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Cognitive deficits are a well-established feature of bipolar disorders (BD), even during periods of euthymia, but risk factors associated with cognitive deficits in euthymic BD are still poorly understood. We aimed to validate classification criteria for the identification of clinically significant cognitive impairment, based on psychometric properties, to estimate the prevalence of neuropsychological deficits in euthymic BD, and identify risk factors for cognitive deficits using a multivariate approach.
We investigated neuropsychological performance in 476 euthymic patients with BD recruited via the French network of BD expert centres. We used a battery of tests, assessing five domains of cognition. Five criteria for the identification of neuropsychological impairment were tested based on their convergent and concurrent validity. Uni- and multivariate logistic regressions between cognitive impairment and several clinical and demographic variables were performed to identify risk factors for neuropsychological impairment in BD.
One cut-off had satisfactory psychometric properties and yielded a prevalence of 12.4% for cognitive deficits in euthymic BD. Antipsychotics use were associated with the presence of a cognitive deficit.
This is the first study to validate a criterion for clinically significant cognitive impairment in BD. We report a lower prevalence of cognitive impairment than previous studies, which may have overestimated its prevalence. Patients with euthymic BD and cognitive impairment may benefit from cognitive remediation.
The relationship between residual depressive symptoms, cognition and functioning in patients with euthymic bipolar disorder is a subject of debate.
To assess whether cognition mediates the association between residual depressive symptoms and functioning in patients with bipolar disorder who were euthymic.
We included 241 adults with euthymic bipolar disorder in a multicentre cross-sectional study. We used a battery of tests to assess six cognition domains. A path analysis was then used to perform a mediation analysis of the relationship between residual depressive symptoms, cognitive components and functioning.
Only verbal and working memory were significantly associated with better functioning. Residual depressive symptoms were associated with poorer functioning. No significant relationship was found between residual depressive symptoms and any cognitive component.
Cognition and residual depressive symptoms appear to be two independent sources of variation in the functioning of people with euthymic bipolar disorder.
Catherine Esnouf, Institut National de la Recherche Agronomique (INRA), Paris,Marie Russel, Institut National de la Recherche Agronomique (INRA), Paris,Nicolas Bricas, Centre de Co-opération Internationale en Recherche Agronomique pour le Développement (CIRAD), Paris
The duALIne project chose to examine the methods used to assess food sustainability in a chapter of its own, separate from the sectorial approaches presented previously, so that this examination could be as open as possible. This chapter focuses in particular on the specific issues posed by food vis-à-vis the methods currently used to measure sustainability. Under this approach, this chapter looks firstly at the complexity of food systems, then how the associated challenges of sustainability could be structured and finally presents some methods and indicators and the research questions they raise.
Measuring performance has become a widespread activity in modern societies. It is the benchmark by which political and economic choices are regularly backed and/or justified. Performance indicators, whatever their objective, have seen exponential development, as have the operators who construct them. Assessing the performance of food systems through the prism of sustainable development is still a recent concern that requires in-depth reflection, both in terms of its scope and of the issue(s) to be assessed on the one hand, and regarding the choices of the sustainable challenges targeted or the assessment methods to be used on the other.
The brain functions as an integrated multi-networked organ. Complex neurocognitive functions are not attributed to a single brain area but depend on the dynamic interactions of distributed brain areas operating in large-scale networks. This is especially important in the field of neurosurgery where intervention within a spatially localized area may indirectly lead to unwanted effects on distant areas. As part of a preliminary integrated work on functional connectivity, we present our initial work on diffusion tensor imaging tractography to produce in vivo white matter tracts dissection.
Diffusion weighted data and high-resolution T1-weighted images were acquired from a healthy right-handed volunteer (25 years old) on a whole-body 3 T scanner. Two approaches were used to dissect the tractography results: 1) a standard region of interest technique and 2) the use of Brodmann's area as seeding points, which represents an innovation in terms of seeds initiation.
