The Role of Telemedicine in Voice Therapy.

Introduction: During the COVID-19 pandemic, our institution adopted telemedicine for voice therapy (VT) as an alternative to in-person sessions, which has been integrated into our routine practice following the pandemic. This study aims to explore factors influencing completion rates among the 2 methods. Method: A retrospective chart review at a single tertiary care institution between 2019 and 2021 was conducted. Patient zip codes were used to determine Neighborhood Atlas® Area Deprivation Index (ADI) scores and travel distance to our institution. Demographic data, Voice Handicap Index (VHI) scores, and completion status were extracted. Results: Between 2019 and 2021, 521 patients were referred to VT at our institution, with 29% opting for telemedicine VT (TVT) sessions and 71% choosing in-person sessions. Seventy-four percent was female, and average age was 57.1 years (range:10-89 years old). No statistically significant differences were observed between the 2 groups regarding sex, age, employment status, or insurance type. Participants in the TVT group demonstrated notably higher completion rates compared to the in-person group [70.0% vs 31.6% (P < .001)]. The TVT group also comprised of a higher percentage of white patients, reported longer travel distances and times to reach therapy, but had comparable ADI scores to the in-person group. Moreover, there were no significant differences in pretreatment VHI scores between the 2 groups or between those who completed therapy versus those who did not (P = .501). Conclusion: Our findings indicate that patients utilizing the telemedicine platform had significantly higher VT completion rates compared to patients appearing in person. These results highlight the importance of being able to offer telemedicine-based options in the management of voice patients.

Parallels of quantum superposition in ecological models: from counterintuitive patterns to eco-evolutionary interpretations of cryptic species.

BACKGROUND: Superposition, i.e. the ability of a particle (electron, photon) to occur in different states or positions simultaneously, is a hallmark in the subatomic world of quantum mechanics. Although counterintuitive at first sight, the quantum world has potential to inform macro-systems of people and nature. Using time series and spatial analysis of bird, phytoplankton and benthic invertebrate communities, this paper shows that superposition can occur analogously in redundancy analysis (RDA) frequently used by ecologists. RESULTS: We show that within individual ecosystems single species can be associated simultaneously with different orthogonal axes in RDA models, which suggests that they operate in more than one niche spaces. We discuss this counterintuitive result in relation to the statistical and mathematical features of RDA and the recognized limitations with current traditional species concepts based on vegetative morphology. CONCLUSION: We suggest that such "quantum weirdness" in the models is reconcilable with classical ecosystems logic when the focus of research shifts from morphological species to cryptic species that consist of genetically and ecologically differentiated subpopulations. We support our argument with theoretical discussions of eco-evolutionary interpretations that should become testable once suitable data are available.

Behavioral observations, heart rate and heart rate variability in horses following oral administration of a cannabidiol containing paste in three escalating doses (part 1/2).

Cannabidiol (CBD) products have been proposed to exert stress- and anxiety-relieving effects in animals. Despite the increasing popularity of CBD for veterinary use, the available research detailing the effects of CBD in horses is limited. The aim of this study (part 1 of 2) was to analyze stress parameters via behavioral observations and heart rate monitoring in healthy horses following single oral administration of a CBD containing paste in different doses. Study products were two pastes for oral administration, one containing CBD and one containing no active ingredient. Pastes were applied as single administrations in consecutive trials with escalating dosages (doses: 0.2, 1.0, 3.0 mg CBD/kg) to a treatment (trial 1: n = 3, trial 2: n = 3, trial 3: n = 5 horses) and a control group (trial 1: n = 3, trial 2: n = 3, trial 3: n = 6 horses) with minimum wash-out periods of seven days in between. Behavioral parameters were evaluated using video recordings to score the levels of sedation including the horses' reactions to acoustic and visual stimuli. Facial expression was assessed using photographs. Evaluation was based on the previously described facial sedation scale for horses (FaceSed) and the Horse Grimace Scale. For baseline values, identical observations were recorded on the day before each paste administration. Both paste administration and behavioral evaluation were performed double blinded. Cardiac beat-to-beat (R-R) intervals were continuously recorded throughout the trial and assessed using heart rate and heart rate variability parameters. Statistical analysis included comparison between treatment and control group over escalating doses and time points using linear mixed models. The CBD paste was well tolerated, and no side effects were observed. Analysis of sedation scores and facial expressions did not indicate significant differences between treatment and control group over the escalating doses. The heart rate was neither reduced, nor were significant changes in heart rate variability observed compared to the control group. Main limitation of this study is the small sample size. Further research is required to determine adequate doses and indications for the use of CBD products in horses.

Drivers of long-term invertebrate community stability in changing Swedish lakes.

