Predicting Pain to Support Exercise Participation after Spinal Cord Injury

The expansion of mobile health and predictive modeling may be worthwhile tools to predict subjective pain experiences and tailor personalized approaches for health behaviors after spinal cord injury (SCI). PURPOSE: This study examined whether exercise (hybrid functional electrical stimulation rowing) and smartphone-based health and mobility data could reliably forecast changes in pain intensity among people with SCI. METHODS: Data were collected via Suunto memory belt (heart rate), rower C2 log cards (training duration/frequency) and personal smartphones (location details, self-reported mood, pain, health, mobility) among (N=12) community-living adults with SCI over 9 months. Data were combined to predict the pain in the subsequent week. We applied nonlinear random forest algorithms, first transforming absolute scores week-to-week: \u270\u27=no change or lessened pain, & \u271\u27=worsened pain. Due to potential data imbalances, we applied k-fold cross-validation for model robustness. We referenced the F1 score (\u3e0.80) to assess model predictive accuracy. RESULTS: Our analysis underscored that while exercise data alone was insufficient for accurate predictions about the forthcoming week’s pain (F1=0.40), combining it with, prior self-reported outcomes considerably enhanced predictive accuracy. We achieved notable success in forecasting pain intensity a week ahead for multiple feature groups. For example, when predicting next week’s pain, we obtained an F1 score of 0.83 using the combination of (current) pain interference, and the difference in the hours out of bed this week and rowing frequency compared to the previous week. Similarly, the triad of TRIMPS, and the difference in pain interference and anxiety this week compared to last week also yielded an F1 score of 0.83, further emphasizing the value of integrating diverse data sources. Partial Dependence Plots revealed a positive nonlinear relationship. CONCLUSION: Through a combination of various exercise, health, mood, and mobility metrics collected in-situ, we were able to predict future pain intensity among exercisers with SCI, as reflected by commendable F1-scores. This underscores the intricacies of pain experiences, and the utility of non-linear predictive models to understand and support exercise participation for those with SCI

Unified Hierarchical Relationship Between Thermodynamic Tradeoff Relations

Recent years have witnessed a surge of discoveries in the studies of thermodynamic inequalities: the thermodynamic uncertainty relation (TUR) and the entropic bound (EB) provide a lower bound on the entropy production (EP) in terms of nonequilibrium currents; the classical speed limit (CSL) expresses the lower bound on the EP using the geometry of probability distributions; the power-efficiency (PE) tradeoff dictates the maximum power achievable for a heat engine given the level of its thermal efficiency. In this study, we show that there exists a unified hierarchical structure encompassing all of these bounds, with the fundamental inequality given by a novel extension of the TUR (XTUR) that incorporates the most general range of current-like and state-dependent observables. By selecting more specific observables, the TUR and the EB follow from the XTUR, and the CSL and the PE tradeoff follow from the EB. Our derivations cover both Langevin and Markov jump systems, with the first proof of the EB for the Markov jump systems and a more generalized form of the CSL. We also present concrete examples of the EB for the Markov jump systems and the generalized CSL.Comment: 19 pages, 4 figure

The fate of North Atlantic Subtropical Mode Water in the FLAME model

Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 44 (2014): 1354–1371, doi:10.1175/JPO-D-13-0202.1.North Atlantic Subtropical Mode Water, also known as Eighteen Degree Water (EDW), has the potential to store heat anomalies through its seasonal cycle: the water mass is in contact with the atmosphere in winter, isolated from the surface for the rest of the year, and reexposed the following winter. Though there has been recent progress in understanding EDW formation processes, an understanding of the fate of EDW following formation remains nascent. Here, particles are launched within the EDW of an eddy-resolving model, and their fate is tracked as they move away from the formation region. Particles in EDW have an average residence time of ~10 months, they follow the large-scale circulation around the subtropical gyre, and stratification is the dominant criteria governing the exit of particles from EDW. After sinking into the layers beneath EDW, particles are eventually exported to the subpolar gyre. The spreading of particles is consistent with the large-scale potential vorticity field, and there are signs of a possible eddy-driven mean flow in the southern portion of the EDW domain. The authors also show that property anomalies along particle trajectories have an average integral time scale of ~3 months for particles that are in EDW and ~2 months for particles out of EDW. Finally, it is shown that the EDW turnover time for the model in an Eulerian frame (~3 yr) is consistent with the turnover time computed from the Lagrangian particles provided that the effects of exchange between EDW and the surrounding waters are included.The authors are thankful for financial support from the U.S. National Science Foundation for S. F. G., M. S. L., Y.-O. K., and J. J. P.2014-11-0

Year-to-year reoutcropping of Eighteen Degree Water in an eddy-resolving ocean simulation

Author Posting. © American Meteorological Society, 2015. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 45 (2015):1189–1204, doi:10.1175/JPO-D-14-0122.1.Winter outcropping of the Eighteen Degree Water (EDW) and its subsequent dispersion are studied using a ° eddy-resolving simulation of the Family of Linked Atlantic Modeling Experiments (FLAME). Outcropped EDW columns in the model simulations are detected in each winter from 1990 to 1999, and particles are deployed in the center of each outcropped EDW column. Subsequently, the trajectories of these particles are calculated for the following 5 yr. The particles slowly spread away from the outcropping region into the nonoutcropping/subducted EDW region south of ~30°N and eventually to the non-EDW region in the greater subtropical gyre. Approximately 30% of the particles are found in non-EDW waters 1 yr after deployment; after 5 yr, only 25% of the particles are found within EDW. The reoutcropping time is defined as the number of years between when a particle is originally deployed in an outcropping EDW column and when that particle is next found in an outcropping EDW column. Of the particles, 66% are found to reoutcrop as EDW in 1 yr, and less than 5% of the particles outcrop in each of the subsequent 4 yr. While the individual trajectories exhibit significant eddy-like motions, the time scale of reoutcropping is primarily set by the mean circulation. The dominance of reoutcropping in 1 yr suggests that EDW outcropping contributes considerably to the persistence of surface temperature anomalies from one winter to the next, that is, the reemergence of winter sea surface temperature anomalies.We gratefully acknowledge the support from the NSF OCE Physical Oceanography program (NSF OCE-0961090 to Y-OK and J-JP; NSF OCE-0960776 to MSL and SFG; and NSF OCE-1242989 to Y-OK).2015-10-0

Forage Rye Cultivars for Animal Feed in Korea

Rye (Secale cereal L.) is well known for its overwintering ability and has the highest tolerance to cold temperature of the small grain cereals such as wheat, barley, and oat. Rye is used as livestock pasture and as green manure in Korea, and its cultivation area for fodder is about 50,000 ha in 2014. Most rye is grown as a fall-sown annual crop, generally called “winter rye”. In Korea, rye cultivation for whole crop silage (WCS) in the winter-season rice field can be considered as a promising way to enhance feed supply. The WCS production can be an efficient way to use farm products as livestock feed, and it can also contribute to increasing farm income. Although rye is inferior in several ways to the predominant cereal crops (wheat, rice, and maize), it will continue to be an important crop for farmers in Korea because of its winter hardiness and early harvesting by rapid growing ability