Impact of Sociodemographic Disparities on Language Outcomes After Cochlear Implantation in a Diverse Pediatric Cohort.

OBJECTIVE: We examined how sociodemographic and audiologic factors affect receptive and expressive language outcomes in children with cochlear implantation. STUDY DESIGN: Retrospective cohort study. SETTING: A hearing loss (HL) clinic at a tertiary center. METHODS: Sociodemographic variables, HL characteristics, age at implantation, and receptive language scores (Preschool Language Scale and the Clinical Evaluation of Language Fundamentals) were collected from patients with congenital HL who received their first implant by 4 years old after January 1, 2007. t Tests, linear regression, Mann-Whitney, Cohen's d, and mediation analysis were used for descriptive statistics and hypothesis testing. RESULTS: Among 79 patients, 42 (53%) were females, 44 (56%) under-represented minorities, and 56 (71%) had public insurance. At least 1 year after implantation, the median receptive language score was 69 (range 50-117). Females (p = .005), having private insurance (p = .00001), having a Cochlear Implant Profile score below 4 (p = .0001), and receiving their implant at or before 12 months of age (p = .0009) were significantly associated with improved receptive language outcomes. Insurance type had a significant effect on receptive language outcomes, independent from age at first implantation (total effect: coef = -13.00, p = .02; direct effect: coef = -12.26, p = .03; indirect effect: coef = -0.75, p = .47). Sociodemographic variables had large effect sizes, with the Cochlear Implant Profile score having the largest effect size (d = 1.3). CONCLUSION: Sociodemographic factors have a large impact on receptive language outcomes. Public insurance is associated with worse receptive language, not mediated by later age at implantation, suggesting that other factors primarily impact language outcomes in publicly insured children with cochlear implants.

Is the clinical head impulse test helpful in cochlear implantation candidacy evaluation?

Objective: Vestibular dysfunction is a known risk of cochlear implantation (CI). However, the utility of the physical exam to screen CI candidates for vestibular dysfunction is not well-studied. The objective of this study is to evaluate the preoperative role of the clinical head impulse test (cHIT) in subjects undergoing CI surgery evaluation. Study Design Setting and Subjects: We conducted a retrospective review of 64 adult CI candidacy cases between 2017 and 2020 at a tertiary health care center. Methods: All patients underwent audiometric testing and evaluation by the senior author. Patients with an abnormal catch-up saccade contralateral to their worse hearing ear during cHIT were referred for formal vestibular testing. Outcomes included clinical and formal vestibular results, operated ear with regard to audiometric and vestibular results, and postoperative vertigo. Results: Among all CI candidates, 44% (n = 28) reported preoperative disequilibrium symptoms. Overall, 62% (n = 40) of the cHITs were normal, 33% (n = 21) were abnormal, and 5% (n = 3) were inconclusive. There was one patient who presented with a false positive cHIT. Among the patients who endorsed disequilibrium, 43% had a positive preoperative cHIT. Fourteen percent of the subjects (n = 9) without disequilibrium had an abnormal cHIT. In this cohort, bilateral vestibular impairment (71%) was more common than unilateral vestibular impairment (29%). In 3% of the cases (n = 2), surgical management was revisited or altered due to cHIT findings. Conclusion: There is a high prevalence of vestibular hypofunction in the CI candidate population. Self-reported assessments of vestibular function are often not congruent with cHIT results. Clinicians should consider incorporating cHITs as part of the preoperative physical exam to potentially avoid bilateral vestibular dysfunction in a minority of patients.

Preserved Cochlear Implant Function After Multiple Electroconvulsive Therapy Treatments.

This case report presents the successful use of multiple treatments of electroconvulsive therapy (ECT) in a patient with a cochlear implant (CI). A 60-year-old man with a left-sided CI and bipolar disorder presented with severe depression. A total of 9 separate sessions of unilateral ECT was administered to the contralateral side of the existing CI. We collected subjective, clinical, and audiological assessment of the patient and the CI prior, during, and after ECT therapy. The patient tolerated ECT well and there were no complications. Unilateral ECT was performed contralateral to the CI without any harm to the patient or implant. Laryngoscope, 131:E1695-E1698, 2021.

