True conversive hallucinations

A case of true conversive hallucinations in a 19-year-old female soldier is described. This phenomenon is rare and should be distinguished from psychotic or dissociative states.     

Initiator and recruited T lymphocytes are distinct subclasses of T lymphocytes.

In previous studies we found that the generation of a cell-mediated immune reaction required interaction between sensitized initiator T lymphocytes (ITL) and syngeneic recruited T lymphocytes (RTL). ITL were sensitized in vitro against allogeneic mouse fibroblasts and injected into a footpad of syngeneic recipient mice. ITL were observed to migrate from the connective tissues of the footpad to the draining popliteal lymph node (PLN). Sensitized ITL recruited RTL from the blood circulation into the PLN. This recruitment led to the generation of immunospecific effector and memory lymphocytes. In the present study we investigated the cellular characteristics of ITL and RTL. We found that both ITL and RTL were positive for Thy-1 antigen, and were absent from B mice. However, ITL and RTL differed in their response to various treatments. ITL were sensitive to adult thymectomy and resistant to antithymocyte serum (ATS). RTL were resistant to adult thymectomy and sensitive to ATS. Sensitized ITL were adherent to nylon wool or histamine-rabbit serum albumin-Sepharose, while RTL were not retained on such columns. ITL and RTL also differed in their relative distributions in various lymphoid organs and migratory pathways. The thymus demonstrated ITL but not RTL activity. In contrast, lymph node lymphocytes were a rich source of RTL, but had no detectable ITL activity. Both ITL and RTL could be demonstrated in the spleen. Thus, ITL and RTL functions are mediated by distinct subclasses of T lymphocytes which reside in particular compartments of the immune system.     

Recruitment of effector lymphocytes by initiator lymphocytes. In vivo migration of in vitro sensitized initiator T lymphocytes.

We previously found that mouse T lymphocytes sensitized in vitro against allo- or syngeneic fibroblasts, upon injection into syngeneic recipients, do not themselves differentiate into effector cells, but recruit effector T lymphocytes within the draining lymph nodes. As a result of sensitization, these initiator lymphocytes acquire a trypsin-sensitive membrane property which is necessary for recruitment. We now report studies on the in vivo migratory behavior of initiator lymphocytes following sensitization. We injected 51Cr-labeled initiator lymphocytes into recipient footpads and found significantly increased migration of sensitized cells to the draining popliteal lymph node (PLN) during the first day. By amputation of the foot at various times, we showed that migration during the first 12-24 hours was critical for subsequent recruitment. Trypsin treatment of initiator lymphocytes abolished this accelerated migration. Lymphocytes triggered nonspecifically by Con A migrated to the PLN like antigen-sensitized cells. We also compared the migration of injected lymphocytes from the footpad to the PLN in graft-versus-host and host-versus-graft reactions, and found these reactions to differ both from each other and from recruitment in terms of lymphocyte migration. These findings are discussed in terms of the physiology of the cell-mediated immune response and the notion of peripheral sensitization.     

The in-vitro effect of fibrinogen, factor XIII and thrombin-activatable fibrinolysis inhibitor on clot formation and susceptibility to tissue plasminogen activator-induced fibrinolysis in hemodilution model

Patients suffering major traumatic or surgical bleeding are often exposed to hemodilution resulting in dilutional coagulopathy. The aim of this study was to evaluate in vitro the effects of fibrinogen, factor XIII and thrombin-activatable fibrinolysis inhibitor (TAFI) on clot formation and resistance to fibrinolysis in hemodilution conditions. Citrated whole blood from 36 healthy volunteers was diluted to 30 and 60% with lactated Ringer's solution. Blood samples were subsequently supplemented with fibrinogen, FXIII, TAFI or their combinations. Rotation thromboelastometry (ROTEM) in whole blood and thrombin generation in plasma were performed in the presence of CaCl? and tissue factor/EXTEM reagent, and fibrinolysis was induced by tissue plasminogen activator (tPA). Hemodilution was expressed by decrease of peak height in thrombin generation and α-angle and maximum clot firmness (MCF) in ROTEM. Fibrinogen, FXIII or TAFI did not correct the decrease in thrombin generation peak height. In ROTEM, spiking of diluted blood with fibrinogen stimulated clot propagation. In tPA-treated blood fibrinogen, FXIII and TAFI increased clot firmness and inhibited fibrinolysis. Stronger protection against fibrinolysis was achieved combining FXIII with TAFI. Hemodilution was associated with inhibition of thrombin generation; however, this effect was not sensitive to blood spiking with fibrinogen, FXIII and TAFI. In ROTEM, these hemostasis agents improved clot strength and decreased clot susceptibility to tPA in nondiluted and to more extent in diluted blood. The maximal protection against fibrinolysis was caused by TAFI. Combining FXIII with TAFI exerted synergistic inhibitory effect on fibrinolysis.     

