extfieldsofstudy: Biology Psychology

text:
Do Sigmoidal Acquisition Curves Indicate Conformity?

The potential for behaviours to spread via cultural transmission has profound implications for our understanding of social dynamics and evolution. Several studies have provided empirical evidence that local traditions can be maintained in animal populations via conformist learning (i.e. copying the majority). A conformist bias can be characterized by a sigmoidal relationship between a behavior’s prevalence in the population and an individual’s propensity to adopt that behavior. For this reason, the presence of conformist learning in a population is often inferred from a sigmoidal acquisition curve in which the overall rate of adoption for the behavior is taken as the dependent variable. However, the validity of sigmoidal acquisition curves as evidence for conformist learning has recently been challenged by models suggesting that such curves can arise via alternative learning rules that do not involve conformity. We review these models, and find that the proposed alternative learning mechanisms either rely on faulty or unrealistic assumptions, or apply only in very specific cases. We therefore recommend that sigmoidal acquisition curves continue to be taken as evidence for conformist learning. Our paper also highlights the importance of understanding the generative processes of a model, rather than only focusing solely on the patterns produced. By studying these processes, our analysis suggests that current practices by empiricists have provided robust evidence for conformist transmission in both humans and non-human animals.

Introduction
There is increasing evidence that many animals, including humans, use social learning strategically; preferentially copying certain individuals, behaviours, or in particular contexts, in order to optimise the quality of information gained 1, 2 . These learning rules will not only determine what behaviours individuals acquire, but will also shape transmission dynamics as information moves through populations, with implications for the formation and patterning of traditions 1, 3-5 . One such social learning strategy is conformist learning, where individuals acquire behaviour with a positive frequency-dependent learning bias, preferentially copying the most common behaviour or the majority of individuals 6,7 . Conformity has been of particular interest in study of cultural evolution, as it is considered one of the central pillars in the emergence of culture 8 . That is, if individuals copy the majority, groups should fix on a single socially learnt tradition that will remain stable over time and be resistant to erosion or invasion by alternative variants, allowing for group specific cultures to establish and persist. Once thought of as a potentially 'human only' trait 6 , numerous models have now argued that conformity could potentially evolve in various species under a wide range of environmental conditions 7,9 . For example, a conformist learning strategy can be adaptive in spatially varying environments where local knowledge is costly to acquire 3,8,10 , as copying the behaviour of the majority of residents will thus be a short-cut to ascertaining best locally adapted behaviour. However, experimentally demonstrating the use of conformist learning in non-human animals has been challenging, particularly in natural populations. A key question has thus become whether conformist learning strategies can be inferred from behavioural data on the acquisition and expression of learnt behaviour.
Recently, two studies provided evidence for conformity in very different systems. van de Waal et al. (2013) 11 trained troops of vervet monkeys (Chlorocebus aethiops) to prefer one of two colours of otherwise identical corn. They found that naïve monkeys adopted the majority behaviour of their group, and that dispersing individuals would switch their choice to match the majority behaviour in the group that they joined.   12 trained great tits (Parus major) to access a food reward by solving a puzzle box by sliding a door either left or right, where puzzle boxes could be opened in either direction. They then observed the spread of this new behaviour from these trained demonstrators across five replicated sub-populations and over two generations. Naïve birds disproportionately copied the most common solving technique (opening direction) in their local foraging group, with traditions for left or right persisting across generations. Similarly to the vervet monkeys 11 , birds that moved between sub-populations with different traditions tended to switch their preference to match the locally common behaviour of the area they immigrated into.
These studies reflect the major lines of evidence used to suggest conformist biases in non-human animals. Firstly, that individuals will change their existing behaviour when faced with a conflict between their preferences and those of the rest of their group, and secondly, that the probability of a naïve individual adopting the most common behaviour will be disproportionately greater than that behaviour's frequency in the group it observes. This second line of evidence can be characterized by a sigmoidal relationship between an individual's probability of acquiring a behaviour and the frequency of its demonstration. When individuals can choose between two behaviours (sometimes including non-adoption as a behaviour), such a sigmoidal relationship indicates how individuals become disproportionately biased towards the common behaviour 3 . It is important here to differentiate acquisition curves from diffusion curves, as both are common topics in the study of social learning. A diffusion curve is the instantaneous or cumulative rate of adopting a behavior in population as a function of time 13 . Our focus is on the acquisition curve, which is the instantaneous rate of adopting a behavior as a function of that behavior's prevalence in the population.
Scaling up, the presence of a sigmoidal acquisition curve at the population level-in which the relationship between the frequency of a behavior and the overall rate at which it is adopted is sigmoidal-has generally thought been to provide evidence of conformist learning in the population in question.Yet is this approach robust? Several recent papers have argued that the answer is a firm 'no' [14][15][16] . These critiques center on the claim that sigmoidal curves can be generated without conformist learning, and therefore that the presence of such curves is not evidence for conformist learning. In the remainder of this paper, we evaluate these claims.
The evidence for conformist learning in wild populations of non-human primates and birds was first challenged by van Leeuwen et al. (2015) 14 . In this paper, they disputed the methodological validity of using the frequency at which a behaviour is being demonstrated when testing for a conformist bias (for example, as in Aplin et al. 2015a 12 ). Instead they argued that a valid test for conformity must compare the probability of adopting behaviours against the overall proportion of individuals performing each behaviour; for example, if a demonstrator performs a behaviour twice, its contribution to the overall frequency of behaviour in a group is only counted once. In response to this critique, Aplin et al. (2015b) 17 reanalysed their previous data to show that their results were robust to either definition of conformist transmission. Aplin et al. 17 further argued that, in empirical practice, there were no cases in which the two definitions conflicted, suggesting that they are functionally equivalent in all but the most exceptional of cases 17 . This argument was further supported by re-analyses of data from van de Waal et al.  16 have issued a more direct challenge to the validity of identifying conformist transmission from population-level sigmoidal acquisition curves. In these papers, they developed a simple computational model in which populations of 100 individuals were randomly initialized with one of two behaviours. Individuals were then allowed to either copy a random demonstrator 15 or choose to copy a demonstrator as determined by a range of different learning rules 16 . In several cases, their modelling results suggested that plotting the frequency of the behaviour against the probability of adoption could lead to sigmoidal acquisition curves even in the absence of conformist learning. In particular, Acerbi et al. (2016) 16 purported to demonstrate two non-conformist learning rules that nevertheless generate conformist curves: one in which individuals have an intrinsic bias for one behaviour ('variant preference'), another in which individuals copy a behaviour from a randomly chosen member of a small subset of the population ('demonstrators subgroup').
Here, we revisit the modelling approaches used by van 16 . Specifically, we reanalyse the models in two regards. First, we examine the evidence that proposed non-conformist learning mechanisms can yield sigmoidal curves. We then consider the argument that the majority should be represented as the frequency of individuals exhibiting a behaviour, rather than by the frequency of the behaviour itself. This reanalysis highlights that in both cases, a number of problematic assumptions and implementation errors distort or exaggerate the results produced. Our aim in this paper is therefore to correct the overly strong claims made in those papers. We conclude that, with proper methodological implementation, a sigmoidal relationship between the adoption of a behaviour and its frequency in the group remains a valid and robust way to identify conformist social learning in wild populations. variant B. The authors examined 10 possible learning biases, and concluded that two non-conformist learning biases could also produce sigmoidal curves: 'variant preferences', and 'demonstrator subgroups'. We address each of these in turn (see also Table 1 for a summary). Before doing so, we first address an underlying assumption of their model that has important implications for any interpretation of its results: the 50/50 variant starting condition.

