While it is known that dietary supplements containing a combination of thermogenic ingredients can increase resting metabolic rate (RMR), the magnitude can vary based on the active ingredient and/or combination of active ingredients. The purpose of this study was to examine the effects of a commercially available thermogenic fat loss supplement on RMR and hemodynamic variables in healthy, resistance trained males.p ≤ 0.05.Ten resistance-trained male participants  volunteered to participate in this randomized, double-blind, placebo controlled cross-over study. Participants underwent two testing sessions separated by at least 24 h. On their first visit, participants arrived to the laboratory after an overnight fast and a 24-h avoidance of exercise, and underwent a baseline RMR, HR, and BP assessment. Next, each participant ingested a thermogenic fat loss supplement (TFLS) or a placebo (PLA) and repeated the RMR, HR, and BP assessments at 60, 120, and 180 min post-ingestion. During the second visit the alternative supplement was ingested and the assessments were repeated in the exact same manner. Data were analyzed via a 2-factor [2x4] within-subjects repeated measures analysis of variance (ANOVA). Post-hoc tests were analyzed via paired samples t-tests. The criterion for significance was set at p = 0.014) was observed. Post-hoc analysis revealed that the TFLS significantly increased RMR at 60-min, 120-min, and 180-min post ingestion (p < 0.05) as compared to baseline RMR values. No significant changes in RMR were observed for the PLA treatment (p > 0.05). Specifically, RMR was increased by 7.8 % , 6.9 % , and 9.1 %  in the TFLS, while the PLA treatment increased RMR by 3.3 % , 3.1 % , and 2.1 %  above baseline at 60, 120, and 180-min post ingestion, respectively. Additionally, the TFLS significantly elevated RMR at the 3-h time point as compared to the PLA treatment . A main effect for groups was observed for systolic blood pressure, and a significant interaction and main effect for time were observed for diastolic blood pressure. It should be noted that although changes in diastolic blood pressure were significant, all values stayed within normal clinical ranges (<80 mmHg).A significant main effect for time relative to raw RMR data (The TFLS led to significant elevations in RMR as compared to baseline. These elevations came with no adverse effect relative to resting heart rate, but a slight increase in blood pressure values. Taken on a daily basis, this TFLS may increase an individual’s overall energy expenditure, however; future studies should investigate if this leads to a reduction in fat mass loss over time. One of the most popular categories of dietary supplements is weight loss supplements, or "fat burners". These supplements contain thermogenic ingredients aimed at increasing resting metabolic rate (RMR) and facilitating fat loss. Recent investigations have identified that commercially available thermogenic supplements and ingredients typically contained in such products can increase RMR in healthy subjects –3, and wCaffeine supplementation has previously been shown to enhance lipolysis and fat oxidation . HoweverIt is generally accepted that caffeine increases resting metabolic rate (RMR) through activation of β2 and β3 adrenergic receptors, as well as activation of cyclic AMP (cAMP) , 12, cauL-carnitine is another ingredient found in many weight loss products due to its role in fat metabolism. The primary function of L-carnitine it to transport long-chain fatty acids across the mitochondrial membrane . Once inOne of the common concerns about using a thermogenic supplement is its effect on hemodynamic variables, such as heart rate (HR) and blood pressure (BP). Previous investigations have suggested that caffeine does have a stimulatory effect on systolic blood pressure (SBP), but the combination of additional ingredients in the product must be taken into consideration. Some trials have shown acute increases in HR and BP following ingestion of thermogenic supplements containing caffeine plus ephedra , 23. OthWhile it is known that these ingredients can have a positive influence on RMR, the magnitude can vary based on the active ingredient and/or combination of active ingredients. Additionally, further safety evaluation is needed on multi-ingredient supplements that contain caffeine because of its potential effect on HR and BP. Therefore, the primary objective for this study was to determine the effects of the thermogenic fat loss supplement Iron Cuts™ on RMR in healthy, resistance-trained males. A secondary objective of this study was to determine the effects of this thermogenic fat loss supplement on resting HR and BP.Ten healthy males  between the ages of 18 and 50 years volunteered to participate in this randomized, double-blind, placebo controlled cross-over study. All participants reported engaging in resistance exercise an average of four days per week. The research protocol was approved by the University of South Florida Institutional Review Board. Following an explanation of all risks and benefits associated with the experimental protocol, each participant gave his informed consent to participate in this study. Participants were screened for participation based on established criteria set forth by the American College of Sports Medicine  within-subjects repeated measures analysis of variance (ANOVA) using SPSS version 22.0. If sphericity could not be assumed, a Huynd-Feldt correction was used to produce a more critical s et al. . AUC wasp > 0.05) [For both supplement treatments, a Shapiro-Wilk’s test ( > 0.05)  and a vi > 0.05) . The sta > 0.05) .p = 0.014), but no treatment x time (p = 0.224) or main effect for group (p = 0.145). Post-hoc analysis revealed that the thermogenic fat loss supplement treatment (Iron Cuts™) significantly increased RMR at 60 min, 120 min, and 180 min post ingestion , as compared to baseline RMR values , elevating RMR 6.2 % above the placebo treatment at this point in time. Relative to AUC comparisons, no significant differences (p = 0.131) were observed between the thermogenic fat loss supplement and placebo treatments  and no main effects for group (p = 0.135) or time (p = 0.110) relative to heart rate for the two supplement treatments. For systolic blood pressure (SBP), no group x time interaction effect (p = 0.433) or main effect for time (p = 0.136) was observed, but a main effect for group (p = 0.024) was revealed. For diastolic blood pressure (DBP), a significant group x time interaction (p = 0.013) and a main effect for time (p = 0.005) were observed, but no main effect for group (p = 0.068). Raw data for RMR and hemodynamic variables is summarized in Table There were no group x time interaction effects , leading to further increases in RMR . TherefoThe secondary purpose of this study was to examine the effects of Iron Cuts™ on resting hemodynamic variables. Acute ingestion of Iron Cuts™ did not significantly alter resting heart rate. However, there was a slight, yet significant, increase in both systolic and diastolic blood pressure. It has been demonstrated that long-term consumption of caffeine has minimal effect on hemodynamic function , 37, 38;The major findings from this study indicate that ingestion of a thermogenic fat loss supplement (Iron Cuts™) containing approximately 200 mg of caffeine, green tea extract, and other ingredients can significantly increase RMR over a three hour time period in healthy males. These elevations came with no adverse effects relative to resting heart rate and only slight increases in blood pressure values. Although the thermogenic fat loss supplement resulted in an elevation in RMR, at this time we are not able to conclude whether this can lead to actual fat loss over time in this population. Future studies should investigate the effectiveness and safety of ingesting the dietary supplement over a longer period of time  to determine if reductions in fat mass are observed.


