguish between the two behaviors as there was no marking on the birds’ bodies. The numbers of birds standing and walking were affected by AV treatments and their interaction with the time of day . More biPanting is one of the expected behaviors for maintaining thermoregulation under heat stress. The AV treatment had no main effect on the number of birds panting . HoweverThe AV treatment significantly affected the number of chickens sitting . More biWing flapping and leg stretching are two common behaviors under heat stress. This study discovered that the AV treatment significantly changed the number of birds flapping their wings and stretching their legs . The nump < 0.05) than the number of those sitting near the outlets for chickens of any age and at any time of day (The chickens moved around the chamber for various purposes. For example, they moved to the feeder location for feeding and moved to the middle of the chamber for drinking. While resting, they tended to sit at various locations. The number of chickens sitting varied at the inlet and the outlet . Under be of day . Time ofUnder heat stress, birds usually reduce their feed intake according to their age up to 42 days ,10. AlthIrrespective of AV treatment, drinking declined with broiler age. Bizeray  and JacoThe birds selected for this experiment were all without leg defects. During the experimental period, the birds’ activity was more involved in either feeding or drinking. The heavier broilers were found to be less likely to walk or stand. It was even observed that they barely made more than two or three steps unless it was required to reach the drinker or feeder. The heavier body weight and stressful weather condition made them sit more often than walk or stand. Li  found reThe absence of sweat glands and the presence of feathers increase broilers’ panting under higher environmental temperature to release excessive body heat. The AV treatments in this study did not significantly impact panting behavior. Hence, the number of chickens panting under the two treatments did not differ significantly. Panting increased with age . On the This study suggests that the number of birds sitting increases with age. Li  found thAlthough the number of chickens engaging in wing flapping or leg stretching was small, the occurrences were observed under stressed conditions. On average, only a couple of chickens flapped their wings or stretched their legs, but at a later age, when the Ta was higher, at most 11 birds in the low AV treatment chamber were found engaging in this activity. They might have been trying to cope with the stressful environment with this behavior.According to the ANOVA test, the blocking factor chamber had a significant impact on the number of chickens feeding, drinking, and panting . AlthougThe birds moved around the chamber floor primarily to drink or eat. They sometimes walked simply to find a better location in the chamber. During warmer periods, the birds tended to sit more often near the inlet to experience a higher speed of air flow passing over their bodies. Since the air entered the chamber through the inlet and the air speed was higher there, the birds under heat stress wanted to sit more often at the inlet side compared to the outlet, where upwind birds may block some flow to reduce the air speed in the downwind zone. From this study, it was found that the heavier birds tended to stay closer to the location where air entered when conditions were warmer or stressful. Hence, it is important to make sure that all the birds in a facility can experience uniform air velocity passing over them to keep them comfortable. Bringing down air flow to birds’ height and properly mixing AV throughout the housing facility could be a good strategy for pacifying birds under heat stress. A collective understanding of these behavioral changes will help in understanding the potential issues associated with broilers’ welfare in the flock in order to take necessary management actions to maintain and/or enhance performance and welfare.These results indicate that high AV treatment significantly changed the number of chickens engaging in feeding, standing, walking, sitting, wing flapping, and leg stretching behavior under heat stress. The numbers of chickens feeding and panting increased with age, but the number of those drinking and sitting declined. The number of chickens drinking and panting varied significantly according to the time of the day. The applied AV did not directly affect drinking and panting, but its two-way interaction with age and time of day significantly altered the prevalence of these behaviors under thermal stress. In general, heavy broilers changed their typical behaviors, such as feeding, drinking, walking, or resting, under heat-stressed conditions in order to adapt to the stressor. Air velocity can enhance the heat release activity of larger and older broiler chickens, ensuring their growth performance and welfare. The findings from this study will help to identify thermal stress through behavioral changes in the birds themselves. This will help producers make necessary management decisions to keep their birds healthy and happy under conditions of environmental stress.


Video recording is essential for behavioral neuroscience research, but the majority of available systems suffer from poor cost-to-functionality ratio. Commercial options frequently come at high financial cost that prohibits scalability and throughput, whereas DIY solutions often require significant expertise and time investment unaffordable to many researchers. To address this, we combined a low-cost Raspberry Pi microcomputer, DIY electronics peripherals, freely available open-source firmware, and custom 3D-printed casings to create Pi USB Cam, a simple yet powerful and highly versatile video recording solution. Pi USB Cam is constructed using affordable and widely available components and requires no expertise to build and implement. The result is a system that functions as a plug-and-play USB camera that can be easily installed in various animal testing and housing sites and is readily compatible with popular behavioral and neural recording software. Here, we provide a comprehensive parts list and step-by-step instructions for users to build and implement their own Pi USB Cam system. In a series of benchmark comparisons, Pi USB Cam was able to capture ultra-wide fields of view of behaving rats given limited object distance and