Starting conditions
In the model of Acerbi et al. 16 , a population of 100 individuals is initialized in which approximately 50% adopt behavioural variant A, and the rest adopt variant B. That is, the probability that an individual performs behaviour A at time = 0 is, on average, 0.5. They then simulate 10000 copying events in which individuals randomly copy the behaviour of another individual, and use a range of rules for selecting these individuals. They record each of these copying events as the history of events. They replicate this process 1000 times. Data is then aggregated across all model runs for the entire length of each simulation in order to describe the full range of variant frequencies, as either A or B goes to fixation. At least two potential problems arise from this methodology. First, in the absence of group structure or anti-conformist drives, a random copying rule implies that the absorption probability increases with the frequency of the variant; in expectation, the population monotonically converges to a single dominant variant. Thus, data for frequencies of variant A between 0.5 and 1 will come exclusively from runs in which uniform adoption of variant A was the absorbing state, while data for frequencies of variant A between 0 and 0.5 will come from runs in which the absorbing state was variant B (with some exceptions for stochastic noise around frequencies of 0.5). A corollary is that the dynamic in which a rare variant arises and becomes dominant is never explored. Second, because data is aggregated from across all time steps, more common variant frequencies will be overrepresented in the data. This should not matter if all aggregated populations are drawn from the same distributions. However, as we show below, errors can arise when populations using disparate rules are averaged together. Acerbi et al. (2016) 16 claim that a non-conformist copying rule, which they call variant preference, can generate sigmoidal curves. The rule is instantiated in the model as follows:

Variant preference
Individuals had a preference for one of the two variants, which was operationalized by endowing individuals with different copying probabilities for the two variants. When presented with a demonstrator showing it, one of the variants was always copied, while the other variant (the less preferred) was copied according to the parameter pLess (pLess was varied between 0 and 1). All individuals preferred the same variant, which was randomly selected at the beginning of each repetition. , p. 7) 16 .
The sigmoidal curves generated stem from the two faulty assumptions described above: averaging data across all runs, and always starting with a 50-50 mix of behavioural variants 16 . We first replicate their result ( Fig. 1, top row middle) of a sigmoidal curve. We then separate runs in which variant A is preferred ( Fig. 1, top row right) from runs in which variant B is preferred ( Fig. 1, top row left). This indicates that the sigmoidal curve is actually an artefact formed by joining together two lines with different slopes. What explains this?
When each individual prefers variant A, the probability of adopting A is simply equal to the probability of encountering an individual exhibiting variant A, which is just the frequency of A. For each individual preferring variant B, the probability of adopting A is the frequency of A times pLess: the probability of encountering an A times the probability that A is adopted in those cases. The sigmoidal curve results from these two lines being stuck together. Of course, two lines being averaged together should not result in a sigmoidal curve. This feature stems from the fact that the frequency of each variant is always initialized at 50%. Because adoption dynamics are always monotonic in expectation (i.e., the frequency of one variant always steadily increases, stochastic noise notwithstanding), the left and right sides of the graphs are generally from completely different batches of simulations, as illustrated by the top row of Fig. 1. In most empirical studies, individuals in the population are not initiated with a particular behaviour at random, but rather will start out naïve, so that the probability of encountering a behavior will start close to 100%, rather than 50%. We ran simulations with a uniform distribution of initial adoption frequencies for variant A ranging from 0% to 100%. The results (Fig. 1, bottom row) illustrate how preferences for either variant are associated with a straight line with slope depending on preference strength. Combining data from both preferences reveals a concave-up curve rather than sigmoidal curve. Such a curve, rather than a straight line, is a result of more data for the dominant variant preference at either end of the frequency spectrum. A simple test supports this interpretation (Fig. 2).

