Genome Architecture Mediates Transcriptional Control of Human Myogenic Reprogramming.

Genome architecture has emerged as a critical element of transcriptional regulation, although its role in the control of cell identity is not well understood. Here we use transcription factor (TF)-mediated reprogramming to examine the interplay between genome architecture and transcriptional programs that transition cells into the myogenic identity. We recently developed new methods for evaluating the topological features of genome architecture based on network centrality.

Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics.

Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients.

Electrical coupling between hippocampal neurons: contrasting roles of principal cell gap junctions and interneuron gap junctions.

There is considerable experimental evidence, anatomical and physiological, that gap junctions exist in the hippocampus. Electrical coupling through these gap junctions may be divided into three types: between principal neurons, between interneurons and at mixed chemical (glutamatergic)/electrical synapses. An approach, combining in vitro experimental with modeling techniques, sheds some light on the functional consequences of electrical coupling, for network oscillations and for seizures.

Self-organized criticality and pattern emergence through the lens of tropical geometry.

Tropical geometry, an established field in pure mathematics, is a place where string theory, mirror symmetry, computational algebra, auction theory, and so forth meet and influence one another. In this paper, we report on our discovery of a tropical model with self-organized criticality (SOC) behavior. Our model is continuous, in contrast to all known models of SOC, and is a certain scaling limit of the sandpile model, the first and archetypical model of SOC.

Baseline multimodal information predicts future motor impairment in premanifest Huntington's disease.

In Huntington's disease (HD), accurate estimates of expected future motor impairments are key for clinical trials. Individual prognosis is only partially explained by genetics. However, studies so far have focused on predicting the time to clinical diagnosis based on fixed impairment levels, as opposed to predicting impairment in time windows comparable to the duration of a clinical trial.

A dual regulation mechanism of histidine kinase CheA identified by combining network-dynamics modeling and system-level input-output data.

It is challenging to decipher molecular mechanisms in biological systems from system-level input-output data, especially for complex processes that involve interactions among multiple components. We addressed this general problem for the bacterial histidine kinase CheA, the activity of which is regulated in chemotaxis signaling complexes by bacterial chemoreceptors. We developed a general network model to describe the dynamics of the system, treating the receptor complex with coupling protein CheW and the P3P4P5 domains of kinase CheA as a regulated enzyme with two substrates, ATP and P1, the phosphoryl-accepting domain of CheA.

Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions.

Simulation and data analysis have evolved into powerful methods for discovering and understanding molecular modes of action and designing new compounds to exploit these modes. The combination provides a strong impetus to create and exploit new tools and techniques at the interfaces between physics, biology, and data science as a pathway to new scientific insight and accelerated discovery. In this context, we explore the rational design of novel antimicrobial peptides (short protein sequences exhibiting broad activity against multiple species of bacteria).

Digital technologies as biomarkers, clinical outcomes assessment, and recruitment tools in Alzheimer's disease clinical trials.

Digital technology is transforming the development of drugs for Alzheimer's disease and was the topic of the Alzheimer's Association's Research Roundtable on its May 23-24, 2017 meeting. Research indicates that wearable devices and unobtrusive passive sensors that enable the collection of frequent or continuous, objective, and multidimensional data during daily activities may capture subtle changes in cognition and functional capacity long before the onset of dementia. The potential to exploit these technologies to improve clinical trials as both recruitment and retention tools as well as for potential end points was discussed.

Directed Self-Assembly of Ge Quantum Dots Using Focused Si Ion Beam Patterning.

We show that templating a Si surface with a focused beam of Si or Si ions can create suitable nucleation sites for the subsequent growth of self-assembled Ge quantum dots by chemical vapor deposition. To determine the mechanism of patterning we use atomic force microscopy to show that, similar to Ga patterning, the formation of a surface pit is required to enable control over Ge quantum dot locations. We find that relatively high implantation doses are required to achieve patterning, and these doses lead to amorphization of the substrate.

Designing Decision-Support Technologies for Patient-Generated Data in Type 1 Diabetes.

People living with type 1 diabetes generate data as a byproduct of diabetes management. The development of decision support technologies can be enabled by harnessing these patient-generated data, but a major challenge is for these technologies to provide meaningful and highly personalized guidance to support individual patients' decision-making processes. In this paper, results from a year-long qualitative study were reported.

Bootstrap-based Feature Selection to Balance Model Discrimination and Predictor Significance: A Study of Stroke Prediction in Atrial Fibrillation.

Atrial fibrillation (AF) is a common cardiac arrhythmias, which increases the risk and severity of ischemic stroke. For predicting ischemic stroke in AF patients, a risk prediction model that can achieve both good model discrimination (e.g.