Whether and how primates are able to maintain long-term affiliative relationships is still under debate. Emotional bookkeeping (EB), the partner-specific accumulation of emotional responses to earlier interactions, is a candidate mechanism that does not require high cognitive abilities. EB is difficult to study in real animals, due to the complexity of primate social life. Therefore, we developed an agent-based model based on macaque behavior, the EMO-model, that implements arousal and two emotional dimensions, anxiety-FEAR and satisfaction-LIKE, which regulate social behavior. To implement EB, model individuals assign dynamic LIKE attitudes towards their group members, integrating partner-specific emotional responses to earlier received grooming episodes. Two key parameters in the model were varied to explore their effects on long-term affiliative relationships: (1) the timeframe over which earlier affiliation is accumulated into the LIKE attitudes; and (2) the degree of partner selectivity. EB over short and long timeframes gave rise to low variation in LIKE attitudes, and grooming partner preferences were only maintained over one to two months. Only EB over intermediate-term timeframes resulted in enough variation in LIKE attitudes, which, in combination with high partner selectivity, enables individuals to differentiate between regular and incidental grooming partners. These specific settings resulted in a strong feedback between differentiated LIKE attitudes and the distribution of grooming, giving rise to strongly reciprocated partner preferences that could be maintained for longer periods, occasionally up to one or two years. Moreover, at these settings the individual’s internal, socio-emotional memory of earlier affiliative episodes (LIKE attitudes) corresponded best to observable behavior (grooming partner preferences). In sum, our model suggests that intermediate-term LIKE dynamics and high partner selectivity seem most plausible for primates relying on emotional bookkeeping to maintain their social bonds. Group-living animals have varied social relationships with their group members. In primates, this variation has been extensively investigated and different types of relationships are distinguished, for example dominance relationships, kinship and affiliative relationships . Affiliative relationships among kin usually show high rates of proximity and mutually high grooming frequencies, and are therefore considered of higher quality than non-kin relationships with scarce grooming e.g., . HoweverSocial relationships are defined as consistent patterns in social interactions between two individuals and are the elements underlying the social (group) structure . WhetherResearch suggests that primates are able to recognize their social relationships with others  and evenThe suggestion from the EMO-model that emotional bookkeeping may explain some behavioral patterns in primates and other species is also consistent with empirical data. For instance, a study on male macaques reported that coalition formation in the mating season could be predicted by affiliation patterns several weeks earlier, even after controlling for affiliation in the mating season itself . Female Agent-based models (ABM) are a well-established tool to systematically study and understand structuring mechanisms in a complex system of interacting entities . The impIn a previous paper , we presIn the EMO-model, social interactions elicit a positive (satisfaction) or negative emotional response (anxiety) in the individuals, which may affect an individual’s subsequent behavior on the short term. Moreover, individuals in the model assign positive or negative attitudes to their group members, which affect partner-specific behavioral tendencies concerning affiliation (LIKE attitude) and agonism (FEAR attitude) over a longer term see . In the Several studies employing ABMs have explored the effect of choosing particular partners on the emergence of relationships: the model of The conditions under which emotional bookkeeping can lead to long-term stability of social bonds are still unclear. Whether social bonds are maintained or exist only temporarily may depend on: (a) how often individuals encounter each other; (b) the impact of affiliative behavior on the perceived quality of the relationship ; (c) how quickly LIKE attitudes  decay over time; and (d) how selective (‘choosy’) individuals are. In the current paper we specifically explore the effect of the latter two factors: temporal dynamics of emotional bookkeeping and individuals’ ‘choosiness,’ on the establishment of long-term affiliative relationships.emotional memory decay and partner selectivity would yield long-term social bonds. Particularly, we were interested in the duration, stability and reciprocity of (strong) social bonds, and in how well the internal representation of affiliative relationships (LIKE attitude) corresponded to its expression in externally observable affiliative behavior (grooming).Thus, we investigated in the EMO-model which temporal conditions of emotional bookkeeping are necessary to yield long-term stability of individualized affiliative relationships. To do this, we systematically varied the decay rate of LIKE attitudes, i.e., the timeframe over which the accumulated value of emotional responses to earlier received affiliation is remembered. Additionally, we varied the degree of partner selectivity (LPS), i.e., the degree to which LIKE attitudes affect the individuals’ partner choice and behavior. We investigated which specific settings of The EMO-model is an Agent-Based Model (ABM), in which model entities possess the same set of behavioral rules but variable internal states, which together regulate the interactions with others in their environment. Internal states are updated after an interaction or, sometimes, e.g., during a grooming bout, almost continuously. In the EMO-model, each model individual has rules that regulate its movements relative to specific others and relative to the whole group. In addition, a model individual has behavioral rules that determine its social interactions with others (action selection). Individuals select probabilistically a behavior from different types of movement , affiliation  and agonism . The internal states comprise an emotional state and emotional attitudes towards other individuals. The emotional state of an individual has three components  that are updated on the basis of its own interactions or on observing escalated fights in its vicinity. The arousal component affects the chance that the individual will select an active behavior rather than resting behavior. In addition, based on previous affiliative interactions from an interaction partner, an individual can develop a particular positive assessment (“LIKE attitude”) of this interaction partner. The model individuals also have a fixed FEAR attitude that is based on fixed differences in dominance. In the EMO-model the environment is purely social and only interactions between individuals affect their behaviors, emotional states and LIKE attitudes.Publications website of the Animal Ecology group . Below, we describe our model according to the updated ODD protocol  that may be used in macaques to develop and maintain social bonds, and their effect on the emergent properties of affiliative relationships. First, we studied the timeframe over which earlier affiliation (in contrast to current affiliation) is incorporated into LIKE attitudes. In other words, the appreciation of affiliation partners may be based either on a short-term or long-term summary of earlier grooming experiences, and this is expected to affect the structure and stability of affiliative relationships. Second, we varied the partner selectivity, i.e., the degree to which individuals select grooming partners with high LIKE attitudes, to study its effect on the stability and duration of affiliative relationships.We simulated the movements and interactions of 20 macaque-like model individuals. These individuals are characterized by a number of state variables see . These cIndividuals are characterized by their dominance strength (myDOM) which ranges from 0.05 (lowest ranking) to 1 , which does not change over time or after interactions . This reOur model entities are further described by their emotional state, consisting of arousal, anxiety and satisfaction , i.e., an individual’s state of alertness and an aversive and a pleasant dimension of the emotional state. Arousal, anxiety and satisfaction change over time depending on the social context the model entity experiences. Model individuals also possess partner-specific emotional attitudes (LIKE and FEAR), which they assign to each other group member. In our model, agonism-related FEAR attitudes are fixed, while affiliation-related LIKE attitudes are dynamically changing over time depending on partner-specific affiliation history .General model parameters are summarized in One time step in the simulation resembles 1 MINUTE. One HOUR consists of 60 MINUTES and we defined 12 HOURS as one DAY, 7 DAYS as 1 WEEK and 50 WEEKS as 1 YEAR. To define one MONTH we divided one YEAR (350 DAYS) into 12 equal periods of ca. 29 DAYS. Simulations were run for 504000 time steps, i.e., 2 YEARS, plus a prior stabilization period.Submodel ‘LIKE Attitudes’). Therefore, settings of high LHW required longer stabilization periods prior to the data-recording period of 2 YEARS, than low LHW (see STAB) was dependent on the setting of LHW and defined as follows:In contrast to earlier papers , we here LHW see . Thus, tNote, that the minimum duration of the stabilization period was set to 100 HOURS.Our model is event-driven. Most social behaviors are modelled as discrete events in time, except for moving, resting and grooming, which are modelled as continuous duration behaviors. Time is modelled on a continuous scale and during a simulation run individuals’ activations are regulated by a timing regime. The general process overview is pictured in Each time, the agent with the lowest schedule time is activated first see . WheneveThus, after activation, the next activation of ego, but also that of interaction partners or bystanders, is scheduled anew. The exact time until an individual’s next activation depends on the behavior performed, received or observed, respectively. As movement, resting and grooming are implemented as duration behaviors, they are performed in bouts. After starting a movement bout ego is activated each 3 s to execute the last movement “step” and to decide whether movement is to be continued. After starting a grooming or resting bout ego is activated anew after 7.5 ± 0.375 min (mean ± SD). Social interactions may involve (and therefore activate) other group members and may also interrupt a grooming or resting bout. Whenever ego receives an attack, it is immediately activated to respond with either fleeing or a counter attack. Whenever ego receives a signal or observes an attack nearby, a fast reaction is required and ego is activated 0.1 ± 0.005 s (mean ± SD) later to select an action.In our model, receiving affiliation affects the general emotional state  of individuals, as well as their affiliative attitude (LIKE attitude) directed to the actor of affiliation. In turn, an individual’s emotional state affects its general short-term