You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila}
Article:
Abstract:
Isolation profoundly influences social behavior in all animals. In humans, isolation has serious effects on health. Drosophila melanogaster is a powerful model to study small-scale, temporally-transient social behavior. However, longer-term analysis of large groups of flies is hampered by the lack of effective and reliable tools. We built a new imaging arena and improved the existing tracking algorithm to reliably follow a large number of flies simultaneously. Next, based on the automatic classification of touch and graph-based social network analysis, we designed an algorithm to quantify changes in the social network in response to prior social isolation. We observed that isolation significantly and swiftly enhanced individual and local social network parameters depicting near-neighbor relationships. We explored the genome-wide molecular correlates of these behavioral changes and found that whereas behavior changed throughout the six days of isolation, gene expression alterations occurred largely on day one. These changes occurred mostly in metabolic genes, and we verified the metabolic changes by showing an increase of lipid content in isolated flies. In summary, we describe a highly reliable tracking and analysis pipeline for large groups of flies that we use to unravel the behavioral, molecular and physiological impact of isolation on social network dynamics in Drosophila.
Introduction:
Social interactions range from simple touch contacts as seen in Caenorhabditis elegans, Drosophila melanogaster( fruit flies) and other animals, to the most complex social behaviors of primates including human beings[1–4]. Social interactions are a key feature of the life style of many species. In socially interacting species, isolation is thought to profoundly influence the response to stress, as shown in humans, monkeys, mice and fruit flies[5–10]. In humans, isolation is known to even affect the onset and progress of diseases[11]. Furthermore, a few studies revealed a potential link between social conditions and gene expression[12, 13]. For example, Cole et al[14] profiled leukocytes from older humans with different social connections and found correlations between genome-wide transcriptional activity and social conditions, specifically the level of loneliness. This suggests that changes in social conditions, particularly the degree of “isolation”, may influence gene expression patterns. To unravel the complex mechanisms of social behaviors, which occur in groups, tools are essential to analyze all interacting individuals at the same time. Whilst most studies still focus on dyadic interactions to limit the complexity of analysis[15–17], these studies cannot reflect the group-level relationships. More recently, researchers have begun to use social networks to analyze the relationships among the whole group of individuals[18–21]. Social network analysis allows to examine and quantify the social relationships in groups, such as the influence of individuals towards the whole group, spread of behavior throughout the group, etc.[22]. For our needs, social network analysis completes the toolset required to study the effects of isolation on group behavior, which includes also molecular and genetic analysis, and individual and grouped physiological and behavioral analysis. Drosophila melanogaster is a very powerful genetic and neuroscience model that can to a certain extent readily be used to study social behavior and its underlying mechanisms. In order to study social behaviors in the fly, for example the effects of isolation on behavior and metabolism, a few key challenges remained open and needed to be addressed. These include appropriate experimental setups, reliability and accuracy of tracking, behavioral classification and detailed social network analysis. Presently, few of these are readily available with the required level of accuracy that we needed for our study on the effects of isolation on social behavior. While there has been enormous progress in the study of pairs of flies in dyadic interactions[2], tools are lacking for larger groups. Especially, the efficient and accurate tracking of large groups of flies is a non-trivial problem. Ctrax, introduced by Branson and colleagues[23], was one of the first powerful algorithms to track multiple unmarked flies under laboratory conditions. While it can identify flies or other animals accurately when apart from each other in high resolution recordings, Ctrax creates tracking errors during interactions( loss and swap of individual identities and creation of new tracks) in a low resolution video[24]. Similar algorithms were used in 3D tracking methods using multiple cameras[25], in which the unexplained assumption was made that each interaction involves only 2 flies. Though such assumption may be valid for small numbers of flies, it may become incorrect as the number of flies increases. idTracker[26] was introduced to track multiple unmarked animals including flies to stop identity errors from propagating after interactions, even with complete occlusions. However, as stated by the authors, for idTracker to be in proper working order, the image of each individual should comprise at least 150 pixels, which is almost ten times more than that of a typical fly in videos acquired with easily accessible and affordable cameras. Therefore, the currently available tracking algorithms are either unreliable or they have too many constraints for simple and efficient usage, thereby making improvements like the one we propose in this paper necessary. The next key issue in the analysis of social behaviors is the classification of various types of behaviors. Manual scoring is observer-dependent and can be arduous when multiple flies are involved[23]. Schneider J, et al[27] proposed a rule-based automated alternative to classify a number of well-defined interactions based on distance, duration and approach angle. However, the approach described in their study is not generic and cannot be applied to classify other behaviors. Also, the natural behavior of a fly is difficult to describe by a set of rules. An interactive machine learning based approach( JAABA) was recently proposed by Kabras et al[28], in which a user iteratively labels specific behaviors, and a machine learning algorithm uses these as training examples to predict future occurrences of the same behavior. However, the iterative process of creating the training dataset might be tedious for long videos. Thus, our approach differs from the above approach both in the classification strategy and in way that the training dataset is extracted. Schneider et al[20] were the first to study social interactions of Drosophila at the group level by generating unweighted dynamic networks. Based on binary social network parameters, they concluded that social isolation had no effect on the networks. On the other hand, Simon et al[5] used a static parameter called the social space index, denoting the distance between resting non-interacting flies, to conclude that 7 days of isolation enlarges the social space between young( 3–4 days old) flies. Our aim in this work is to extend these initial studies on social isolation, by investigating the effect of isolation on the social structure of flies at group-level over a long period of time. Although a social interaction network can be useful to quantify social behaviors, it has its limitations when comparing different conditions, such as different biological repeats of an experiment. In that case, the intuition is to average the parameters of the social networks obtained from the individual repeats, which is only valid for a homogenous network structure[20]. This is certainly not true in our case, since in our social isolation experiments, each time a new batch of flies is used. In this paper, we propose a novel method to average social networks by matching individual flies, such that the variance of network parameters is reduced. In summary, in this paper we describe an effective and reliable pipeline to image and analyze large groups of walking male flies. The pipeline consists of 1) an efficient and relatively low-cost acquisition setup, 2) an improved tracking algorithm( error-free for up to 16 male flies) based on a graph-theoretical framework, 3) a machine learning method for the classification of social interactions, and 4) a novel algorithm for computing an average social network to analyze the social structure. We apply this pipeline to study behavioral changes due to isolation and correlate these with changes in genome-wide gene expression. Our results show strong and specific effects of isolation on social network dynamics, metabolic gene expression and animal physiology.