produced high image quality while maintaining consistent frame rates even under low-light and no-light conditions relative to a standard, commercially available USB camera. Video recordings were easily scaled using free, open-source software. Altogether, Pi USB Cam presents an elegant yet simple solution for behavioral neuroscientists seeking an affordable and highly flexible system to enable quality video recordings. Video capture is increasingly necessary for neuroscience research where neural and behavioral data are synchronized to reveal correlative and causal relationships. This relies on a recording system that can capture quality videos without significant alterations to preexisting experimental conditions , enables easy online and offline analysis by commonly used software, and offers high scalability to increase throughput. However, the high cost and poor flexibility of commercially available options leave the role of an ideal video recording system unfulfilled. Here, we present a DIY video recording solution that combines affordable electronics hardware and custom 3D-printed components with sophisticated open-source software to make a simple, yet powerful USB camera that satisfies almost any recording need.Video recorded data are a crucial component of behavioral neuroscience research. While a number of commercially available recording solutions exist, few, if any, satisfy all or most of the needs of a typical laboratory. Many behavioral assays, particularly those intended for use with nocturnal species, are conducted under low-light or no-light conditions to mimic the portion of the circadian cycle during which animals are the most active. Therefore, an ideal video recording system enables video acquisition under both ambient and infrared (IR) illumination. Moreover, spatial limitations imposed by testing apparatuses frequently require capture of a large field of view from a relatively short object distance. Although some commercially available options designed specifically for behavioral neuroscience research meet these criteria, they are often expensive and unique to a specific testing apparatus limiting scalability and flexibility of use. Given these limitations, researchers frequently repurpose commercial USB webcams for video acquisition because of their affordability and accessibility. However, webcams are rarely intended for recording under low-light to no-light conditions, nor for capturing a large field of view under circumstances when subjects are at a relatively short distance from the camera.DIY solutions that make use of low-cost, single board microcomputers such as Raspberry Pi have gained the attention of behavioral neuroscientists as laboratories attempt to address the shortcomings of existing commercially available solutions. For example, To address this gap, we designed a versatile, low-cost, DIY video recording solution that requires no specialized expertise or equipment. Pi USB Cam is low-/no-light compatible and accepts a variety of wide-angle lenses. The system utilizes open-source, actively maintained software that enables true plug-and-play capability. Combined with custom 3D-printed components and freely available open-source video acquisition software, this system offers highly versatile implementation and scalability across numerous behavioral testing conditions. Here, we provide detailed step-by-step instructions for hardware and software setup and demonstrate its ease of use and superiority in terms of field of view and low-light recording over commercial counterparts.Pi USB Cam is comprised of a Raspberry Pi board  and a wide-angle day-night vision camera . This camera comes equipped with a motorized IR-CUT filter that is automatically triggered based on ambient light intensity to accommodate for both bright-light and low-/no-light recording, as well as an out-of-the-box 170° [diagonal field of view (DFOV)] × 140° [horizontal FOV (HFOV)] fisheye lens. The camera uses a 5MP OV5647 sensor, which can support up to 30 frames per second (FPS) at 1080p when used with the suggested firmware. Additionally, the camera comes attached with two 850-nm IR LEDs to facilitate image acquisition in low-/no-light conditions.Show-me webcam, an open-source firmware that enables a Raspberry Pi connected camera to be booted as a simple USB camera. While both models are compatible as the base hardware for building a Pi USB Cam, we chose Pi 4B over Zero because of its added processing power, which is significantly more advantageous if the Pi board were to be repurposed for GUI applications. Of note, while the Raspberry Pi Zero 2 was recently released, it is not yet supported by Show-me webcam, although this is likely to change in near future. Legacy boards like Pi 3+ and earlier releases that are no longer widely accessible for purchase are not supported by Show-me webcam.We selected the Raspberry Pi four Model B to run the camera, which was the latest release of the main product line at the time of publication. This model and the more affordable and compact Raspberry Pi Zero are readily supported by Table 1-1.A complete list of store-bought components is provided in Insert a clean micro-SD card with a minimum storage of 64MB into your computer A.Show-me webcam image file to your computer from the developer’s GitHub page https://github.com/showmewebcam/showmewebcam  describes how to access and adjust camera setting parameters on a Windows PC using an open-source software called PuTTY (https://github.com/showmewebcam/showmewebcam).Camera software settings such as brightness and white balance can also be readily adjusted to suit specific recording needs, such as improving the image color when recording under low-light conditions 3B. Usersed PuTTY G, which Device Manager and locate the COM port number of the camera under Ports (COM & LPT)  port on your host PC the Pi USB Cam is using, open M & LPT) E.Device Manager to confirm which COM port is used by which camera.Note: if you have more than one USB serial device connected to your computer, you can disconnect and then reconnect Pi USB Cam while monitoring hardware changes in https://www.putty.org. To establish a serial connection with the Pi USB Cam, first make sure that Session is selected under Category . Click Open to log into the Pi USB Cam , and Auto Exposure, White Balance, and ISO Sensitivity Auto to Manual (G2), to improve the image coloring under low-light conditions  or R . To terminate the