Results are presented as tri-dimensional tractography images. The uncinate fasciculus, the inferior longitudinal fasciculus, the inferior fronto-occipital fasiculus, the corticospinal tract, the corpus callosum, the cingulum, and the optic radiations where studied by conventional seeding approach, while Broca's and Wernicke's areas, the primary motor as well as the primary visual cortices were used as seeding areas in the second approach.
We report state-of-the-art tractography results of some of the major white matter bundles in a normal subject using DTI. Moreover, we used Brodmann's area as seeding areas for fiber tracts to study the connectivity of known major functional cortical areas.
Progress on separating the long-lived fission products has notably implied basic research on specific host matrices, especially for the immobilization of cesium. Barium hollandite (BaAl2Ti6O16) ceramics have received considerable interest because of their high cesium incorporation ability and chemical stability. This study deals with the preparation of hollandite in the BaxCsy(Al,Fe)2x+yTi8–2x-yO16 (x+y<2) compositional range by an oxide route. Different parameters such as the grain size of the precursor or the temperature and duration of sintering were changed in order to optimize ceramics synthesis. To estimate the hollandite radiation resistance, external electron irradiation experiments (simulating the β particles emitted by radioactive cesium) were performed on hollandite of simple composition. The irradiation-induced defects were studied by Electron Paramagnetic Resonance (EPR) spectroscopy and their nature is discussed.
Initiatives for the sustainable development of aquaculture have so far focused on the
production of codes of conduct, of best management practices, of standards etc., most of
which have been developed by international organisations, the industrial sector and non
governmental organisations. They were, to a large extent, produced using a “top
down” process and inspired by models from intensive industrial shrimp and sea fish farming
(mainly salmon). However, most of global aquaculture production comes from small- and
medium-sized farms, essentially in Asia which contributes 92% of the total world
aquaculture production volume. The objective of this article is to define the contours of
systemic typologies that are able to express the sustainability conditions of aquaculture
systems. The proposed approach builds on surveys of aquaculture systems which differ in
terms of their biogeographical nature (temperate/tropical and north/south countries) or
their farming techniques and their governance systems. This work is a prerequisite to any
attempt at an individualised and comparative evaluation of specific aquaculture systems
from either global or territorial viewpoints. In order to go beyond the cleavage of a
typology based on the differentiation between developed and developing countries, three
typologies were produced. These typologies allow for discriminatory variables to be
identified such as for example the marketing methods or the pace of innovation: a
structural typology, a functional typology and a systemic typology. Finally, the
representations of aquaculture activity and of its sustainability that producers have of
the 4 different types that emerge from the systemic typology were recorded and
Heretofore, we learned that bilinguals better detected letters in inter-lingual homographs when the context language ascribed a content role to the homograph as compared to a function role. In previous work the target homographs appeared in passages that were of a single language. The present work investigated whether this letter detection pattern would hold if both languages were activated by intermixing languages in a passage. Results suggested that despite intermixing of languages that would excite competing function and content meanings, local sentence context was sufficient to engender a content over function word advantage for inter-lingual homographs that was reminiscent of that obtained with homogenous text.
Tests of inter-lingual homographs that have different meanings across two languages support models postulating initial non-selective access to competing language representations, e.g. Bilingual Interactive Activation (BIA) model. Most such research assessed inter-lingual homographs in the absence of connected text. Here a letter detection paradigm was used that required subjects to detect letters in words in connected text. Prior work with this paradigm suggested that readers respond to only one interpretation of an intra-lingual homograph when detecting letters. Three experiments described here indicate that letter detection patterns to inter-lingual homographs are similar, i.e. detection reflects only a context appropriate interpretation. However, the demonstration that text role, text cohesiveness and bilingual fluency affect inter-lingual letter detection (Experiments 1 and 2), and that word role affects detection even though target frequency is constant across inter-lingual meanings (Experiment 3) indicates that selectivity is in response to post-lexical processes. Thus, results are seen as compatible with tenets of the BIA model.
