Bayesian Network structure learning algorithm for highly missing and non imputable data: Application to breast cancer radiotherapy data.

It is not uncommon for real-life data produced in healthcare to have a higher proportion of missing data than in other scopes. To take into account these missing data, imputation is not always desired by healthcare experts, and complete case analysis can lead to a significant loss of data. The algorithm proposed here, allows the learning of Bayesian Network graphs when both imputation and complete case analysis are not possible. The learning process is based on a set of local bootstrap learnings performed on complete sub-datasets which are then aggregated and locally optimized. This learning method presents competitive results compared to other structure learning algorithms, whatever the mechanism of missing data.

Development of a micromanipulation method for single cell isolation of prokaryotes and its application in food safety.

For nearly a century, conventional microbiological methods have been standard practice for detecting and identifying pathogens in food. Nevertheless, the microbiological safety of food has improved and various rapid methods have been developed to overcome the limitations of conventional methods. Alternative methods are expected to detect low cell numbers, since the presence in food of even a single cell of a pathogenic organism may be infectious. With respect to low population levels, the performance of a detection method is assessed by producing serial dilutions of a pure bacterial suspension to inoculate representative food matrices with highly diluted bacterial cells (fewer than 10 CFU/ml). The accuracy of data obtained by multiple dilution techniques is not certain and does not exclude some colonies arising from clumps of cells. Micromanipulation techniques to capture and isolate single cells from environmental samples were introduced more than 40 years ago. The main limitation of the current micromanipulation technique is still the low recovery rate for the growth of a single cell in culture medium. In this study, we describe a new single cell isolation method and demonstrate that it can be used successfully to grow various types of microorganism from picked individual cells. Tests with Gram-positive and Gram-negative organisms, including cocci, rods, aerobes, anaerobes, yeasts and molds showed growth recovery rates from 60% to 100% after micromanipulation. We also highlight the use of our method to evaluate and challenge the detection limits of standard detection methods in food samples contaminated by a single cell of Salmonella enterica.

Bonobos and orangutans, but not chimpanzees, flexibly plan for the future in a token-exchange task.

Non-human animals, including great apes, have been suggested to share some of the skills for planning that humans commonly exhibit. A crucial difference between human and non-human planning may relate to the diversity of domains and needs in which this skill is expressed. Although great apes can save tools for future use, there is little evidence yet that they can also do so in other contexts. To investigate this question further, we presented the apes with a planning token-exchange task that differed from standard tool-use tasks. Additionally, we manipulated the future outcome of the task to investigate planning flexibility. In the Exchange condition, subjects had to collect, save and transport tokens because they would need them 30 min later to exchange them for food with a human, i.e., "bring-back" response. In the Release condition, the collection and transport of tokens were not needed as no exchange took place after 30 min. Out of 13 subjects, eight solved the task at least once in the Exchange condition, with chimpanzees appearing less successful than the other species. Importantly, three individuals showed a clear differential response between conditions by producing more "bring-back" responses in the Exchange than in the Release conditions. Those bonobo and orangutan individuals hence adapted their planning behavior according to changing needs (i.e., they brought tokens back significantly more often when they would need them). Bonobos and orangutans, unlike chimpanzees, planned outside the context of tool-use, thus challenging the idea that planning in these species is purely domain-specific.

Effect of continuous positive airway pressure treatment on short-term memory performance over 24 h of sustained wakefulness in patients with obstructive sleep apnea-hypopnea syndrome.

BACKGROUND: Few studies have examined the impact of continuous positive airway pressure (CPAP) therapy on short-term memory (STM) over sustained wakefulness in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS). We have investigated if impaired STM can be reversed by CPAP treatment in a 24-h sustained wakefulness paradigm. METHODS: Our follow-up study was conducted with repeated-memory tasks within 12 OSAHS patients and 10 healthy controls who underwent three 32-h sessions, one before CPAP (T0) and the second (T3) and the third (T6), after 3 and 6 months of treatment, respectively, for OSAHS patients. Each session included one night of sleep followed by 24h of sustained wakefulness, during which both groups performed STM tasks including both digit span (DS) and Sternberg tasks. RESULTS: Untreated OSAHS patients had no deficit in the forward DS task measuring immediate memory but were impaired in STM, especially working memory assessed by the complex Sternberg task and the backward DS. However, only performance in the latter was improved after 6 months of CPAP treatment. CONCLUSIONS: Because the high level of memory scanning required high speed in information processing, persistent impairment on the complex Sternberg task may be attributable to working memory slowing, possibly enhanced by sustained wakefulness.

Short-term memory performances during sustained wakefulness in patients with obstructive sleep apnea-hypopnea syndrome.

Both working and immediate memories were assessed every 4h by specific short-term memory tasks over sustained wakefulness in 12 patients with obstructive sleep apnea and hypopnea syndrome (OSAHS) and 10 healthy controls. Results indicated that OSAHS patients exhibited lower working memory performances than controls on both backward digit span and complex Sternberg tasks. Speed and accuracy on Sternberg tasks were affected by memory load in both groups. However, immediate memory was not impaired in OSAHS patients. Diurnal and nocturnal SaO(2) were correlated with speed and accuracy high-speed memory scanning performance on Sternberg tasks in patients. These results suggest specific working memory deficits associated with OSAHS over sustained wakefulness with a possible deficiency in the central executive responsible for the higher information processing, in addition to a potentially insufficient storage capacity. Among OSAHS patients, working memory ability involved in high-speed memory scanning may be impaired by chronic hypoxemia.

Multidimensional fitting for multivariate data analysis.

Large multidimensional data matrices are frequent in biology. However, statistical methods often have difficulties dealing with such matrices because they contain very complex data sets. Consequently variable selection and dimensionality reduction methods are often used to reduce matrix complexity, although at the expense of information conservation. A new method derived from multidimensional scaling (MDS) is presented for the case where two matrices are available to describe the same population. The presented method transforms one of the matrices, called the target matrix, with some constraints to make it fit with the second matrix, referred to as the reference matrix. The fitting to the reference matrix is performed on the distances computed for the two matrices, and the transformation depends on the problem at hand. A special feature of this method is that a variable can be only partially modified. The method is applied on the exclusive-or (XOR) problem and then on a biological application with large-scale gene expression data.

Assessing phytoplankton structure and spatio-temporal dynamics in a freshwater ecosystem using a powerful multiway statistical analysis.

Phytoplankton dynamics and diversity are particularly difficult to analyze, especially when (i) the scale of the analysis is situated at the species level, (ii) such a diversity is high, (iii) the study covers several seasons, and (iv) sampling has been performed at many stations of the ecosystem. Fortunately, some powerful statistical methods have been developed with which each species identified can be considered in detailed spatio-temporal analyses. The Partial Triadic Analysis, a method issued from the STATIS family, was applied on a dataset corresponding to 6 stations of the largest French reservoir (Reservoir Marne) sampled 22 times over two years (2006-2007) between March and September. Three key sampling periods that were consistent with those exhibited with the Plankton Ecology Group model (i.e. early spring, late spring-early summer, late summer-early autumn) were unambiguously recognized, with some specific species associated with each of them. Furthermore, a potential reference sampling station was identified among all stations investigated, an information very relevant to both scientists and water managers. It remains that 3 other stations could also be monitored, regularly or from time to time, because of specific phytoplankton characteristics.