Search for: All records

Nature serves as a rich source of molecules with immense chemical diversity. Aptly named, these ‘natural products’ boast a wide variety of environmental, medicinal and industrial applications. Type II polyketides, in particular, confer substantial medicinal benefits, including antibacterial, antifungal, anticancer and anti-inflammatory properties. These molecules are produced by enzyme assemblies known as type II polyketide synthases (PKSs), which use domains such as the ketosynthase chain-length factor and acyl carrier protein to produce polyketides with varying lengths, cyclization patterns and oxidation states. In this work, we use a novel bioinformatic workflow to identify biosynthetic gene clusters (BGCs) that code for the core type II PKS enzymes. This method does not rely on annotation and thus was able to unearth previously ‘hidden’ type II PKS BGCs. This work led us to identify over 6000 putative type II PKS BGCs spanning a diverse set of microbial phyla, nearly double those found in most recent studies. Notably, many of these newly identified BGCs were found in non-actinobacteria, which are relatively underexplored as sources of type II polyketides. Results from this work lay an important foundation for future bioprospecting and engineering efforts that will enable sustainable access to diverse and structurally complex molecules with medicinally relevant properties. 

Abstract Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective. 

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.