probability of executing certain behaviors. In this way, emotional processes regulate spontaneous behaviors as well as appropriate responses to received behaviors. Partner-specific LIKE attitudes, which summarize earlier received affiliation from specific individuals on a shorter- or longer-term, depending on the setting of LHW (LIKE-HISTORY WEIGHT) in the model. This models differences in decay rate of the memory of affiliative interactions. LIKE attitudes in turn affect the probability of affiliating with these individuals and, in this way, regulate the development and maintenance of affiliative relations via LPS (LIKE-PARTNER SELECTIVITY). This ranges from no use of the LIKE-attitude (LPS = 0), representing a lack of emotional bookkeeping, to the strong directing of partner selection based on LIKE attitudes (LPS = 0.99). Varying the settings of LHW and LPS yields insights on the stability and duration of individual-specific affiliative relationships.In agent-based models, individual behavior is imposed by the model rules, while group-level properties are usually not implemented explicitly into the model, but rather emerge from the interactions of the lower-level entities, i.e., the individuals. In our model, behavioral patterns, e.g., affiliation, aggression and proximity, are emergent properties arising from the interactions of the model entities. The structure of the network of LIKE attitudes and group level properties such as reciprocity and partner-specificity are an emergent property arising from the interrelation between emotional attitudes and affiliative behavior.The model entities change their behavior in response to changes in their general emotional state  and their emotional attitudes towards others (see submodel LIKE Attitudes) and individuals (implicitly) seek to increase satisfaction and to decrease anxiety. As appropriate behavior is mediated by emotional processes, this yields a homeostatic regulation system. In this way, we aimed to produce adaptive (in the sense of flexible) behavior and emerging group properties that are representative of observations of the social behavior of real primates.Individuals in the dynamic attitude model regularly update their partner-specific LIKE attitudes assigned to other group members, based on earlier grooming received from these individuals. This may be seen as a (basic) form of learning. The LIKE attitude represents the memory of the summarized positive emotion elicited by affiliative behavior received from a particular individual. This is similar to a learning process with reinforcement of the LIKE-attitude after receiving affiliative behavior from a particular individual and fading of the LIKE-attitude when no affiliative behavior from this particular individual is received. Emotional bookkeeping thus provides individuals with information on valuable affiliation partners, which may dynamically change over time according to these partners’ behavior. See the section on LIKE attitudes below for a detailed description of how LIKE attitudes are updated during bouts of grooming and during periods of non-grooming.Individuals in our model may perceive the location, certain behaviors and signals of other group members, but only ‘locally’ within certain distances and within a specific view angle . The exact distances depend on the salience of the perceivable information. Individuals are able to perceive (or know) the dominance strength of other group members and perception of a group member elicits ego’s internal valuation of this group member, i.e., its FEAR and LIKE attitude that are assigned to this specific individual.Social interactions in our model can be categorized as affiliative, submissive and aggressive behaviors. Affiliation comprises grooming, affiliative signalling and approaching; submission comprises leaving, submissive signalling and avoiding; and aggression comprises attacking and aggressive signalling. Interactions are implemented identically as described in the introductory paper on the EMO-model .Potential interaction partners are the 10 nearest recognizable individuals (within MAX˙DIST and ego’s current view angle). The potential behavioral probabilities towards these 10 (or less) individuals depend on ego’s emotional attitudes (FEAR and LIKE) assigned to those individuals and ego’s general emotional state . One behavior towards one specific interaction partner is chosen randomly according to those probabilities.Some social interactions can only be performed towards group members within a certain distance. Individuals within INTERACT˙DIST (1 m) can be groomed, left or attacked. Individuals within PERS˙DIST (5 m) can receive  signals. Individuals within MAX˙DIST (50 m) can be approached and individuals within PERS˙DIST (5 m) can be avoided.In our model, many processes are not implemented deterministically, but include some degree of stochasticity, to produce variability in those processes. Those processes include action selection, the determination of the winner of an escalated fight, the random walk procedure and the timing regime.For the analysis of our model, we only used data that were recorded during the last 504.000 time steps of each simulation run, i.e., the 2 YEARS after the stabilization phase, to avoid transient spatial and social group effects due to the initial random placement.Dyadic LIKE attitudes were sampled every 3.5 DAYS and then averaged (per dyad) over the last YEAR for each simulation run. Dyadic grooming rates were recorded (per dyad) over each recording interval of 3.5 DAYS and then divided by the duration of the recording interval to obtain average hourly behavioral rates.Note that LIKE attitudes and grooming are always directed from an actor to a receiver and are thus not symmetric by definition.To illustrate the variation in LIKE attitudes over time, we also provide snapshots of the LIKE attitudes at single points in time  and F4. To calculate long-term measures, such as the number of preferred partners, the bout duration and the total duration of grooming partner preferences, grooming rates were sampled over the whole recording period, i.e., over 2 YEARS.x-coordinates and the y-coordinates of the 20 individuals were drawn randomly from a predefined circular sphere with an arbitrary diameter of 50 m. The individual’s initial heading was set to a random orientation between 1° and 360° and the initial view angle was set at 120° for each individual. Each individual’s level of arousal was set to the default arousal level (0.09) and the level of anxiety and satisfaction was set to 0.0. LIKE attitudes were initialized at 0.0. The initial schedule time for each individual was drawn randomly from a normal distribution with a mean of 1 min and a standard deviation of 0.05 min.The initial settings are summarized in This section describes the main processes implemented in the EMO-model cf , as far In our model, an individual’s arousal level, i.e., its responsiveness or tendency to be active cf. , changesIn our model, anxiety level, i.e., an individual’s general ‘fearfulness’ in response to negative stimuli within the current social environment, was scaled between 0 (not anxious) and 1 (highly anxious). Anxiety increases immediately  and 1 (highly satisfied). There are two limit values (0 and 1), towards which the satisfaction level can change over time, depending on the behavioral context of the individual. Satisfaction increases towards the limit value 1 when receiving grooming (rate: 0.1/min)(GR˙SAT˙INC) or when giving grooming (rate: 0.05/min)(GG˙SAT˙INC). Withouti) and another group member (j) and are calculated as FEARij = myDOMj − myDOMi, i.e., ranging from −0.95 to +0.95. Thus, FEAR attitudes are directional and not symmetric. Based on the fixed dominance strengths (myDOM), FEAR attitudes are fixed over the course of our simulation and are, thus, not affected by social interactions. Yet, they do affect the individual’s valuation of its potential aggression risk related to the respective group member. High FEAR attitudes result in decreased probabilities of aggression  and increased probabilities of submission  towards the respective group member.In our model, individuals assign a partner-specific FEAR attitude to each group member. FEAR attitudes resemble the difference in dominance strength between an individual  to 1 . LIKE attitudes are not symmetric per se, as they are dynamically updated dependent on earlier affiliation received from specific individuals. A high LIKE attitude affects ego’s judgement of the affiliative ‘value’ of the respective group member and results in increased probabilities of affiliation  towards this specific group member.LIKEij depends on individual i’s current increase in satisfaction in response to grooming time received exclusively from individual j. The current satisfaction level of i due to the grooming received from partner j is represented by the partner-specific variable PARTNER˙SATij, which increases during the grooming with a rate of 0.1/min (GR˙SAT˙INC). The LIKE attitude from i to j is updated by combining the value of the last LIKEij with the current satisfaction level PARTNER˙SATij, as follows: nt is the current time, tn−1 is the time of the last update and (nt − tn−1) is the time since the last update (in MINUTES). LIKEij(nt) is the updated (new) value of the LIKE attitude from i to j and LIKEij(tn−1) is the former level of LIKE. LHW (LIKE-HISTORY WEIGHT) weighs how strongly the updated LIKE attitude depends on earlier  affiliation history as opposed to recent  affiliation. The value of the parameter LHW differs per simulation run.LIKE attitudes are dynamically updated upon receiving grooming. The exact increase of LIKEij(nt)) combines the current level of satisfaction associated with the partner (PARTNER˙SATij(nt)) with the recent history of partner-specific satisfaction levels summarized in the former level LIKEij(tn−1) (upper line in the above equation). In this updated LIKE attitude, the former level of LIKE (LIKEij(tn−1)) and the current level of satisfaction associated with the partner (PARTNER˙SATij(nt)) are weighted by LHW and (nt − tn−1), respectively. The higher the value of LHW, the less the updated LIKE attitudes are influenced by the current satisfaction level. When, however, the current satisfaction level PARTNER˙SATij(nt) exceeds this new LIKE value, LIKEij(nt) is set to the level of PARTNER˙SATij, ensuring that LIKE attitudes are always higher than or equal to the current partner-specific satisfaction level (lower line in the above equation). ij decreases quickly after being groomed, this satisfaction is integrated in the LIKE attitude, which is retained in memory and only slowly decays over time, with the rate of decay depending on the value of LHW  for LIKEij to decrease from 1.0 to 0.5. Higher LHW results in a longer half-life, i.e., in slower dynamics, of LIKE attitudes (see ij = PARTNER ˙SATij), LIKE decreases equally fast as satisfaction levels. Note further, that the quick increase of LIKE attitudes during receiving grooming is not dependent on the setting of LHW.Whenever PARTNER˙SATij) determine how long a high LIKE attitude is maintained.In sum, current affiliation received from a partner may quickly increase LIKE and/or maintain a high level of LIKE, while the lack of current affiliation will result in a slowly decreasing LIKE