Randomized controlled trial to improve primary care follow-up among emergency department patients.

STUDY OBJECTIVE: Primary care (PC) follow-up for discharged emergency department (ED) patients provides patients with further medical attention. We conducted a pilot randomized controlled trial to determine whether using a freely-available physician appointment-booking website results in higher self-reported PC follow-up. METHODS: We randomized discharged patients whom treating physicians determined PC follow-up was important and who possessed health insurance but had no PC provider to one of three groups: (1) a PC appointment booked through the booking website prior to ED discharge; (2) written information on how to use the booking website; or (3) usual care (i.e. standard follow-up instructions). We phoned subjects two weeks after the ED visit to determine whether they had completed a PC follow-up visit. We also asked subjects about their satisfaction with obtaining a PC appointment, satisfaction with the ED visit, symptom resolution and subsequent ED visits. The self-reported PCP follow-up rate was compared among the study groups by estimating the risk difference (RD) and 95% CI between usual care and each intervention group. RESULTS: 272 subjects were enrolled and randomized and 68% completed the two-week telephone follow-up interview. The self-reported PCP follow-up rate was higher (52%) among subjects whose appointment was booked on the website before ED discharge (RD = 16%; 95% CI -1%, 34%) and lower (25%) for subjects who received booking website information (RD = 13%; 95% CI -32%, 7%) compared to subjects (36%) in the usual care group. A higher percentage of subjects in the booking group were more likely to report being extremely or very satisfied with obtaining a PC appointment (78%) compared to those who received booking website information (54%) or usual care (40%). CONCLUSION: Among ED patients that providers judged PC follow-up is important, using a booking website to schedule an appointment before ED discharge resulted in a higher but not statistically significant self-reported PC follow-up rate. This intervention warrants further investigation in a study with a larger sample size and objective follow-up visit data.

Machine learning for autonomous crystal structure identification.

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Seeing the Errors You Feel Enhances Locomotor Performance but Not Learning.

In human motor learning, it is thought that the more information we have about our errors, the faster we learn. Here, we show that additional error information can lead to improved motor performance without any concomitant improvement in learning. We studied split-belt treadmill walking that drives people to learn a new gait pattern using sensory prediction errors detected by proprioceptive feedback. When we also provided visual error feedback, participants acquired the new walking pattern far more rapidly and showed accelerated restoration of the normal walking pattern during washout. However, when the visual error feedback was removed during either learning or washout, errors reappeared with performance immediately returning to the level expected based on proprioceptive learning alone. These findings support a model with two mechanisms: a dual-rate adaptation process that learns invariantly from sensory prediction error detected by proprioception and a visual-feedback-dependent process that monitors learning and corrects residual errors but shows no learning itself. We show that our voluntary correction model accurately predicted behavior in multiple situations where visual feedback was used to change acquisition of new walking patterns while the underlying learning was unaffected. The computational and behavioral framework proposed here suggests that parallel learning and error correction systems allow us to rapidly satisfy task demands without necessarily committing to learning, as the relative permanence of learning may be inappropriate or inefficient when facing environments that are liable to change.

Nonlinear machine learning and design of reconfigurable digital colloids.

Digital colloids, a cluster of freely rotating "halo" particles tethered to the surface of a central particle, were recently proposed as ultra-high density memory elements for information storage. Rational design of these digital colloids for memory storage applications requires a quantitative understanding of the thermodynamic and kinetic stability of the configurational states within which information is stored. We apply nonlinear machine learning to Brownian dynamics simulations of these digital colloids to extract the low-dimensional intrinsic manifold governing digital colloid morphology, thermodynamics, and kinetics. By modulating the relative size ratio between halo particles and central particles, we investigate the size-dependent configurational stability and transition kinetics for the 2-state tetrahedral (N = 4) and 30-state octahedral (N = 6) digital colloids. We demonstrate the use of this framework to guide the rational design of a memory storage element to hold a block of text that trades off the competing design criteria of memory addressability and volatility.

Blocking trial-by-trial error correction does not interfere with motor learning in human walking.