Educational lectures enhance knowledge about the human immunodeficiency virus (HIV) and reduce risky behavior and fear among methadone maintenance treatment patients

Abstract

Background: The aim of this study was to characterize human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS)-related knowledge and stigma among methadone maintenance treatment (MMT) patients and evaluate the contribution of an educational lecture in reducing risky behavior and unjustified overprotective behavior due to fear and stigma among MMT patients. Methods: Patients from an MMT clinic within a tertiary medical center were invited to an educational lecture on HIV/AIDS. Seventy participants (of current 330) were chosen by a random sample (December 2015), plus at-risk patients and HIV patients. Attendee compliance and change in scores of questionnaires on knowledge (modified HIV-K-Q-22) and on sexual and injection behaviors were studied. Results: Forty-six patients (65.7% compliance) attended the lecture, and their knowledge and behavior scores improved 2?weeks post-lecture (knowledge: from 14.2?±?3 to 19.0?±?2.2 [P?<?.0005], sexual behavior: from 12.1?±?2.9 to 8.8?±?3.0 [P?<?.0005], and injection behavior: from 7.3?±?6.2 to 0.2?±?1.3 [P?<?.0005]). The unjustified fear of proximity to HIV carriers reported by 50% attendees fell to 35% post-lecture. Eight months post-lecture, the scores on knowledge and risky behavior of 21 randomly chosen attendees were still better than pre-lecture scores (knowledge: 15.4?±?2.3 vs. 17.2?±?1.8 [paired t test, P?=?.001], sexual behavior: 13.2?±?2.3 vs. 9.7?±?2.9 [P?<?.0005], and injection behavior: 9.3?±?5.6 vs. 2.8?±?3.1 [P?<?.0005]). Drug abuse and treatment adherence were not related to intervention and to risky behavior. Conclusions: More knowledge, less fear, and less risky behavior immediately and at 8?months post-lecture reflect the success and importance of the educational intervention. Future efforts are needed in order to reduce ignorance and fear associated with HIV/AIDS.     