attitude. How slow LIKE attitudes decrease in the absence of received grooming, depends on the setting of LHW. Thus, LHW in combination with the frequency and duration of grooming interactions eliciting satisfaction  the distance of the individual to ego, (b) ego’s emotional state , (c) ego’s FEAR and LIKE attitudes assigned to the potential interaction partners and (d) the parameter setting of LIKE-PARTNER SELECTIVITY (LPS), i.e., the degree to which high LIKE attitudes are important for the selection of affiliation partners. The emotional state facilitates behavior that is appropriate to the individual’s position and situation within the social group in general, while emotional attitudes facilitate behavior that is appropriate towards specific group members.First, the 10 (or less) potential interaction partners are determined. Then the possible behaviors towards each of these individuals are determined dependent on their distance to ego. Finally, the probabilities for those possible behaviors towards each potential interaction partner are calculated. According to these probabilities, one of the possible behavior-partner combinations is randomly selected and executed. The details on the calculation of the probabilities for affiliation, aggression, submission and avoidance are described in j increases with increased LIKEij (given that LPS > 0) and with increased intrinsic affiliation motivation of ego, i.e., with higher satisfaction or lower anxiety level. At LPS = 0, the level of LIKEij has no effect on the affiliation probability. With higher LPS the probability to choose j rather than k as grooming partner depends more strongly on the difference between LIKEij and LIKEik  and within the currently employed view angle. Group members are always perceived within 1 m (INTERACT˙DIST). The view angle is by default 120° (VIEW˙ANGLE) or else 360° (MAX˙ANGLE) when ego is scanning. Model entities can judge whether at least three other group members are present within 20 m (NEAR˙DIST) and within the currently employed view angle. Furthermore, individuals in our model are capable to judge whether their distance to the furthest group member exceeds 100 m (FAR˙DIST). The two latter criteria are used by ego to decide whether grouping behavior should be executed.Individuals can also perceive signals, which were directed at them from others within 5 m (PERS˙DIST) when in ego’s view angle. Individuals orient to their interaction partner and orient towards an escalated fight. In these situations, if ego or one of the interaction partners was scanning, scanning was stopped.When employing scanning behavior, an individual is turning its head right and left, thus expanding its view angle to 360°(MAX˙ANGLE) instead of the default view angle of 120°(VIEW˙ANGLE). Individuals can only employ scanning when resting, and not during moving bouts or social interactions. The probability to perform scanning behavior increases with ego’s current arousal level see .Concerning movement behavior, individuals in our model may either move towards or from other group members  or they may execute random movement within the group. Movement behavior in our model takes time and is implemented as movement bouts with a constant speed (0.6 m/s), which is reasonable for macaques . During After ending a movement bout ego always performs a proximity update. Ego checks whether any individuals towards which it directs a (high) FEAR attitude  are now (or still) nearer than 5 m (PERS˙DIST), as this will update the arousal level ego approaches over time (myAROUSAL˙LIMIT) . AdditioBefore selecting a social behavior, model entities always check whether grouping should be executed. Grouping will be selected if less than three (MIN˙OTHERS) group members are located within 20 m (GROUP˙DIST) and 360°(MAX˙ANGLE) or whenever any group member is further away from ego than 100 m (FAR˙DIST). When grouping is to be performed, ego simply approaches any randomly selected group member.In our model, grooming and resting behavior are implemented as duration behaviors, which are executed in bouts. When starting a grooming or resting bout, ego’s next activation is scheduled several minutes later to choose its new behavior. Ego may be disturbed and activated earlier in response to receiving an attack or a signal, or after observing an escalated fight nearby. The average schedule time for grooming was set at 7.5 ± 0.375 min (mean ± SD). This resulted in average grooming durations of 5.7 to 6.3 min . Groominijw = 1∕(1 + exp(−η∗(myDOMi − myDOMj))), where η determines the slope of the sigmoidal win chance curve  of all bouts of grooming partner preference of a dyad over the total recording period of two YEARS. To calculate the total duration of grooming partner preference, we determined the sum of all bout durations of grooming partner preference of a dyad over the total recording period of two YEARS.To examine the relation between LIKE attitudes and grooming rates, we calculated the row-wise Pearson correlation coefficients  between To assess the reciprocity of grooming partner preferences at the group level we calculated the Kendall’s tau row-wise matrix correlation between the matrix of the total durations of grooming partner preferences over two YEARS and its transposed matrix  using thFirst, we are interested in how the pattern of developed LIKE attitudes within the group depended on the different settings in our model, i.e., the half-life of the LIKE attitudes  maintained. When LHW was given the extreme value of 21,600 , we found almost no differentiation in average LIKE attitudes among the dyads  had almost no effect on the distribution of LIKE attitudes . This was different at intermediate LHW settings . Here, in the null model setting (LPS = 0), where individuals selected affiliation partners irrespective of their LIKE attitudes, LIKE attitudes were relatively equally distributed among all group members . At highi to j is the average over one YEAR and therefore summarizes all LIKE relationships from i to j that developed  over the whole period of one YEAR. To also illustrate the dynamics of the LIKE attitudes over time, we compared the averaged LIKE attitudes of Note, that in From the LIKE snapshot plots, we noticed that at intermediate LHW settings and in the absence of partner selectivity , individuals developed high LIKE attitudes towards only a few individuals simultaneously, strongly suggesting that they regularly switched partners in an almost random fashion over time  grooming rates were decreased and this decrease was stronger at faster LIKE dynamics  . High seTo explain the effect of LPS and LHW on grooming rates in more detail, we examined the distribution of dyadic grooming within the group. First, for the null model setting . This resulted in increasingly lower grooming rates throughout the whole group , LIKE attitudes decreased fast  between dyads that do not regularly affiliate, which were mainly the distant-ranked dyads. Increased selectivity (LPS) resulted in increasingly lower grooming rates among distant-ranked dyads  and 3B. Finally, at slow LIKE dynamics , LIKE attitudes did decrease only very slowly over time and individuals developed high LIKE attitudes towards most other group members, even towards less regular (distant-ranking) groomers , individuals groomed more up the hierarchy than down the hierarchy and groomed slightly more similar-ranking individuals than distant-ranking ones. High LPS and fast LIKE dynamics (LHW = 0) resulted in very little grooming, due to the lack of partners with high LIKE attitudes. At high LPS and intermediate LIKE dynamics, individuals groomed similar-ranking partners more selectively and grooming became more symmetric. With slow LIKE dynamics , the grooming distribution at high LPS was very similar to low LPS, as individuals developed high LIKE attitudes to most group members.We were interested in how well the LIKE attitudes in our model corresponded to the actual grooming rates of the individuals in our model. LIKE attitudes are an internal, summarized representation of earlier received grooming from other individuals. When comparing visually the matrices of the average LIKE attitudes with the average dyadic grooming rates  and 3B, As described earlier, at the null model setting (LPS = 0), LHW did not affect the grooming rates, as individuals did not use their LIKE attitudes in this setting. Grooming rates and the decision whom to groom are, in this setting, purely determined by proximity and dominance rank, independently of the LIKE attitudes. Yet, the level of LIKE attitudes did  depend on LHW, where higher LHW resulted in higher average LIKE attitudes (see first column in Higher partner selectivity (LPS) generally resulted in a higher correlation between LIKE attitudes and grooming rates. This came about due to the stronger feedback between LIKE attitudes and grooming partner choices caused by higher selectivity (LPS). At higher LPS, LIKE attitudes were determined by grooming and did also feedback on future decisions on whom to groom. This resulted in higher reciprocity in grooming rates and LIKE attitudes at higher LPS, compared to lower LPS. As grooming rates and LIKE attitudes were more symmetric at higher LPS, they were also more overlapping and corresponding to each other, resulting in higher correlation coefficients between grooming rates and LIKE attitudes.Furthermore, intermediate LHW settings resulted in higher correlation coefficients between LIKE attitudes and grooming rates, than low or high LHW, especially when LPS was high  LIKE attitudes correspond most to  grooming rates, when individuals are very selective in their partner choice (LPS ≥ 0.9) and when LIKE attitudes incorporated emotional responses to earlier affiliative behavior over intermediate-term periods, i.e., when LHW ranges between 180 and 5400. In these settings we obtained correlation coefficients higher than 0.77 and even up to 0.95 (at LHW = 720 and LPS = 0.99).preferred grooming partner , called the bout duration of grooming partner preferences. We also investigated the sum of the durations of all bouts of grooming partner preference per dyad, summed over the total recording period of two YEARS, i.e., the total duration of grooming partner preferences.The main interest of this simulation study is the question whether emotional bookkeeping enables individuals to maintain stable long-term affiliative partner preferences over time. To answer this question, we present the average number of consecutive MONTHS for which an individual remained another individual’s bout duration of grooming partner preferences was between 1.25 and 1.7 MONTHS , the average bout durations of grooming partner preferences  and high selectivity (LPS), individuals developed differentiated LIKE attitudes towards others, based on partner-specific affiliative history. From the average The total duration of grooming partner preferences was lower than 6 MONTHS for most settings of LPS and LHW . This suThe bout duration and the total duration of partner preferences were longest for LHW = 720 and LPS = 0.99, meaning that in this setting individuals were able to maintain the most stable partner preferences over time. These stable preferences emerge because intermediate-term LIKE dynamics generates a differentiation between regular and incidental groomers