Leafflower plant/leafflower moth brood pollination mutualisms are widespread in the Paleotropics. Leafflower moths pollinate leafflower plants, but their larvae consume a subset of the hosts' seeds. These interactions are highly phylogenetically constrained: six clades of leafflower plants are each associated with a unique clade of leafflower moths (Epicephala). Here, we report a previously unrecognized basal seventh pollinating Epicephala lineageassociated with the highly derived leafflower clade Glochidionin Asia. Epicephala lanceolaria is a pollinator and seed predator of Glochidion lanceolarium. Phylogenetic inference indicates that the ancestor of E. lanceolaria most likely shifted onto the ancestor of G. lanceolarium and displaced the ancestral allospecific Epicephala pollinator in at least some host populations. The unusual and apparently coadapted aspects of the G. lanceolarium/E. lanceolaria reproductive cycles suggest that plant-pollinator coevolution may have played a role in this displacement and provide insights into the dynamics of host shifts and trait coevolution in this specialized mutualism.

Les interfaces cerveau-machine (ICM) permettent de contrôler une machine directement à partir de l'activité cérébrale. Le P300-Speller, en particulier, pourrait offrir à des patients complètement paralysés, la possibilité de communiquer sans l'aide de la parole ou du geste. Nous avons cherché à améliorer cette communication en étudiant la coadaptation entre cerveau et machine. Nous avons d'abord montré que l'adaptation d'un utilisateur peut être partiellement perçue, en temps-réel, à travers les modulations de sa réponse électrophysiologique aux feedbacks de la machine. Nous avons ensuite proposé, testé et évalué les effets sur l'utilisateur de plusieurs approches permettant d'améliorer l'interaction, notamment : la correction automatique des erreurs, grâce à la reconnaissance en temps-réel des réponses aux feedbacks ; une stimulation dynamique permettant de diminuer le risque d'erreur tout en réduisant l'inconfort lié aux stimulations ; un processus automatique de décision adaptative, en fonction de l'état de vigilance du sujet. Nos résultats montrent la présence de réponses aux feedbacks spécifiques des erreurs et modulées par l'attention ainsi que par la surprise du sujet face au résultat de l'interaction. Par ailleurs, si l'efficacité de la correction automatique est variable d'un sujet à l'autre, le nouveau mode de stimulation comme la décision adaptative apparaissent comme très avantageux et leur utilisation a un effet positif sur la motivation. Dans la perspective d'études cliniques pour évaluer l'utilité des ICM pour la communication, ces travaux soulignent et quantifient l'intérêt de développer des interfaces capables de s'adapter à chaque utilisateur / Brain-computer interfaces (BCI) aim at enabling the brain to directly control an artificial device. In particular, the P300-Speller could offer patients who cannot speak and neither move, to communicate again. This work consisted in improving this communication by implementing and studying a coadaptation between the brain and the machine. First, on the user side, we showed that adaptation is reflected in real-time by modulations of the electrophysiological responses to the feedbacks from the machine. Then, on the computer side, we proposed, tested and evaluated the effect on the user, of several approaches that endow the machine with adaptive behavior, namely: Automatic correction of errors, based on real-time recognition of feedback responses; Dynamic stimulation to increase spelling accuracy as well as to reduce the discomfort associated with the traditional row/column stimulation paradigm; Adaptive decision making for optimal stopping, depending on the attentional state of the user. Our results show the presence of feedback responses which are error specific and modulated by attention as well as user's surprise with respect to the outcome of the interaction. Besides, while the interest of automatic correction is highly subject-dependant, the new stimulation mode and the adaptive decision method proved clearly beneficial and their use had a significant positive impact on subject's motivation. In the perspective of clinical studies to assess the usefulness of ICM for communication, this work highlights and quantifies the importance of developing adaptive interfaces that are tailored to each every individual

Brain-computer interfaces (BCIs) create a new form of communication and control for humans by translating brain activity directly into actions performed by a computer. This new field of research, best known for its breakthroughs in enabling fully paralyzed or locked-in patients to communicate and control simple devices, has resulted in a variety of remarkable technological developments. However, the field is still in its infancy, and facilitating control of a computer application via thought in a broader context involves a number of a challenges that have not yet been met. Advancing BCIs beyond the experimental phase continues to be a struggle. End-users have rarely been reached, except for in the case of a few highly specialized applications which require continual involvement of BCI experts. While these applications are profoundly beneficial for the patients they serve, the potential for BCIs is much broader in scope and powerful in effect. Unfortunately, the current approaches to brain-computer interfacing research have not been able to address the primary limitations in the field: the poor reliability of most BCIs and the highly variable performance across individuals. In addition to this, the modes of control available to users tend to be restrictive and unintuitive (\emph{e.g.}, imagining complex motor activities to answer ``Yes" or ``No" questions). This thesis presents a novel approach that addresses both of these limitations simultaneously. Brain-computer interfacing is currently viewed primarily as a machine learning problem, wherein the computer must learn the patterns of brain activity associated with a user's mental commands. In order to simplify this problem, researchers often restrict mental commands to those which are well characterized and easily distinguishable based on \emph{a priori} knowledge about their corresponding neural correlates. However, this approach does not fully recognize two properties of a BCI which makes it unique to other human-computer interfaces. First, individuals can vary widely with respect to the patterns of activation associated with how their brains generate similar mental activity and with respect to which kinds of mental activity have been most trained due to life experience. Thus, it is not surprising that BCIs based on predefined neural correlates perform inconsistently for different users. Second, for a BCI to perform well, the human and the computer must become a cohesive unit such that the computer can adapt as the user's brain naturally changes over time and while the user learns to make their mental commands more consistent and distinguishable given feedback from the computer. This not only implies that BCI use is a skill that must be developed, honed, and maintained in relation to the computer's algorithms, but that the human is the fundamental component of the system in a way that makes human learning just as important as machine learning. In this thesis it is proposed that, in the long term, a generalized BCI that can discover the appropriate neural correlates of individualized mental commands is preferable to the traditional approach. Generalization across mental strategies allows each individual to make better use of their own experience and cognitive abilities in order to interact with BCIs in a more free and intuitive way. It is further argued that in addition to generalization, it is necessary to develop improved training protocols respecting the potential of the user to learn to effectively modulate their own brain activity for BCI use. It is shown through a series of studies exploring generalized BCI methods, the influence of prior non-BCI training on BCI performance, and novel methods for training individuals to control their own brain activity, that this new approach based on balancing the roles of the user and the computer according to their respective capabilities is a promising avenue for advancing brain-computer interfacing towards a broader array of applications usable by the general population. / Thesis / Doctor of Philosophy (PhD)

