Seven focus groups at a university campus were formed to identify college men's health concerns, barriers to seeking help, and recommendations to help college men adopt healthier lifestyles. Content analysis was used to identify and organize primary patterns in the focus-group data. Results of the study revealed that the college men were aware that they had important health needs but took little action to address them. The participants identified both physical and emotional health concerns. Alcohol and substance abuse were rated as the most important issues for men. The greatest barrier to seeking services was the men's socialization to be independent and conceal vulnerability. The most frequently mentioned suggestions for helping men adopt healthier lifestyles were offering health classes, providing health information call-in service, and developing a men's center. Implications of the results are discussed.

v, A canine model simulating both cervical spondylosis and its results in delayed progressive myelopathy is presented. This model allowed control of compression, an ongoing assessment of neurological deficits, and evaluation using diagnostic images, frequent electrophysiological tests, local blood flow measurements, and postmortem histological examinations. Subclinical cervical cord compression was achieved in 14 dogs by placing a Teflon washer posteriorly and a Teflon screw anteriorly, producing an average of 29% stenosis of the spinal canal. Four dogs undergoing sham operations were designated as controls. Twelve of the animals undergoing compression developed delayed and progressive clinical signs of myelopathy, with a mean latent period to onset of myelopathy of 7 months.Spinal cord blood flow studies using the hydrogen clearance method showed a significant transient increase in blood flow immediately after compression and a decrease before sacrifice. Somatosensory evoked potential studies indicated progressive deterioration during the period of compression. Magnetic resonance images revealed intramedullary changes. Histological studies showed abnormalities overwhelmingly within the gray matter, including changes in vascular morphology, loss of large motor neurons, necrosis, and cavitation. Axonal degeneration and obvious demyelination were rarely seen. The most profound morphological changes occurred at the site of greatest compression. It is proposed that a momentary arrest of microcirculation occurs during extension of the neck because of loss of the reserve space in the compromised spinal canal. This microcirculatory disturbance is predominant in the watershed area of the cord and mainly affects the highly vulnerable anterior horn cells, leading to neuronal death, necrosis, and eventual cavitation at the junction of the dorsal and anterior horns. Additional supportive evidence of this hypothesis was derived from the literature. KEY WORDS 9 cervical spondylosis 9 cervical myelopathy 9 spinal cord compression 9 magnetic resonance imaging 9 somatosensory evoked potentials 9 dog

Language-modeling-based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.

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Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.