An analytical model of variant preference
The variant preference rule is equivalent to what Boyd and Richerson (1985) 3 called a direct bias. It is easy to derive a simple mathematical model of such a bias, and doing so will help to illustrate the dynamics of the individual-based model discussed above. Each individual observes another individual at random and decides whether to adopt their behaviour. Let p be the frequency of variant A. Upon observation, the probability of adopting one's preferred variant is 1, and the probability of   16 in which individuals prefer one of the two variants. Includes best fit linear and sigmoid curves to data. Top row: Initial frequency of variant A always initialized to 50%, as in Acerbi et al. Bottom row: Initial frequency varied across runs from 0% to 100% in increments of 0.1%. Centre column: Runs in which variant A is preferred and runs in which variant B is preferred are averaged together. Right and left columns: variant A or B is always preferred, respectively.
adopting the less-preferred variant is q (equivalent to pLess in the Acerbi et al. model 16 ). Finally, let r be the frequency of individuals who prefer variant A, in order to accommodate a mixed population of preference types. It now follows that the probability that a randomly chosen individual adopts variant A is given by: The first term on the right side of this equation is the probability that the observer initially exhibits variant A and continues to do so after the observation, while the second term is the probability that the observer initially exhibits variant B but switches behavior after the observation. Focusing on variant A, we see that if everyone in the population favours the same variant, we should observe strict concave up or down curves, as in Fig. 3A. If there is an even mix of both types, or if we average over an even number of runs of both types, the curves will average out, and we end up with a straight line. However, a sigmoidal curve can once again be generated from the assumption that the population is always initialized with 50% of each variant. Whenever one variant is favoured, that variant will always increase, and so no data will ever be recorded for frequencies less than 0.5. Rather, frequencies below 0.5 of variant A will only be observed when variant B is preferred (r <0.5). Combining these two situations leads to the union of two distinct curves, and can generate an artefactual sigmoid curve, as in Fig. 3B. However, this bears little resemblance to any realistic data that would be collected in an empirical study.
Why does this analytical model give an r-shaped curve for the case where all individuals prefer variant A, while the model of Acerbi et al. 16 gives a straight line? The difference allows us to notice yet another important modelling artefact. Acerbi et al. took as their dependent measure the probability that any randomly selected copying event in which the focal individual was exposed to another individual with variant A resulted in that focal individual adopting variant A. This counts events in which the focal individual was already exhibiting variant A and then encountered an A, and so "copied" the variant she already had. However, it does not count events in which the focal individual started with variant A and encountered a B, but refrained from copying the newly encountered variant. In our analytical model (Fig. 3), the dependent variable is, instead, the probability that a random individual will end up demonstrating variant A, given the current frequency of individuals exhibiting variant A in the population. In other words, the analytical model looks at the behaviours expressed in the population regardless of who encountered whom, and so counts the case where a focal individual started with variant A, encountered a B, and refrained from copying the newly encountered variant. Thus, the probability of demonstrating variant A when it is preferred is higher in our model than in the model of Acerbi et al. 16 . We view this as a more reasonable assumption, since the probability of adopting a behavioural variant should be enhanced by exhibiting an internal bias for that particular variant. We note also that our analytical result mirrors a very similar result obtained previously by Boyd and Richerson (1985, p. 207) 3 .
We also believe that the dependent measure of adoption used in our analytical model is more in line with empirical methodology than the measure of adoption used by Acerbi et al. 16 . The sample sizes that empirical studies deal with will often be substantially smaller than the size of the populations being modelled. A consequence of this is that researchers will use all of the data available to them (i.e. not ignore some cases-as when individuals starting with variant A encounter B and refrains from copying it).
In summary, the variant preference copying rule does not generate sigmoidal curves when unrealistic modelling assumptions are removed. By investigating why a sigmoid was produced in the model by Acerbi et al. 16 , we have shown that the variant preference rule is not a valid competing hypothesis for conformist learning when considering the causes of sigmoidal acquisition curves in results from empirical studies.

Demonstrator subgroups
The second copying rule purported by Acerbi et al. (2016) 16 to yield sigmoidal acquisition curves was called demonstrators subgroup. This rule corresponds to a situation in which individuals exhibit a model bias, for example by copying dominant or prestigious individuals (e.g., a 'copy dominant males' rule), and works as follows:

5/12
At the beginning of each repetition, a subset S of individuals in the population, of size Dm (Dm = 5, 10, 20, 50), was randomly chosen. At each time step, each individual was paired with a demonstrator chosen from the subset S and always copied it. Demonstrators themselves changed their state according to the same rule. (Acerbi et al., p. 7) 16 .
We first replicate the results of Acerbi et al. 16 (Fig. 4A). Unlike variant preference, the sigmoidal curve generated is robust to variation in the initial frequency of each variant when averaged across simulations using a uniform distribution of initial frequencies (Fig. 4B). If, however, the behavioural variant that is preferentially copied is initially rare, the sigmoidal curve becomes increasingly skewed, approaching an r-shaped curve (Fig. 4C). The reason the 'demonstrators subgroup' rule generates a sigmoidal curve is as follows. Due to the well-known phenomenon of drift in small groups 19,20 , random copying among the demonstrators will soon create a majority and then a uniform bloc. When one variant has a majority in the demonstrators subgroup, that variant will be copied at a rate higher than chance, because its frequency will increase more rapidly in the small subgroup than in the population at large. Once the variant reaches fixation in the subgroup, it will be copied with certainty by members of the population, evidenced by the horizontal flattening out of the acquisition curve for small and large frequencies of variant A in Fig. 4A (the severity of these lines is reduced in Fig. 4B because the initial frequencies of the variant in the subgroup and in the larger population are less strongly coupled). By restricting analysis to conditions in which variant A is initially rare, we lose the strong horizontal line for low frequencies of A (Fig. 4C), in part because adopting variant A will in those cases be a rare event.
The acquisition curve generated by the 'demonstrators subgroup' rule is more severe (characterized by larger jumps in the second derivative) than the curve generated by conformist transmission; however, it can be classified as a sigmoidal curve. Should researchers therefore be concerned that their sigmoidal data may reflect a model bias (e.g. a strategy of copying dominant individuals) rather than a majority bias? Probably not. Acerbi et al. (2016) 16 showed that the demonstrators subgroup rule reliably generated a sigmoidal curve only when both the total group size and the relative number of demonstrators were very small. Specifically, a sigmoidal curve provided a better fit than a line only when N ≤ 100 and Dm <= 5. Practically speaking, this is likely equate to exclusively copying one or a few specific individuals, rather than preferentially copying a type or class of individual. In such cases, it should be relatively easy for researchers to identify this sort of dynamic and to test the demonstrators subgroup hypothesis against more direct conformist biases absent dominant leaders. How to measure the majority Conformist learning occurs when the more common behavioural variant is preferentially adopted. Empirically, there are at least two ways to measure just how common a particular variant is. You can count the number of individuals exhibiting each variant, or you can count the total instances of each variant. In the latter case, some individuals may be counted more than once. Two papers by van Leeuwen et al. 14,15 purported to show how using the frequency of observed behaviours as a measure of the majority, rather than the frequency of individuals exhibiting those behaviours, can lead to incorrect conclusions about the presence of conformist learning. To illustrate this, van Leeuwen et al. 15 constructed a model similar to the above, and compared acquisition curves produced using the frequency of behaviours and the frequency of individuals. They found that measuring the 6/12 frequency of behaviours could produce artefactual sigmoidal curves even when individuals copied the behaviors of randomly chosen individuals, and so claimed that a reliable indicator of conformity could be derived only by measuring the frequency of individuals. Firstly, it is worth noting that when implementing their model, the authors recorded the complete history of behaviours expressed by the population up to the point where the observer made their decision. This implementation suggests that observers use the complete history of all behaviours exhibited by all individuals when choosing between variants, or at least that empirical researchers analyse their data under this assumption. We would argue that this is highly implausible; rather, copying individuals will observe only a small subset of the population during a decision-making event. Ideally, researchers would like to be able to quantify exactly what focal individuals have actually paid attention to prior to exhibiting their behaviour. To date, this has been methodologically challenging, and so instead most researchers have recorded the frequency of behaviours (or of individuals undertaking behaviours) in some constrained time prior to the focal individual making its decision. This approach attempts to estimate what the individual might have plausibly been able to observe in its current learning period. For example,   12 used a 245 second period prior to each bird's first solve (a mean of 6 previous solves) as their 'history of behaviour' time-window, and based this value on behavioural patterns observed from extensive studies in their population.
Here we re-ran the baseline model from van Leeuwen et al (2016) 15 and Acerbi et al (2016) 16 , in which individuals randomly select one individual to copy (Fig. 5A). Taking the entire history of behaviours produced by individuals in the population replicated the same sigmoidal response that was produced in the original studies (Fig. 5B). However, if the history was limited to the previous 10 behaviours, we found that the relationship was perfectly linear, as expected under a random copying strategy (Fig. 5C).
However if one is not going to use the complete history of behaviours, then how long should the time window for data collection be? A limitation of using a time window approach for considering the behavioural history of demonstrations is that the size of the time window is not always obvious, as highlighted above and in the critique by van Leeuwen et al (2016) 15 . Could the choice of time-window influence the likelihood of erroneously producing a sigmoidal curve in the absence of conformist copy? To test this, we measured the sensitivity of the relationship between frequency of behaviours observed and the probability of performing the behaviour to the size of the time window (Fig. 6). We found that a linear model provided a much better fit to the data than a sigmoidal model up to window sizes that were still quite large (more than 2000 observations). From this, we can conclude that an incorrect inference of conformist transmission when copying is in fact random will be very unlikely if any time-windowing is applied when calculating frequency of behaviours.