Research on ecosystem stability has had a strong focus on local systems. However, environmental change often occurs slowly at broad spatial scales, which requires regional-level assessments of long-term stability. In this study, we assess the stability of macroinvertebrate communities across 105 lakes in the Swedish "lakescape." Using a hierarchical mixed-model approach, we first evaluate the environmental pressures affecting invertebrate communities in two ecoregions (north, south) using a 23 year time series (1995-2017) and then examine how a set of environmental and physical variables affect the stability of these communities. Results show that lake latitude, size, total phosphorus and alkalinity affect community composition in northern and southern lakes. We find that lake stability is affected by species richness and lake size in both ecoregions and alkalinity and total phosphorus in northern lakes. There is large heterogeneity in the patterns of community stability of individual lakes, but relationships between that stability and environmental drivers begin to emerge when the lakescape, composed of many discrete lakes, is the focal unit of study. The results of this study highlight that broad-scale comparisons in combination with long time series are essential to understand the effects of environmental change on the stability of lake communities in space and time.

Adaptive capacity in ecosystems.

Understanding the adaptive capacity of ecosystems to cope with change is crucial to management. However, unclear and often confusing definitions of adaptive capacity make application of this concept difficult. In this paper, we revisit definitions of adaptive capacity and operationalize the concept. We define adaptive capacity as the latent potential of an ecosystem to alter resilience in response to change. We present testable hypotheses to evaluate complementary attributes of adaptive capacity that may help further clarify the components and relevance of the concept. Adaptive sampling, inference and modeling can reduce key uncertainties incrementally over time and increase learning about adaptive capacity. Such improvements are needed because uncertainty about global change and its effect on the capacity of ecosystems to adapt to social and ecological change is high.

An equation-of-state-meter of quantum chromodynamics transition from deep learning.

A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.

A quantitative framework for assessing ecological resilience.

Quantitative approaches to measure and assess resilience are needed to bridge gaps between science, policy and management. In this paper, we revisit definitions of resilience and suggest a quantitative framework for assessing ecological resilience sensu Holling (1973). Ecological resilience as an emergent ecosystem phenomenon can be decomposed into complementary attributes (scales, adaptive capacity, thresholds and alternative regimes) that embrace the complexity inherent to ecosystems. Quantifying these attributes simultaneously provides opportunities to move from the assessment of specific resilience within an ecosystem towards a broader measurement of its general resilience. We provide a framework, based on testable hypotheses, which allows assessment of complementary attributes of ecological resilience. By implementing the framework in adaptive approaches to management, inference and modeling, key uncertainties can be reduced incrementally over time and learning about the general resilience of dynamic ecosystems maximized. Such improvements are needed because uncertainty about global environmental change impacts and their effects on resilience is high. Improved resilience assessments will ultimately facilitate an optimized use of limited resources for management.

Vortical Fluid and Λ Spin Correlations in High-Energy Heavy-Ion Collisions.

Fermions become polarized in a vortical fluid due to spin-vorticity coupling, and the polarization density is proportional to the local fluid vorticity. The radial expansion converts spatial vortical structures in the transverse plane to spin correlations in the azimuthal angle of final Λ hyperons' transverse momentum in high-energy heavy-ion collisions. Using a (3+1)D viscous hydrodynamic model with fluctuating initial conditions from a multiphase transport (AMPT) model, we reveal two vortical structures that are common in many fluid dynamic systems: a right-handed toroidal structure around each beam direction for transverse vorticity and pairing of longitudinal vortices with opposite signs in the transverse plane. The calculated azimuthal correlation of the transverse spin is shown to have a cosine form plus an offset due to the toroidal structure of the transverse vorticity around the beam direction and the global spin polarization. The longitudinal spin correlation in the azimuthal angle shows an oscillatory structure due to multiple vorticity pairs in the transverse plane. Mechanisms of these vortical structures, physical implications of hyperon spin correlations, dependence on colliding energy, rapidity, centrality, and sensitivity to the shear viscosity are also investigated.

Increasing the perceptual salience of relationships in parallel coordinate plots.

We present three extensions to parallel coordinates that increase the perceptual salience of relationships between axes in multivariate data sets: (1) luminance modulation maintains the ability to preattentively detect patterns in the presence of overplotting, (2) adding a one-vs.-all variable display highlights relationships between one variable and all others, and (3) adding a scatter plot within the parallel-coordinates display preattentively highlights clusters and spatial layouts without strongly interfering with the parallel-coordinates display. These techniques can be combined with one another and with existing extensions to parallel coordinates, and two of them generalize beyond cases with known-important axes. We applied these techniques to two real-world data sets (relativistic heavy-ion collision hydrodynamics and weather observations with statistical principal component analysis) as well as the popular car data set. We present relationships discovered in the data sets using these methods.

Exploring Ensemble Visualization.

An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.

Comparative Visualization of Ensembles Using Ensemble Surface Slicing.

By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call Ensemble Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.