Resilience of the zooplankton community in the northeast Gulf of Mexico during and after the Deepwater Horizon oil spill.

We evaluated the resilience of the zooplankton community to the Deepwater Horizon oil spill in the northeast Gulf of Mexico, by assessing abundance, biomass, spatial distribution, species composition, and diversity indices during spring, summer, and winter, May 2010 to August 2014. SEAMAP samples collected between spring and summer 2005-2009 were analyzed as a baseline. Our results did not indicate that there was a long-term impact from the oil spill, but did demonstrate that environmental variability and riverine processes strongly governed zooplankton community dynamics. Zooplankton abundances during the oil spill (spring 2010) were not significantly different from abundances during spring 2011 and 2012. Summer 2010 abundances were the highest observed for the 2005 to 2014 period, due to high river discharge, high chlorophyll, and aggregation in eddies. High densities of the dinoflagellate, Noctiluca, during the oil spill, and the copepod, Centropages velificatus, and larvaceans in all years, suggest that these taxa warrant further investigation. Ecosystem connectivity (zooplankton transport by currents into the oil spill region), high fecundity, relatively short generation times, and refugia in deeper depths are key factors in zooplankton resilience to major perturbations. This study serves as a baseline for assessment of future impacts to this system.

A double-stranded RNA mycovirus confers hypovirulence-associated traits to Botrytis cinerea.

Botrytis cinerea CCg425 contains a 33-nm isometric mycovirus whose genome is a 6.8-kb double-stranded RNA (dsRNA) molecule. Virulence bioassays, performed by direct plug mycelial inoculation on bean plant leaves, showed that B. cinerea CCg425 displays less fungal aggressivity than B. cinerea CKg54, a virulent fungal strain that is not infected by dsRNA mycoviruses. B. cinerea CCg425 also showed lower laccase activity and conidiation rate than B. cinerea CKg54. Furthermore, infection of B. cinerea CKg54 with viral particles purified from B. cinerea CCg425 resulted in diminished virulence of the infected fungus. Collectively, our results indicate that mycovirus infection confers hypovirulence to the fungal host.

Recognizing plankton images from the shadow image particle profiling evaluation recorder.

We present a system to recognize underwater plankton images from the shadow image particle profiling evaluation recorder (SIPPER). The challenge of the SIPPER image set is that many images do not have clear contours. To address that, shape features that do not heavily depend on contour information were developed. A soft margin support vector machine (SVM) was used as the classifier. We developed a way to assign probability after multiclass SVM classification. Our approach achieved approximately 90% accuracy on a collection of plankton images. On another larger image set containing manually unidentifiable particles, it also provided 75.6% overall accuracy. The proposed approach was statistically significantly more accurate on the two data sets than a C4.5 decision tree and a cascade correlation neural network. The single SVM significantly outperformed ensembles of decision trees created by bagging and random forests on the smaller data set and was slightly better on the other data set. The 15-feature subset produced by our feature selection approach provided slightly better accuracy than using all 29 features. Our probability model gave us a reasonable rejection curve on the larger data set.

Fast support vector machines for continuous data.

Support vector machines (SVMs) can be trained to be very accurate classifiers and have been used in many applications. However, the training time and, to a lesser extent, prediction time of SVMs on very large data sets can be very long. This paper presents a fast compression method to scale up SVMs to large data sets. A simple bit-reduction method is applied to reduce the cardinality of the data by weighting representative examples. We then develop SVMs trained on the weighted data. Experiments indicate that bit-reduction SVM produces a significant reduction in the time required for both training and prediction with minimum loss in accuracy. It is also shown to typically be more accurate than random sampling when the data are not overcompressed.