Discussion
Three recent studies have questioned the empirical evidence for conformist copying in non-human animals, and re-opened the debate as to whether sigmoidal acquisition curves can be used as evidence for conformity. In this paper, we examined the different processes that generated sigmoidal curves in these published models, and find that non-conformist learning processes for sigmoidal outcomes were almost exclusively underpinned by flawed or unrealistic modelling assumptions. Our work further highlights how these assumptions misrepresent the types of analyses conducted by empiricists. More specifically, we first examined the non-conformist learning mechanisms purported to yield sigmoidal curves. We showed that one of them, variant preference, failed to do so when more realistic behavioural and analytical assumptions were used. The second mechanism, demonstrator subgroup, can generate weakly sigmoidal curves under some conditions, but is unlikely to be mistaken for Table 1. Summary of problematic issues found in the models by Acerbi et al (2016) and Van Leeuwin et al (2016). These issues result in the incorrect or misleading conclusions drawn from the simulations. While some issues do not strongly affect the shapes of the curve in the results of the papers, they incorrectly implement the biological process that the papers seek to investigate (for example, animals do not copy themselves) or incorrectly represent the analytical approaches used by empirical studies (for example studies count only the first demonstration by an individual).

Issue Explanation Starting conditions
The population was always initiated with a probability of 0.5 for each behaviour. This is unrealistic from empirical perspective, as a natural spreading process would start with one innovator (proportions near 0 or 1). This assumption exacerbates the potential for an artefactual sigmoidal response. Had range of initial conditions been used to assess model robustness, which is common practice in agent-based modelling, the strong claims made by the modelling papers would not have been supported. Pooling data All of the frequency data across each of the 1000 replicates were pooled, without noting variation across replicates. This aggregates multiple categories of response into a single category. This practice can eliminate discovery of multimodal data and provides no information on the range of outcomes that are possible. This approach can graft different responses together, such as combining two linear responses that then appear as an artefactual sigmoid. More generally, data with categorically different starting conditions should not be pooled.

Full behaviour history
The full history of all behaviours was used to calculate frequency of behaviours used by agents in the model when making a behavioural choices. This approach misrepresents what has been done in existing empirical studies. It also describes a biologically implausible process that assumes individuals respond to a distant past that potentially includes thousands of observations. Using data restricted to recent history when calculating the state of the system alleviates any potential mistaken results. What counts as 'copying' events Every event was counted in which an individual encountered behavioural variant A, including when an individual did not change what action it performs. However, events in which an individual retained variant A after encountering variant B were not counted. This increases the appearance of a sigmoidal acquisition curve (as opposed to either a linear or r-shaped curve) because individuals that continuously perform variant A when the population behaviour becomes entrenched are all counted as copying action A. Defining social learning events is challenging, so striving for consistency and robustness is important. Individuals are their own targets for social learning Individuals could select themselves as demonstrators, which confounds data on social learning. Similarly, a focal individual's behaviours were counted in its assessment of the historical frequency of observed events. Whilst in a population of 100 or more individuals this issue had relatively little impact on the results, the effect may be much greater in a smaller population. conformist transmission and is easy to control for empirically. Second, we considered the argument that the majority should be represented as the frequency of individuals exhibiting a behaviour, rather than by the frequency of the behaviour itself, as the latter can also generate sigmoidal patterns under random copying. We showed that this conclusion stems from an unrealistic modelling assumption that bears little relevance to the overwhelming majority of empirical research.
Our motivation for this paper is neither to provide a general critique of an individual-based modelling approach for understanding social learning and animal behaviour, nor to critique the general caution that alternative generative mechanisms at the individual level may be responsible for population-level patterns. Formal models, including individual-based models, have been critically important in the development of rich theories for understanding social behaviour and evolution. We are also sympathetic to the view that there can exist multiple causal pathways for population-level phenomenal; see, for example, Frank (2009) 21 on why power laws appear ubiquitous in complex systems. Rather we support publishing guidelines highlighting common errors, in line with the current movement towards a more robust scientific literature [22][23][24][25] . Indeed, our study highlights the importance of carefully exploring the factors that drive patterns in a model. A mathematical or computational model acts as a sort of logical engine for turning assumptions into conclusions 26,27 . Those assumptions concern the delineation of the parts of a system (often individual organisms), the properties and initial distributions of those parts, and the dynamic interactions by which the population changes 28 . The validity of the conclusions drawn from a model therefore depends fundamentally on the extent to which its assumptions adequately represent reality. All models must make unrealistic assumptions if they are to act as useful abstractions. However, the robustness of any model's results to idiosyncratic assumptions must be carefully assessed if we wish to avoid drawing erroneous conclusions when constructing analogies between models and reality.
Individuals can exhibit various attentional biases during the process of social learning that will have consequences both for acquisition patterns and for what traditions become established at the group level 1, 2, 29 . While a conformist learning bias is one the clearest examples of such an effect, multiple different social learning strategies have been identified across a wide variety of species, many of which could potentially also influence transmission dynamics 1,4,13 . It is therefore a valuable exercise to explore whether different social learning strategies could produce similar acquisition patterns, and we applaud the effort to do so. In our reanalysis of Acerbi et al. 16 , we conclude that, indeed, one such social learning rule can generate sigmoidal acquisition curves superficially similar to conformist learning: the exclusive copying of a subgroup of potential demonstrators, a form of model bias here called the 'demonstrator subgroup' rule. However, it is important to note that this rule only generates a sigmoidal curve when both total group size and the number of demonstrators are small-that is, for populations of 100 individuals or fewer, with five or fewer demonstrators-and even then the sigmoidal nature of the curve is apparent only for very low and very high variant frequencies. For example, a social learning biases such as 'copy adult males' would be very unlikely to generate such a acquisition pattern, while 'copy the single most dominant male' might. This rule is therefore important to consider as an alternative explanation. However, we believe that empirical researchers have shown themselves to be capable of identifying candidate social learning strategies that might be operating in their system, and devising experiments to test hypotheses against one another. Two recent examples include movement direction in baboons 30 , for which conformist learning was supported, and nest site navigation in honeybees (reviewed in Seeley 2010 31 ), in which the preferentially copied individuals were readily identifiable. Finally, 'copy the dominant' biases have been explored in vervet monkeys 32 and thoroughly discussed in the context of additional evidence for conformity in this species 11, 18 .

