Recognition judgments and the performance of the recognition heuristic depend on the size of the reference class

In a series of three experiments, participants made inferences about which one of a pair of two objects scored higher on a criterion. The first experiment was designed to contrast the prediction of Probabilistic Mental Model theory (Gigerenzer, Hoffrage, & Kleinbölting, 1991) concerning sampling procedure with the hard-easy effect. The experiment failed to support the theory's prediction that a particular pair of randomly sampled item sets would differ in percentage correct; but the observation that German participants performed practically as well on comparisons between U.S. cities (many of which they did not even recognize) than on comparisons between German cities (about which they knew much more) ultimately led to the formulation of the recognition heuristic. Experiment 2 was a second, this time successful, attempt to unconfound item difficulty and sampling procedure. In Experiment 3, participants' knowledge and recognition of each city was elicited, and how often this could be used to make an inference was manipulated. Choices were consistent with the recognition heuristic in about 80% of the cases when it discriminated and people had no additional knowledge about the recognized city (and in about 90% when they had such knowledge). The frequency with which the heuristic could be used affected the percentage correct, mean confidence, and overconfidence as predicted. The size of the reference class, which was also manipulated, modified these effects in meaningful and theoretically important ways.

Most residues on the floor of the antigen binding site of the class I MHC molecule H-2Kd influence peptide presentation.

A panel of 15 single alanine substitutions on the floor of the peptide binding groove of the murine class I histocompatibility molecule H-2Kd has been analyzed. All but two mutant molecules were expressed on the cell surface, and were tested for peptide binding and presentation to specific cytotoxic T lymphocytes. Eleven out of 13 mutant molecules appeared to be functionally altered. Five of the substituted residues were involved in the presentation of all peptides tested. Three participated in the presentation of certain peptides but not others. Three other residues participated in epitope formation through indirect interactions. Only two mutations had no detectable effect.

Combining molecular evolution and environmental genomics to unravel adaptive processes of MHC class IIB diversity in European minnows (Phoxinus phoxinus)

Host-pathogen interactions are a major evolutionary force promoting local adaptation. Genes of the major histocompatibility complex (MHC) represent unique candidates to investigate evolutionary processes driving local adaptation to parasite communities. The present study aimed at identifying the relative roles of neutral and adaptive processes driving the evolution of MHC class IIB (MHCIIB) genes in natural populations of European minnows (Phoxinus phoxinus). To this end, we isolated and genotyped exon 2 of two MHCIIB gene duplicates (DAB1 and DAB3) and 1665 amplified fragment length polymorphism (AFLP) markers in nine populations, and characterized local bacterial communities by 16S rDNA barcoding using 454 amplicon sequencing. Both MHCIIB loci exhibited signs of historical balancing selection. Whereas genetic differentiation exceeded that of neutral markers at both loci, the populations' genetic diversities were positively correlated with local pathogen diversities only at DAB3. Overall, our results suggest pathogen-mediated local adaptation in European minnows at both MHCIIB loci. While at DAB1 selection appears to favor different alleles among populations, this is only partially the case in DAB3, which appears to be locally adapted to pathogen communities in terms of genetic diversity. These results provide new insights into the importance of host-pathogen interactions in driving local adaptation in the European minnow, and highlight that the importance of adaptive processes driving MHCIIB gene evolution may differ among duplicates within species, presumably as a consequence of alternative selective regimes or different genomic context.

PPARgamma but not PPARalpha ligands are potent repressors of major histocompatibility complex class II induction in atheroma-associated cells.