serial connection, first press Q to quit the camera-ctl interface, and then close the PuTTY session window.Once finished, press To adjust the focus on the camera lens, first loosen the screw that secures the lens firmly inside the M12 lens holder, as indicated in IR-sensitive cameras need to be coupled with adequate IR illumination to produce high-quality images in low-/no-light conditions. The wide-angle day-night vision camera from Arducam that we suggest using in this configuration comes with two 850-nm IR LEDs attached and powered by the camera board B. Howeve10.1523/ENEURO.0224-22.2022.f4-1Extended Data Figure 4-1A, Gather all essential parts: (1) electrical wire AWG22, noninsulated ring terminal connector AWG22-16 #4 stud size, heat shrink tubing 1/8”, (2) Astro crimping tool with B-Jaw for noninsulated terminals, and wire stripper. B–D, Strip the wire to expose ∼4 mm of conducting wire, comparable to the length of the wire barrel of the connector. D, Cut one short segment of 1/8” heat shrinking tubing that is long enough to cover the entire wire barrel (≥6 mm) and put it on the wire before crimping. E, Crimp the connector to the bare wire using the first die (DIN 0.5–1.5 mm2) on the B-Jaw of the Astro crimping tool. F, Make sure to visually inspect and perform a gentle pull test to confirm the crimp is successful. G, Fully cover the entire wire barrel with heat shrink tubing and use a heat gun to shrink the tubing. Download Figure 4-1, TIF file.Step-by-step instructions for crimping ring terminal connectors. 10.1523/ENEURO.0224-22.2022.tab1-2Extended Data Table 1-2n = 8) for the upper bound includes the cost of one set of tools and supplies as well as the cost for eight sets of essential equipment. Our university rate for 3D-printing services of $30/kg of PLA plastic was used to calculate the upper bound cost of 3D-printed components, whereas the lower bound was calculated as the expense for only the purchase of plastic for those laboratories that already have a 3D printer. A complete camera  electrical wire AWG22, male or female pin terminal connector, plastic housing, (2) Astro crimping tool with H-Jaw for open barrel terminals, and wire stripper. B, C, Strip the wire to expose ∼2.5 mm of conducting wire. D, Insert the stripped wire into the pin terminal connector while making sure that the bare wire falls within the wire barrel of the connector and the wire insulation is inside the insulation barrel. Gently bend the insulation barrel around the wire insulation to prevent connector or wire from moving out of place during crimping. E, Crimp the connector to the wire using the middle die (DIN 0.5 mm2) on the H-Jaw of the Astro crimping tool. Make sure that the open barrel is facing towards the “nest” of the crimping die and both wire barrel and insulation barrel are positioned in the appropriate spots of the crimping die. F, Visually inspect and perform a gentle pull test to confirm the crimp is successful. G, Insert the crimped connector into a plastic housing. Visually inspection and gentle pull test should be performed to make sure that the connector is securely housed in order to avoid issues with mating pins. Download Figure 4-2, TIF file.Step-by-step instructions for crimp pin terminal connectors. https://pinout.xyz/pinout/3v3_power). Exceeding that limit using high-power LEDs may cause a “brownout” of the Pi and potential safety concerns. Our testing indicated that the two IR LED boards that came with the Arducam day-night vision camera draw ≤200 mA of current from the 3v3 pin when connected in parallel. For higher power LEDs, it is recommended to use either the 5v power pin, which provides a higher current draw of ∼1.5 A (according to: https://pinout.xyz/pinout/5v_power), or an external battery pack or power supply unit. For alternative LED options and related hardware, see Extended Data Table 1-1.Note: the 3v3 power pin of the Pi board can safely provide up to 500 mA of current (according to: (2) to a pair of 3v3 power and ground pins on the Pi board.Following the step where the LED boards were taken off from the main camera board B, cut th(1) down the middle into two separate red and black wires. Split one end of wires (2) and (3) by ∼3 cm and the other end of each ∼1 cm.Split wire (1).Install two noninsulated ring connectors on either end of wire (2) separated by ∼3 cm and two male pin connectors housed in one 1x2 plastic housing on the other end.Install two ring connectors on the end of wire (3) separated by ∼3 cm and two female pin connectors housed in one 1x2 plastic housing on the other end.Install two female pin connectors each housed in 1x1 plastic housings on the end of wire Crimp the appropriate electrical connectors to the stripped wires as shown in Note: for detailed instructions on how to crimp ring and pin type connectors, see Extended Data Wire the LED boards in parallel as shown in (1) and (2) to the LED boards using the screws that came with camera module.Secure the ring connectors on wires (3) to any pair of 3v3 power and ground pins on the Pi board 40-pin header  and (3) via their respective pin connectors to complete the circuit.Connect wires Note: red wires are used for power (+) and black wires for ground (–). Make sure the polarities of all components align before completing the circuit.Test functionality by powering up the Pi USB Cam and confirming that the LED unit is illuminated under low-/no-light conditions.Install heat sinks on the back of the LED boards to prevent overheating F. OptionPi USB Cam affords a high degree of scalability that is often desired for high throughput experiments. Use of the freely available, open-source video acquisition software, OBS (Open Broadcaster Software) Studio enables preview of live video feeds on screen for real-time behavioral monitoring, live-stream video feeds over Internet, and simultaneous video acquisition from multiple camera sources into separate local files for offline behavioral analysis. OBS Studio is compatible with all three major operating systems; however, we recommend using a Windows PC as the host computer because of added functionalities including video configuration settings that are not available on other platforms. Scalability is limited only by the hardware of the host computer . We recommend using a Windows desktop that allows for add-on USB PCIe expansion cards to provide additional USB controllers and greater bandwidth for multicamera recordings.https://obsproject.com/wiki/. Note