9/12
Does a sigmoidal acquisition curve at the population-level uniquely indicate conformist transmission? We cannot say for certain. However, the recent works by van Leeuwen et al. 15 and Acerbi et al. 16 provide no credible evidence that alternative mechanisms could generate such curves under reasonable conditions. This does not mean that no such mechanism exists, nor does it eliminate the possibility that environmental or network constraints could contribute to the emergence of sigmoidal acquisition curves in some circumstances. Indeed, it remains an important question as to whether alternative mechanisms can produce those stable and resilient group-level cultures that are so indicative of conformity 3 , and we encourage further research on this topic. However, if such conditions exist, they are not any that have been previously proposed for the social acquisition of information. In the end, a population-level acquisition curve is a somewhat crude tool with which to investigate the cognitive and behavioural strategies used by individuals. More complex methods and models that can incorporate things like individual heterogeneity, population structure, and the possibility that individuals use multiple learning strategies simultaneously are surely needed [33][34][35] . Yet for the time being, in the presence of a sigmoidal acquisition curve, conformist transmission should be viewed as a highly probable antecedent.

Methods
In this study, we re-evaluated the modelling results described in Acerbi et al. 16 and Van Leeuwen et al. 15 . Our implementations relied on the MATLAB source code provided by Acerbi et al. 36 . We modified this code in only two ways. The first modification was to relax the starting conditions in which the probability for each variant was 0.5 (Figures 1, 4). The second modification is to record, after each copying event, the frequency of behaviours over the previous T solves, where T = 10 ( Figure 5) or ranged from 0 to 10000 ( Figure 6). In Table 1, we list some additional implementation issues that we identified while working these models. However, because we were interested in isolating and demonstrating the primary mechanisms underpinning the apparent emergence of sigmoidal curves under random copying, and we did not otherwise alter the original code provided by the authors. Following Acerbi et al., all acquisition data were plotted along with their best-fit linear and sigmoid curves. Analysis of the time window size was based on a replication of the model of Van Leeuwen et al. 15 in R, the code for which is provided in the online supporting materials.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
The Role of Angiogenesis in Cancer Treatment

A number of anti-angiogenesis drugs have been FDA-approved and are being used in cancer treatment, and a number of other agents are in different stages of clinical development or in preclinical evaluation. However, pharmacologic anti-angiogenesis strategies that arrest tumor progression might not be enough to eradicate tumors. Decreased anti-angiogenesis activity in single mechanism-based anti-angiogenic strategies is due to the redundancy, multiplicity, and development of compensatory mechanism by which blood vessels are remodeled. Improving anti-angiogenesis drug efficacy will require identification of broad-spectrum anti-angiogenesis targets. These strategies may have novel features, such as increased porosity, and are the result of complex interactions among endothelial cells, extracellular matrix proteins, growth factors, pericyte, and smooth muscle cells. Thus, combinations of anti-angiogenic drugs and other anticancer strategies such as chemotherapy appear essential for optimal outcome in cancer patients. This review will focus on the role of anti-angiogenesis strategies in cancer treatment.