Peroxisome proliferator-activated receptors (PPARs) are essential in glucose and lipid metabolism and are implicated in metabolic disorders predisposing to atherosclerosis, such as diabetes and dyslipidemia. Conversely, antidiabetic glitazones and hypolipidemic fibrate drugs, known as PPARgamma and PPARalpha ligands, respectively, reduce the process of atherosclerotic lesion formation, which involves chronic immunoinflammatory processes. Major histocompatibility complex class II (MHC-II) molecules, expressed on the surface of specialized cells, are directly involved in the activation of T lymphocytes and in the control of the immune response. Interestingly, expression of MHC-II has recently been observed in atherosclerotic plaques, and it can be induced by the proinflammatory cytokine interferon-gamma (IFN-gamma) in vascular cells. To explore a possible role for PPAR ligands in the regulation of the immune response, we investigated whether PPAR activation affects MHC-II expression in atheroma-associated cells. In the present study, we demonstrate that PPARgamma but not PPARalpha ligands act as inhibitors of IFN-gamma-induced MHC-II expression and thus as repressors of MHC-II-mediated T-cell activation. All different types of PPARgamma ligands tested inhibit MHC-II. This effect of PPARgamma ligands is due to a specific inhibition of promoter IV of CIITA and does not concern constitutive expression of MHC-II. Thus, the beneficial effects of antidiabetic PPARgamma activators on atherosclerotic plaque development may be partly explained by their repression of MHC-II expression and subsequent inhibition of T-lymphocyte activation.

Patrons d'incorporació sociolaboral immigració llatinoamericana en dos contestos urbans, Barcelona i Manchester

La present memòria de treball presenta les principals conclusions del projecte titulat “Patrons d'incorporació sociolaboral immigració llatinoamericana en dos contestos urbans, Barcelona i Manchester" finançat per la convocatòria Projectes Batista i Roca PBR 2009 i executat pels grups de recerca GEDIME (Grup d’estudis de migracions i minories ètniques) de la UAB, i el Global Urban Research Centre (GURC-University of Manchester). El projecte s’aproxima, des d’un pla estructural i des d’una anàlisi comparativa, al context de recepció d’ambdós territoris (context institucional, social i econòmic), que determina l’estructura d’oportunitats dels immigrants i condiciona les seves estratègies com agents socials, en interacció amb les diferents situacions de cada immigrant (nivell educatiu, situació familiar, cicle vital, classe social, etc.). A través dels seminaris i workshops realitzats en ambdues universitats participants, s'ha establert vincles de col•laboració estable entre els dos grups de recerca implicats en el projecte. Aquest marc de cooperació es valora com una iniciativa que ha de transcendir el temps de desenvolupament d’aquest estudi i que ha de donar lloc a la presentació de nous projectes competitius, publicacions conjuntes, etc. Aquesta opció, valorada com a fonamental pels dos centres de recerca, ha servit per generar un flux d’informació científica permanent entre ambdós centres de recerca.

Adherence as a predictor of the development of class-specific resistance mutations: the Swiss HIV Cohort Study.

BACKGROUND: Non-adherence is one of the strongest predictors of therapeutic failure in HIV-positive patients. Virologic failure with subsequent emergence of resistance reduces future treatment options and long-term clinical success. METHODS: Prospective observational cohort study including patients starting new class of antiretroviral therapy (ART) between 2003 and 2010. Participants were naïve to ART class and completed ≥1 adherence questionnaire prior to resistance testing. Outcomes were development of any IAS-USA, class-specific, or M184V mutations. Associations between adherence and resistance were estimated using logistic regression models stratified by ART class. RESULTS: Of 314 included individuals, 162 started NNRTI and 152 a PI/r regimen. Adherence was similar between groups with 85% reporting adherence ≥95%. Number of new mutations increased with increasing non-adherence. In NNRTI group, multivariable models indicated a significant linear association in odds of developing IAS-USA (odds ratio (OR) 1.66, 95% confidence interval (CI): 1.04-2.67) or class-specific (OR 1.65, 95% CI: 1.00-2.70) mutations. Levels of drug resistance were considerably lower in PI/r group and adherence was only significantly associated with M184V mutations (OR 8.38, 95% CI: 1.26-55.70). Adherence was significantly associated with HIV RNA in PI/r but not NNRTI regimens. CONCLUSION: Therapies containing PI/r appear more forgiving to incomplete adherence compared with NNRTI regimens, which allow higher levels of resistance, even with adherence above 95%. However, in failing PI/r regimens good adherence may prevent accumulation of further resistance mutations and therefore help to preserve future drug options. In contrast, adherence levels have little impact on NNRTI treatments once the first mutations have emerged.