Discussion:
In this paper, we describe the establishment of a novel framework for the accurate tracking of multiple flies, their behavior classification and a method to study social network dynamics. We use this framework to study the effects of isolation on social network dynamics and performed the first genome-wide gene expression analysis to correlate transcriptomic and behavioral changes upon social isolation in male Drosophila. Importantly, this pipeline uses easily available hardware to record long videos of interacting flies. The use of a graph-theoretic structure for the tracking algorithm allows to detect a constant number of flies in each frame, thus it is highly reliable and accurate and no further steps are required to correct for broken tracks. We confirmed this by the manual tracking of 2 randomly chosen videos. The supervised machine learning approach that we used for classification is generic and allows to extend the study of mere “touch” interactions to other types of interactions such as “sniff” or “chase”. Therefore, this platform is well suited for an accurate large-scale, long-term assessment of social behavior in flies. In theory, this approach is transferrable to any other species. Additionally, the interfaces between different components of the pipeline are compatible with other established methods, so that other researchers can adapt their experiments to our pipeline with minimum effort. In this study, we implement for the first time the use of an average network for social touch interactions for a large group of male flies. Previously,[20] work used social network analysis to study group behavior in flies. In their research, the dynamic iterative network was built based on the consecutive interactions among the individuals, for example 33 consecutive interactions were used for iterative network construction in most results, rather than a fixed period of interactions. When they compared the differences between networks, they averaged the parameters of networks, which might lose information of heterogeneity within the network since the flies are not in similar position/functions within the group. Unlike network analysis in other fields[45–47], individuals( flies) in our research are not the same across networks, which could create hierarchy within the group. Taking the heterogeneity of network into account, matching individuals across the networks while studying multiple network is crucial for accurate analysis of behaviors. The average network built in our work based on graph matching of individuals first minimized the variance across the networks hence generates closer result to reality. Having developed these tools, we applied them to study the consequences of isolation on social network dynamics. The results showed that social isolation affects the interactions between isolated flies by increased locomotion activity and local social interactions, with minimal effects on the global pattern of social interactions. Social isolation in humans is a risk factor for, and may contribute to, poorer overall cognitive performance and executive function, increased negativity and depressive cognition[48]. There are more and more studies trying to unravel the relations between social environment changes, for example, isolation, and gene expression, to understand the process of the social influence. Previous studies on leukocyte of elderly human being have revealed some connections between social loneliness to immune system gene expression changes[14]. A study on mice shows[49] showing upregulated brain-derived neurotrophic factor upon social isolation. We tracked the changes in gene expression over social isolation time and found that changes in gene expression occurred very early together with, and even before some of, the behavioral changes. These gene expression alterations were highly specific, affecting mainly metabolism, and very transient. Further experiment demonstrated that isolation has caused increase in the weight of Drosophila compared to that of control group, both in the gross weight( wet weight, dry weight, and ether weight) and normalized lipid content. It is tantalizing to speculate that a transient alteration in the metabolic state of flies upon social isolation plays a potentially causal role in long term behavioral changes, but this needs to be studied in much greater depth in the future.
Social isolation severely affects the behavior and physiology of social animals , including humans . The fruit fly is a powerful model for studying the mechanisms of development , health and disease and is also used to study social behaviors such as mating and aggression . However , these studies are limited to examining few individuals for shorts amounts of time , due to the lack of effective computational tools for the analysis of large groups over prolonged time . To overcome this hurdle , we built a new behavioral arena and developed new software that accurately tracks many flies simultaneously over long time periods . The arena is cheap and easy to build and the software works with low resolution videos . Using these improved tools , we studied social isolation in groups of male flies . We found that isolation caused flies to form stronger interactions with neighboring flies in their social network . These behavioral changes were preceded by transient changes in the expression of metabolism genes and eventually resulted in isolated flies accumulating fat , as has been previously observed in studies in mice and humans . Our study opens the door for the use of fruit flies in future studies of social isolation .