Introduction
Angiogenesis is a normal and complex process controlled by certain biomolecules produced in the body. Endogenous local or systemic chemical signals coordinate functions of endothelial cells and smooth muscle cells to repair damaged blood vessels. The generation of new blood vessels is from pre-existing blood cells via the "sprouting" of endothelial cells, thus expanding the vascular tree ( Figure 1A) [1,2]. Steps toward angiogenesis include protease production, endothelial cell migration, and proliferation, vascular tube formation, anastomosis of newly formed tubes, synthesis of a new basement membrane, and incorporation of pericytes and smooth muscle cells ( Figure 1B).
After activation of endothelial cells by angiogenic stimuli, proteolytic enzymes are produced, which degrade the perivascular extracellular matrix (ECM) and the basement membrane. Endothelial cells proliferate and migrate into the perivascular area, forming "primary sprouts". Subsequent lumenation of these primary sprouts leads to formation of capillary loops, which is followed by synthesis of a new basement membrane and blood vessel maturation to complete tube-like structures through which blood can flow [3].
Physiological angiogenesis processes are crucial during embryo development, wound healing, and collateral formation for improved organ perfusion. However, abnormally accelerated angiogenesis processes or pathological angiogenesis are associated with various disorders, including ocular neovascularization, which leads to a loss of vision. In comparison with chemical signals that induce blood formation, there is another type of chemical signal known as an angiogenesis inhibitor (Table 1). These signals may systematically disrupt blood vessel formation or support removal of existing vessels. Inhibitors function by acting on several proteins that have been identified as angiogenic activators, including vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF, FGF2), angiogenin, transforming growth factor (TGF)-α, TGF-β, tumor necrosis factor (TNF)-α, platelet-derived endothelial growth factor, granulocyte colony-stimulating factor, placental growth factor, interleukin-8 (IL-8), hepatocyte growth factor, and epidermal growth factor [4]. It is very important to keep a balance between activators and inhibitors, and this balance regulates vascular homeostasis.  In comparison with chemical signals that induce blood formation, there is another type of chemical signal known as an angiogenesis inhibitor (Table 1). These signals may systematically disrupt blood vessel formation or support removal of existing vessels. Inhibitors function by acting on several proteins that have been identified as angiogenic activators, including vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF, FGF2), angiogenin, transforming growth factor (TGF)-α, TGF-β, tumor necrosis factor (TNF)-α, platelet-derived endothelial growth factor, granulocyte colony-stimulating factor, placental growth factor, interleukin-8 (IL-8), hepatocyte growth factor, and epidermal growth factor [4]. It is very important to keep a balance between activators and inhibitors, and this balance regulates vascular homeostasis. Among them, VEGF is a powerful angiogenic agent in neoplastic tissues, and VEGF receptors (VEGFR) have been widely studied in the field of neoplastic vascularization. For example, by generation of VEGF and its secretion into neighboring tissue, the tumor cells will be able to feed on the new blood vessels.
Although it was thought for many years that the spread of cancer cells and growth of localized tumors beyond a few millimeters in size requires local angiogenesis in which tumor cells produce new blood vessels by releasing pro-angiogenic chemical signals, recent studies have reported that tumors like brain, lung, and liver can co-opt and grow along existing vessels without evoking new vessel growth [31]. Normal cells proximal to cancer cells may also support a pro-angiogenic response via signaling molecules. Local neovascularization supplies growing tumors with oxygen and essential nutrients, supports tumor extension and invasion into nearby normal tissue, and is essential to distant metastasis [32,33].

Angiogenesis Mechanism in Cancer
It is well known that in healthy cells, oxygen tension is key in the regulation of angiogenesis, and endothelial cells (ECs) and smooth muscle cells (SMCs) have various oxygen-sensing mechanisms, including oxygen-sensitive NADPH oxidases, endothelial nitric oxide synthase (eNOS), and heme-oxygenases [34]. Vascular cells also express a different class of oxygen sensors that interface with the hypoxia-inducible transcription factor (HIF) family, which in turn is an important molecular interface for relaying adaptations to changes in oxygen tension. Each of the three isoforms of HIFα (HIF-1-3) can heterodimerize with the aryl hydrocarbon receptor nuclear translocator (HIFβ/ARNT) subunit to form an active transcriptional complex that initiates expression of hundreds of genes, including those regulating cell survival, metabolism, and angiogenesis [35]. In order to grow or locally metastasize, tumor tissue also needs oxygen and nutrients that will be provided by blood vessels [32] because the primary function of blood vessels is to carry the oxygen that we breathe. The presence and abundance of oxygen correlates with the metabolism of endothelial cells in which oxygen can be consumed to form either sprouts in vitro [36] or a vascular network in vivo [37]. Because oxygen is key in cell growth (both healthy cells and cancer cells), hypoxic tumor cells (tumor cells that have been deprived of oxygen) will not divide ( Figure 2). In growing cancers, endothelial cells are vigorously active because of the release of many proteins, such as EGF, estrogen, basic and acidic FGF, IL-8, prostaglandin E1 and E2, TNF-α, and VEGF, that can activate endothelial cell growth and motility when the anti-angiogenic factors' production is reduced [32,38]. VEGF and bFGF are particularly important to tumor angiogenesis [32], but the redundancy of (other) pro-angiogenic factors helps explain the current suboptimal effectiveness in the oncology of the pharmacological inhibitors of single endogenous angiogenic agents.
In comparison to other naturally occurring angiogenesis inhibitors such as angiostatin, endostatin, interferons, IL-1 and IL-12, tissue inhibitor of metalloproteinases, and retinoic acid [38][39][40], we previously reported that physiological concentrations of thyroid hormone are pro-angiogenic by multiple mechanisms. This raises the possibility that thyroid hormone (thyroxine) is a model of non-protein stimulators of angiogenesis that may contribute to clinical resistance to anti-angiogenesis drugs [41][42][43]. We also introduced compound MR-49 as a novel pro-angiogenesis modulator that is synthesized from tetraiodothyroacetic acid (tetrac), a deaminated derivative of thyroxine hormone. MR-49 expressed a pro-angiogenic rather than an anti-angiogenic activity of tetrac [44]. Prostaglandin E2 (PGE2) as a mitogen in epithelial tumor cells is another example of a non-protein stimulator of angiogenesis in the vascular endothelium. It has also been also shown that the overexpression of cyclooxygenase-2 (an enzyme for conversion of arachidonic acid to prostaglandin H2) is accompanied by enhanced expression and production of angiogenic factors such as VEGF, FGF-2, HIF-1, matrix metalloproteinases (MMPs), and adhesion receptors of the integrin families. Therefore, it has been found that, with a high output of PGE2 via expression of cyclooxygenase-2, angiogenesis causes tumor development [45,46]. Furthermore, the CCN family of matricellular proteins are cytokines linking cells to the extracellular matrix. CCN3 is pro-angiogenic, while CCN5 is anti-angiogenic [47][48][49][50]. Multimerin 2 (MMRN2) has anti-angiogenesis effects, and its down-modulation occurs in the context of tumor-associated angiogenesis [51,52]. angiogenic [47][48][49][50]. Multimerin 2 (MMRN2) has anti-angiogenesis effects, and its down-modulation occurs in the context of tumor-associated angiogenesis [51,52].

