Park University is a private university in Parkville, Missouri. It was founded in 1875. In the fall of 2017, Park had an enrollment of 11,457 students.
Following the June 24, 2022 Dobbs v. Jackson Supreme Court ruling, which overturned the federal right to abortion established in Roe v. Wade, hundreds of employers publicly announced policies covering out-of-state employee travel for abortions and related care. Leveraging data from Indeed and Glassdoor, we first document that companies with more female and more Democratic-leaning employees and executives were more likely to announce these policies. We then examine the causal impact such announcements had on recruitment, job satisfaction, and pay by introducing a new methodology to recover similar employers who did not make announcements using workers' revealed preferences in job search. Difference-in-differences estimates reveal that for announcing companies: (i) vacancies received more job seeker interest, particularly in Democratic-leaning states and female-dominated jobs in states with "trigger" laws that outlawed abortion, (ii) satisfaction with management fell amongst existing employees, particularly in male-dominated jobs, and (iii) posted wages increased, especially for companies where employee sentiment declined. These results highlight the complicated trade-off employers face from engaging in sociopolitical dialogue, in particular how signals of company culture can help recruit new workers but alienate current ones.
The roughness length ( z_0 ) and displacement height ( z_d ) are essential surface-layer parameters in numerical models (e.g., weather, climate, wall-modeled LES, etc.). This work evaluates the consistency of z_0 and z_d estimates from morphometric and anemometric methods using data from two eddy-covariance flux towers (AmeriFlux US-INg and US-INc) in Indianapolis, IN. Results show inconsistencies in estimated z_0 and z_d values depending on the chosen method. The two evaluated anemometric methods estimate non-physical values of z_d when compared to roughness elements surrounding both towers. Additionally, predictions of mean wind speed using surface-layer similarity theory with morphometric estimates exhibit a bias during near-neutral and stable conditions relative to observations. The overestimation of mean wind speed by surface layer similarity theory is consistent with previous observational and modeling studies in urban areas, suggesting that the application of similarity theories to urban environments may have limitations. Differentiation of vegetation from built structures appears to impact morphometric z_0 and z_d estimates, particularly where vegetation is abundant; however, it has little impact on correcting biases in the similarity theory. Specifically, we find that existing similarity theories using morphometric estimates underestimate integral velocity and length scales, and the degree of underestimation depends on the stability conditions. Accounting for the degree of anisotropy in surface-layer turbulence helps reduce the biases between similarity theories and observations during unstable conditions, but not in near-neutral cases. Future work is needed to identify the cause of such biases for near-neutral conditions.
Eukaryotic genes usually encode proteins and contain exons in different size, including micro and small exon categories. However, genome-wide gene exon-intron organizations and their conservation across angiosperms have not been investigated. Among exons of protein-coding genes from 46 angiosperms and four gymnosperms, 35
Objective Parkinson's disease (PD) is a progressive neurodegenerative disorder in which early diagnosis remains difficult due to subtle and heterogeneous symptoms. Speech impairments, particularly hypokinetic dysarthria, often appear early and offer promise as non-invasive biomarkers for detection. This study investigates whether quantitative speech-derived acoustic features can serve as reliable, non-invasive biomarkers for early detection of PD by analysing dysphonia measures extracted from sustained phonation recordings. Method A two-pronged analytical framework was used. First, exploratory data analysis was performed on 16 dysphonia features from 5875 sustained phonation samples collected from 42 individuals with idiopathic PD to examine feature distributions, correlations, and redundancies. Second, principal component analysis was applied to address multicollinearity among vocal features, and the resulting components were used as predictors in multiple regression-based machine learning models. Ensemble, kernel-based, and linear models were compared using standard metrics. Result Ensemble models delivered the strongest predictive performance. Random forest explained 91% of variance ( R 2 = 0.910 for motor Unified Parkinson's Disease Rating Scale (UPDRS); 0.901 for total UPDRS), with root mean squared error (RMSE) = 2.39 and 8.33 and mean absolute error (MAE) = 1.85 and 6.95, respectively. Gradient boosting explained 90% of variance ( R 2 = 0.900 for motor and total UPDRS), with RMSE = 2.52 and 8.42, and MAE = 1.86 and 7.14. Linear models performed substantially worse, consistently yielding R 2 < 0.12, indicating limited ability to capture nonlinear patterns in dysphonia characteristics. Conclusion Speech-derived acoustic biomarkers, when paired with machine learning, especially ensemble methods, show strong potential for accurate, scalable, and cost-effective assessment of PD severity. These findings highlight the potential of speech-derived acoustic biomarkers, coupled with machine learning, as scalable, cost-effective, and objective tools for improving diagnostic precision and enabling earlier intervention in PD.