and high LPS allows for the selection of regular grooming partners.total time that A was a preferred partner of B to the total time that B was a preferred partner of A. We calculated the group level reciprocity of the matrix of total preferred partner durations, using Kendall’s tau row-wise matrix correlation. A high taurw indicates a high group-level reciprocity over the total period of 2 YEARS.We also investigated whether grooming partner preference was reciprocated in our model and how the degree of reciprocity depended on the different settings in our model. To do this, we compared the rw > 0.875) at intermediate LIKE dynamics (LHW = 180 or 720) and highest selectivity , independently of LHW, as here individuals did not use their LIKE attitudes to choose grooming partners . At incrLong-lasting stable reciprocal preferences for particular grooming partners emerged in the EMO-model when two conditions were met: individuals possess an intermediate-term timeframe of emotional bookkeeping, and they are very selective in choosing grooming partners towards whom they assign a high LIKE attitude.Only a high partner selectivity enabled individuals to differentiate between regular and incidental groomers. However, a strong distinction could only be made when LIKE dynamics were intermediate-term , but not short-term or long-term, because only this resulted in enough variation in LIKE attitudes between regular and incidental grooming partners. Using this variation as a substrate, high selectivity caused individuals to maintain affiliative relationships with regular groomers and to ‘neglect’ incidental groomers. In turn, differential grooming partner choice reinforced the differentiation of LIKE attitudes. Due to this strong feedback between grooming and LIKE attitudes, both grooming and LIKE attitudes were most reciprocated in this setting and resulted in the longest, most consistent and reciprocal partner preferences.In contrast, at fast LIKE dynamics (low LHW) the memory of the accumulated emotional response to affiliative events decayed quickly relative to the rate of grooming events. Thus, high LIKE attitudes were maintained only for short periods, which prevented the establishment of a strong feedback between LIKE attitudes and grooming. Therefore, partner preferences were much less reciprocal. At slow LIKE dynamics (high LHW) the memory of accumulated emotional responses to affiliative events decayed slowly; in other words, LIKE attitudes remained high after little grooming and thus towards many individuals, including incidental grooming partners. This resulted in little variation in LIKE attitudes. Consequently, even high partner selectivity did not result in strong partner specificity in interactions. Thus, only intermediate-term LIKE dynamics resulted in low LIKE attitudes towards incidental, and high LIKE attitudes towards regular groomers. Note, that these results depend crucially on the decay rate of the socio-emotional memory (the LIKE attitudes) and the degree of partner selectivity. In a different model study , individIn the absence of any partner selectivity (LPS = 0), affiliation patterns were mainly based on proximity and, to some extent, dominance rank. Selected grooming partners were not consistent in time, as partners changed continuously and arbitrarily. Moreover, partner preferences were not symmetric. At low partner selectivity (low LPS), individuals still interacted with many partners, preventing the maintenance of relationships with preferred partners that were assigned high LIKE attitudes. Only when high partner selectivity limited the number of potential interaction partners, the feedback loop between repeated affiliative events and high LIKE attitudes could emerge and thus also mutually high LIKE attitudes.If real primates possess LIKE attitudes to integrate the emotional response to earlier received affiliation, it is expected that this internal representation is also reflected in external, observable behavior. In the EMO-model, this was the case in settings that resulted in partner-specific affiliative relationships. Here, the internal representation of an affiliative relationship (LIKE attitude) corresponded best to the externally measurable behavior (grooming). All in all, these model outcomes suggest that settings of intermediate-term LIKE dynamics and high partner selectivity seem most plausible for primates relying on emotional bookkeeping to maintain their social bonds.In the EMO-model, at intermediate-term LIKE dynamics (LHW = 720) the half-life of the partner-specific emotional memory was 720 MINUTES . At high partner selectivity (LPS = 0.99) grooming partner preferences were stable across 2.25 MONTHS, on average. In this setting, many similar-ranking dyads maintained their reciprocal grooming partner preferences across 4 MONTHS and occasionally even across one or two YEARS.Interestingly, the conditions that resulted in long total durations of grooming partner preferences were slightly less specific to the parameter settings: LHW between 180 and 5,400 and LPS between 0.9 and 0.99 yielded comparable total preferred partner durations . While aAlthough the EMO-model yielded partner-specific relationships that lasted for several MONTHS, relationships across one or two YEARS were very rare. Thus, emotional bookkeeping may not be sufficient to generate general long-term partner-specific relationships. Other mechanisms may be required or some specifications of the way emotional bookkeeping was implemented in the model might be modified (see below).The conditions under which the EMO-model allows maintaining long-term stable social bonds seem very limiting, since the socio-emotional memory of affiliative events  should only last for an intermediate period  and a very high partner selectivity is required.In the EMO-model, the impact of an affiliative event on the LIKE attitude is independent from the current value of the LIKE attitude . However, affiliative events may have a stronger effect when the LIKE attitude is low or high, either promoting relationship quality when it is not yet formed or, in contrast, enhancing existing relationships. Recent empirical data suggest the latter possibility, since in wild chimpanzees only grooming with regular partners releases oxytocin , suggestAlso, in the EMO-model there is no limit to the number of individuals assigned a high LIKE attitude. However, empirical data suggest that primates have social bonds with a limited number of group members, called grooming cliques . While oβ-endorphins and is rewarding for both the receiving and grooming individual  a direct positive link between satisfaction and the connected LIKE attitude. However, in real animals the relative impact of satisfaction and anxiety may be more similar or even reversed. Primates probably groom not only to reduce anxiety , since gdividual . The relpartner-specific affiliation patterns, and they were only found in very specific conditions. Furthermore, in the model, only intermediate-term (not short- or long-term) emotional memory of received affiliation from group members and a strong preference for LIKEd partners led to a strong self-reinforcing feedback between high LIKE attitudes and high grooming rates, which generated reciprocal LIKE attitudes and reciprocal grooming that lasted for prolonged periods of time. The EMO-model employs a relatively conservative manner to update LIKE attitudes and has not incorporated self-reinforcing processes where affiliative interactions with preferred partners exert a stronger LIKE increase than interactions with non-preferred partners. Recent empirical data suggest, however, that such biased updates may actually be present in primates. Since such biased updates will more easily give rise to differentiated relationships with group members, in real primates the conditions that allow emotional bookkeeping to generate prolonged reciprocal affiliative attitudes and behavior may actually be more common than in the EMO-model.In sum, the EMO-model suggests that reciprocal social partner preferences and reciprocal affiliation patterns could result from emotional bookkeeping. In fact, in the EMO-model only emotional bookkeeping generates 10.7717/peerj.1488/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.1488/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.1488/supp-3Supplemental Information 3Click here for additional data file.


During neuronal degenerative diseases, microcircuits undergo severe structural alterations, leading to remodeling of synaptic connectivity. This can be particularly well observed in the retina, where photoreceptor degeneration triggers rewiring of connections in the retina’s first synaptic layer (e.g., Strettoi et al.,  Additionally, these two retinal networks will provide crucial insight into how distinct degenerative networks within the same tissue may interact with each other.This work was supported by the Deutsche Forschungsgeme-inschaft .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Deafferentation results not only in sensory loss, but also in a variety of alterations in the postsynaptic circuitry. These alterations may have detrimental impact on potential treatment strategies. Progressive loss of photoreceptors in retinal degenerative diseases, such as retinitis pigmentosa and age-related macular degeneration, leads to several changes in the remnant retinal circuitry. Müller glial cells undergo hypertrophy and form a glial seal. The second- and third-order retinal neurons undergo morphological, biochemical and physiological alterations. A result of these alterations is that retinal ganglion cells (RGCs), the output neurons of the retina, become hyperactive and exhibit spontaneous, oscillatory bursts of spikes. This aberrant electrical activity degrades the signal-to-noise ratio in RGC responses, and thus the quality of information they transmit to the brain. These changes in the remnant retina, collectively termed “retinal remodeling”, pose challenges for genetic, cellular and bionic approaches to restore vision. It is therefore crucial to understand the nature of retinal remodeling, how it affects the ability of remnant retina to respond to novel therapeutic strategies, and how to ameliorate its effects. In this article, we discuss these topics, and suggest that the pathological state of the retinal output following photoreceptor loss is reversible, and therefore, amenable to restorative strategies. C receptors are downregulated, whereas others, such as AMPA, GABAA, and glycine receptors are upregulated following photoreceptor loss  and a modified AAQ, DENAQ, act as photoswitches for the KProducing artificial vision in blind animals and humans by electrically stimulating the degenerating retina with a prosthetic “chip” is no longer a fantasy. There are two approaches to stimulating the retina: subretinal, where a light-sensitive photodiode array substitutes the missing photoreceptors and stimulates the remnant circuit, and epiretinal, where RGCs are stimulated directly. Devices of both types have been approved for human use and provide measurable visual acuity and rudimentary object localization/recognition (Ahuja et al., ex-vivo, both in subretinal and epiretinal configurations (Ghezzi et al., Considering that inner retina responds remarkably to loss of photoreceptors, a biological