that unless otherwise stated all videos and snapshots of videos included in figures were acquired at 480 p and 30 FPS using OBS Studio.The following protocol describes how to set up OBS Studio on a Windows desktop for multi-Pi USB Cam video recordings. For streaming over network, adding audio recording, and many more applications, users can visit the OBS Wiki at Connect one or more Pi USB Cams to a host Windows desktop that is optionally installed with USB PCIe expansion cards for added USB bandwidth A.Device Manager, click View followed by Devices by connection, and confirm that no more than three Pi USB Cams are connected to each Host Controller , uncheck the auto-configuration wizard, and click OK to finish creating the new profile , repeat steps 6–7 to create one “scene collection” for each camera. Then, launch one more instance of OBS Studio for each additional camera by double-clicking the desktop shortcut B1 follow warning B2. In ea warning B3. To co warning B4 or in 10.1523/ENEURO.0224-22.2022.f6-1Extended Data Figure 6-1A, One scene with multiple video sources operating within one instance of OBS Studio recorded into a single video file. B, Multiple scenes each with their own video source operating within one instance of OBS Studio. Recording produces video from a single scene/video source. C, Multiple scene collections each with their own video source running in independent instances of OBS Studio produce independent video files. Image source: https://github.com/obsproject/obs-studio/blob/master/UI/forms/images/obs_256x256.png. Download Figure 6-1, TIF file.Schematics showing several options to preview and/or record from multiple video sources. in vivo calcium imaging, combine video recordings with optical signal to correlate behavioral events with neural activity. The potential for dropped or duplicate frames, which we have experienced with commercially available USB webcams, poses nontrivial challenges for offline data analysis in these kinds of experiments. Recordings with dropped frames appear fast forward and have shorter duration during playback by third-party media players, which often assume a nominal frame rate that is higher than the actual frame rate achieved. Recordings with duplicate frames appear choppy, and thus have lower temporal resolution than desired, despite the high frame rate achieved. Both issues result in additional and unnecessary workload during postprocessing to accurately align behavioral and neural data. To evaluate the frame rate performance of Pi USB Cam, videos of freely behaving adult male Long–Evans and Sprague Dawley rats were acquired from inside separate operant boxes (MED Associates) using a Pi USB Cam and a generic commercial webcam (Logitech C930e webcam). Camera performance was assessed under both red and white house light illumination.Many neuroscience techniques, such as https://www.tdt.com/docs/sdk/offline-data-analysis/offline-data-matlab/overview/). For each video frame, Synapse stores two timestamps: one for the frame onset and one for offset. The onset timestamp of the very last frame was taken as the recording length, as the offset timestamp of the last frame was stored as inf instead of a real number. The true frame rate achieved was calculated using the following equation:For dropped frame analysis, Synapse (Tucker-Davis Technologies TDT), an acquisition platform commonly used in neuroscience research, was used to acquire video recordings from both cameras simultaneously. Five videos of ∼5-min duration each were captured into AVI format in each lighting condition from each camera at the maximum resolution (640 × 480) and frame rate (20FPS) supported by Synapse. For each recording, the total number of frames and timestamps of each frame were read from Synapse data block using MATLAB TDTbin2mat function . Videos with fisheye distortion were subsequently corrected using the method described above. Position and locomotor measures were tracked offline using ANY-maze software (Stoelting Co). Each measure obtained was averaged across all five videos and compared using a one-way ANOVA.To assess the effects of fisheye distortion and digital correction on locomotor activity measurement, position tracking was performed on videos with low distortion, fisheye distortion, and distortion digitally corrected. Five videos of ∼5-min duration of adult female Long–Evans rats exploring two contextually distinct compartments in a standard conditioned place preference apparatus (MED Associates) were acquired in OBS Studio from two Pi USB Cams in tandem. One camera was equipped with a 70° HFOV low-distortion lens and the other with a 100° HFOV fisheye lens  × 140° (HFOV) fisheye lens. Notably, in this orientation, the camera was placed <1 cm above the testing apparatus therefore requiring essentially no additional vertical space for video acquisition A. In conCamera performance was qualitatively assessed under no-visible light condition B, and sePi USB Cam also outperformed the commercial webcam in terms of frame rate maintenance. Unlike the Pi USB Cam, the commercial webcam was unable to maintain the set frame rate of 20 FPS when interfacing with Synapse, losing on average 505.60 ± 69.40 frames per min under white house light illumination and 702 ± 0.00 frames per min under red house light illumination . This reMovie 1.Comparison of Pi USB Cam and commercial webcam performance using specialized neural recording software for video acquisition.10.1523/ENEURO.0224-22.2022.video.1Movie 2.Comparison of Pi USB Cam and commercial webcam performance using OBS Studio.10.1523/ENEURO.0224-22.2022.video.2The recommended Arducam day-night vision camera can produce high quality video images under both bright and low-/no-light conditions with relative ease , 4 and cMovie 3.Example video showing versatile implementation of Pi USB Cam for behavioral monitoring.10.1523/ENEURO.0224-22.2022.video.3Movie 4.Example video showing versatile implementation of Pi USB Cam for rodent home cage monitoring.10.1523/ENEURO.0224-22.2022.video.4Table 1-1), each of which is associated with a different field of view and accompanying degree of image distortion. Our research needs necessitate positioning of the Pi USB Cam off-center from the testing arena to accommodate tether access through the roof opening. However, this orientation has the potential to result in uneven image distortion across the field of view as distortion tends to be minimal at the center of the fisheye field of view and progressively enlarged toward the radial edge of the image . Enable the ShaderFilter plugin and fisheye correction for each video source (C3–C7). Download Figure 8-1, TIF file.Digital correction of fisheye distortion. 