Observations and models for needle-tissue interactions

The asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. In this study we present a mechanics-based model that calculates the deflection of the needle embedded in an elastic medium. Microscopic observations for several needle- gel interactions were used to characterize the interactions at the bevel tip and along the needle shaft. The model design was guided by microscopic observations of several needle- gel interactions. The energy-based model formulation incor- porates tissue-specific parameters such as rupture toughness, nonlinear material elasticity, and interaction stiffness, and needle geometric and material properties. Simulation results follow similar trends (deflection and radius of curvature) to those observed in macroscopic experimental studies of a robot- driven needle interacting with different kinds of gels. These results contribute to a mechanics-based model of robotic needle steering, extending previous work on kinematic models

Tracking Charge Migration with Frequency-Matched Strobo-Spectroscopy

We present frequency-matched strobo-spectroscopy (FMSS) of charge migration (CM) in bromodiacetylene, simulated with time-dependent density-functional theory. CM+FMSS is a pump-probe scheme that uses a frequency-matched HHG-driving laser as an independent probe step following the creation of a localized hole on the bromine atom that induces CM dynamics. We show that the delay-dependent harmonic yield tracks the phase of the CM dynamics through its sensitivity to the amount of electron density on the bromine end of the molecule. FMSS takes advantage of the intrinsic attosecond time resolution of the HHG process, in which different harmonics are emitted at different times and thus probe different locations of the electron hole. Finally, we show that the CM-induced modulation of the HHG signal is dominated by the recombination step of the HHG process, with negligible contribution from the ionization step

Removal of Acid Yellow 25 from Aqueous Solution by Chitin Prepared from Waste Snow Crab Legs

Acid Yellow 25 (AY25) is used in the textile industry for dyeing of natural and synthetic fibers, and is also used as a coloring agent in paints, inks, plastics, and leathers. Effluents from such industries are major sources of water pollution. Hence, it is important to find simple, efficient, and inexpensive ways to remove these dyes from wastewater. Here, we determined the suitability of chitin extracted from waste crab legs as an adsorbent for removing AY25 dye. The adsorption kinetics was modeled using pseudo-first order, pseudo-second order, and intraparticle diffusion equations to determine the rate controlling step. Results showed that the pseudo-second order adsorption mechanism is predominant, and the overall rate of the dye adsorption process is therefore controlled by an adsorption reaction. Adsorption isotherms were analyzed by utilizing the Langmuir, Freundlich, Dubinin-Radushkevich (D-R) and Temkin isotherm models at 23˚C, with data collected by using various initial dye concentrations with different chitin dosages. Our results show the highest correlation with the Langmuir model, consistent with the fact that chitin contains both a monolayer and homogeneous adsorption sites. Based on the D-R model, the adsorption of AY25 dye onto chitin is via chemisorption. Furthermore, we have concluded that the rate constants of both pseudo-second order adsorption and film diffusion are correlated to the initial dye concentrations and chitin dosages. In conclusion, chitin from waste crab legs is a very suitable adsorbent material that is capable of rapidly removing up to 95% of the initial concentration of AY25 dye at a pH of 2 and room temperature

The relationship between cannabis outcome expectancies and cannabis refusal self-efficacy in a treatment population

Background and aims: Self-efficacy beliefs and outcome expectancies are central to Social Cognitive Theory (SCT). Alcohol studies demonstrate the theoretical and clinical utility of applying both SCT constructs. This study examined the relationship between refusal self-efficacy and outcome expectancies in a sample of cannabis users, and tested formal mediational models. Design: Patients referred for cannabis treatment completed a comprehensive clinical assessment, including recently validated cannabis expectancy and refusal self-efficacy scales. Setting: A hospital alcohol and drug out-patient clinic. Participants: Patients referred for a cannabis treatment [n=1115, mean age 26.29, standard deviation (SD) 9.39]. Measurements: The Cannabis Expectancy Questionnaire (CEQ) and Cannabis Refusal Self-Efficacy Questionnaire (CRSEQ) were completed, along with measures of cannabis severity [Severity of Dependence Scale (SDS)] and cannabis consumption. Findings: Positive (β=-0.29,

A Comparison of Administrative and Physiologic Predictive Models in Determining Risk Adjusted Mortality Rates in Critically Ill Patients

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r(2) for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting "report cards" or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models

Imperfect Sound Forever: Loudness Wars, Listening Formations and the History of Sound Reproduction

The purpose of this paper is to provide some historical perspective on the so-called loudness war. Critics of the loudness war maintain that the average volume level of popular music recordings has increased dramatically since the proliferation of digital technology in the 1980s, and that this increase has had detrimental effects on sound quality and the listening experience. My point is not to weigh in on this debate, but to suggest that the issue of loudness in sound recording and playback can be traced back much earlier than the 1980s. In fact, loudness has been a source of pleasure, a target of criticism, and an engine of technological change since the very earliest days of commercial sound reproduction. Looking at the period between the turn-of-the-century format feud to the arrival of electrical amplification in the 1920s, I situate the loudness war within a longer historical trajectory, and demonstrate a variety of ways in which loudness and volume have been controversial issues in – and constitutive elements of – the history of sound reproduction. I suggest that the loudness war can be understood in relation to a broader cultural history of volume

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Symptom Dimensions in OCD: Item-Level Factor Analysis and Heritability Estimates

To reduce the phenotypic heterogeneity of obsessive-compulsive disorder (OCD) for genetic, clinical and translational studies, numerous factor analyses of the Yale-Brown Obsessive Compulsive Scale checklist (YBOCS-CL) have been conducted. Results of these analyses have been inconsistent, likely as a consequence of small sample sizes and variable methodologies. Furthermore, data concerning the heritability of the factors are limited. Item and category-level factor analyses of YBOCS-CL items from 1224 OCD subjects were followed by heritability analyses in 52 OCD-affected multigenerational families. Item-level analyses indicated that a five factor model: (1) taboo, (2) contamination/cleaning, (3) doubts, (4) superstitions/rituals, and (5) symmetry/hoarding provided the best fit, followed by a one-factor solution. All 5 factors as well as the one-factor solution were found to be heritable. Bivariate analyses indicated that the taboo and doubts factor, and the contamination and symmetry/hoarding factor share genetic influences. Contamination and symmetry/hoarding show shared genetic variance with symptom severity. Nearly all factors showed shared environmental variance with each other and with symptom severity. These results support the utility of both OCD diagnosis and symptom dimensions in genetic research and clinical contexts. Both shared and unique genetic influences underlie susceptibility to OCD and its symptom dimensions.Obsessive Compulsive FoundationTourette Syndrome AssociationAnxiety Disorders Association of AmericaAmerican Academy of Child and Adolescent Psychiatr