We investigate in this chapter the case of linear neural networks, named associative memories by T. Kohonen (Figure 3.1). We begin by specializing the heavy algorithm we have studied in the general case of adaptive systems to the case of neural networks, where controls are matrices. It shows how to modify the last synaptic matrix that has learned a set of patterns for learning a new pattern without forgetting the previous patterns.
Because right-inverses of tensor products are tensor products of right-inverses, we observe that the heavy algorithm has a Hebbian character: The heavy algorithm states that the correction of a synaptic matrix during learning is the product of activities in both presynaptic and postsynaptic neurons. This added feature that plain vectors do not enjoy justifies the specifics of systems controlled by matrices instead of vectors.
We then proceed with associative memories with postprocessing, with multilayer and continuous-layer associative memories. We conclude this chapter with associative memories with gates, where the synaptic matrices link conjuncts (i.e., subsets) of presynaptic neurons with each postsynaptic neuron. They allow computation of any Boolean function. They require a short presentation of fuzzy sets.
We present in this appendix the tests of the external and internal algorithms conducted by Nicolas Seube at Thomson-SINTRA to control the tracking of an exosystem by an autonomous underwater vehicle (AUV). This system has three degrees of freedom (planar motion), six state variables (positions, heading, and their derivatives), and three controls (thruster forces). The dynamics of an AUV are highly nonlinear, coupled, and sometimes fully interacting, thus making it difficult to control by the usual methods. Moreover, the dynamics are poorly known, because only approximate hydrodynamic models are available for realworld vehicles. Finally, we need to involve the marine currents that can significantly perturb the dynamics of the AUV.
In addition, the problem of controlling an AUV cannot be linearized about a single velocity axis because all vehicle velocities usually have the same range; conventional linear control techniques clearly are unable to provide adequate performance by the control systems.
We shall present three different learning rules that address the problems of uniform minimization and adaptive learning by a set-valued feedback control map. The three classes of algorithms presented here have been tested in the case of the Japanese Dolphin AUV.
In particular, it is shown that the gradient step size is critical for the external rule, but is not critical for the uniform external algorithm. The latter could also be applied to pattern-classification problems, and may provide a plausible alternative method to stochastic gradient algorithms.
The projection theorem allows construction of the orthogonal right-inverse of a linear surjective operator A, associating with any datum y the solution x to the equation Ax = y with minimal norm. In the same way, it allows construction of the orthogonal left-inverse of a linear injective operator A, associating with any datum y the solution x to the equation Ax = ȳ, where ȳ is the orthogonal projection of y onto the image of A. More generally, when A is any linear operator between finite-dimensional vector spaces, the pseudoinverse of A associates with any datum y the solution x (with minimal norm) to the equation Ax = ȳ, where ȳ is the orthogonal projection of y onto the image of A.
These definitions show how useful the concept of the pseudoinverse is in many situations. It is used explicitly or implicitly in many domains of statistics and data analysis. It is then quite natural that the pseudoinverse plays an important role in the use of adaptive systems in learning algorithms of patterns.
This is what we do to construct the heavy algorithm for adaptive systems that are affine with respect to the controls. Because we are looking for synaptic matrices when we deal with neural networks, we have to make a short pause to study the structure of the space of linear operators, of its dual, and of a tensor product of linear operators.
This book is devoted to some mathematical methods that arise in two domains of artificial intelligence: neural networks and qualitative physics. Professor Aubin makes use of control and viability theory in neural networks and cognitive systems, regarded as dynamical systems controlled by synaptic matrices, and set-valued analysis that plays a natural and crucial role in qualitative analysis and simulation. This allows many examples of neural networks to be presented in a unified way. In addition, several results on the control of linear and nonlinear systems are used to obtain a 'learning algorithm' of pattern classification problems, such as the back-propagation formula, as well as learning algorithms of feedback regulation laws of solutions to control systems subject to state constraints. This book will be of value to anyone with an interest in neural networks and cognitive systems.