Minors held by majors: the H13 minor histocompatibility locus defined as a peptide/MHC class I complex

The products of minor histocompatibility (H) loci are serious barriers to tissue transplantation even among major histocompatibility complex (MHC) identical individuals, frequently causing chronic graft rejection and graft versus host disease. Over 50 minor H loci map to mouse autosomal chromosomes but none are known at the molecular level. By expression cloning, we identified the H13 locus, a classical minor H locus first detected 30 years ago by the trait of graft rejection. The H13a allele is located on chromosome 2 and encodes a novel protein that yields the rare naturally processed nonapeptide SSVVGVWYL (SVL9) for presentation by the Db MHC class I molecule. The SVL9 peptide binds Db MHC despite the absence of the consensus binding motif, and a conservative methyl group substitution (Valine 4 <--> Isoleucine) explains why reciprocal T cell responses are elicited in H13a and H13b congenic strains.     

Multi-Cohort Intelligence algorithm: an intra- and inter-group learning behaviour based socio-inspired optimisation methodology

ABSTRACT

A Multi-Cohort Intelligence (Multi-CI) metaheuristic algorithm in emerging socio-inspired optimisation domain is proposed. The algorithm implements intra-group and inter-group learning mechanisms. It focusses on the interaction amongst different cohorts. The performance of the algorithm is validated by solving 75 unconstrained test problems with dimensions up to 30. The solutions were comparing with several recent algorithms such as Particle Swarm Optimisation (PSO), Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Self-Adaptive Differential Evolution Algorithm, Comprehensive Learning Particle Swarm Optimisation, Backtracking Search Optimisation Algorithm, and Ideology Algorithm. The Wilcoxon signed-rank test was carried out for the statistical analysis and verification of the performance. The proposed Multi-CI outperformed these algorithms in terms of the solution quality including objective function value and computational cost, i.e. computational time and functional evaluations. The prominent feature of the Multi-CI algorithm along with the limitations is discussed as well. In addition, an illustrative example is also solved and every detail is provided.     

Advances in SHRUTI—A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony

We are capable of drawing a variety of inferences effortlessly, spontaneously, and with remarkable efficiency—as though these inferences are a reflex response of our cognitive apparatus. This remarkable human ability poses a challenge for cognitive science and computational neuroscience: How can a network of slow neuron-like elements represent a large body of systematic knowledge and perform a wide range of inferences with such speed? The connectionist model SHRUTI attempts to address this challenge by demonstrating how a neurally plausible network can encode a large body of semantic and episodic facts, systematic rules, and knowledge about entities and types, and yet perform a wide range of explanatory and predictive inferences within a few hundred milliseconds. Relational structures (frames, schemas) are represented in SHRUTI by clusters of cells, and inference in SHRUTI corresponds to a transient propagation of rhythmic activity over such cell-clusters wherein dynamic bindings are represented by the synchronous firing of appropriate cells. SHRUTI encodes mappings across relational structures using high-efficacy links that enable the propagation of rhythmic activity, and it encodes items in long-term memory as coincidence and coincidence-error detector circuits that become active in response to the occurrence (or non-occurrence) of appropriate coincidences in the on going flux of rhythmic activity. Finally, “understanding” in SHRUTI corresponds to reverberant and coherent activity along closed loops of neural circuitry. Over the past several years, SHRUTI has undergone several enhancements that have augmented its expressiveness and inferential power. This paper describes some of these extensions that enable SHRUTI to (i) deal with negation and inconsistent beliefs, (ii) encode evidential rules and facts, (iii) perform inferences requiring the dynamic instantiation of entities, and (iv) seek coherent explanations of observations.     

Platelet and neutrophil distributions in pump oxygenator circuits. III. Influence of nitric oxide gas infusion

The authors used quantitative gamma scintigraphy to evaluate the influence of nitric oxide gas on platelet and neutrophil deposition in Cobe Duo microporous oxygenators during cardiopulmonary bypass (CPB). The effects of nitric oxide gas on circulating platelet and neutrophil counts and platelet function also were assessed. Animals were prepared by standard methods. Cells were harvested, labeled (111 In platelet and 99mTc neutrophil), infused, and recirculated. Nitric oxide gas, a guanylate cyclase pathway promoter, was infused int he Duo gas port at 500 ppm (t = 0-60 min), increased to 1,000 (t = 60-80 min), and stopped (final, 10 min). Images were taken at 10-15 min intervals during CPB. Standard isotope image corrections were made. No differences between nitric oxide gas and control experiments were observed for flow, pressure, hematocrit, or replacement volume. Nitric oxide gas infusion significantly (p < 0.05) reduced both platelet adherence to the oxygenator and in vitro platelet aggregation. Neutrophil adhesion tended to be lower, and circulating platelet and neutrophil counts tended to be higher with nitric oxide gas infusion. Results of in vitro aggregometry studies using rabbit platelets indicate that the class V phosphodiesterase inhibitor zaprinast can strongly enhance the inhibitory effects of nitric oxide. The authors conclude nitric oxide gas is a promising platelet sparing agent in the setting of CPB.     

Bayes results for classical Pareto distribution via Gibbs sampler,with doubly-censored observations

The paper considers the full Bayes analysis of the Pareto distribution when the observations are doubly censored, and provides sample-based estimates of posterior distributions using Gibbs sampler algorithm. The approach is not only computationally simple but fully explores the low-dimensional posterior surfaces-which otherwise seems difficult. Complexities through censored data always arise in life testing experiments; these complexities are no longer problems with the Gibbs sampler algorithm, unlike the situations with nonsample-based approaches