University of Wisconsin-Madison

About: University of Wisconsin-Madison is a education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 108707 authors who have published 237594 publications receiving 11883575 citations.

Genome sequence and analysis of the tuber crop potato.

Abstract: Potato (Solanum tuberosum L.) is the world's most important non-grain food crop and is central to global food security. It is clonally propagated, highly heterozygous, autotetraploid, and suffers acute inbreeding depression. Here we use a homozygous doubled-monoploid potato clone to sequence and assemble 86% of the 844-megabase genome. We predict 39,031 protein-coding genes and present evidence for at least two genome duplication events indicative of a palaeopolyploid origin. As the first genome sequence of an asterid, the potato genome reveals 2,642 genes specific to this large angiosperm clade. We also sequenced a heterozygous diploid clone and show that gene presence/absence variants and other potentially deleterious mutations occur frequently and are a likely cause of inbreeding depression. Gene family expansion, tissue-specific expression and recruitment of genes to new pathways contributed to the evolution of tuber development. The potato genome sequence provides a platform for genetic improvement of this vital crop.

Individual-patient monitoring in clinical practice: are available health status surveys adequate?

Abstract: Interest has increased in recent years in incorporating health status measures into clinical practice for use at the individual-patient level. We propose six measurement standards for individual-patient applications: (1) practical features, (2) breadth of health measured, (3) depth of health measured, (4) precision for cross-sectional assessment, (5) precision for longitudinal monitoring and (6) validity. We evaluate five health status surveys (Functional Status Questionnaire, Dartmouth COOP Poster Charts, Nottingham Health Profile, Duke Health Profile, and SF-36 Health Survey) that have been proposed for use in clinical practice. We conducted an analytical literature review to evaluate the six measurement standards for individual-patient applications across the five surveys. The most problematic feature of the five surveys was their lack of precision for individual-patient applications. Across all scales, reliability standards for individual assessment and monitoring were not satisfied, and the 95% Cls were very wide. There was little evidence of the validity of the five surveys for screening, diagnosing, or monitoring individual patients. The health status surveys examined in this paper may not be suitable for monitoring the health and treatment status of individual patients. Clinical usefulness of existing measures might be demonstrated as clinical experience is broadened. At this time, however, it seems that new instruments, or adaptation of existing measures and scaling methods, are needed for individual-patient assessment and monitoring.

Addiction motivation reformulated: an affective processing model of negative reinforcement.

Abstract: This article offers a reformulation of the negative reinforcement model of drug addiction and proposes that the escape and avoidance of negative affect is the prepotent motive for addictive drug use. The authors posit that negative affect is the motivational core of the withdrawal syndrome and argue that, through repeated cycles of drug use and withdrawal, addicted organisms learn to detect interoceptive cues of negative affect preconsciously. Thus, the motivational basis of much drug use is opaque and tends not to reflect cognitive control. When either stressors or abstinence causes negative affect to grow and enter consciousness, increasing negative affect biases information processing in ways that promote renewed drug administration. After explicating their model, the authors address previous critiques of negative reinforcement models in light of their reformulation and review predictions generated by their model.

The Limitations of Deep Learning in Adversarial Settings

Abstract: Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to misclassify. In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs. In an application to computer vision, we show that our algorithms can reliably produce samples correctly classified by human subjects but misclassified in specific targets by a DNN with a 97% adversarial success rate while only modifying on average 4.02% of the input features per sample. We then evaluate the vulnerability of different sample classes to adversarial perturbations by defining a hardness measure. Finally, we describe preliminary work outlining defenses against adversarial samples by defining a predictive measure of distance between a benign input and a target classification.