Side Effects in Anti-Angiogenic Therapy
It has been reported that angiogenesis inhibitors might potentially interfere with many normal body processes such as wound healing [53], blood pressure [54], kidney function, fetal development, reproduction, and increased risk of clots in the arteries that would result in stroke or heart attack [53,55,56]. As an example, hypertension is one of the most observed side effects of systemic inhibition of VEGF signaling, which is also one of the most manageable side effects with the use of standard anti-hypertensive medications. Treatment of cancer by the inhibition of VEGF signaling will cause endothelial dysfunction by decreasing the level of VEGF, which will eventually result in hypertension.
Under normal conditions, VEGF is known to release vasodilator nitric oxide (NO) in vessel walls by upregulating endothelial nitric oxide synthase (eNOS) and prostacyclin (PGI2), resulting in vasodilation, through the activation of the mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K) downstream pathways [57][58][59][60]. Therefore, by inhibition of VEGF, the production of NO will be decreased, which will promote vasoconstriction, increase the peripheral resistance and eventually elevate blood pressure [61].
Because angiogenesis is required for wound healing, high levels of VEGF are produced during the repairing of normal wound. It has been reported that the inhibition of VEGF in angiogenesis therapy could interfere with normal angiogenesis and cause a disruption in the angiogenesis process or lead to a delay of the wound-healing process. In this regard, the treatment of patients having metastatic colorectal cancer with bevacizumab showed an increase in post-surgical wound healing complications, including wound dehiscence and impaired wound healing [62]. It was thought earlier that anti-angiogenesis strategies by blocking tumor angiogenesis would limit permeability of its own and other adjunct therapies. However, data demonstrated that treatment with anti-angiogenic drugs results in a more efficient normalized vasculature that might allow for improved tumor delivery of drugs [63][64][65][66][67][68][69][70]. Additionally, other anti-angiogenesis strategies such as anti-αvβ3 integrin have been exploited for enhanced active targeted delivery into tumors [71,72].

Examples of Angiogenesis Inhibitors for Cancer Therapy
Angiogenesis inhibitors can be designed to block the formation of new blood vessels, and the growth of tumors would thereby be halted but not eliminated; hence, anti-angiogenesis monotherapies are not effective in humans as was hoped for [73,74]. Thus, combinatorial treatments with conventional chemotherapy drugs are required. These inhibitors sometimes may not eliminate tumors and in order to achieve optimal treatment, a combination of anti-angiogenesis and conventional chemotherapy may be required.
In general, angiogenesis inhibitors can be classified into two main group of inhibitors: (i) direct inhibitors that target endothelial cells in the growing vasculature, and (ii) indirect inhibitors that target either tumor cells or the other tumor-associated stromal cells [75].

Side Effects in Anti-Angiogenic Therapy
It has been reported that angiogenesis inhibitors might potentially interfere with many normal body processes such as wound healing [53], blood pressure [54], kidney function, fetal development, reproduction, and increased risk of clots in the arteries that would result in stroke or heart attack [53,55,56]. As an example, hypertension is one of the most observed side effects of systemic inhibition of VEGF signaling, which is also one of the most manageable side effects with the use of standard anti-hypertensive medications. Treatment of cancer by the inhibition of VEGF signaling will cause endothelial dysfunction by decreasing the level of VEGF, which will eventually result in hypertension.
Under normal conditions, VEGF is known to release vasodilator nitric oxide (NO) in vessel walls by upregulating endothelial nitric oxide synthase (eNOS) and prostacyclin (PGI2), resulting in vasodilation, through the activation of the mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K) downstream pathways [57][58][59][60]. Therefore, by inhibition of VEGF, the production of NO will be decreased, which will promote vasoconstriction, increase the peripheral resistance and eventually elevate blood pressure [61].
Because angiogenesis is required for wound healing, high levels of VEGF are produced during the repairing of normal wound. It has been reported that the inhibition of VEGF in angiogenesis therapy could interfere with normal angiogenesis and cause a disruption in the angiogenesis process or lead to a delay of the wound-healing process. In this regard, the treatment of patients having metastatic colorectal cancer with bevacizumab showed an increase in post-surgical wound healing complications, including wound dehiscence and impaired wound healing [62]. It was thought earlier that anti-angiogenesis strategies by blocking tumor angiogenesis would limit permeability of its own and other adjunct therapies. However, data demonstrated that treatment with anti-angiogenic drugs results in a more efficient normalized vasculature that might allow for improved tumor delivery of drugs [63][64][65][66][67][68][69][70]. Additionally, other anti-angiogenesis strategies such as anti-αvβ3 integrin have been exploited for enhanced active targeted delivery into tumors [71,72].

