Olfactory cues potentiate learning of distant visuospatial information

The influence of proximal olfactory cues on place learning and memory was tested in two different spatial tasks. Rats were trained to find a hole leading to their home cage or a single food source in an array of petri dishes. The two apparatuses differed both by the type of reinforcement (return to the home cage or food reward) and the local characteristics of the goal (masked holes or salient dishes). In both cases, the goal was in a fixed location relative to distant visual landmarks and could be marked by a local olfactory cue. Thus, the position of the goal was defined by two sets of redundant cues, each of which was sufficient to allow the discrimination of the goal location. These experiments were conducted with two strains of hooded rats (Long-Evans and PVG), which show different speeds of acquisition in place learning tasks. They revealed that the presence of an olfactory cue marking the goal facilitated learning of its location and that the facilitation persisted after the removal of the cue. Thus, the proximal olfactory cue appeared to potentiate learning and memory of the goal location relative to distant environmental cues. This facilitating effect was only detected when the expression of spatial memory was not already optimal, i.e., during the early phase of acquisition. It was not limited to a particular strain.

Adult native septic arthritis: 10 years in review in order to establish local guidelines on empiric antibiotic therapy.

Objective: Antibiotic stewardship includes development of practice guidelines incorporating local microbiology and resistance patterns. In case of septic arthritis (SA), addition of vancomycin to the empiric therapy and broad-spectrum antibiotherapy in some clinical settings are subjects of discussion. Our objective was to review the local epidemiology of native septic arthritis in adults, in order to establish local guidelines for empiric therapy. Methods: Retrospective study based on positive synovial fluid cultures and hospital discharge diagnoses of SA obtained from 1999 to 2008 in patients _16 years. Medical records were reviewed to assess the diagnosis and complete relevant clinical information. Results: During this ten-year period, we identified 233 SA on native joints in 231 patients. 107 episodes (46%) were obtained through positive synovial fluid cultures, and 126 episodes (54%) through the discharge diagnosis. 147 SA (63%) were large joint infections (LJI). 35 SA (15%) occurred in intravenous drug users. Preexisting arthropathy was present in 51% of cases. 42% of patients with small joint infection (SJI) were diabetic, vs. 23% with LJI (p = 0.003). When available, synovial fluid direct examination was positive in 35% of cases. Etiologic agents are reported in the table. Five of the 11 MRSA SA (45%) occurred in known carriers. SJI were more frequently polymicrobial (24% vs. 1%, p<0.001). For LJI, an empiric treatment with amoxicillin/clavulanate (A/C) would have been appropriate in 85% of cases. MRSA (8 cases) and tuberculous (7 cases) arthritis would have been the most frequently untreated pathogens. Addition of vancomycin to A/C in MRSA carriers would rise the adequacy to 87%. In contrast, A/C would cover only 75% of SJI (82% if restricted to non-diabetic patients). MRSA (3 cases) and P. aeruginosa (9 cases, 7 monomicrobial) would be the main untreated pathogens. An anti-pseudomonal penicillin would have been appropriate in 94% of cases of SJI (P = 0.002 vs. A/C, p = 0.19 if diabetic patients not included). Conclusions: Treatment with A/C seems adequate for empiric coverage of LJI in our setting. Broad-spectrum antibiotherapy was significantly superior for SJI in diabetic patients, due to different causative bacteria. In an area of low MRSA incidence, our results do not justify a systematic empiric therapy for MRSA, which should be considered in a known carrier.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Social structure emerges via the interaction between local ecology and individual behaviour.

1. The formation of groups is a fundamental aspect of social organization, but there are still many questions regarding how social structure emerges from individuals making non-random associations. 2. Although food distribution and individual phenotypic traits are known to separately influence social organization, this is the first study, to our knowledge, experimentally linking them to demonstrate the importance of their interaction in the emergence of social structure. 3. Using an experimental design in which food distribution was either clumped or dispersed, in combination with individuals that varied in exploratory behaviour, our results show that social structure can be induced in the otherwise non-social European shore crab (Carcinus maenas). 4. Regardless of food distribution, individuals with relatively high exploratory behaviour played an important role in connecting otherwise poorly connected individuals. In comparison, low exploratory individuals aggregated into cohesive, stable subgroups (moving together even when not foraging), but only in tanks where resources were clumped. No such non-foraging subgroups formed in environments where food was evenly dispersed. 5. Body size did not accurately explain an individual's role within the network for either type of food distribution. 6. Because of their synchronized movements and potential to gain social information, groups of low exploratory crabs were more effective than singletons at finding food. 7. Because social structure affects selection, and social structure is shown to be sensitive to the interaction between ecological and behavioural differences among individuals, local selective pressures are likely to reflect this interaction.

Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers

Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.

Working memory in schizophrenia: dynamic visuo-spatial information processing

Working memory, commonly defined as the ability to hold mental representations on line transiently and to manipulate these representations, is known to be a core deficit in schizophrenia. The aim of the present study was to investigate the visuo-spatial component of the working memory in schizophrenia, and more precisely to what extent the dynamic visuo-spatial information processing is impaired in schizophrenia patients. For this purpose we used a computerized paradigm in which 29 patients with schizophrenia (DSMIV, Diagnostic Interview for Genetic Studies) and 29 age and sex matched control subjects (DIGS) had to memorize a plane moving across the computer screen and to identify the observed trajectory among 9 plots proposed together. Each trajectory could be seen max. 3 times if needed. The results showed no difference between schizophrenia patients and controls regarding the number of correct trajectory identified after the first presentation. However, when we determine the mean number of correct trajectories on the basis of 3 trials, we observed that schizophrenia patients are significantly less performant than controls (Mann-Whitney, p _ 0.002). These findings suggest that, although schizophrenia patients are able to memorize some dynamic trajectories as well as controls, they do not profit from the repetition of the trajectory presentation. These findings are congruent with the hypothesis that schizophrenia could induce an unbalance between local and global information processing: the patients may be able to focus on details of the trajectory which could allow them to find the right target (bottom-up processes), but may show difficulty to refer to previous experience in order to filter incoming information (top-down processes) and enhance their visuo-spatial working memory abilities.

Building legitimacy by criticising the pharmaceutical industry: a qualitative study among prescribers and local opinion leaders.

PRINCIPLES: The literature has described opinion leaders not only as marketing tools of the pharmaceutical industry, but also as educators promoting good clinical practice. This qualitative study addresses the distinction between the opinion-leader-as-marketing-tool and the opinion-leader-as-educator, as it is revealed in the discourses of physicians and experts, focusing on the prescription of antidepressants. We explore the relational dynamic between physicians, opinion leaders and the pharmaceutical industry in an area of French-speaking Switzerland. METHODS: Qualitative content analysis of 24 semistructured interviews with physicians and local experts in psychopharmacology, complemented by direct observation of educational events led by the experts, which were all sponsored by various pharmaceutical companies. RESULTS: Both physicians and experts were critical of the pharmaceutical industry and its use of opinion leaders. Local experts, in contrast, were perceived by the physicians as critical of the industry and, therefore, as a legitimate source of information. Local experts did not consider themselves opinion leaders and argued that they remained intellectually independent from the industry. Field observations confirmed that local experts criticised the industry at continuing medical education events. CONCLUSIONS: Local experts were vocal critics of the industry, which nevertheless sponsor their continuing education. This critical attitude enhanced their credibility in the eyes of the prescribing physicians. We discuss how the experts, despite their critical attitude, might still be beneficial to the industry's interests.