Detection of illicit drug residues from wastewater provides a new route toward community level assessment of drug abuse that is critical to public health. However, traditional chemistry analytical tools such as liquid chromatography in tandem with mass spectrometry cannot meet the large-scale testing requirement in terms of cost, throughput, and...
The purpose of the work is to enable students of botany to identify
accurately Oregon ferns, both as living plants and as dried specimens.
Therefore, it provides vegetative keys to the families, genera
and species of the ferns (Class FILICINAE) found in Oregon. Correct
names have been determined using the...
Clinical mastitis (CM), the most prevalent and costly disease in dairy cows, is diagnosed most commonly shortly after calving. Current indicators do not satisfactorily predict CM. This study aimed to develop a robust and comprehensive mass spectrometry-based metabolomic and lipidomic workflow using untargeted ultra-performance liquid chromatography high-resolution mass spectrometry for...
We take for granted how quickly we, as humans, form mental models of the world around us. By the time we are toddlers, we have an observable intuition around the physical rules of the world. Stacking blocks such that they don’t fall over becomes such a trivial task, that it...
Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this...
As the link between human microbiomes and health has become more established, the interest in applying statistical approaches to microbiome data to understand the mechanisms behind these links has grown. However, microbiome data is often of unmanageable size, and consequently, producing quality lower dimensional representations of samples is a significant...
The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....
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Xiaoli Fern
The problem of document classification has been widely studied in machine