From the Cover: Algorithms,games, and evolution

Even the most seasoned students of evolution, starting with Darwin himself, have occasionally expressed amazement that the mechanism of natural selection has produced the whole of Life as we see it around us. There is a computational way to articulate the same amazement: “What algorithm could possibly achieve all this in a mere three and a half billion years?” In this paper we propose an answer: We demonstrate that in the regime of weak selection, the standard equations of population genetics describing natural selection in the presence of sex become identical to those of a repeated game between genes played according to multiplicative weight updates (MWUA), an algorithm known in computer science to be surprisingly powerful and versatile. MWUA maximizes a tradeoff between cumulative performance and entropy, which suggests a new view on the maintenance of diversity in evolution.Precisely how does selection change the composition of the gene pool from generation to generation? The field of population genetics has developed a comprehensive mathematical framework for answering this and related questions (1). Our analysis in this paper focuses particularly on the regime of weak selection, now a widely used assumption (2, 3). Weak selection assumes that the differences in fitness between genotypes are small relative to the recombination rate, and consequently, through a result due to Nagylaki et al. (4) (see also ref. 1, section II.6.2), evolution proceeds near linkage equilibrium, a regime where the probability of occurrence of a certain genotype involving various alleles is simply the product of the probabilities of each of its alleles. Based on this result, we show that evolution in the regime of weak selection can be formulated as a repeated game, where the recombining loci are the players, the alleles in those loci are the possible actions or strategies available to each player, and the expected payoff at each generation is the expected fitness of an organism across the genotypes that are present in the population. Moreover, and perhaps most importantly, we show that the equations of population genetic dynamics are mathematically equivalent to positing that each locus selects a probability distribution on alleles according to a particular rule which, in the context of the theory of algorithms, game theory, and machine learning, is known as the multiplicative weight updates algorithm (MWUA). MWUA is known in computer science as a simple but surprisingly powerful algorithm (see ref. 5 for a survey). Moreover, there is a dual view of this algorithm: each locus may be seen as selecting its new allele distribution at each generation so as to maximize a certain convex combination of (i) cumulative expected fitness and (ii) the entropy of its distribution on alleles. This connection between evolution, game theory, and algorithms seems to us rife with productive insights; for example, the dual view just mentioned sheds new light on the maintenance of diversity in evolution.Game theory has been applied to evolutionary theory before, to study the evolution of strategic individual behavior (see, e.g., refs. 6, 7). The connection between game theory and evolution that we point out here is at a different level, and arises not in the analysis of strategic individual behavior, but rather in the analysis of the basic population genetic dynamics in the presence of sexual reproduction. The main ingredients of evolutionary game theory, namely strategic individual behavior and conflict between individuals, are extraneous to our analysis.We now state our assumptions and results. We consider an infinite panmictic population of haplotypes involving several unlinked (i.e., fully recombining) loci, where each locus has several alleles. These assumptions are rather standard in the literature. They are made here to simplify exposition and algebra, and there is no a priori reason to believe that they are essential for the results, beyond making them easily accessible. For example, Nagylaki’s theorem (4), which is the main analytical ingredient of our results, holds even in the presence of diploidy and partial recombination.Nagylaki’s theorem states that weak selection in the presence of sex proceeds near the Wright manifold, where the population genetic dynamics becomes (SI Text)xit+1(j)=1Xtxit(j)(Fit(j)),where xit(j) is the frequency of allele j of locus i in the population at generation t, X is a normalizing constant to keep the frequencies summing to 1, and Fit(j) is the mean fitness at time t among genotypes that contain allele j at locus i (see ref. 4 and SI Text). Under the assumption of weak selection, the fitnesses of all genotypes are close to one another, say within the interval [1 − ε, 1 + ε], and so the fitness of genotype g can be written as F_g = 1 + εΔ_g, where ε is the selection strength, assumed here to be small, and Δ_g ∈ [−1, 1] can be called the differential fitness of the genotype. With this in mind, the equation above can be writtenxit+1(j)=1Xtxit(j)(1+ϵΔit(j)),[1]where Δit(j) is the expected differential fitness among genotypes that contain allele j at locus i (see Fig. 1 for an illustration of population genetics at linkage equilibrium).Open in a separate windowFig. 1.Equations of population genetics formulated in the 1930s constitute the standard mathematical way of understanding evolution of a species by tracking the frequencies of various genotypes in a large population. In the simple example shown here, a haploid organism with two genetic loci A and B has three alleles in each of its two loci named A1, A2, A3 and B1, B2, B3 for a total of nine genotypes. In A we show the fitness of each genotype, that is, its expected number of offspring. The fitness numbers shown in A are all close to each other, reflecting weak selection, where the individual alleles’ contributions to fitness are typically minuscule. Initially, each genotype occurs in the population with some frequency; in this particular example these numbers are initially equal (B); naturally, their sum over all nine genotypes is 1 (frequencies are truncated to the fourth decimal digit). C shows how the genotype frequencies evolve in the next generation: each individual of a given genotype produces a number of offspring that is proportional to its fitness shown in A, and the resulting offspring inherits the alleles of its parents with equal probability (because we are assuming, crucially, sexual reproduction). The genotype frequencies in the next generation are shown in C, calculated through the standard recurrence equations of population genetics. We also show in the margins of the table the allele frequencies, obtained by adding the genotype frequencies along the corresponding row or column. Ten generations later, the frequencies are as shown in D.We now introduce the framework of game theory (see Fig. 2 for an illustration) and the MWUA (SI Text), studied in computer science and machine learning, and rediscovered many times over the past half-century; as a result of these multiple rediscoveries, the algorithm is known with various names across subfields: “the experts algorithm” in the theory of algorithms, “Hannan consistency” in economics, “regret minimization” in game theory, “boosting” and “winnow” in artificial intelligence, etc. Here we state it in connection to games, which is only a small part of its applicability (see SI Text for an introduction to the MWUA in connection to the so-called “experts problem” in computer science).Open in a separate windowFig. 2.A simple coordination game is played by two players: the row player, who chooses a row, and the column player, who chooses a column. After the two players make a choice, they both receive (or both pay, in case of a negative entry) the same amount of money, equal to the number at the chosen row and column (A). Coordination games are the simplest possible kind of a game, one in which the strategic interests of all players are completely aligned—that is to say, there is no conflict at all. They are of interest when it is difficult for the players to know these numbers, or to communicate and agree on a mutually beneficial combination (in this example, third row and second column). Notice that this particular coordination game is closely related to the fitness landscape shown in Fig. 1A: If P is a payoff in this game, the corresponding entry of Fig. 1A is equal to 1 + ε ⋅ P, where ε is a small parameter here taken to be 0.01. Suppose that each of the two players chooses each of the three options with some probability, initially 1/3 for all (B); in game theory such probabilistic play is called a mixed action. How do we expect these probabilities to change over repeated play? One famous recipe is the MWUA, in which a player “boosts” the probability of each option by multiplying it by 1 + εG, where G is the expected amount of money this option is going to win the player in the current round of play, and ε is the same small parameter as above. For example, the second action of the row player has G equal to 2 (the average of 3, −1, and 4), and so the probability of playing the second row will be multiplied by 1.02. Then these weights are “renormalized” so they add up to 