BackgroundEpidemiology and surveillance of neonatal sepsis helps in implementation of rational empirical antibiotic strategy.ObjectiveTo study the frequency of bacterial isolates of early onset neonatal sepsis (EONS) and their sensitivity pattern.MethodsIn this retrospective study, a case of EONS was defined as an infant who had clinical signs or born to mothers with potential risk factors for infection, in whom blood culture obtained within 72 hours of life, grew a bacterial pathogen. Blood culture sample included a single sample from peripheral vein or artery. Relevant data was obtained from the unit register or neonatal case records.ResultsOf 2182 neonates screened, there were 389 (17.8%) positive blood cultures. After excluding coagulase-negative Staphylococci (160), we identified 229 EONS cases. Preterm neonates were 40.6% and small for gestational age, 18.3%. Mean birth weight and male to female ratio were 2344.5 (696.9) g and 1.16:1 respectively. Gram negative species represented 90.8% of culture isolates. Pseudomonas (33.2%) and Klebsiella (31.4%) were common among them. Other pathogens included Acinetobacter (14.4%), Staphylococcus aureus (9.2%), E.coli (4.4%), Enterobacter (2.2%), Citrobacter (3.1%) and Enterococci (2.2%). In Gram negative group, best susceptibility was to Amikacin (74.5%), followed by other aminoglycosides, ciprofloxacin and cefotaxime. The susceptibility was remarkably low to ampicillin (8.4%). Gram positive group had susceptibility of 42.9% to erythromycin, 47.6% to ciprofloxacin and above 50% to aminoglycosides. Of all isolates, 83.8% were susceptible to either cefotaxime or amikacinConclusionGram-negative species especially Pseudomonas and Klebsiella were the predominant causative organisms. Initial empirical choice of cefotaxime in combination with amikacin appeared to be rational choice for a given cohort.

Our findings suggest that depressed individuals differ from healthy controls in the neural substrates involved with processing social information. In depression, the nucleus accumbens and dorsal caudate may underlie abnormalities in processing information linked to the fairness and rewarding aspects of other people's decisions.

Cognitive functions rely on the extensive use of information stored in the brain, and the searching for the relevant information for solving some problem is a very complex task. Human cognition largely uses biological search engines, and we assume that to study cognitive function we need to understand the way these brain search engines work. The approach we favor is to study multimodular network models, able to solve particular problems that involve searching for information. The building blocks of these multimodular networks are the context dependent memory models we have been using for almost 20 years. These models work by associating an output to the Kronecker product of an input and a context. Input, context and output are vectors that represent cognitive variables. Our models constitute a natural extension of the traditional linear associator. We show that coding the information in vectors that are processed through association matrices, allows for a direct contact between these memory models and some procedures that are now classical in the Information Retrieval field. One essential feature of context-dependent models is that they are based on the thematic packing of information, whereby each context points to a particular set of related concepts. The thematic packing can be extended to multimodular networks involving input-output contexts, in order to accomplish more complex tasks. Contexts act as passwords that elicit the appropriate memory to deal with a query. We also show toy versions of several 'neuromimetic' devices that solve cognitive tasks as diverse as decision making or word sense disambiguation. The functioning of these multimodular networks can be described as dynamical systems at the level of cognitive variables.

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Graphs have been increasingly utilized in the characterization of complex networks from diverse origins, including different kinds of semantic networks. Human memories are associative and are known to support complex semantic nets; these nets are represented by graphs. However, it is not known how the brain can sustain these semantic graphs. The vision of cognitive brain activities, shown by modern functional imaging techniques, assigns renewed value to classical distributed associative memory models. Here we show that these neural network models, also known as correlation matrix memories, naturally support a graph representation of the stored semantic structure. We demonstrate that the adjacency matrix of this graph of associations is just the memory coded with the standard basis of the concept vector space, and that the spectrum of the graph is a code invariant of the memory. As long as the assumptions of the model remain valid this result provides a practical method to predict and modify the evolution of the cognitive dynamics. Also, it could provide us with a way to comprehend how individual brains that map the external reality, almost surely with different particular vector representations, are nevertheless able to communicate and share a common knowledge of the world. We finish presenting adaptive association graphs, an extension of the model that makes use of the tensor product, which provides a solution to the known problem of branching in semantic nets.

We named "Minsky's problem" the challenge of building up a cognitive architecture able to perform a good diagnosis based on multiple criteria that arrive one by one as successive clues. This is a remarkable human information processing capability, and a desirable ability for an artificial expert system. We present a general cognitive design that solves Minsky's problem and a neural network implementation of it that uses distributed associative memories. The type of architecture we present is based on the interaction between an "attribute-object associator (AOA)" and an "intersection filter (IF)" of successive evoked objects, with the intermediation of a working (short-term) memory.