10.1523/ENEURO.0224-22.2022.f8-2Extended Data Figure 8-2A), 140° (B), 118° (C), 100° (D), and 73° (E) for overhead viewing in a standard operant box. Snapshots of raw videos acquired from a centered position at the corresponding object distances showing comparable captured fields (2), and the same videos after the fisheye image distortion was digitally corrected using the OBS ShaderFilter plugin (3). Power settings used are indicated on each image. *Indicates the default lens that comes with the Arducam day-night vision camera. Download Figure 8-2, TIF file.Comparison of Pi USB Cam-compatible fisheye lenses in terms of object distance and fisheye correction. Pictures showing the object distance of a Pi USB Cam (1) equipped with various M12 fisheye lenses including 180° , after digital distortion correction (B), and from a video acquired in tandem using a 70° HFOV low-distortion lens (C). Representative center-point tracking plots (2) and heatmaps (3) from the same video show similar results. D, Effects of fisheye distortion and correction on position tracking accuracy were demonstrated by comparing various locomotor activity measures, including the total distance travelled (1) and average speed (2) during the entire test duration, as well as the total distance travelled , average speed , and time spent  in either side of the testing apparatus. Download Figure 8-3, TIF file.Effects of fisheye distortion on position tracking and locomotor measures. Snapshots of ANY-maze position tracking (1) performed on a video acquired with a 100° HFOV fisheye lens without distortion correction (Table 1-1). For example, for overhead monitoring of a standard conditioned place preference testing apparatus with a working area of 32.70” L × 8.25” W × 8” H, we found that a 100° (HFOV) fisheye lens was ideally suited to capture the entire testing arena when the camera was positioned ∼30 cm overhead  = 6.538, p = 0.0462; Extended Data post hoc comparisons failed to identify a significant difference between precorrection and low-distortion  or postcorrection and low-distortion . While no other statistically significant differences were observed, visual inspection of the data makes clear that the difference between precorrection and low-distortion is lessened by digital correction. Therefore, digital fisheye correction is likely to improve tracking accuracy in experiments where absolute, rather than relative, measures are a high priority.As an alternative to using low-distortion lenses, which often require larger object distance that is incompatible with many experimental set ups, fisheye distortion can be digitally corrected using the OBS ShaderFilter plugin . This is particularly important given that camera costs can present significant challenges to scalability and throughput. Augmented by the burgeoning technology of 3D printing, Pi USB Cam can be installed in various commonly used behavioral testing and housing settings without the need for additional modifications to the testing apparatus or additional costly parts.Hardware flexibility is the main advantage of Pi USB Cam over commercially available webcams. Indeed, despite their out-of-the-box user friendliness, the hardware of most commercial webcams is simply not designed to acquire high quality video recordings in confined spaces and/or poor lighting conditions. In contrast, Pi USB Cam can be used with a wide range of camera modules that offer IR sensitivity for low-light recording, IR correction for bright-light recording, fisheye lenses for wide-angle recording, low-distortion lenses for accurate position tracking, etc. Because of its DIY nature, the cost of building Pi USB Cam is comparable to, if not more affordable than, repurposed commercial webcams and a fraction of the cost of video capture systems specialized for neuroscience research  necessitate use of a standalone microphone to capture audio independent of video feed, which can then be synchronized online or offline depending on the recording software used. As with any open-source DIY solution, Pi USB Cam software will require regular and timely updates in tandem with upgrades to Raspberry Pi hardware and operating system. However, the well-established open-source community of Show-me webcam developers has a strong history of providing regular support and upgrades for the firmware. This is unlike previous niche DIY video solutions, which have often relied on a single individual to perform upgrades and consequently have had difficulty surviving beyond initial hardware and operating system versions (While Pi USB Cam offers an easy-to-use, flexible, and affordable option for behavioral neuroscientists to acquire video recordings, several limitations of our system should be noted. Previous approaches using Raspberry Pi cameras are designed such that the microcomputer performs the heavy lifting of video encoding . The advversions . FinallyIn summary, Pi USB Cam is a highly versatile and affordable DIY video recording solution for real-time behavioral monitoring and offline analysis. Requiring minimum time, expertise, and financial commitment to implement, Pi USB Cam offers behavioral neuroscientists a powerful, yet simple, solution for high quality and high-throughput behavioral data collection. We encourage users to reference the current manuscript when implementing Pi USB Cam in their own experiments.