Movements can be learned implicitly in response to new environmental demands or explicitly through instruction and strategy. The former is often studied in an environment that perturbs movement so that people learn to correct the errors and store a new motor pattern. Here, we demonstrate in human walking that implicit learning of foot placement occurs even when an explicit strategy is used to block changes in foot placement during the learning process. We studied people learning a new walking pattern on a split-belt treadmill with and without an explicit strategy through instruction on where to step. When there is no instruction, subjects implicitly learn to place one foot in front of the other to minimize step-length asymmetry during split-belt walking, and the learned pattern is maintained when the belts are returned to the same speed, i.e., postlearning. When instruction is provided, we block expression of the new foot-placement pattern that would otherwise naturally develop from adaptation. Despite this appearance of no learning in foot placement, subjects show similar postlearning effects as those who were not given any instruction. Thus locomotor adaptation is not dependent on a change in action during learning but instead can be driven entirely by an unexpressed internal recalibration of the desired movement.

Machine learning assembly landscapes from particle tracking data.

Bottom-up self-assembly offers a powerful route for the fabrication of novel structural and functional materials. Rational engineering of self-assembling systems requires understanding of the accessible aggregation states and the structural assembly pathways. In this work, we apply nonlinear machine learning to experimental particle tracking data to infer low-dimensional assembly landscapes mapping the morphology, stability, and assembly pathways of accessible aggregates as a function of experimental conditions. To the best of our knowledge, this represents the first time that collective order parameters and assembly landscapes have been inferred directly from experimental data. We apply this technique to the nonequilibrium self-assembly of metallodielectric Janus colloids in an oscillating electric field, and quantify the impact of field strength, oscillation frequency, and salt concentration on the dominant assembly pathways and terminal aggregates. This combined computational and experimental framework furnishes new understanding of self-assembling systems, and quantitatively informs rational engineering of experimental conditions to drive assembly along desired aggregation pathways.

A marching-walking hybrid induces step length adaptation and transfers to natural walking.

Walking is highly adaptable to new demands and environments. We have previously studied adaptation of locomotor patterns via a split-belt treadmill, where subjects learn to walk with one foot moving faster than the other. Subjects learn to adapt their walking pattern by changing the location (spatial) and time (temporal) of foot placement. Here we asked whether we can induce adaptation of a specific walking pattern when one limb does not "walk" but instead marches in place (i.e., marching-walking hybrid). The marching leg's movement is limited during the stance phase, and thus certain sensory signals important for walking may be reduced. We hypothesized that this would produce a spatial-temporal strategy different from that of normal split-belt adaptation. Healthy subjects performed two experiments to determine whether they could adapt their spatial-temporal pattern of step lengths during the marching-walking hybrid and whether the learning transfers to over ground walking. Results showed that the hybrid group did adapt their step lengths, but the time course of adaptation and deadaption was slower than that for the split-belt group. We also observed that the hybrid group utilized a mostly spatial strategy whereas the split-belt group utilized both spatial and temporal strategies. Surprisingly, we found no significant difference between the hybrid and split-belt groups in over ground transfer. Moreover, the hybrid group retained more of the learned pattern when they returned to the treadmill. These findings suggest that physical rehabilitation with this marching-walking paradigm on conventional treadmills may produce changes in symmetry comparable to what is observed during split-belt training.

Nonlinear machine learning of patchy colloid self-assembly pathways and mechanisms.

Bottom-up self-assembly offers a means to synthesize materials with desirable structural and functional properties that cannot easily be fabricated by other techniques. An improved understanding of the structural pathways and mechanisms by which self-assembling materials spontaneously form from their constituent building blocks is of value in understanding the fundamental principles of assembly and in guiding inverse building block design. We present an approach to infer systematically assembly pathways and mechanisms by nonlinear data mining of molecular simulation trajectories using diffusion maps. We have validated our methodology in applications to Brownian dynamics simulations of the assembly of anisotropic "patchy colloids" into polyhedral aggregates. For particles designed to form tetrahedral aggregates, we identify two divergent assembly pathways leading to chains of interlocking dimers and tetramers and chains of interlocking trigonal planar trimers. For particles designed to assemble icosahedral aggregates, our approach recovers two distinct assembly pathways corresponding to monomeric addition and budding from a disordered liquid phase. These assembly routes were previously reported by inspection of simulation trajectories by Wilber et al. ( J. Chem. Phys. 2007, 127, 085106 ; J. Chem. Phys. 2009, 131, 175102 ), validating the capacity of our approach to systematically recover assembly mechanisms previously discernible only by trajectory visualization.