process, it is unclear how it would respond to an artificial device in the long term. Although the initial success is promising, more work is required to improve specificity, spatial and temporal resolution, contrast sensitivity, and intraocular packaging (Eiber et al., Several recent discoveries suggest that the endogenous regenerative capacity of retina can be exploited for repair. Specifically, Müller glia have been shown to exhibit stem cell properties. In response to retinal injury in fish, Müller cells and the ciliary marginal zone cells can regenerate all retinal neurons, resulting in complete functional recovery (Braisted et al., The mammalian Müller glia, however, have limited regenerative capacity. They have been shown to re-enter cell cycle and express stem-cell and mature-retinal-cell markers, such as PKC-α, NSE, recoverin, rhodopsin, calretinin, NeuN, and prox1 following photoreceptor loss, but they do not generate functional photoreceptors (Ooto et al., in vitro (Lawrence et al., Several approaches to promote neuroprotective and regenerative capacity of Müller glia in mammalian retina seem promising. Müller glia secrete several neuroprotective growth factors, such as bFGF, BDNF, GDNF, NGF, and CNTF (Wen et al., More than a decade of research has revealed the molecular to circuit-level details of retinal remodeling, but several challenges remain. For example, it is unclear how events, such as dendritic retraction, aberrant neuritogenesis, cell migration, and ectopic synapse formation, are connected temporally. We know how some retinal neurotransmitter receptors or ion channels respond to loss of photoreceptors, but it is unclear how this occurs at the level of specific cell types.Longitudinal transcriptional profiling of specific cells may help identify potential signaling pathways and molecular targets, as has been done for amyotrophic lateral sclerosis (Saxena et al., Many of the promising treatment approaches, such as retinal prostheses, gene therapy, optogenetics, photoswitches, and endogenous regeneration will likely benefit from advances in specific cell targeting. One possibility that has not been explored sufficiently is to combine these treatment approaches. A recent study combined optogenetic expression of ChR2 in RGCs with retinal prostheses and showed that driving the stimulator with retina’s neural code elicits RGC firing similar to normal retina (Nirenberg and Pandarinath, Recent work on biochemical mechanisms underlying oscillatory activity in retina may help design newer drug therapies for retinal degeneration. For example, blocking gap junctions with meclofenamic acid has been shown to eliminate oscillatory activity in RGCs (Trenholm et al., All listed authors, have made substantial, direct, and intellectual contribution to the work, and approved it for publication.This study was funded by National Brain Research Centre, India.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Increased circulating cathepsin S levels have been linked to increased risk of cardiometabolic diseases and cancer. However, whether cathepsin S is a modifiable risk factor is unclear. We aimed to investigate the effects of a prudent diet on plasma cathepsin S levels in healthy individuals.ad libitum prudent diet, i.e. healthy Nordic diet (ND) or a control group  for 6 weeks. Whereas all foods in the ND were provided, the control group was advised to consume their habitual diet throughout the study. The ND was in line with dietary recommendations, e.g. low in saturated fats, sugars and salt, but high in plant-based foods rich in fibre and unsaturated fats.Explorative analyses of a randomized study were performed in 88 normal to slightly overweight and hyperlipidemic men and women (aged 25 to 65) that were randomly assigned to The ND significantly decreased cathepsin S levels (from 20.1 (+/-4.0 SD) to 19.7 μg/L (+/-4.3 SD)) compared with control group (from 18.2 (+/-2.9 SD) to 19.1 μg/L (+/-3.8 SD)). This difference remained after adjusting for sex and change in insulin sensitivity (P = 0.03), and near significant after adjusting for baseline cathepsin S levels (P = 0.06), but not for change in weight or LDL-C. Changes in cathepsin S levels were directly correlated with change in LDL-C.ad libitum healthy Nordic diet decreased cathepsin S levels in healthy individuals, possibly mediated by weight loss or lowered LDL-C. These differences between groups in cathepsin S were however not robust and therefore need further investigation.Compared with a habitual control diet, a provided  Cathepsin S is highly expressed in antigen presenting cells –3 and haElevated circulating cathepsin S concentrations predict mortality in elderly men  and it had libitum, on plasma levels of cathepsin S in the NORDIET-trial. A secondary aim was to investigate the relationships between changes in cathepsin S concentrations and changes in cardiometabolic risk factors.As cathepsin S appears to be a novel promising risk marker of both cardiometabolic and malignant diseases, it is important to examine the effect of diet on cathepsin S concentrations. We hypothesized that a healthy nordic diet (ND) could reduce plasma levels of cathepsin S. The aim was to investigate the effects of a ND, eaten 1, body mass index (BMI) ≥20 and ≤ 31 kg m2, for women and men respectively [During December 2007, subjects living in Bollnäs, Sweden, were recruited by advertisements in the local newspaper. The inclusion criteria were healthy (as assessed by a physician), men and women between 25 and 65 years, plasma LDL-C ≥3.5 mmol/L¯ectively . SubjectThe study was conducted between February and May, 2008. Eighty-eight subjects were randomly assigned to one of two groups: a ND or a control group following their usual diet.http://www.controlled-trials.com); International Standard Randomized Controlled Trial Number (ISRTCTN): 77759305.Clinical and laboratory assessments were performed at baseline and after 6 weeks. The trial was conducted in accordance with the CONSORT statement and registered in the Current Controlled Trials database , LDL-C, HDL-C, systolic blood pressure (SBP) and diastolic blood pressure (DBP).All main meals were provided to the subjects in the ND group. The assessment of diet and the change in diet during the study has been described previously .ad libitum.The ND was based on the Nordic nutrition recommendations (NNR)  and contThe subjects in the control group were instructed to follow their habitual diet, eaten ad libitum, and continue their usual physical activity.Bodyweight was measured (kg) in light clothing without shoes, on a digital scale. Blood pressure was measured manually in a sitting position after 5 minutes rest. Two measurements were performed with a 2 minutes interval, and the average value was calculated.Venous blood was collected after a 12 h fast and plasma separated and frozen in -70°C before analyses. Plasma cathepin S was measured by ELISA , DY1183, R&D Systems). The intraassay CV was 7%. Glucose, total cholesterol, TG and HDL-C plasma concentrations were measured using a Roche Diagnostics Cobas® 6000. Plasma LDL-C was calculated by Friedewalds formula . Plasma Data are presented as mean ± SD. Per protocol analysis was used to assess effects of diet on outcome measures. Unpaired T-test was used to assess differences in plasma levels of cathepsin S during follow up, between the two groups, and as a second step, we used ANCOVA to adjust for sex, weight and cardiometabolic risk factors. To assess associations between change in cathepsin S and change in cardiometabolic risk factors linear regression and Pearson’s correlation was used. A two-tailed P-value of 0.05 was regarded as significant. STATA, version 11 was used for statistical analysis.Only two subjects (one in each group) dropped out , leavingAs previously reported , ND reduThe ND decreased plasma cathepsin S levels, from 20.1 (+/-4.05 SD) to 19.7 μg/L (+/-4.3 SD) whereas the control group changed from 18.2 (+/-2.9 SD) to 19.1 μg/L (+/-3.8 SD) , which remained after adjusting for sex (P = 0.04). Furthermore, change in cathepsin S were correlated with change in LDL-C (P = 0.03) and total cholesterol (P = 0.01). These associations remained significant after adjusting for sex (P = 0.03 and 0.01 respectively). Change in cathepsin S did not correlate with changes in insulin sensitivity, TG, HDL-C, SBP or DBP Table .Table 2ad libitum ND for 6 weeks slightly decreased levels of plasma cathepsin S in normal or slightly overweight individuals, compared with the control group. Change in circulating cathepsin S concentrations were correlated with changes in body weight, LDL-C and total cholesterol suggesting that these factors may mediate the effect on cathepsin S levels.Adherence to an ad libitum diet reduces weight and cathepsin S levels also in non-obese subjects, including men. The decreased cathepsin S levels did not remain significant after adjusting for weight change, suggesting that weight reduction mediated some of the dietary effect on cathepsin S. Adjusting for baseline levels of cathepsin S resulted in a P-value of 0.06 which may indicate lack of statistical power, rather than a lack of effect on cathepsin S. However, regression-towards-the mean effect cannot be completely excluded. Cathepsin S is strongly associated with cardiovascular risk factors, such as elevated triglycerides [To our knowledge, there are no studies investigating the effects of a prudent diet on cathepsin S concentrations. In accordance with our results, studies in obese women, showed that energy restriction and weight loss reduced cathepsin S mRNA and cathepsin S release in adipose tissue as well as serum levels , 18. Bodycerides  and LDL-ycerides . Subjectycerides  and it iHigher levels of circulating cathepsin S are associated with insulin sensitivity . In the The strengths of this study include the randomized controlled design, and also all foods were provided to the ND group ensuring high compliance and low drop-out rates. It should however be noted that some of the observed effect on cathepsin S levels was likely to be caused by the fact that all foods was provided to the ND, but not the control group. Also, these results cannot be directly translated to clinical settings were dietary advice alone is given. Further studies are needed where all foods are provided to both ND and control groups, or dietary advice is given to both groups. Although all subjects in both groups were instructed to maintain their physical activity level during the study, possible differences between groups in physical activity level and smoking were not assessed and may thus have introduced some bias. Since only Caucasian Swedish subjects were included, generalizability to other ethnic groups is unclear. Further, it should be noted that this is a post-hoc study of the NORDIET trial, and thus there was no power calculation done on cathepsin S when designing this trial.These results suggest that a prudent diet comprising healthy Nordic foods may moderately reduce cathepsin S levels in non-obese men and women. This association seems to be partly, mediated by diet-induced weight loss and/or reduced LDL-C concentrations. Given the close link between cathepsin S and various obesity-linked diseases and increased mortality risk, the present results warrant further investigation in further studies.


