Stanford University

About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.

How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions.

Abstract: The secretion of glucocorticoids (GCs) is a classic endocrine response to stress. Despite that, it remains controversial as to what purpose GCs serve at such times. One view, stretching back to the time of Hans Selye, posits that GCs help mediate the ongoing or pending stress response, either via basal levels of GCs permitting other facets of the stress response to emerge efficaciously, and/or by stress levels of GCs actively stimulating the stress response. In contrast, a revisionist viewpoint posits that GCs suppress the stress response, preventing it from being pathologically overactivated. In this review, we consider recent findings regarding GC action and, based on them, generate criteria for determining whether a particular GC action permits, stimulates, or suppresses an ongoing stressresponse or, as an additional category, is preparative for a subsequent stressor. We apply these GC actions to the realms of cardiovascular function, fluid volume and hemorrhage, immunity and inflammation, metabolism, neurobiology, and reproductive physiology. We find that GC actions fall into markedly different categories, depending on the physiological endpoint in question, with evidence for mediating effects in some cases, and suppressive or preparative in others. We then attempt to assimilate these heterogeneous GC actions into a physiological whole. (Endocrine Reviews 21: 55‐ 89, 2000)

A Behavioral Theory of the Firm

Abstract: Provides a theory of decision making within business organizations. Contrary to the economic theory of the firm, which sees firms as profit-maximizing entities, the authors advocate a theory based on empirical observation of actual firm decision-making. Various features of firm decision-making are identified. First, firms are coalitions of participants whose individual goals may and often do conflict. How this conflict is resolved is determined by the firm's bargaining process. This process is constrained by past behavior and decisions. Second, the authors reject the notion that firms are one-dimensional profit-maximizers in favor of a view of firms as entities with many different objectives that will accept suboptimal outcomes if they are above a minimum level. Congruent with this, a firm's search activity occurs in response to a perceived problem and is limited in scope. The effect is that firm policies will change only incrementally. Also impeding radical policy change is the fact that firms react to uncertainty using standardized decision rules. Using this theory, two computer models of business decision-making are presented and compared with actual results. These models are shown to have especially good predictive power. (CAR)

Sparse MRI: The application of compressed sensing for rapid MR imaging.

Abstract: The sparsity which is implicit in MR images is exploited to significantly undersample k -space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressedsensing, images with a sparse representation can be recovered from randomly undersampled k -space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the 1 norm of a transformed image, subject to data

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Abstract: We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.

Agricultural sustainability and intensive production practices

Abstract: A doubling in global food demand projected for the next 50 years poses huge challenges for the sustainability both of food production and of terrestrial and aquatic ecosystems and the services they provide to society. Agriculturalists are the principal managers of global useable lands and will shape, perhaps irreversibly, the surface of the Earth in the coming decades. New incentives and policies for ensuring the sustainability of agriculture and ecosystem services will be crucial if we are to meet the demands of improving yields without compromising environmental integrity or public health.