Affordability of products and services is an economic benefit that should accrue to consumers, whether they are corporations, government agencies or individuals. This concept of affordability goes beyond conventional wisdom that considers affordability as the ability to pay the price of a product or service. This dissertation defines and explores a broader concept of affordability – one of fitness to perform at the level of quality required by the consumer, to perform at that level whenever the product or service is used, and to do so with minimum consumption of resources. This concept of affordability is applied to technological systems by using the complexity sciences concept of fitness as the metaphor for technological systems' fitness. During a system design evolution, the specific design outcome is determined by that set of design search paths followed – it is path dependent. Dynamic mechanisms create, dictate and maintain path dependence. Initial conditions define the start and direction of a path. During subsequent design steps, positive feedback influences the designer to continue on that path. This dissertation describes underlying mechanisms that create, dictate and maintain path dependence; discusses the effects of path dependence on system design and system affordability; models these effects using system dynamics modeling; and suggests actions to address its effects. This dissertation also addresses several types of fitness landscapes, and suggests that the Data Envelopment Analysis (DEA) solution space is a form of fitness landscape suitable for evaluating the efficiency, and thus the fitness, of research and development (R&D) projects. It describes the use of DEA to evaluate and select Department of Defense (D0D) R&D projects as a new application of DEA. / Ph. D.

Les interfaces cerveau-machine (ICM) permettent de contrôler une machine directement à partir de l'activité cérébrale. Le P300-Speller, en particulier, pourrait offrir à des patients complètement paralysés, la possibilité de communiquer sans l'aide de la parole ou du geste. Nous avons cherché à améliorer cette communication en étudiant la coadaptation entre cerveau et machine. Nous avons d'abord montré que l'adaptation d'un utilisateur peut être partiellement perçue, en temps-réel, à travers les modulations de sa réponse électrophysiologique aux feedbacks de la machine. Nous avons ensuite proposé, testé et évalué les effets sur l'utilisateur de plusieurs approches permettant d'améliorer l'interaction, notamment : - la correction automatique des erreurs, grâce à la reconnaissance en temps-réel des réponses aux feedbacks ; - une stimulation dynamique permettant de diminuer le risque d'erreur tout en réduisant l'inconfort lié aux stimulations ; - un processus automatique de décision adaptative, en fonction de l'état de vigilance du sujet. Nos résultats montrent la présence de réponses aux feedbacks spécifiques des erreurs et modulées par l'attention ainsi que par la surprise du sujet face au résultat de l'interaction. Par ailleurs, si l'efficacité de la correction automatique est variable d'un sujet à l'autre, le nouveau mode de stimulation comme la décision adaptative apparaissent comme très avantageux et leur utilisation a un effet positif sur la motivation. Dans la perspective d'études cliniques pour évaluer l'utilité des ICM pour la communication, ces travaux soulignent et quantifient l'intérêt de développer des interfaces capables de s'adapter à chaque utilisateur.

The cytochrome bc1 complex of the mitochondrial electron transport chain accomplishes the enzymatic reaction known as the modified Q-cycle. In the Q-cycle the bc1 complex transports protons from the matrix to the intermembrane space of the mitochondria, creating the proton gradient used to make ATP. The energy to move these protons is obtained by shuttling electrons from the coenzyme ubiquinol (QH2) to coenzyme ubiquinone (Q) and the mobile cytochrome c. This well studied complex is ideal for examining molecular adaptation because it consists of ten different subunits, it functions as a dimer, and it includes at least five different active sites. The program TreeSAAP was used to characterize molecular adaptation in the bc1 complex and identify specific amino acid sites that experienced positive destabilizing (radical) selection. Using this information and three-dimensional structures of the protein complex, selection was characterized in terms of coevolution and coadaptation. Coevolution is described as reciprocal local biochemical shifts based on phylogenetic location and results in overall maintenance. Coadaptation, on the other hand, is more dynamic and is described as coordinated local biochemical shifts based on phylogenetic location which results in overall adaptation. In this study both coevolution and coadaptation were identified in various locations on the protein complex near the active sites. Sites in the pore region of cyt c1 were shown to exhibit coevolution, in other words maintenance, of many biochemical properties, whereas sites on helix H of cyt b, which flanks the active sites Qo and Qi, were shown to exhibit coadaptation, in other words coordinated shifts in the specific properties equilibrium constant and solvent accessible reduction ratio. Also, different domains of the protein exhibited significant shifts in drastically different amino acid properties: the protein imbedded in the membrane demonstrated shifts in mainly functional properties, while the part of the complex in the intermembrane space demonstrated shifts in conformational, structural, and energetic properties.