Identification of naturally processed hepatitis C virus-derived major histocompatibility complex class I ligands.

Fine mapping of human cytotoxic T lymphocyte (CTL) responses against hepatitis C virus (HCV) is based on external loading of target cells with synthetic peptides which are either derived from prediction algorithms or from overlapping peptide libraries. These strategies do not address putative host and viral mechanisms which may alter processing as well as presentation of CTL epitopes. Therefore, the aim of this proof-of-concept study was to identify naturally processed HCV-derived major histocompatibility complex (MHC) class I ligands. To this end, continuous human cell lines were engineered to inducibly express HCV proteins and to constitutively express high levels of functional HLA-A2. These cell lines were recognized in an HLA-A2-restricted manner by HCV-specific CTLs. Ligands eluted from HLA-A2 molecules isolated from large-scale cultures of these cell lines were separated by high performance liquid chromatography and further analyzed by electrospray ionization quadrupole time of flight mass spectrometry (MS)/tandem MS. These analyses allowed the identification of two HLA-A2-restricted epitopes derived from HCV nonstructural proteins (NS) 3 and 5B (NS3₁₄₀₆₋₁₄₁₅ and NS5B₂₅₉₄₋₂₆₀₂). In conclusion, we describe a general strategy that may be useful to investigate HCV pathogenesis and may contribute to the development of preventive and therapeutic vaccines in the future.

Purification and ligand binding of a soluble class I major histocompatibility complex molecule consisting of the first three domains of H-2Kd fused to beta 2-microglobulin expressed in the baculovirus-insect cell system.

A recombinant baculovirus encoding a single-chain murine major histocompatibility complex class I molecule in which the first three domains of H-2Kd are fused to beta 2-microglobulin (beta 2-m) via a 15-amino acid linker has been isolated and used to infect lepidopteran cells. A soluble, 391-amino acid single-chain H-2Kd (SC-Kd) molecule of 48 kDa was synthesized and glycosylated in insect cells and could be purified in the absence of detergents by affinity chromatography using the anti-H-2Kd monoclonal antibody SF1.1.1.1. We tested the ability of SC-Kd to bind antigenic peptides using a direct binding assay based on photoaffinity labeling. The photoreactive derivative was prepared from the H-2Kd-restricted Plasmodium berghei circumsporozoite protein (P.b. CS) peptide 253-260 (YIPSAEKI), a probe that we had previously shown to be unable to bind to the H-2Kd heavy chain in infected cells in the absence of co-expressed beta 2-microglobulin. SC-Kd expressed in insect cells did not require additional mouse beta 2-m to bind the photoprobe, indicating that the covalently attached beta 2-m could substitute for the free molecule. Similarly, binding of the P.b. CS photoaffinity probe to the purified SC-Kd molecule was unaffected by the addition of exogenous beta 2-m. This is in contrast to H-2KdQ10, a soluble H-2Kd molecule in which beta 2-m is noncovalently bound to the soluble heavy chain, whose ability to bind the photoaffinity probe is greatly enhanced in the presence of an excess of exogenous beta 2-m. The binding of the probe to SC-Kd was allele-specific, since labeling was selectively inhibited only by antigenic peptides known to be presented by the H-2Kd molecule.

Stochastic dominance and absolute risk aversion

In this paper we proose the infimum of the Arrow-Pratt index of absoluterisk aversion as a measure of global risk aversion of a utility function.We then show that, for any given arbitrary pair of distributions, thereexists a threshold level of global risk aversion such that all increasingconcave utility functions with at least as much global risk aversion wouldrank the two distributions in the same way. Furthermore, this thresholdlevel is sharp in the sense that, for any lower level of global riskaversion, we can find two utility functions in this class yielding oppositepreference relations for the two distributions.

The achievable region approach to the optimal control of stochastic systems

The achievable region approach seeks solutions to stochastic optimisation problems by: (i) characterising the space of all possible performances(the achievable region) of the system of interest, and (ii) optimisingthe overall system-wide performance objective over this space. This isradically different from conventional formulations based on dynamicprogramming. The approach is explained with reference to a simpletwo-class queueing system. Powerful new methodologies due to the authorsand co-workers are deployed to analyse a general multiclass queueingsystem with parallel servers and then to develop an approach to optimalload distribution across a network of interconnected stations. Finally,the approach is used for the first time to analyse a class of intensitycontrol problems.