Examples of Angiogenesis Inhibitors for Cancer Therapy
Angiogenesis inhibitors can be designed to block the formation of new blood vessels, and the growth of tumors would thereby be halted but not eliminated; hence, anti-angiogenesis monotherapies are not effective in humans as was hoped for [73,74]. Thus, combinatorial treatments with conventional chemotherapy drugs are required. These inhibitors sometimes may not eliminate tumors and in order to achieve optimal treatment, a combination of anti-angiogenesis and conventional chemotherapy may be required.
In general, angiogenesis inhibitors can be classified into two main group of inhibitors: (i) direct inhibitors that target endothelial cells in the growing vasculature, and (ii) indirect inhibitors that target either tumor cells or the other tumor-associated stromal cells [75].
In direct inhibition of angiogenesis, inhibitors such as angiostatin, endostatin, arrestin, canstatin, and tumstatin are known as fragments released on proteolysis of distinct ECM molecules and prevent vascular endothelial cells from proliferating and migrating in response to a spectrum of angiogenesis inducers, including VEGF, bFGF, IL-8, and PDGF [14,[76][77][78]. It has also been reported that the direct anti-angiogenic effect can be attributed to integrin receptors accompanied by several intracellular signaling pathways [14]. For example, Eikesdal et al. identified the critical amino acids (L, V, and D) within tumstatin, known as an inhibitor of endothelial cell proliferation, that confer anti-angiogenic and antitumor activity to tumstatin peptide, which is associated with the expression of the adhesion receptor, α v β 3 integrin, on tumor endothelial cells [79].
As mentioned above, indirect angiogenesis inhibitors will block the expression or activity of pro-angiogenic proteins like EGFR [80]. For example, Ciardiello et al. evaluated the anti-angiogenic and antitumor activity of gefitinib (ZD1839; Iressa ® ), a small molecule known as an EGFR tyrosine kinase inhibitor (TKI) in human colon (GEO, SW480, and CaCo2), breast (ZR-75-1 and MCF-7 ADR), ovarian (OVCAR-3), and gastric (KATO III and N87) cancer cells, that co-expresses TGF-α and EGFR (pro-angiogenic factor) [81]. Additionally, the U.S. FDA has approved a number of angiogenesis inhibitors for the treatment of cancers ( Figure 3, Table 2). R. K. Jain reported that for both direct or indirect anti-angiogenic therapy, the balance between pro-angiogenic and anti-angiogenic factors will be restored through the reduction of vessel permeability and hypoxia and enhancement of the homogeneity of blood flow and perivascular cells coverage [82]. In direct inhibition of angiogenesis, inhibitors such as angiostatin, endostatin, arrestin, canstatin, and tumstatin are known as fragments released on proteolysis of distinct ECM molecules and prevent vascular endothelial cells from proliferating and migrating in response to a spectrum of angiogenesis inducers, including VEGF, bFGF, IL-8, and PDGF [14,[76][77][78]. It has also been reported that the direct anti-angiogenic effect can be attributed to integrin receptors accompanied by several intracellular signaling pathways [14]. For example, Eikesdal et al. identified the critical amino acids (L, V, and D) within tumstatin, known as an inhibitor of endothelial cell proliferation, that confer anti-angiogenic and antitumor activity to tumstatin peptide, which is associated with the expression of the adhesion receptor, αvβ3 integrin, on tumor endothelial cells [79].
As mentioned above, indirect angiogenesis inhibitors will block the expression or activity of proangiogenic proteins like EGFR [80]. For example, Ciardiello et al. evaluated the anti-angiogenic and antitumor activity of gefitinib (ZD1839; Iressa ® ), a small molecule known as an EGFR tyrosine kinase inhibitor (TKI) in human colon (GEO, SW480, and CaCo2), breast (ZR-75-1 and MCF-7 ADR), ovarian (OVCAR-3), and gastric (KATO III and N87) cancer cells, that co-expresses TGF-α and EGFR (proangiogenic factor) [81]. Additionally, the U.S. FDA has approved a number of angiogenesis inhibitors for the treatment of cancers ( Figure 3, Table 2). R. K. Jain reported that for both direct or indirect antiangiogenic therapy, the balance between pro-angiogenic and anti-angiogenic factors will be restored through the reduction of vessel permeability and hypoxia and enhancement of the homogeneity of blood flow and perivascular cells coverage [82]. For example, bevacizumab, a recombinant humanized monoclonal antibody to VEGF and known by its brand name, Avastin ® , blocks tumor cell-derived VEGF-A, impairing the development of new vessels and leading to tumor starvation and, consequently, growth inhibition [83]. It has been observed that the side effects of bevacizumab increased when it was combined with chemotherapy. For example, in the treatment of colorectal cancer treated with IFL (a chemotherapy regimen consisting of concurrent treatment with irinotecan, leucovorin (folinic acid), and fluorouracil) in combination with bevacizumab, bleeding complications were observed [84,85]. Combination therapy of bevacizumab with carboplatin and paclitaxel improved the overall response and time to progression in patients with advanced or recurrent non-small-cell lung cancer, but severe or fatal pulmonary hemorrhage has been observed [86]. In ovarian cancer, the combination of platinumbased chemotherapy with bevacizumab delayed progression and improved survival for newly diagnosed ovarian cancer patients after initial surgery [87]. For example, bevacizumab, a recombinant humanized monoclonal antibody to VEGF and known by its brand name, Avastin ® , blocks tumor cell-derived VEGF-A, impairing the development of new vessels and leading to tumor starvation and, consequently, growth inhibition [83]. It has been observed that the side effects of bevacizumab increased when it was combined with chemotherapy. For example, in the treatment of colorectal cancer treated with IFL (a chemotherapy regimen consisting of concurrent treatment with irinotecan, leucovorin (folinic acid), and fluorouracil) in combination with bevacizumab, bleeding complications were observed [84,85]. Combination therapy of bevacizumab with carboplatin and paclitaxel improved the overall response and time to progression in patients with advanced or recurrent non-small-cell lung cancer, but severe or fatal pulmonary hemorrhage has been observed [86]. In ovarian cancer, the combination of platinum-based chemotherapy with bevacizumab delayed progression and improved survival for newly diagnosed ovarian cancer patients after initial surgery [87]. Thalidomide, with the brand name of Immunoprin, is known for the treatment of multiple myeloma and other types of cancers that express angiogenic cytokines such as VEGF and bFGF [88]. Lenalidomide (Revlimid ® ) is a derivative of thalidomide, and it is employed for the treatment of multiple myeloma and a specific type of myelodysplastic syndrome (MDS) [89].
Sunitinib (previously known as SU11248) is a TKI and is used to treat kidney cancer; it was also approved by the FDA to treat renal cell carcinoma (RCC) and imatinib-resistant gastrointestinal stromal tumors. Sunitinib also showed promising activity in the treatment of other tumors such as neuroendocrine tumors [90], advanced non-small-cell lung cancer [91], breast cancer [92], and colorectal cancer [93]. Sorafenib (Nexavar ® ) is a TKI drug that is approved for the treatment of liver cancer (hepatocellular carcinoma) [94], kidney cancer (advanced renal cell carcinoma) [95], and radioactive iodine-resistant advanced thyroid carcinoma [96].
Temsirolimus (Torisel ® ) was approved by the FDA in 2007 for the treatment of advanced RCC. It is an inhibitor of the mammalian target of rapamycin (mTOR), an enzyme that regulates cell growth and proliferation [97]. By activation of mTOR, c-Myc, and HIF-1α will be stimulated, which results in an increase in genes that promote VEGF-associated angiogenesis, proliferation (cyclin D1), and cell survival (survivin) [98]. Temsirolimus is also known to disrupt angiogenesis, which plays an important role in the development and progression of RCC [99][100][101]. The combination of temsirolimus with vorinostat showed higher anticancer activity compared with temsirolimus alone in both in vitro and in vivo models of RCC. The effectiveness of the combination was due to a decrease of the surviving levels, apoptotic induction, and improved reduction of angiogenesis [100]. Axitinib (Inlyta ® ) is another FDA-approved small molecule TKI shown in clinical trials to induce partial response for the treatment of RCC and several other tumor types. Phase II trials with this agent alone or in combination with chemotherapeutic drugs were reported in several types of malignancy [102]. Additional FDA-approved chemotherapeutic drugs are listed in Table 2.

Conclusions
Angiogenesis plays a significant role in tumor progression. Effective inhibition of tumor angiogenesis might arrest or halt tumor progression but would not eradicate the tumor as a stand-alone therapy, especially with a single mechanism anti-angiogenic agent. Hence, the combination of an anti-angiogenesis agent and chemotherapy might be essential for effective tumor treatment.

Conflicts of Interest:
The authors declare no conflict of interest. However, the authors are pursuing the development of broad-spectrum anti-angiogenesis compounds, which are not dealt with in this review.