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Mental state monitoring is a critical component of current and future human-machine interfaces, including semi-autonomous driving and flying, air traffic control, decision aids, training systems, and will soon be integrated into ubiquitous products like cell phones and laptops. Current mental state assessment approaches supply quantitative measures, but their only frame of reference is generic population-level ranges. What is needed are physiological biometrics that are validated in the context of task performance of individuals. Using curated intake experiments, we are able to generate personalized models of three key biometrics as useful indicators of mental state; namely, mental fatigue, stress, and attention. We demonstrate improvements to existing approaches through the introduction of new features. Furthermore, addressing the current limitations in assessing the efficacy of biometrics for individual subjects, we propose and employ a multi-level validation scheme for the biometric models by means of -fold cross-validation for discrete classification and regression testing for continuous prediction. The paper not only provides a unified pipeline for extracting a comprehensive mental state evaluation from a parsimonious set of sensors (only EEG and ECG), but also demonstrates the use of validation techniques in the absence of empirical data. Furthermore, as an example of the application of these models to novel situations, we evaluate the significance of correlations of personalized biometrics to the dynamic fluctuations of accuracy and reaction time on an unrelated threat detection task using a permutation test. Our results provide a path toward integrating biometrics into augmented human-machine interfaces in a judicious way that can help to maximize task performance.

Targeted memory reactivation (TMR) during slow-wave oscillations (SWOs) in sleep has been demonstrated with sensory cues to achieve about 5-12% improvement in post-nap memory performance on simple laboratory tasks. But prior work has not yet addressed the one-shot aspect of episodic memory acquisition, or dealt with the presence of interference from ambient environmental cues in real-world settings. Further, TMR with sensory cues may not be scalable to the multitude of experiences over one's lifetime. We designed a novel non-invasive non-sensory paradigm that tags one-shot experiences of minute-long naturalistic episodes in immersive virtual reality (VR) with unique spatiotemporal amplitude-modulated patterns (STAMPs) of transcranial electrical stimulation (tES). In particular, we demonstrated that these STAMPs can be re-applied as brief pulses during SWOs in sleep to achieve about 10-20% improvement in the metamemory of targeted episodes compared to the control episodes at 48 hours after initial viewing. We found that STAMPs can not only facilitate but also impair metamemory for the targeted episodes based on an interaction between pre-sleep metamemory and the number of STAMP applications during sleep. Overnight metamemory improvements were mediated by spectral power increases following the offset of STAMPs in the slow-spindle band (8-12 Hz) for left temporal areas in the scalp electroencephalography (EEG) during sleep. These results prescribe an optimal strategy to leverage STAMPs for boosting metamemory and suggest that real-world episodic memories can be modulated in a targeted manner even with coarser, non-invasive spatiotemporal stimulation.

Fighter pilots must study models of aircraft dynamics before learning complex maneuvers and tactics. Similarly, autonomous fighter aircraft applications may benefit from a model-based learning approach. Instead of using a preexisting physics model of a given aircraft, a machine learning system can learn a predictive model of the aircraft physics from training data. Furthermore, it can model interactions between multiple friendly aircraft, enemy aircraft, and the environment. Such a system can also learn to represent state variables that are not directly observable, as well as dynamics that are not hard coded. Existing model-based methods use a deep neural network that takes observable state information and agent actions as input and provides predictions of future observations as output. The proposed method builds upon this approach by adding a residual feedforward skip connection from some of the inputs to all of the outputs of the deep neural network. Further innovations address numerical conditioning issues as well as periodic discontinuities of angular quantities such as bearing or heading. The methods in this article also extend techniques from model-based reinforcement learning control to the domain of adversarial multi-agent environments. In previous literature, these model-based methods have only been used for controlling individual agents. Instead of using a traditional Recurrent Neural Network (RNN) to learn a representation of the world state, the novel method also uses a compressive encoding scheme. This is based on an augmented version of the same neural network that is used for predictive modeling.

Medical civil military operations (MCMO) are part of military civil capacity-building efforts within the full spectrum of military operations, from war to military operations other than war. In 2008-2009 during Operation Iraqi Freedom, the Division Surgeon's Section (DSS) of the 25th Infantry Division (25ID) and Multinational Division-North developed an innovative MCMO program in northern Iraq. The program centered on understanding and mapping key relationships, empowering brigade-level programs, and leveraging technology to identify and share best practices. The DSS mapped the critical relationships within and between the three entities affecting MCMO: the government of Iraq (GOI), Department of State (DOS), and the Department of Defense (DOD). A division MCMO working group was then created along with processes to facilitate MCMO project execution and program management. The structure and organization of the 25ID MCMO program lend themselves to other operational environments requiring synchronization of medical capacity-building efforts.