Grooming is a widespread, essential, and complex behavior with social and affiliative valence in the non-human primate world. Its impact at the autonomous nervous system level has been studied during allogrooming among monkeys living in a semi-naturalistic environment. For the first time, we investigated the effect of human grooming to monkey in a typical experimental situation inside laboratory. We analyzed the autonomic response of male monkeys groomed by a familiar human (experimenter), in terms of the heart rate (HR) and heart rate variability (HRV) at different body parts. We considered the HRV in both the time  and the frequency domain . For this purpose, we recorded the electrocardiogram of two male rhesus monkeys seated in a primate chair while the experimenter groomed their mouth, chest, or arm. We demonstrated that (1) the grooming carried out by a familiar human determined a decrement of the HR and an increment of the HRV; (2) there was a difference in relation to the groomed body part. In particular, during grooming the mouth the HRV was higher than during grooming the arm and the chest. Taken together, the results represent the first evidence that grooming carried out by a familiar human on experimental monkeys has the comparable positive physiological effect of allogrooming between conspecifics. Moreover, since the results underlined the positive modulation of both HR and HRV, the present study could be a starting point to improve the well-being of non-human primates in experimental condition by means of grooming by a familiar person. Social grooming is a widespread behavior among mammals, birds, and arthropods. Self-grooming is directed toward the individual’s own body, while allogrooming is carried out on others’ body parts, inaccessible or invisible to self-grooming. Although the primary biological function of allogrooming is to take care of the body surface of others, many studies demonstrated its social function in many animals –8 and esThe grooming among non-human primates is characterized by bimanual actions with rhythmic sweeps and plucking movements of the fingernails in precision grip, while being directed at addressing skin debris, spots, blemishes, ectoparasites, or vegetation trapped in the fur . AllogroIn addition to the numerous studies related to allogrooming, also the behavioral and physiological impact of grooming conducted by humans on monkeys or other animals has been explored. For example, the effect of human contact in horses , cats 3, dogs 3–35, and Concerning non-human primates, Taira and Rolls  demonstrThe aim of the present study was to investigate the physiological effect of human grooming to experimental non-human primates. We investigated the modulation of the HR and HRV of two experimental male rhesus monkeys receiving grooming by a familiar human (the experimenter) in relation to three different body parts .Macaca mulatta) aged 4 and 5 years, and weighing 4 and 4.7 kg, respectively. All experimental protocols were approved by the Veterinarian Animal Care and Use Committee of the University of Parma, and complied with European law on the humane care and use of laboratory animals.The subjects of the present study were two experimental male rhesus monkeys . Each cage was of 180 cm height, 90 cm width, and 120 cm depth. In the middle of the height of each cage, it was possible to insert one or two panels for the monkey to sit on. The litter was located immediately below the cage. In the bottom part of the cage, two containers were located, one for water and one for food, which were filled by the experimenter. The monkeys were provided with food and water once a day, usually in the morning. Inside the cage, each monkey had access to toys, mirrors, and swings. Moreover monkeys had visual, auditory, and olfactory contact to each other and were able to touch and groom with the neighboring monkeys. All cages were in an air-conditioned room maintained at a consistent temperature of 25–26°C. The well-being and health conditions of the monkeys were constantly monitored by the institutional veterinary doctor of the University of Parma .The two monkeys that we used for the experiment were also used for our another previous study. Once the previous study was finished, we performed the present one, from January 2014 to May 2014. The experimenters and the laboratory were the same. Before the start of the first experiment, the monkeys were habituated to sit in the primate chair in the laboratory, to interact with the experimenters, and to perform a fixation task. The criteria to determine whether the monkeys learned the procedure were: (1) when the monkeys learned to sit in the primate chair, i.e., they sat without struggle; (2) when the monkeys learned the task, i.e., they performed it almost without error (error rate: about 10–20%). We used juice as reward after correctly performed each trial.At the end of the familiarization process and the training, a head fixation system was implanted. The surgery was performed under general anesthesia , followed by post-surgical pain medication . The fix®) attached on the back of the monkey, the signal was acquired at 1000 Hz sample rate, amplified by means of CED 1902 (Cambridge Electronic Design®) and stored by Spike2 program (Cambridge Electronic Design®) . Since our purpose was to investigate the effect of grooming during experimental situation, we repeated all conditions of the previous experimental situation (head fixed and fixation task). In order to perform ECG, we used surface electrodes (Medtronic) Figure .Each recording was conducted in the laboratory, in the morning before feeding the monkeys, in four different conditions, and each for six consecutive minutes: fixation task (Ft), grooming arm (GA), grooming chest (GC), and grooming mouth (GM) conditions. A 1-min rest period was inserted between each condition in order to minimize any potentially overlapping effects. For each day of recording, Ft was always taken as the first condition. The three grooming conditions were not recorded in the same order each day, but rather the order was changed day by day. The Ft condition consisted of: fixation of a green light (LED) in front of monkey for 1–2 s; wait 800 ms, and receive a juice reward; waiting 1 s without any request; start the next trial. The monkey had to perform consecutively the task for 6 min  monitoring that the monkey fixated the green LED when requested. Due to the experimental conditions, the monkey was unable to see the experimenter or the movements, but was solely able to feel the grooming stimulation. In this manner, we avoided any visual influence. Since the aim of the present study was to evaluate the effect of grooming in the experimental situation, we did not analyze the performance of monkey but if the monkey ceased performing the Ft, the experiment was stopped and recommenced after a pause. The monkey remained inside the laboratory not more than 4 h a day.Each day we recorded 1 Ft and 1 or more sets of the three grooming conditions. With one monkey, we performed experiment for 15 days, acquiring 15 Ft, 15 GA, 15 GC, and 15 GM. With the other monkey, we performed experiment for 4 days, acquiring a total of 4 Ft, 12 GA, 12 GC, and 12 GM. In sum, we collected 19 Ft (15 + 4), 27 GA (15 + 12), 27 GC (15 + 12), and 27 GM (15 + 12). The number of days in which recordings were carried out differs between the two monkeys since the decision to perform experiment was dependent on the behavior of the subject, whereby if the monkeys did not perform the tasks, we stopped experiment to avoid any stressful consequences.N = 2), since we investigated the effect of human grooming on monkeys carried out by the same familiar person under the previous experimental condition. The two monkeys were both male and almost the same age, while both experienced identical experimental and dietary conditions, and the experimenter applying the grooming was the same person, familiar to both of them. For these reasons, the sample is indeed highly uniform. Accordingly, the age, sex, and other variables such as diet and/or experimental and training conditions that could interfere with the results for each subject are not considered as factors in this study.Moreover, it is necessary to underline that the subjects of the present study were those already involved in a previous experiment  were extracted with a custom script and exported as a text file to the Kubios HRV software  to obtain the HR (1/min) and HRV parameters. We analyzed the central 5 min of the six recording minutes, from 30 s after the start of condition to 30 s before the end. For the frequency domain analysis of the HRV, the power spectrum (of each 5-min recording) was obtained with a fast Fourier transform-based method . We considered the power of the low frequency  and high-frequency  bands, both expressed in absolute values (square milliseconds). The intervals of the power of the LF and HF bands were in accordance with the literature , 68, 69.In the time domain of the HRV, we evaluated the SD of RR intervals  and the square root of the mean of the squares of the successive differences between adjacent RRs  and the ratio RMSSD/SDNN. RMSSD estimates high-frequency variations in HR, and therefore the activity of parasympathetic nervous system, while SDNN is a non-specific index of both the sympathetic activity and parasympathetic activity , 75. TheFigure In the present study, we evaluated for the first time the HR and HRV of active grooming carried out by a familiar human (experimenter) on experimental rhesus monkeys in three different body parts . Since the here presented preliminary data were obtained from only two subjects, we did not apply statistical analysis. Nevertheless, we controlled the age, sex, and food diet between subjects. Thus, we will focus the discussion on physiological aspects of grooming done by human to experimental monkeys. The familiarization and a good interaction between the experimenter and each monkey were necessary in order to establish a presumably positive relationship between them. In fact, if monkeys did not want to be groomed by experimenter, they rejected experimenter both in the laboratory and in the cage. When it was established a good interaction with experimenter, the monkeys wanted to be groomed by the experimenter, but not by strangers.The comparison between each grooming condition and the baseline revealed that the HR decreased during grooming of the arm, chest, and mouth. The results are in line with existing studies related to allogrooming, that is, grooming among free-ranging monkeys in a naturalistic environment and situation , 19, 27.SDNN, RMSSD, HF, and LF are positively correlated to the parasympathetic branch of the