8 resultados para H-2 PRODUCTION

em Boston University Digital Common


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Background Chronic illness and premature mortality from malaria, water-borne diseases, and respiratory illnesses have long been known to diminish the welfare of individuals and households in developing countries. Previous research has also shown that chronic diseases among farming populations suppress labor productivity and agricultural output. As the illness and death toll from HIV/AIDS continues to climb in most of sub-Saharan Africa, concern has arisen that the loss of household labor it causes will reduce crop yields, impoverish farming households, intensify malnutrition, and suppress growth in the agricultural sector. If chronic morbidity and premature mortality among individuals in farming households have substantial impacts on household production, and if a large number of households are affected, it is possible that an increase in morbidity and mortality from HIV/AIDS or other diseases could affect national aggregate output and exports. If, on the other hand, the impact at the household farm level is modest, or if relatively few households are affected, there is likely to be little effect on aggregate production across an entire country. Which of these outcomes is more likely in West Africa is unknown. Little rigorous, quantitative research has been published on the impacts of AIDS on smallholder farm production, particularly in West Africa. The handful of studies that have been conducted have looked mainly at small populations in areas of very high HIV prevalence in southern and eastern Africa. Conclusions about how HIV/AIDS, and other causes of chronic morbidity and mortality, are affecting agriculture across the continent cannot be drawn from these studies. In view of the importance of agriculture, and particularly smallholder agriculture, in the economies of most African countries and the scarcity of resources for health interventions, it is valuable to identify, describe, and quantify the impact of chronic morbidity and mortality on smallholder production of important crops in West Africa. One such crop is cocoa. In Ghana, cocoa is a crop of national importance that is produced almost exclusively by smallholder households. In 2003, Ghana was the world’s second-largest producer of cocoa. Cocoa accounted for a quarter of Ghana’s export revenues that year and generated 15 percent of employment. The success and growth of the cocoa industry is thus vital to the country’s overall social and economic development. Study Objectives and Methods In February and March 2005, the Center for International Health and Development of Boston University (CIHD) and the Department of Agricultural Economics and Agribusiness (DAEA) of the University of Ghana, with financial support from the Africa Bureau of the U.S. Agency for International Development and from Mars, Inc., which is a major purchaser of West African cocoa, conducted a survey of a random sample of cocoa farming households in the Western Region of Ghana. The survey documented the extent of chronic morbidity and mortality in cocoa growing households in the Western Region of Ghana, the country’s largest cocoa growing region, and analyzed the impact of morbidity and mortality on cocoa production. It aimed to answer three specific research questions. (1) What is the baseline status of the study population in terms of household size and composition, acute and chronic morbidity, recent mortality, and cocoa production? (2) What is the relationship between household size and cocoa production, and how can this relationship be used to understand the impact of adult mortality and chronic morbidity on the production of cocoa at the household level? The study population was the approximately 42,000 cocoa farming households in the southern part of Ghana’s Western Region. A random sample of households was selected from a roster of eligible households developed from existing administrative information. Under the supervision of the University of Ghana field team, enumerators were graduate students of the Department of Agricultural Economics and Agribusiness or employees of the Cocoa Services Division. A total of 632 eligible farmers participated in the survey. Of these, 610 provided complete responses to all questions needed to complete the multivariate statistical analysis reported here.

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A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.

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This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production with and without jaw perturbations.

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This article describes the VITEWRITE model for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in the outflow command to a given synergy occurs. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. Each synergy exhibits a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

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This paper describes a model of speech production called DIVA that highlights issues of self-organization and motor equivalent production of phonological units. The model uses a circular reaction strategy to learn two mappings between three levels of representation. Data on the plasticity of phonemic perceptual boundaries motivates a learned mapping between phoneme representations and vocal tract variables. A second mapping between vocal tract variables and articulator movements is also learned. To achieve the flexible control made possible by the redundancy of this mapping, desired directions in vocal tract configuration space are mapped into articulator velocity commands. Because each vocal tract direction cell learns to activate several articulator velocities during babbling, the model provides a natural account of the formation of coordinative structures. Model simulations show automatic compensation for unexpected constraints despite no previous experience or learning under these constraints.

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This article describes a neural network model that addresses the acquisition of speaking skills by infants and subsequent motor equivalent production of speech sounds. The model learns two mappings during a babbling phase. A phonetic-to-orosensory mapping specifies a vocal tract target for each speech sound; these targets take the form of convex regions in orosensory coordinates defining the shape of the vocal tract. The babbling process wherein these convex region targets are formed explains how an infant can learn phoneme-specific and language-specific limits on acceptable variability of articulator movements. The model also learns an orosensory-to-articulatory mapping wherein cells coding desired movement directions in orosensory space learn articulator movements that achieve these orosensory movement directions. The resulting mapping provides a natural explanation for the formation of coordinative structures. This mapping also makes efficient use of redundancy in the articulator system, thereby providing the model with motor equivalent capabilities. Simulations verify the model's ability to compensate for constraints or perturbations applied to the articulators automatically and without new learning and to explain contextual variability seen in human speech production.

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Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309)

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This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.