Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69–0.99) and a specificity 0.84 (95% CI 0.58–0.94) with an AUC of 0.93 (95% CI 0.89–1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85–1.00) and specificity of 0.95 (95% CI 0.74–0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.     

Solving the N-Queens problem with a binary Hopfield-type network

The application of a discrete Hopfield-type neural network to solving the NP-Hard optimization problem — the N-Queens Problem (NQP) — is presented. The applied network is binary, and at every moment each neuron potential is equal to either 0 or 1. The network can be implemented in the asynchronous mode as well as in the synchronous one with n parallel running processors. In both cases the convergence rate is up to 100%, and the experimental estimate of the average computational complexity is polynomial. Based on the computer simulation results and the theoretical analysis, the proper network parameters are established. The behaviour of the network is explained.     

Mapping of neuropeptide Y expression in Octopus brains

Neuropeptide Y (NPY) is an evolutionarily conserved neurosecretory molecule implicated in a diverse complement of functions across taxa and in regulating feeding behavior and reproductive maturation in Octopus. However, little is known about the precise molecular circuitry of NPY-mediated behaviors and physiological processes, which likely involve a complex interaction of multiple signal molecules in specific brain regions. Here, we examined the expression of NPY throughout the Octopus central nervous system. The sequence analysis of Octopus NPY precursor confirmed the presence of both, signal peptide and putative active peptides, which are highly conserved across bilaterians. In situ hybridization revealed distinct expression of NPY in specialized compartments, including potential “integration centers,” where visual, tactile, and other behavioral circuitries converge. These centers integrating separate circuits may maintain and modulate learning and memory or other behaviors not yet attributed to NPY-dependent modulation in Octopus. Extrasomatic localization of NPY mRNA in the neurites of specific neuron populations in the brain suggests a potential demand for immediate translation at synapses and a crucial temporal role for NPY in these cell populations. We also documented the presence of NPY mRNA in a small cell population in the olfactory lobe, which is a component of the Octopus feeding and reproductive control centers. However, the molecular mapping of NPY expression only partially overlapped with that produced by immunohistochemistry in previous studies. Our study provides a precise molecular map of NPY mRNA expression that can be used to design and test future hypotheses about molecular signaling in various Octopus behaviors.