Given that the alkali-activation of OTOP1 and the required key residues were conserved in the six representative vertebrates, we cautiously propose that OTOP1 participates in alkaline sensation in vertebrates. Thus, our study identified OTOP1 as an alkali-activated channel.Although alkaline sensation is critical for survival, alkali-activated receptors are yet to be identified in vertebrates. Here, we showed that the OTOP1 channel can be directly activated by extracellular alkali. Notably, OTOP1 biphasically mediated proton influx and efflux with extracellular acid and base stimulation, respectively. Mutations of K221 and R554 at the S5–S6 and S11–S12 linkers significantly reduced alkali affinity without affecting acid activation, suggesting that different domains are responsible for acid- and alkali-activation of OTOP1. The selectivity for H pH sensation is critical for survival of vertebrates. Here, authors found six vertebrate OTOP1 channels that were highly conserved and directly activated by extracellular alkali. Key mutations of OTOP1 reduced alkali affinity without affecting acid activation. Several proton channels have been identified, including the influenza M2 protein, an essential viral component for cell infection2, and the HV1 channel, which favors proton extrusion from cells5. A new proton-selective channel  was recently identified, which exhibits little structural similarity to M2, HV1, or other ion channels6. Activated by acid, OTOP1 is an essential component of sour taste perception and is functionally conserved in Drosophila11. While the underlying mechanisms of acidic pH sensation have been extensively studied, little is known about how animals sense external alkaline pH. Wang et al. recently showed that transmembrane channel-like protein 1 (TMC-1) mediates alkaline sensation in C. elegans12. However, the alkali-activated primary sensation receptors are yet to be identified in vertebrates.Nearly all physiological processes in the body depend on appropriate acid–base balance. Specific proton-permeable channels mediate proton transport across the membrane and regulate cellular and extracellular pH as well as membrane potential13. In mice and zebrafish, mutant alleles of Otopetrin1 can lead to nonsyndromic otoconia agenesis and consequent balance deficits without affecting normal peripheral anatomy and neural circuitry15. OTOP1 is also conserved in the timing, formation, size, and shape of developing otoliths, which are important for vestibular mechanosensory transduction of linear motion and gravity16. OTOP1 attenuates obesity-induced adipose tissue inflammation and plays an adaptive role in maintaining metabolic homeostasis in obesity13. Furthermore, OTOP1 is functionally conserved and an essential component of sour taste perception11.OTOP1 is highly expressed in brown and white adipose tissue, mast cells, adrenal glands, and vestibular and taste cellsIn this work, we investigated the potential function of OTOP1 in alkaline sensation. We found that it was biphasically modulated by acid (pH ≤ 6.0) and alkali (pH ≥ 8.0), mediating proton influx and efflux with extracellular acid and base stimulation, respectively. We also proposed that alkali activation is a shared feature of various vertebrate OTOP1 channels and that alkali-induced gating differs from that induced by acid.6, protons exhibited concentration-dependent induction of the inward macroscopic currents of mouse OTOP1 (mOTOP1) heterologously expressed in human embryonic kidney (HEK293T) cells. As shown in Supplementary Fig. o) decreased, indicating that OTOP1 can be functionally expressed and activated by acid. To determine whether OTOP1 responds to other stimuli, we applied alkali to the OTOP1 channels and found that OTOP1 also generated ionic currents. Increasing pHo elicited large outward currents in OTOP1-transfected cells, and the current magnitude increased with pHo  of 5.30 μM.As OTOP1 is considered a proton-selective ion channel, we first characterized the acid-activation properties of OTOP1. Consistent with the previous observationspHo Fig. . The curpHo Fig. . Thus, tErev). For predominantly alkali-induced OTOP1 currents, we used Zn2+ to block the OTOP1 currents and focused on the Zn2+-sensitive component of the currents. As it is difficult to dissolve Zn2+ under alkaline pH, we applied 1 mM Zn2+ to the OTOP1 currents evoked by varying alkaline pH. As exhibited in Fig. Erev shifted toward more negative voltages with increasing pHo, and measured Erev(EM) closely followed the Nernst prediction (EH) for an H+-selective ion channel.We also measured the potential at which the current reverses, i.e., reversal potential  curves from experiments in which OTOP1 currents were evoked in response to a pH 10.5 stimulus and ramp depolarizations  applied for every second showed a shift in Erev as a function of time , respectively, to ensure the same switching time between different alkaline solutions and the extracellular solution. Activation of OTOP1 in response to pH-variable solutions was measured by fitting the time course of the current upon solution exchange with a single exponential equation. In response to pH 8.0, the time constant for activation of OTOP1 was 195.75 ± 43.29 ms, while the time constants for alkali activation at pH 8.5 and 9.0 were 168.70 ± 31.24 and 120.45 ± 34.66 ms Fig. , respect pH Fig. . These ro, while point mutations of K221A  in the S5–S6 linker and R554A  in the S11–S12 linker largely reduced current amplitude  equation to calculate the ion selectivity of the acid- and alkali-induced OTOP1 channels. By measuring Erev after the replacement of NMDG+ by Na+ and with high and low concentrations of Na+ in the extracellular solution, the selectivity ratio of acid-activated OTOP1 for Na+ relative to H+ was 6.97 × 10−5, while that of alkali-activated OTOP1 was 3.96 × 10−8 . OTOP1 was reported to be endogenously expressed in type III TRCs, which were identified by the presence of a unique marker, PKD2L1 protein18. Our immunofluorescence analysis revealed that the OTOP1 colocalized with PKD2L1, indicating the presence of OTOP1 in TRCs  was identified in whole-genome sequences, and OTOP1 eukaryotic expression plasmids were constructed. All six representative species exhibited a normal response to extracellular acid. Notably, alkali at pH 9.0 elicited large outward currents in all six species  or intracellular  alkalization25. In addition, TRPV1 is the first receptor with a striking ability to be activated by both acidic (extracellular) and basic (intracellular) pH22. However, the cellular and molecular nature of external alkaline sensations are poorly understood, and no alkali-activated primary receptors have been confirmed in vertebrates20. Here, we report that extracellular alkali could directly activate OTOP1 in a concentration-dependent manner. OTOP1 was previously considered to be exclusively activated by protons, with selective permeation of protons upon extracellular acid application6. Extracellular application of alkali mediated the efflux of multiple cations, such as H+, Na+, K+, Cs+, and Li+, out of the cytoplasm, which may be related to the maintenance of acid–base balance and excitability of cells, as reported previously26. For instance, Otop2l, an OTOP ortholog in sea urchins, promotes