Effect of Pacifier Design on Nonnutritive Suck Maturation and Weight Gain in Preterm Infants: A Pilot Study

Background: Pacifiers are effective in promoting oral feeding by increasing the maturation of nonnutritive sucking to nutritive suck in preterm neonates. It is unclear whether pacifier design can influence suck dynamics and weight loss during the first week of life. Objectives: This pilot study examined the feasibility of studying the effect of pacifier design on suck maturation and weight loss in preterm neonates. Methods: Twenty-five preterm neonates (mean [SD] birth weight 1791 [344.9] grams, mean [SD] gestational age 33.1 [1.2] weeks) were studied in a single newborn intensive care unit. Neonates were assigned to either an orthodontic pacifier (n = 13) or a bulb-shaped pacifier (n = 12) immediately after birth. Suck dynamics (cycles per minute, total compressions per minute, cycle bursts, and amplitude) were assessed with an NTrainer (Innara Health, Olathe, Kansas). Weight was recorded during the first week of life on day 1.2 ( ±2.5 days) and day 6.0 ( ±2.1 days). Descriptive statistics were applied to analyze data. Results: No significant differences were seen between groups with respect to birth weight and gestational age. Reproducible nonnutritive sucking measurements could be obtained with the NTrainer, with both types of pacifiers. No differences were detected in nonnutritive sucking dynamics or weight loss over time within each group or between groups. Conclusions: Data indicate that it is feasible to measure nonnutritive sucking dynamics and associated weight loss in relation to pacifier design in preterm neonates. Larger trials over longer time periods are needed to determine whether pacifier design influences suck dynamics and maturation, oromotor function, feeding/weight loss, and dental formation in preterm neonates. ( Curr Ther Res Clin Exp. 2020; 81:XXX–XXX

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: The emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. Thus, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. The NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. The hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Automatic Nonnutritive Suck Waveform Discrimination and Feature Extraction in Preterm Infants

Background and Objective: +e emergence of the nonnutritive suck (NNS) pattern in preterm infants reflects the integrity of the brain and is used by clinicians in the neonatal intensive care unit (NICU) to assess feeding readiness and oromotor development. A critical need exists for an integrated software platform that provides NNS signal preprocessing, adaptive waveform discrimination, feature detection, and batch processing of big data sets across multiple NICU sites. +us, the goal was to develop and describe a crossplatform graphical user interface (GUI) and terminal application known as NeoNNS for single and batch file time series and frequency-domain analyses of NNS compression pressure waveforms using analysis parameters derived from previous research on NNS dynamics. Methods. NeoNNS was implemented with Python and the Tkinter GUI package. +e NNS signal-processing pipeline included a low-pass filter, asymmetric regression baseline correction, NNS peak detection, and NNS burst classification. Data visualizations and parametric analyses included time- and frequency-domain view, NNS spatiotemporal index view, and feature cluster analysis to model oral feeding readiness. Results. 568 suck assessment files sampled from 30 extremely preterm infants were processed in the batch mode (\u3c50 minutes) to generate time- and frequency-domain analyses of infant NNS pressure waveform data. NNS cycle discrimination and NNS burst classification yield quantification of NNS waveform features as a function of postmenstrual age. Hierarchical cluster analysis (based on the Tsfresh python package and NeoNNS) revealed the capability to label NNS records for feeding readiness. Conclusions. NeoNNS provides a versatile software platform to rapidly quantify the dynamics of NNS development in time and frequency domains at cribside over repeated sessions for an individual baby or among large numbers of preterm infants at multiple hospital sites to support big data analytics. +e hierarchical cluster feature analysis facilitates modeling of feeding readiness based on quantitative features of the NNS compression pressure waveform

Self-similar turbulent dynamo

The amplification of magnetic fields in a highly conducting fluid is studied numerically. During growth, the magnetic field is spatially intermittent: it does not uniformly fill the volume, but is concentrated in long thin folded structures. Contrary to a commonly held view, intermittency of the folded field does not increase indefinitely throughout the growth stage if diffusion is present. Instead, as we show, the probability-density function (PDF) of the field strength becomes self-similar. The normalized moments increase with magnetic Prandtl number in a powerlike fashion. We argue that the self-similarity is to be expected with a finite flow scale and system size. In the nonlinear saturated state, intermittency is reduced and the PDF is exponential. Parallels are noted with self-similar behavior recently observed for passive-scalar mixing and for map dynamos.Comment: revtex, 4 pages, 5 figures; minor changes to match published versio

