871 resultados para Information in biology


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In this report we analyze the Topic 5 report’s recommendations for reducing nitrogen losses to the Gulf of Mexico (Mitsch et al. 1999). We indicate the relative costs and cost-effectiveness of different control measures, and potential benefits within the Mississippi River Basin. For major nonpoint sources, such as agriculture, we examine both national and basin costs and benefits. Based on the Topic 2 economic analysis (Diaz and Solow 1999), the direct measurable dollar benefits to Gulf fisheries of reducing nitrogen loads from the Mississippi River Basin are very limited at best. Although restoring the ecological communities in the Gulf may be significant over the long term, we do not currently have information available to estimate the benefits of such measures to restore the Gulf’s long-term health. For these reasons, we assume that measures to reduce nitrogen losses to the Gulf will ultimately prove beneficial, and we concentrate on analyzing the cost-effectiveness of alternative reduction strategies. We recognize that important public decisions are seldom made on the basis of strict benefit–cost analysis, especially when complete benefits cannot be estimated. We look at different approaches and different levels of these approaches to identify those that are cost-effective and those that have limited undesirable secondary effects, such as reduced exports, which may result in lost market share. We concentrate on the measures highlighted in the Topic 5 report, and also are guided by the source identification information in the Topic 3 report (Goolsby et al. 1999). Nonpoint sources that are responsible for the bulk of the nitrogen receive most of our attention. We consider restrictions on nitrogen fertilizer levels, and restoration of wetlands and riparian buffers for denitrification. We also examine giving more emphasis to nitrogen control in regions contributing a greater share of the nitrogen load.

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This paper is concerned with the role of information in the servitization of manufacturing which has led to “the innovation of an organisation’s capabilities and processes as equipment manufacturers seek to offer services around their products” (Neely 2009, Baines et al 2009). This evolution has resulted in an information requirement (IR) shift as companies move from discrete provision of equipment and spare parts to long-term service contracts guaranteeing prescribed performance levels. Organisations providing such services depend on a very high level of availability and quality of information throughout the service life-cycle (Menor et al 2002). This work focuses on whether, for a proposed contract based around complex equipment, the Information System is capable of providing information at an acceptable quality and requires the IRs to be examined in a formal manner. We apply a service information framework (Cuthbert et al 2008, McFarlane & Cuthbert 2012) to methodically assess IRs for different contract types to understand the information gap between them. Results from case examples indicate that this gap includes information required for the different contract types and a set of contract-specific IRs. Furthermore, the control, ownership and use of information differs across contract types as the boundary of operation and responsibility changes.

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Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.

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Acoustic communication in drosophilid flies is based on the production and perception of courtship songs, which facilitate mating. Despite decades of research on courtship songs and behavior in Drosophila, central auditory responses have remained uncharacterized. In this study, we report on intracellular recordings from central neurons that innervate the Drosophila antennal mechanosensory and motor center (AMMC), the first relay for auditory information in the fly brain. These neurons produce graded-potential (nonspiking) responses to sound; we compare recordings from AMMC neurons to extracellular recordings of the receptor neuron population [Johnston's organ neurons (JONs)]. We discover that, while steady-state response profiles for tonal and broadband stimuli are significantly transformed between the JON population in the antenna and AMMC neurons in the brain, transient responses to pulses present in natural stimuli (courtship song) are not. For pulse stimuli in particular, AMMC neurons simply low-pass filter the receptor population response, thus preserving low-frequency temporal features (such as the spacing of song pulses) for analysis by postsynaptic neurons. We also compare responses in two closely related Drosophila species, Drosophila melanogaster and Drosophila simulans, and find that pulse song responses are largely similar, despite differences in the spectral content of their songs. Our recordings inform how downstream circuits may read out behaviorally relevant information from central neurons in the AMMC.

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Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D - F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation. © 2012 IEEE.

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People are alarmingly susceptible to manipulations that change both their expectations and experience of the value of goods. Recent studies in behavioral economics suggest such variability reflects more than mere caprice. People commonly judge options and prices in relative terms, rather than absolutely, and display strong sensitivity to exemplar and price anchors. We propose that these findings elucidate important principles about reward processing in the brain. In particular, relative valuation may be a natural consequence of adaptive coding of neuronal firing to optimise sensitivity across large ranges of value. Furthermore, the initial apparent arbitrariness of value may reflect the brains' attempts to optimally integrate diverse sources of value-relevant information in the face of perceived uncertainty. Recent findings in neuroscience support both accounts, and implicate regions in the orbitofrontal cortex, striatum, and ventromedial prefrontal cortex in the construction of value.

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Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

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The task of word-level confidence estimation (CE) for automatic speech recognition (ASR) systems stands to benefit from the combination of suitably defined input features from multiple information sources. However, the information sources of interest may not necessarily operate at the same level of granularity as the underlying ASR system. The research described here builds on previous work on confidence estimation for ASR systems using features extracted from word-level recognition lattices, by incorporating information at the sub-word level. Furthermore, the use of Conditional Random Fields (CRFs) with hidden states is investigated as a technique to combine information for word-level CE. Performance improvements are shown using the sub-word-level information in linear-chain CRFs with appropriately engineered feature functions, as well as when applying the hidden-state CRF model at the word level.