A First Step Towards Behavioral Coaching for Managing Stress: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning.

Psychological stress is a major contributor to the adoption of unhealthy behaviors, which in turn accounts for 41% of global cardiovascular disease burden. While the proliferation of mobile health apps has offered promise to stress management, these apps do not provide micro-level feedback with regard to how to adjust one's behaviors to achieve a desired health outcome. In this paper, we formulate the task of multi-stage stress management as a sequential decision-making problem and explore the application of reinforcement learning to provide micro-level feedback for stress reduction.

StressHacker: Towards Practical Stress Monitoring in the Wild with Smartwatches.

In modern life, the nonstop and pervasive stress tends to keep us on long-lasting high alert, which over time, could lead to a broad range of health problems from depression, metabolic disorders to heart diseases. However, there is a stunning lack of practical tools for effective stress management that can help people navigate through their daily stress. This paper presents the feasibility evaluation of StressHacker, a smartwatch-based system designed to continuously and passively monitor one's stress level using bio-signals obtained from the on-board sensors.

Signal and noise extraction from analog memory elements for neuromorphic computing.

Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors.

Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions.

Aim And Objective: Adverse drug reactions (ADRs) present a major burden for patients and the healthcare industry. Various computational methods have been developed to predict ADRs for drug molecules. However, many of these methods require experimental or surveillance data and cannot be used when only structural information is available.

Efficient algorithms for polyploid haplotype phasing.

Background: Inference of haplotypes, or the sequence of alleles along the same chromosomes, is a fundamental problem in genetics and is a key component for many analyses including admixture mapping, identifying regions of identity by descent and imputation. Haplotype phasing based on sequencing reads has attracted lots of attentions. Diploid haplotype phasing where the two haplotypes are complimentary have been studied extensively.

High coherence plane breaking packaging for superconducting qubits.

We demonstrate a pogo pin package for a superconducting quantum processor specifically designed with a nontrivial layout topology (e.g., a center qubit that cannot be accessed from the sides of the chip).

Design principles for enhancing phase sensitivity and suppressing phase fluctuations simultaneously in biochemical oscillatory systems.

Biological systems need to function accurately in the presence of strong noise and at the same time respond sensitively to subtle external cues. Here we study design principles in biochemical oscillatory circuits to achieve these two seemingly incompatible goals. We show that energy dissipation can enhance phase sensitivity linearly by driving the phase-amplitude coupling and increase timing accuracy by suppressing phase diffusion.

Readmission prediction via deep contextual embedding of clinical concepts.

Objective: Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk.

Switching-State Dynamical Modeling of Daily Behavioral Data.

Emerging wearable and environmental sensor technologies provide health professionals with unprecedented capacity to continuously collect human behavioral data for health monitoring and management. This enables new solutions to mitigate globally emerging health problems such as obesity. With such outburst of dynamic sensor data, it is critical that appropriate mathematical models and computational methods are developed to translate the collected data into accurate characterization of the underlying health dynamics, enabling more reliable personalized monitoring, prediction, and intervention of health status changes.

On designing of a low leakage patient-centric provider network.

Background: When a patient in a provider network seeks services outside of their community, the community experiences a leakage. Leakage is undesirable as it typically leads to higher out-of-network cost for patient and increases barrier for care coordination, which is particularly problematic for Accountable Care Organization (ACO) as the in-network providers are financially responsible for quality of care and outcome. We aim to design a data-driven method to identify naturally occurring provider networks driven by diabetic patient choices, and understand the relationship among provider composition, patient composition, and service leakage pattern.

Nanomaterial-Based Plasmon-Enhanced Infrared Spectroscopy.

Surface-enhanced infrared absorption (SEIRA) has attracted increasing attention due to the potential of infrared spectroscopy in applications such as molecular trace sensing of solids, polymers, and proteins, specifically fueled by recent substantial developments in infrared plasmonic materials and engineered nanostructures. Here, the significant progress achieved in the past decades is reviewed, along with the current state of the art of SEIRA. In particular, the plasmonic properties of a variety of nanomaterials are discussed (e.

Design principles and optimal performance for molecular motors under realistic constraints.

The performance of a molecular motor, characterized by its power output and energy efficiency, is investigated in the motor design space spanned by the stepping rate function and the motor-track interaction potential. Analytic results and simulations show that a gating mechanism that restricts forward stepping in a narrow window in configuration space is needed for generating high power at physiologically relevant loads. By deriving general thermodynamics laws for nonequilibrium motors, we find that the maximum torque (force) at stall is less than its theoretical limit for any realistic motor-track interactions due to speed fluctuations.