1, yielding the marginal probabilities shown in C. The probabilities after 10 such rounds of play are shown in D. Comparing now the numbers in the margins of Figs. 1D and ​and2D,2D, we notice that they are essentially the same. This is what we establish mathematically in this paper: the two processes—repeated coordination games played through multiplicative updates, and evolution under weak selection—are essentially identical. This conclusion is of interest because the MWUA is known in computer science to be surprisingly powerful.A game has several players, and each player i has a set A_i of possible actions. Each player also has a utility, capturing the way whereby her actions and the actions of the other players affect this player’s well-being. Formally the utility of a player is a function that maps each combination of actions by the players to a real number (intuitively denoting the player’s gain, in some monetary unit, if all players choose these particular actions). In general, rather than choosing a single action, a player may instead choose a mixed or randomized action, that is, a probabilistic distribution over her action set. Here we only need to consider coordination games, in which all players have the same utility function—that is, the interests of the players are perfectly aligned, and their only challenge is to coordinate their choices effectively. Coordination games are among the simplest games; the only challenge in such a game is for the players to “agree” on a mutually beneficial action.How do the players choose and adjust their choice of randomized (mixed) actions over repeated play? Assume that at time t, player i has mixed action xit, assigning to each action j ∈ A_i the probability xit(j). The MWUA algorithm (5) adjusts the mixed strategy for player i in the next round of the game according to the following rule:xit+1(j)=1Ztxit(j)(1+ϵuit(j)),[2]where Z^t is a normalizing constant designed to ensure that ∑jxit(j)=1, so xit+1 is a probability distribution; ε is a crucial small positive parameter, and uit(j) denotes the expected utility gained by player i choosing action j in the regime of the mixed actions by the other players effective at time t. This algorithm (i) is known to converge to the min–max actions if the game is two-player zero-sum; (ii) is also shown here to converge to equilibrium for the coordination games of interest in the present paper (SI Text, Corollary 5); (iii) is a general “learning algorithm” that has been shown to be very successful in both theory and practice; and (iv) if, instead of games, it is applied to a large variety of optimization problems, including linear programming, convex programming, and network congestion, it provably converges to the optimum quite fast.It can be now checked that the two processes expressed in Eqs. 1 and 2, evolution under natural selection in the presence of sex and multiplicative weight updates in a coordination game, are mathematically identical (SI Text, Theorem 3). That is, the interaction of weak selection and sex is equivalent to the MWUA in a coordination game between loci in which the common utility is the differential fitness of the organism. The parameter ε in the algorithm, which, when small signifies that the algorithm is taking a “longer-term view” of the process to be solved (SI Text), corresponds to the selection strength in evolution, i.e., the magnitude of the differences between the fitness of various genotypes.The MWUA is known in computer science as an extremely simple and yet unexpectedly successful algorithm, which has surprised us time and again by its prowess in solving sophisticated computational problems such as congestion minimization in networks and convex programming in optimization. The observation that multiplicative weight updates in a coordination game are equivalent to evolution under sex and weak selection makes an informative triple connection between three theoretical fields: evolutionary theory, game theory, and the theory of algorithms–machine learning.So far we have presented the MWUA by “how it works” (informally, it boosts alleles proportionally to how well they do in the current mix). There is an alternative way of understanding the MWUA in terms of “what it is optimizing.” That is, we imagine that the allele frequencies of each locus in each generation are the result of a deliberate optimization by the locus of some quantity, and we wish to determine that quantity.Returning to the game formulation, define Uit(j)=∑τ=0tuiτ(j) to be the cumulative utility obtained by player i by playing strategy j over all t first repetitions of the game, and consider the quantity∑jxit(j)Uit(j)−1ϵ∑jxit(j)ln⁡xit(j).[3]The first term is the current (at time t) expected cumulative utility. The second term of 3 is the entropy (expected negative logarithm) of the probability distribution {x_i(j), j = 1, … |A_i|}, multiplied by a large constant 1/?. Suppose now that player i wished to choose the probabilities of actions xit(j)s with the sole goal of maximizing the quantity 3. This is a relatively easy optimization problem, because the quantity 3 to be maximized is strictly concave, and therefore it has a unique maximum, obtained through the Karush–Kuhn–Tucker conditions of optimality (8) (SI Text, section 4):Uit(j)−1ϵ(1+ln⁡xit(j))+μt=0.[Here μ^t is the Lagrange multiplier associated with the constraint ∑jxit(j)=1 seeking to keep the xit(j)s a probability distribution; see SI Text.] Subtracting this equation from its homolog with t replaced by t + 1, and applying the approximation exp(ϵuit(j))≈(1+ϵuit(j)), we obtain the precise Eq. 2 (the normalization Z^t is obtained from μ^t and μ^t+1; see SI Text for the more detailed derivation).Thus, because Eqs. 1 and 2 are identical, we conclude that, in the weak selection regime, natural selection is tantamount to each locus choosing at each generation its allele frequencies in the population so as to maximize the sum of the expected cumulative differential fitness over the alleles, plus the distribution’s entropy. Note that quantity 3 is maximized by genes, not by individuals, and that, interestingly, it is maximized with respect to current frequencies while being dependent (through U^t) on all past frequencies, and although there is some precedent to the use of “historical fitness” (9), its importance in this context is unexpected.This alternative view of selection provides a new insight into an important question in evolutionary biology, namely: How is genetic diversity maintained in the presence of natural selection (10)? That the MWUA process enhances the entropy of the alleles’ distribution (while at the same time optimizes expected cumulative utility) hints at such a mechanism. In fact, entropy is enhanced inversely proportional to s (the quantity corresponding in the population genetics domain to the parameter ε), the selection strength: The weaker the selection, the more it favors high entropy. Naturally, entropy will eventually vanish when the process quiesces at equilibrium: One allele per locus will eventually be fixed, and in fact this equilibrium may be a local, as opposed to global, fitness maximum. However, we believe that it is interesting and significant that the entropy of the allele distribution is favored by selection in the transient; in any event, mutations, environmental changes, and finite population effects are likely to change the process before equilibrium is reached. This new way of understanding the maintenance of variation in evolution (selection as a tradeoff between fitness and entropy maximization) is quite different from previous hypotheses for the maintenance of variation (e.g., refs. 11, 12). Another rather surprising consequence of this characterization is that, under weak selection, all past generations, no matter how distant, have equal influence on the change in the allele mix of the current generation.Our discussion has focused on the evolution of a fixed set of alleles; that is, we have not discussed mutations. Mutations are, of course, paramount in evolution, as they are the source of genetic diversity, and we believe that introducing mutations to the present analysis is an important research direction. Here we focus on the selection process, which is rigorously shown to be tantamount to a tradeoff, for each locus, between maximizing diversity and maximizing expected cumulative fitness.We can now note a simple yet important point. Because multiplicative weight updates by the loci operate in the presence of sex, the triple connection uncovered in this paper is informative for the “queen of problems in evolutionary biology,” namely the role of sex in evolution (13, 14). The notion that the role of sex is the maintenance of diversity has been critiqued (15), because sex does not always increase diversity, and diversity is not always favorable. The MWUA connection sheds new light on the debate, because sex is shown to lead to a tradeoff between increasing entropy and increasing (cumulative) fitness.The connection between the three fields, evolution, game theory, and learning algorithms, described here was not accessible to the founders of the modern synthesis, and we hope that it expands the mathematical tracks that can be traveled in evolution theory.     