cellular acid efflux critical for biomineralization. The ubiquitous occurrence of OTOP families in calcifying systems across the animal kingdom suggests a conserved physiological function, with pH mediated at the mineralization site26. Given the deep phylogenetic origin of otopetrins in animals19, our results and those of others indicate that the cellular pH sensation and regulation may be inherent features of otopetrins.Like acid, alkaline pH also induces gustatory responses and nociception+ relative to Na+, further suggesting that acid- and alkali-activation are likely independent gating processes in OTOP1. Based on OTOP1 structural analysis and molecular dynamics, Saotome et al. proposed three potential proton conduction pathways, including the N and C domains and the intrasubunit interface between them27. Although the structure of mOTOP1 has not yet been established, AlphaFold-based structural predication of mOTOP128 suggests that K221 and R554 are located at positions near the putative pore within the intrasubunit interface  in the OTOP1 channel. As shown in Supplementary Fig. tilted and mergulhador) significantly reduced acid-induced inward current amplitude, as previously reported15, and slowed the acid-induced rate. We compared all alkali-induced currents for wild-type and mutant OTOP1 by normalizing to pH 4.0 as exhibited in Fig. tilted and mergulhador) were significantly lower than the wild-type OTOP1 channel  with a holding potential at −80 mV. In comparison with our recordings, alkali-induced OTOP1 currents were a little small, which could be explained by the lower holding potential. These results further support the conclusion of alkali-induced OTOP1 activation. Strikingly, mOTOP2 is open at neutral pH and exhibited large outward currents in response to alkaline solutions of pH 9.0 and 10.0, while mOTOP3-transfected cells showed no outward currents in response to any of the alkaline stimuli31. Among the six vertebrate species tested, the OTOP1 channels in mammals, birds, reptiles, and amphibians were all alkali-activated, suggesting a conservative pattern of alkali activation in vertebrates. Moreover, key residues (K221 and R554 at S5–S6 and S11–S12 linkers) of OTOP1 that affect alkali affinity were highly conserved in the vertebrates. Consistent with our findings, the S5–S6 and S11–S12 linkers in a refined topological model of the otopetrin family exhibit maximum evolutionary constraints and length conservatism in vertebrates19. The high sequence and functional similarity of the OTOP1 channels suggest a conserved role in alkaline pH sensation. This hypothesis is consistent with our observation that extracellular alkali application induced inward currents in a physiological environment . The health status of C57BL/6L mice was monitored daily and only healthy mice were used in experiments.Gallus gallus (Gene ID: 422844), Xenopus tropicalis (Gene ID: 100495027), Chelonia mydas (Gene ID: 102932521), Dermochelys coriacea (Gene ID: 119854908), Papio anubis (Gene ID: 101001867), Mus musculus (Gene ID: 21906), and Homo sapiens (Gene ID: 133060). We optimized OTOP1 gene codons of the above species into codons for human expression systems. The OTOP1 gene sequences were synthesized and cloned into the pcDNA3.1 eukaryotic expression plasmid by Sangon Biotech (Shanghai). After screening positive clones with ampicillin resistance, sequencing was performed to obtain the pcDNA3.1 plasmid containing the correct OTOP1 sequence. All mOTOP1 point mutations were constructed using the Mut Express® II Fast Mutagenesis Kit V2 . Point mutations of mOTOP1 were confirmed by sequencing.OTOP1 gene sequences were obtained from the NCBI database for the following species: 2 and 95% O2 in Dulbecco’s modified Eagle medium (DMEM)  containing 10% fetal bovine serum , and 1% penicillin/streptomycin (Gibco). According to the manufacturer’s instructions, the HEK293T cells were transiently transfected with 5 μg of channel plasmid and 0.5 μg of enhanced green fluorescent protein (eGFP) plasmid using Lipofectamine 3000 transfection reagent . After 24 h of transfection, cells with eGFP were selected for patch-clamp recording.The HEK293T cells were purchased from the Kunming Cell Bank, Kunming Institute of Zoology, Chinese Academy of Sciences. Cells were routinely cultured at 37 °C in 5% CO2, 2 CaCl2, 20 d-glucose, 10 HEPES, pH 7.4 with NaOH. Taste cells were isolated from adult C57BL/6L mice (8–12 weeks). After cervical dislocation and execution of the mice, tongues were removed. A mixture of Tyrode’s solution added with 1 mg/mL Elastase  and 2.5 mg/mL Dispase II (Sigma Aldrich) was injected between the epithelium and muscle of the isolated tongues from the root to the tip of the tongues, and then incubated in 5% CO2 and 95% O2 in Tyrode’s solution for 30 min6. The tongue epithelium is peeled off and the clipped epithelium with circumvallate papillae is incubated in the enzyme mixture for 50 min at room temperature. Transfer digested circumvallate papillae into the dish with round glass coverslips, and dissect circumvallate papillae gently with a surgical razor in Tyrode’s solution. The isolated taste cells were cultured at room temperature and used for experiments after 3–7 h.Preparation of Tyrode’s solution (in mM): 145 NaCl, 5 KCl, 1 MgClAs described previously, macroscopic currents were recorded using a HEKA EPC10 amplifier controlled by PatchMaster software (HEKA). Whole-cell recordings were performed at 0 mV. Patch pipettes were prepared from borosilicate glass (WPI) using a Flaming/Brown Micropipette Puller (SUTTER P-97) and fire-polished to a resistance of 3–5 MΩ by Polisher (NARISHIGETM MF-830). Tube switching data of the gravity-driven system were transmitted to the amplifier in real-time and processed using PatchMaster to record the corresponding current change data of each tube solution in real time. Millisecond solution exchange was achieved with a fast-step perfusion system  custom modified to hold microcapillary tubes in a linear array.2, and 10 HEPES (pH adjusted to 7.2 with HCl). The artificial OTOP1 extracellular solutions with different pH in the perfusion system contained (in mM): 160 NMDG-Cl, 2 CaCl2, and 10 buffers based on pH . The artificial setting was used for OTOP1 channel recordings unless otherwise stated. OTOP1 extracellular solutions with different pH were perfused to membrane patch by a fast-step perfusion system.For OTOP1 channel recordings, the artificial pipette