autonomous nervous system , 75, whiThe effect of grooming carried out by humans on animals in relation to different body parts was studied by Normando et al.  in respeSince grooming can have an affiliative quality –18, it cHere, we report that the grooming carried out by familiar humans determines an increment of HRV parameters associated with the vagal activation. Importantly, we employed HRV analysis for the first time in order to evaluate the modulation of the autonomous system in psychologically related situations, such as grooming in rhesus monkey. These results represent the first indirect evidence of the positive relaxing effect of the human-to-monkey grooming, so we can assume that it has a positive autonomic effect  comparable to the one evoked by the interpersonal skin-to-skin contact in humans.Further investigations will be necessary to confirm that the vagal activation proposed by our results by means of HRV evaluation is also present during allogrooming among monkeys.Due to these autonomic responses and the affiliative value of grooming for monkey, the data here presented could be useful in order to reduce the stress under which experimental animals could be for experimental conditions. Recently Viktor and Annie Reinhardt  have hypNevertheless, we highlight a number of open questions that require further investigation. First, the present study featured the evaluation of the effect of grooming on male monkeys, but not females. Moreover, the two monkeys included in the study were 4 and 5 years of age, and both were of a similar weight. For these reasons, the study presented here could be considered as a starting point to evaluate if there is a physiological difference, in terms of HR and/or HRV, between female and male monkeys of different ages and weights during both grooming by humans and allogrooming. An additional aspect to explore would be to investigate behavior indices and measure the serum and/or urinal concentration of acetylcholine, norepinephrine, or cortisol, in order to evaluate the effect of grooming on the different aspects of the autonomous nervous system.In conclusion, the present study represents the first evidence that the human grooming of monkeys in a controlled experimental situation elicits similar physiological effects as those seen in allogrooming among monkeys situated in their own natural environment. The results presented here could, thus, represent an important starting point from which to enhance the welfare of laboratory rhesus monkeys under experimental conditions by means of human grooming.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We review evidence for cross-modal cortical re-organization in clinical populations with hearing loss. Cross-modal plasticity refers to the ability for an intact sensory modality  to recruit cortical brain regions from a deprived sensory modality  to carry out sensory processing. We describe evidence for cross-modal changes in hearing loss across the age-spectrum and across different degrees of hearing impairment, including children with profound, bilateral deafness with cochlear implants, single-sided deafness before and after cochlear implantation, and adults with early-stage, mild-moderate, age-related hearing loss. Understanding cross-modal plasticity in the context of auditory deprivation, and the potential for reversal of these changes following intervention, may be vital in directing intervention and rehabilitation options for clinical populations with hearing loss. Brain plasticity is an often overlooked yet important factor that may influence clinical outcomes in hearing impaired individuals who receive intervention via hearing aids or cochlear implants. Moreover, brain plasticity provides the framework upon which rehabilitation and therapy initiatives for these clinical populations could be based. Plasticity, in its broadest form, refers to the brain’s ability to change, for neurons and networks to alter their function as a result of intrinsically or extrinsically driven factors. Here, we review brain plasticity in hearing loss, including experience-driven plasticity, cross-modal, and intra-modal plasticity in early development and adulthood. Using electroencephalographic (EEG) techniques, we provide evidence of cross-modal re-organization in patients with hearing loss across the age spectrum and discuss its possible clinical implications.During development, both intrinsic and extrinsic factors have a profound influence on cortical maturation of the auditory system. In infants and young children, the cortical auditory evoked potential (CAEP) response can be used as a biomarker in assessment of the development of the central auditory pathways. For normal hearing infants and young children, the morphology of the CAEP response is dominated by a single obligatory, positive peak known as the P1 component. In early childhood, the P1 CAEP response occurs around 200 and 300 milliseconds. During development, an intrinsically regulated period of synaptogenesis occurs in auditory cortex, the peak of which occurs between 3.5 and 4 years of age, and after which extrinsically driven factors such as sensory stimulation cause refinement and pruning of central auditory pathways .The period of synaptogenesis early in development coincides with a critical or sensitive period for cortical auditory development in children, in which the central auditory pathways are maximally plastic. During this sensitive period, extrinsic sensory stimulation has the ability to profoundly shape the development and organization of the auditory cortex ,10,11,12The N1 component of the CAEP response also demonstrates clinical utility as a biomarker in assessing cortical auditory development in children with hearing loss. Unlike the positive P1 component which arises from auditory cortex, cortical sources of the N1 arise from higher-order auditory cortex and cortico-cortico coupling—processes that facilitate higher-order auditory and language processing ,21. In tTaken together, the aforementioned studies provide important validation for the idea of early implantation in bilateral deaf children. However, what these studies do not explain is the reason why late-implanted children rarely develop normal speech and language skills. One theory is that auditory deprivation may affect cortical connectivity, and that alterations in this connectivity may consequently affect normal development of speech and language abilities . Animal Cross-modal cortical re-organization is a form of cortical plasticity. When cortical regions do not receive adequate sensory input , these brain regions become vulnerable to recruitment from other sensory modalities like vision, which may repurpose auditory areas for visual processing . For exaet al. (2007) found that among cochlear implanted children, those that had good speech and language outcomes recruited dorsal networks including dorsolateral pre-frontal cortex, while those that had poor clinical outcomes recruited ventral networks showing high metabolic activity in ventral visual regions [There is also clear evidence of cross-modal re-organization during development, for example, in children with cochlear implants. Lee and colleagues have conducted several studies in which speech perception outcomes following cochlear implantation are positively correlated with pre-implant resting hypometabolism in auditory cortex measured via fluorodeoxyglucose positron emission tomography (FDG-PET) ,33,34. T regions . Signifi regions .While the aforementioned studies suggest cross-modal plasticity in groups of children, in a recent investigation , we examThough the data presented above represent case study evidence and should accordingly be interpreted as such, these findings add to the growing body of evidence that visual and somatosensory cross-modal re-organization does occur in some cochlear implant recipients, and that cross-modal recruitment may be related to speech perception outcomes ,38,39,40As the criteria for cochlear implantation in children expands, clinical evidence of cross-modal plasticity may play a critical role in determining whether implantation may be beneficial in non-typical cases of pediatric deafness. For example, cochlear implantation for cases of single-sided deafness (SSD) is not currently approved by the Federal Drug Administration (FDA) in the United States. Research demonstrating the efficacy of cochlear implantation in both adults and children with SSD is limited to behavioral evidence, though emerging data suggests increased speech perception abilities in quiet and in noise, as well as improvement in binaural hearing in some patients with SSD following cochlear implantation ,42,43,44vs. somatosensory cross-modal plasticity in hearing loss. For example, it’s possible that persistence of visual cross-modal reorganization is maintained by the functional role that vision plays in everyday communication, consistent with previous reports that visual intra-modal plasticity may be beneficial for audiovisual speech perception in cochlear implantation  sensory input ,56. For Importantly, and perhaps surprisingly, evidence of cross-modal re-organization has been seen not just in the extreme case of long-term severe-profound deafness and in adults and children, but also in adults with lesser degrees of hearing loss . For exaIn addition to cross-modal sensory re-organization, there is also growing evidence of compensatory changes in cortical resource allocation in older adults and adults with early-stage, age-related, hearing loss. For example, our group  recordedWhether or not audiological intervention has the ability to reverse maladaptive changes in cortical resource allocation and cross-modal plasticity and/or to improve cognitive function remains unknown. In a recent study by Mosnier and colleagues (2015) in a group of elderly patients , 81% of The recurrent theme outlined in this review is that auditory deprivation results in brain re-organization, and that these brain changes may have a considerable impact on variability in clinical outcomes in patients with hearing loss. This review provides evidence of cortical cross-modal re-organization, whereby cortical auditory brain regions are recruited in deafness or in hearing loss to carry out processing in other sensory modalities such as vision, somatosensation. More studies are needed to further elucidate the clinical applications of cross-modal re-organization in patients with hearing loss. To this end, we have demonstrated several examples of cross-modal re-organization in individual subjects with hearing loss including children with cochlear implants who are excellent and average users and show differing levels of cross-modal re-organization  and a ch