Strong minimax lower bounds for learning

Minimax lower bounds for concept learning state, for example, thatfor each sample size $n$ and learning rule $g_n$, there exists a distributionof the observation $X$ and a concept $C$ to be learnt such that the expectederror of $g_n$ is at least a constant times $V/n$, where $V$ is the VC dimensionof the concept class. However, these bounds do not tell anything about therate of decrease of the error for a {\sl fixed} distribution--concept pair.\\In this paper we investigate minimax lower bounds in such a--stronger--sense.We show that for several natural $k$--parameter concept classes, includingthe class of linear halfspaces, the class of balls, the class of polyhedrawith a certain number of faces, and a class of neural networks, for any{\sl sequence} of learning rules $\{g_n\}$, there exists a fixed distributionof $X$ and a fixed concept $C$ such that the expected error is larger thana constant times $k/n$ for {\sl infinitely many n}. We also obtain suchstrong minimax lower bounds for the tail distribution of the probabilityof error, which extend the corresponding minimax lower bounds.

On prediction of individual sequences

Sequential randomized prediction of an arbitrary binary sequence isinvestigated. No assumption is made on the mechanism of generating the bit sequence. The goal of the predictor is to minimize its relative loss, i.e., to make (almost) as few mistakes as the best ``expert'' in a fixed, possibly infinite, set of experts. We point out a surprising connection between this prediction problem and empirical process theory. First, in the special case of static (memoryless) experts, we completely characterize the minimax relative loss in terms of the maximum of an associated Rademacher process. Then we show general upper and lower bounds on the minimaxrelative loss in terms of the geometry of the class of experts. As main examples, we determine the exact order of magnitude of the minimax relative loss for the class of autoregressive linear predictors and for the class of Markov experts.

Contractual resolutions of financial distress

In a financial contracting model, we study the optimal debt structure to resolve financial distress. Weshow that a debt structure where two distinct debt classes co-exist - one class fully concentrated andwith control rights upon default, the other dispersed and without control rights - removes the controllingcreditor's liquidation bias when investor protection is strong. These results rationalize the use and theperformance of floating charge financing, debt financing where the controlling creditor takes the entirebusiness as collateral, in countries with strong investor protection. Our theory predicts that the efficiency ofcontractual resolutions of financial distress should increase with investor protection.

Beyond Smith's rule: An optimal dynamic index, rule for single machine stochastic scheduling with convex holding costs

Most research on single machine scheduling has assumedthe linearity of job holding costs, which is arguablynot appropriate in some applications. This motivates ourstudy of a model for scheduling $n$ classes of stochasticjobs on a single machine, with the objective of minimizingthe total expected holding cost (discounted or undiscounted). We allow general holding cost rates that are separable,nondecreasing and convex on the number of jobs in eachclass. We formulate the problem as a linear program overa certain greedoid polytope, and establish that it issolved optimally by a dynamic (priority) index rule,whichextends the classical Smith's rule (1956) for the linearcase. Unlike Smith's indices, defined for each class, ournew indices are defined for each extended class, consistingof a class and a number of jobs in that class, and yieldan optimal dynamic index rule: work at each time on a jobwhose current extended class has larger index. We furthershow that the indices possess a decomposition property,as they are computed separately for each class, andinterpret them in economic terms as marginal expected cost rate reductions per unit of expected processing time.We establish the results by deploying a methodology recentlyintroduced by us [J. Niño-Mora (1999). "Restless bandits,partial conservation laws, and indexability. "Forthcomingin Advances in Applied Probability Vol. 33 No. 1, 2001],based on the satisfaction by performance measures of partialconservation laws (PCL) (which extend the generalizedconservation laws of Bertsimas and Niño-Mora (1996)):PCL provide a polyhedral framework for establishing theoptimality of index policies with special structure inscheduling problems under admissible objectives, which weapply to the model of concern.