On the impact of the cutoff time on the performance of algorithm configurators

Algorithm conigurators are automated methods to optimise the parameters of an algorithm for a class of problems. We evaluate the performance of a simple random local search conigurator (Param- RLS) for tuning the neighbourhood size k of the RLS k algorithm. We measure performance as the expected number of coniguration evaluations required to identify the optimal value for the parameter. We analyse the impact of the cutof time κ (the time spent evaluat- ing a coniguration for a problem instance) on the expected number of coniguration evaluations required to ind the optimal parameter value, where we compare conigurations using either best found itness values (ParamRLS-F) or optimisation times (ParamRLS-T). We consider tuning RLS k for a variant of the Ridge function class ( Ridge* ), where the performance of each parameter value does not change during the run, and for the OneMax function class, where longer runs favour smaller k . We rigorously prove that ParamRLS- F eiciently tunes RLS k for Ridge* for any κ while ParamRLS-T requires at least quadratic κ . For OneMax ParamRLS-F identiies k = 1 as optimal with linear κ while ParamRLS-T requires a κ of at least Ω ( n log n ) . For smaller κ ParamRLS-F identiies that k > 1 performs better while ParamRLS-T returns k chosen uniformly at random

Research priorities in hypertrophic cardiomyopathy: report of a Working Group of the National Heart, Lung, and Blood Institute.

Hypertrophic cardiomyopathy (HCM) is a myocardial disorder characterized by left ventricular (LV) hypertrophy without dilatation and without apparent cause (ie, it occurs in the absence of severe hypertension, aortic stenosis, or other cardiac or systemic diseases that might cause LV hypertrophy). Numerous excellent reviews and consensus documents provide a wealth of additional background.1–8 HCM is the leading cause of sudden death in young people and leads to significant disability in survivors. It is caused by mutations in genes that encode components of the sarcomere. Cardiomyocyte and cardiac hypertrophy, myocyte disarray, interstitial and replacement fibrosis, and dysplastic intramyocardial arterioles characterize the pathology of HCM. Clinical manifestations include impaired diastolic function, heart failure, tachyarrhythmia (both atrial and ventricular), and sudden death. At present, there is a lack of understanding of how the mutations in genes encoding sarcomere proteins lead to the phenotypes described above. Current therapeutic approaches have focused on the prevention of sudden death, with implantable cardioverter defibrillator placement in high-risk patients. But medical therapies have largely focused on alleviating symptoms of the disease, not on altering its natural history. The present Working Group of the National Heart, Lung, and Blood Institute brought together clinical, translational, and basic scientists with the overarching goal of identifying novel strategies to prevent the phenotypic expression of disease. Herein, we identify research initiatives that we hope will lead to novel therapeutic approaches for patients with HCM

Simulations of small-scale turbulent dynamo

We report an extensive numerical study of the small-scale turbulent dynamo at large magnetic Prandtl numbers Pm. A Pm scan is given for the model case of low-Reynolds-number turbulence. We concentrate on three topics: magnetic-energy spectra and saturation levels, the structure of the field lines, and the field-strength distribution. The main results are (1) the folded structure (direction reversals at the resistive scale, field lines curved at the scale of the flow) persists from the kinematic to the nonlinear regime; (2) the field distribution is self-similar and appears to be lognormal during the kinematic regime and exponential in the saturated state; and (3) the bulk of the magnetic energy is at the resistive scale in the kinematic regime and remains there after saturation, although the spectrum becomes much shallower. We propose an analytical model of saturation based on the idea of partial two-dimensionalization of the velocity gradients with respect to the local direction of the magnetic folds. The model-predicted spectra are in excellent agreement with numerical results. Comparisons with large-Re, moderate-Pm runs are carried out to confirm the relevance of these results. New features at large Re are elongation of the folds in the nonlinear regime from the viscous scale to the box scale and the presence of an intermediate nonlinear stage of slower-than-exponential magnetic-energy growth accompanied by an increase of the resistive scale and partial suppression of the kinetic-energy spectrum in the inertial range. Numerical results for the saturated state do not support scale-by-scale equipartition between magnetic and kinetic energies, with a definite excess of magnetic energy at small scales. A physical picture of the saturated state is proposed.Comment: aastex using emulateapj; 32 pages, final published version; a pdf file (4Mb) of the paper containing better-quality versions of figs. 5, 8, 12, 15, 17 is available from http://www.damtp.cam.ac.uk/user/as629 or by email upon request

The Run Control and Monitoring System of the CMS Experiment

The CMS experiment at the LHC at CERN will start taking data in 2008. To configure, control and monitor the experiment during data-taking the Run Control and Monitoring System (RCMS) was developed. This paper describes the architecture and the technology used to implement the RCMS, as well as the deployment and commissioning strategy of this important component of the online software for the CMS experiment