Intrinsic characteristics of the proton pump in the luminal membrane of a tight urinary epithelium. The relation between transport rate and delta mu H.

A number of tight urinary epithelia, as exemplified by the turtle bladder, acidify the luminal solution by active transport of H+ across the luminal cell membrane. The rate of active H+ transport (JH) decreases as the electrochemical potential difference for H+ [delta mu H = mu H(lumen) - mu H(serosa)] across the epithelium is increased. The luminal cell membrane has a low permeability for H+ equivalents and a high electrical resistance compared with the basolateral cell membrane. Changes in JH thus reflect changes in active H+ transport across the luminal membrane. To examine the control of JH by delta mu H in the turtle bladder, transepithelial electrical potential differences (delta psi) were imposed at constant acid-base conditions or the luminal pH was varied at delta psi = 0 and constant serosal PCO2 and pH. When the luminal compartment was acidified from pH 7 to 4 or was made electrically positive, JH decreased as a linear function of delta mu H as previously described. When the luminal compartment was made alkaline from pH 7 to 9 or was made electrically negative, JH reached a maximal value, which was the same whether the delta mu H was imposed as a delta pH or a delta psi. The nonlinear JH vs. delta mu H relation does not result from changes in the number of pumps in the luminal membrane or from changes in the intracellular pH, but is a characteristic of the H+ pumps themselves. We propose a general scheme, which, because of its structural features, can account for the nonlinearity of the JH vs. delta mu H relations and, more specifically, for the kinetic equivalence of the effects of the chemical and electrical components of delta mu H. According to this model, the pump complex consists of two components: a catalytic unit at the cytoplasmic side of the luminal membrane, which mediates the ATP-driven H+ translocation, and a transmembrane channel, which mediates the transfer of H+ from the catalytic unit to the luminal solution. These two components may be linked through a buffer compartment for H+ (an antechamber).

Computer Simulation of Crowd Dynamics and Destructive Crowd Behavior

The social processes that lead to destructive behavior in celebratory crowds can be studied through an agent-based computer simulation. Riots are an increasingly common outcome of sports celebrations, and pose the potential for harm to participants, bystanders, property, and the reputation of the groups with whom participants are associated. Rioting cannot necessarily be attributed to the negative emotions of individuals, such as anger, rage, frustration and despair. For instance, the celebratory behavior (e.g., chanting, cheering, singing) during UConn’s “Spring Weekend” and after the 2004 NCAA Championships resulted in several small fires and overturned cars. Further, not every individual in the area of a riot engages in violence, and those who do, do not do so continuously. Instead, small groups carry out the majority of violent acts in relatively short-lived episodes. Agent-based computer simulations are an ideal method for modeling complex group-level social phenomena, such as celebratory gatherings and riots, which emerge from the interaction of relatively “simple” individuals. By making simple assumptions about individuals’ decision-making and behaviors and allowing actors to affect one another, behavioral patterns emerge that cannot be predicted by the characteristics of individuals. The computer simulation developed here models celebratory riot behavior by repeatedly evaluating a single algorithm for each individual, the inputs of which are affected by the characteristics of nearby actors. Specifically, the simulation assumes that (a) actors possess 1 of 5 distinct social identities (group memberships), (b) actors will congregate with actors who possess the same identity, (c) the degree of social cohesion generated in the social context determines the stability of relationships within groups, and (d) actors’ level of aggression is affected by the aggression of other group members. Not only does this simulation provide a systematic investigation of the effects of the initial distribution of aggression, social identification, and cohesiveness on riot outcomes, but also an analytic tool others may use to investigate, visualize and predict how various individual characteristics affect emergent crowd behavior.