Author index, Volume 70

Authors Index

Author index, Volume 70     

Noradrenergic sympathetic innervation of the spleen: V. Acute drug-induced depletion of lymphocytes in the target fields of innervation results in redistribution of noradrenergic fibers but maintenance of compartmentation

Sympathetic noradrenergic fibers follow the vasculature into the white pulp of the spleen and branch from the periarteriolar plexuses into T lymphocyte zones. These lymphocytes, reported to express adrenergic receptors, are contacted directly by norepinephrine (NE) terminals and are putative targets of the locally released NE. Although this splenic innervation has been studied extensively, the functional interdependence of the lymphocytes and the sympathetic innervation is not well understood. To assess the effect of acute lymphocyte loss on the splenic innervation, T and B lymphocytes were depleted through treatment with cyclophosphamide (CY) or hydrocortisone acetate (HC). Despite reductions in spleen weight and cellularity, the total NE content (pmol) per spleen did not change. However, the NE concentration increased in the treated spleens. Although the general compartmental organization of the noradrenergic fibers in the treated spleens was similar to that of controls, the NE fibers redistributed and increased in density around the smaller central arteries in lymphocyte depleted spleens. The accommodation of these NE fibers to the changing environment of the white pulp suggests that the innervation to the spleen remains chemically stable despite a large disruption to the normal splenic milieu, but is capable of plasticity in the face of a shrinking white pulp.