solutions contained (in mM):160 NMDG-Cl, 2 CaCl2, 2 MgCl2, and 10 buffer based on pH .For the taste receptor cells (TRCs) recordings, the physiological setting was as follows: pipette solution contained (in mM) 140 KCl, 0.5 EGTA, 10 HEPES, 2 Mg-ATP (pH adjusted to 7.3 with KOH), and extracellular solution contained (in mM) 140 NaCl, 3 KCl, 2 CaCl6. Prior to the alkaline extracellular solution (pH 9.0) application, the intracellular and extracellular solutions (pH 7.4) contained 160 mM Li+, Cs+, K+, or Na+, respectively. Upon application at pH 9.0, the extracellular solutions were switched from monovalent cations  to 160 mM NMDG+, yielding enlarged outward currents. In the ion permeability experiment of Ca2+ in Supplementary Fig. + in the extracellular solution was replaced with gradient concentrations of Ca2+ under the condition of maintaining the same osmotic pressure.In the ionic selectivity experiment in Fig. Erev of the alkali-activated current of the mOTOP1 channel in the Zn2+-sensitive fraction, is obtained by subtracting the alkali-activated I–V curve with the addition of Zn2+ from the alkali-activated I–V curve. Vm was held at −120 mV and ramped to +80 mV. pH 8.00, holding potential was −43.75 mV; pH 8.25, holding potential was −58.54 mV; pH 8.50, holding potential was –73.32 mV; pH 8.75, holding potential was −88.10 mV.In Fig. Erev recordings, posteriori corrections were made to minimize the effects of liquid junction potentials. Liquid junction potentials in Figs. 33 and were corrected posthoc. In order to minimize junction potentials between bath solutions and “sewer pipe”, a 3 M KCl salt bridge, with a well-chlorided silver wire inside, was used as the bath ground. Junction potentials of <2 mV were not corrected.In the + to H+ ion permeability when the mOTOP1 channel was activated by acid: After the cells were clamped, the membrane potential was held at EH (−78 mV), then ramped from −120 to +80 mV (1 V/s). Erev was measured from the Zn2+-sensitive part of the current6. Zn2+ was added to the OTOP1 perfusion extracellular solution as control at a concentration of 10 mM. The pipette solutions contained (in mM): 130 TMA–methane sulfonate, 5 TEA–Cl, 2 Mg–ATP, 5 EGTA, 2.4 CaCl2, and 80 MES (pH adjusted to 6.0 with TMA–OH). The OTOP1 extracellular solutions in the perfusion system contained (in mM): 130 Na–methane sulfonate or 130 NMDG–methane sulfonate, 2 CaCl2, and 100 MES (pH adjusted to 5.5 with NMDG–OH). 200 µM amiloride was added to the extracellular solutions to block endogenous acid-sensing ion channels.The ratio of Na+ to H+ ion permeability when the mOTOP1 channel was activated by alkaline: After the cells were clamped, the membrane potential was held at EH (+29 mV), then ramped from −120 to +80 mV (1 V/s). Erev was measured from the Zn2+-sensitive part of the current. Zn2+ was added to the OTOP1 perfusion extracellular solution as a control at a concentration of 1 mM. The pipette solutions contained (in mM): 160 NMDG-Cl, 2 CaCl2, and 10 HEPES (pH adjusted to 7.2–7.4 with HCl). The OTOP1 extracellular solutions in the perfusion system contained (in mM): 130 Na–methane sulfonate or 130 NMDG–methane sulfonate, 2 CaCl2, and 100 Tris (pH adjusted to 8.5 with HCl).The ratio of Na34, we used the following formulas  equationformulas  and 2)  34, we u2 and 95% O2), digested, and spread onto cell crawls for at least 1 h. The pHrodo™ Red AM (10 μL) and PowerLoad™ (100 μL) were mixed in 10 mL of OTOP1 channel extracellular solution. The solution (2 mL) was then added to each small dish of cells and incubated at 37 °C for 15–30 min to allow the pH indicator to enter the cells. Cells were stimulated to produce pHrodo™ Red fluorescence using pH 4.0 solution  and pH 9.5 solution 6.The pH fluorescence imaging experiments in Fig. After incubation, changes in pH fluorescence of the HEK293T cells were captured using a Hamamatsu C4742 CCD digital camera controlled by MetaFluor Software (Molecular Devices). A 560-nm excitation filter of the LED light source (X-Cite 120LED) was used for the excitation of pHrodo™ Red AM. The pHrodo™ Red AM fluorescence was filtered using a 580-nm emission filter for detection. MetaMorph software (Molecular Devices) was used to process and analyze the fluorescence images. Fluorescence intensity was normalized by the initial fluorescence intensity of each cell. Six sets of fluorescence values were selected during the periods of different stimuli, and after calculating the mean value of fluorescence intensity for each cell, significant differences between mOTOP1 and untransfected cells were statistically analyzed.The isolated taste cells were plated onto 10 mm coverslips in Tyrode’s solution for 3–7 h. Then cells were fixed with 4% paraformaldehyde  in phosphate buffered saline  for 15 min and washed three times with PBS. Cells were permeabilized in 0.2% Triton X-100 (Solarbio) in PBS for 20 min at room temperature and washed three times with PBS. Then the cells were blocked for 30 min in 2% BSA (BioFroxx) in PBS. After the above treatment, cells were incubated in primary antibody  at 4 °C overnight. After extensive washing in PBS, Cells were mounted with Aqua-Poly/Mount . Images were acquired on an Olympus Flouview 1000 confocal microscope. Related to Fig. n  =  5 for 1 × 10−7 M Zn2+, n  = 5 for 1 × 10−6 M Zn2+; n  =  3 for 1 × 10−5 M Zn2+; n  =  3 for 1 × 10−4 M Zn2+; n  =  5 for 1 × 10−3 M Zn2+. Figure n  =  9 for pH 8.00, n  =  7 for pH 8.25, and pH 8.50; n  =  5 for pH 8.75. Figure n  =  4 for pH 8.0, 9.5, 10.0, and 10.5; n  =  6 for pH 8.5; n  =  5 for pH 9.0. Figure n  =  4 for Na+, n  =  6 for K+, n  =  7 for Li+, n  =  4 for Cs+; mOTOP1: n  =  5 for Na+, n  =  3 for K+, n  =  3 for Li+, n  =  6 for Cs+. The experiments in Figs. The number of experiments (patches) n for some figure panels is listed below. Figure 35. The structural model of mOTOP1 was generated in AlphaFold v2.028. All experiments were repeated at least three times independently. Off-line data analysis was performed using IgorPro v6.04. All statistical values are shown as means ± SEM, and the n value represents the sample size of the experiment. Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software Inc). *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.Multiple sequence alignment and maximum-likelihood phylogenetic analysis were performed using MEGA-X softwareFurther information on research design is available in the Supplementary InformationReporting Summary