Long-term retention of memories critically depends on consolidation processes, which occur during slow-wave oscillations (SWOs) in non-rapid eye movement (NREM) sleep. We designed a non-invasive system that can tag one-shot experiences of naturalistic episodes within immersive virtual reality (VR) with unique spatiotemporal amplitude-modulated patterns (STAMPs) of transcranial electrical stimulation (tES). We demonstrate that these STAMPs can be re-applied during UP states of SWOs on two consecutive nights to achieve a 19.43% improvement in the metamemory of targeted episodes at 48 hours after the one-shot viewing, compared to the control episodes. Further, we found an interaction between pre-sleep metamemory of targeted episodes and the number of STAMP applications for those episodes during sleep, and that STAMPs elicit increases in left temporal slow-spindle (9-12 Hz) power that are predictive of overnight metamemory improvements. These results prescribe an optimal strategy to leverage STAMPs for boosting metamemory based on pre-sleep performance and tracking the STAMP-induced biomarker during sleep, and suggest that real-world episodic memories can be modulated in a targeted manner even with coarser, non-invasive spatiotemporal stimulation. Footnotes * highlight a potential mechanism as well as our uniqueness with respect to prior work on targeted memory reactivation

DNase I hypersensitive sites (DHSs) are markers of regulatory DNA and have underpinned the discovery of all classes of cis -regulatory elements including enhancers, promoters, insulators, silencers and locus control regions. Here we present the first extensive map of human DHSs identified through genome-wide profiling in 125 diverse cell and tissue types. We identify ∼2.9 million DHSs that encompass virtually all known experimentally validated cis -regulatory sequences and expose a vast trove of novel elements, most with highly cell-selective regulation. Annotating these elements using ENCODE data reveals novel relationships between chromatin accessibility, transcription, DNA methylation and regulatory factor occupancy patterns. We connect ∼580,000 distal DHSs with their target promoters, revealing systematic pairing of different classes of distal DHSs and specific promoter types. Patterning of chromatin accessibility at many regulatory regions is organized with dozens to hundreds of co-activated elements, and the transcellular DNase I sensitivity pattern at a given region can predict cell-type-specific functional behaviours. The DHS landscape shows signatures of recent functional evolutionary constraint. However, the DHS compartment in pluripotent and immortalized cells exhibits higher mutation rates than that in highly differentiated cells, exposing an unexpected link between chromatin accessibility, proliferative potential and patterns of human variation. An extensive map of human DNase I hypersensitive sites, markers of regulatory DNA, in 125 diverse cell and tissue types is described; integration of this information with other ENCODE-generated data sets identifies new relationships between chromatin accessibility, transcription, DNA methylation and regulatory factor occupancy patterns. ENCODE: a map of accessible chromatin This paper describes the first extensive map of human DNaseI hypersensitive sites — markers of regulatory DNA — in 125 diverse cell and tissue types. Integration of this information with other data sets generated by ENCODE (Encyclopedia of DNA Elements) identified new relationships between chromatin accessibility, transcription, DNA methylation and regulatory-factor occupancy patterns. Evolutionary-conservation analysis revealed signatures of recent functional constraint within DNaseI hypersensitive sites.

Regulatory factor binding to genomic DNA protects the underlying sequence from cleavage by DNase I, leaving nucleotide-resolution footprints. Using genomic DNase I footprinting across 41 diverse cell and tissue types, we detected 45 million transcription factor occupancy events within regulatory regions, representing differential binding to 8.4 million distinct short sequence elements. Here we show that this small genomic sequence compartment, roughly twice the size of the exome, encodes an expansive repertoire of conserved recognition sequences for DNA-binding proteins that nearly doubles the size of the human cis –regulatory lexicon. We find that genetic variants affecting allelic chromatin states are concentrated in footprints, and that these elements are preferentially sheltered from DNA methylation. High-resolution DNase I cleavage patterns mirror nucleotide-level evolutionary conservation and track the crystallographic topography of protein–DNA interfaces, indicating that transcription factor structure has been evolutionarily imprinted on the human genome sequence. We identify a stereotyped 50-base-pair footprint that precisely defines the site of transcript origination within thousands of human promoters. Finally, we describe a large collection of novel regulatory factor recognition motifs that are highly conserved in both sequence and function, and exhibit cell-selective occupancy patterns that closely parallel major regulators of development, differentiation and pluripotency. DNase I footprinting in 41 cell and tissue types reveals millions of short sequence elements encoding an expansive repertoire of conserved recognition sequences for DNA-binding proteins. ENCODE: transcription-factor footprints DNaseI footprinting detects DNA sequences that are protected from cleavage by DNaseI because they are bound by regulatory factors. Studying these footprints in 41 diverse cell and tissue types, the authors describe millions of short sequence elements that are conserved recognition sequences for DNA-binding proteins. The effort nearly doubles the size of the human cis -regulatory lexicon and provides insight into chromatin states and levels of evolutionary conservation. A large collection of novel regulatory-factor recognition motifs that closely parallel major regulators of development, differentiation and pluripotency is also described.

Regulatory factor binding to genomic DNA protects the underlying sequence from cleavage by DNase I, leaving nucleotide-resolution 'footprints'. Using genomic DNaseI footprinting across 41 diverse cell and tissue types, we detected 45 million transcription factor occupancy events within regulatory regions, representing differential binding to 8.4 million distinct short sequence elements. Here we show that this small genomic sequence compartment, roughly twice the size of the exome, encodes an expansive repertoire of conserved recognition sequences for DNA-binding proteins that nearly doubles the size of the human cis-regulatory lexicon. We find that genetic variants affecting allelic chromatin states are concentrated in footprints, and that these elements are preferentially sheltered from DNA methylation. High-resolution DNase I cleavage patterns mirror nucleotide-level evolutionary conservation and track the crystallographic topography of protein-DNA interfaces, indicating that transcription factor structure has been evolutionarily imprinted on the human genome sequence. We identify a stereotyped 50-base-pair footprint that precisely defines the site of transcript origination within thousands of human promoters. Finally, we describe a large collection of novel regulatory factor recognition motifs that are highly conserved in both sequence and function, and exhibit cell- selective occupancy patterns that closely parallel major regulators of development, differentiation and pluripotency.