Madison Area Technical College

The magnitude of thyroid cancer overdiagnosis in the US, accounting for the true increase in underlying disease, has not been quantified. The implications of reducing thyroid ultrasonography use for these rates has not been calculated. To estimate US population-level rates of papillary thyroid cancer (PTC) overdiagnosis, and to identify the implications of reducing thyroid ultrasonography use for thyroid cancer incidence and overall mortality. This study used a validated simulation model (Papillary Thyroid Carcinoma Microsimulation Model) of contemporary PTC incidence in the adult US population between 1991 and 2019. Data analysis was conducted from June 2024 to August 2025. Thyroid cancer incidence and referrals for ultrasonography of nonpalpable thyroid nodules. Model-estimated relative and absolute rates of PTC overdiagnosis, stratified by sex. The model estimated changes in the rates of thyroid cancer incidence and overall mortality if ultrasonography rates were reduced. The simulation included all adults, aged 18 years or older, in the US. Between 1991 and 2019, the model estimated that 72% to 94% of PTC cases were overdiagnosed. While the proportion of overdiagnosis was slightly higher for women compared with men (75%-95% vs 63%-90%), the absolute rate of overdiagnosis was higher among women compared with men (13-17 per 100 000 individuals vs 3-5 per 100 000 individuals). This rate translated into 443 212 to 573 705 women and 107 804 to 154 504 men who were overdiagnosed with PTC over 28 years. Reducing use of thyroid ultrasonography for nonpalpable nodules by 33% and 67% would have decreased PTC incidence by 17% (18 to 15 per 100 000 individuals) and 41% (18 to 11 per 100 000 individuals) in 2019, respectively, a less than 0.1% change in overall mortality. This study found that thyroid cancer overdiagnosis in the US remains substantial, even after accounting for possible increases in true incidence. This finding suggests an opportunity to reduce unnecessary thyroid ultrasonography referrals, particularly for nonpalpable nodules, and to reduce unnecessary diagnoses and treatment-related harms without increasing mortality.

Ecohydrologic processes change urban water and energy cycles, influencing convective rainfall. Coupled land-atmosphere models, however, lack the ability to represent urban ecohydrology. This study integrates fine-scale urban ecohydrologic processes in

This study explores a lightweight fine-tuning approach using the VITS model to generate high-fidelity, personalized TTS outputs with minimal data. The paper constructed a small-scale speech dataset from a public Figure by extracting short, high-quality audio segments from publicly available video platforms. The dataset was processed into an LJSpeech-compatible format and used to fine-tune a pretrained VITS model within the Coqui-Ttsframework, with the discriminator module disabled to enhance training stability. Objective and subjective evaluations-including Word Error Rate (WER), Character Error Rate (CER), Mean Opinion Score (MOS), and pitch-related RMSE-demonstrate significant improvements in naturalness and speaker similarity compared to the baseline XTTS model. The results show a 12.8% increase in MOS and enhanced consistency in speech prosody. This research illustrates the feasibility of adapting neural TTS models to unique speaker characteristics in low-resource settings and highlights both the technical potential and ethical implications of voice cloning technologies.

INTRODUCTIONShort‐chain fatty acids (SCFAs), produced by gut microbes, influence Alzheimer's disease (AD) pathology in animals. Less is known about SCFAs and AD in humans. We profiled feces of adults along the AD continuum to investigate gut microbiome and SCFA associations with AD pathology and cognition.METHODSWe measured SCFAs and bacterial abundances in fecal samples from 287 participants in the Wisconsin Alzheimer's Disease Research Center and Wisconsin Registry for Alzheimer's Prevention. We performed regressions examining associations between SCFAs or gut microbes and AD pathology and cognition.RESULTSFecal propionate, isovalerate, and propionate‐producing bacteria are inversely associated with amyloid status. Mediation analysis found that propionate mediates sex‐specific associations between SCFAs and cerebrospinal fluid biomarkers. SCFA levels are associated with slower cognitive decline.DISCUSSIONThese results link SCFAs and propionate‐producing bacteria with AD. This may inform efforts to leverage diet and specific bacteria to boost SCFA production and potentially ameliorate AD progression.HighlightsFecal SCFAs link to lower amyloid burden and microglial activation, notably in females.SCFA‐producing gut microbes have reduced abundance in amyloid‐positive participants.Fecal propionate mediates relationships between gut microbes and amyloid status.SCFAs are associated with slower plasma pTau217 accumulation in females.SCFAs are associated with slower cognitive decline.

Sustainable agricultural inputs are essential for enhancing food security, promoting development, and ensuring resilience in rapidly emerging economies; however, studies utilizing the advanced autoregressive distributed lag (ARDL) approach to investigate their impact on agriculture-driven economic growth remain limited. This research aims to fill this gap by employing a dynamic ARDL simulation model to analyze the relationship between sustainable agricultural inputs and economic growth in the agricultural sector, utilizing annual time series data spanning from 1983 to 2023. The analysis includes unit root tests, which affirm the stationarity of all variables, and the ARDL bounds test, which reveals a significant long-run cointegrating relationship among them. Our findings indicate that the expansion of irrigated areas and increased usage of chemical fertilizers contribute significantly to agricultural economic growth in both the short and long run, while agricultural mechanization only positively affects growth in the short run. Notably, the variable representing crop-sown land does not exhibit a statistically significant impact on agricultural economic growth across both time frames. Furthermore, the results from novel DYARDL simulations, which assess the implications of 10% positive and negative shocks, further substantiate both short-run and long-run analysis outcomes. The insights generated from this study highlight the critical role that sustainable agricultural inputs play in agricultural economic dynamics and provide evidence-based recommendations for fostering resilient, low-input systems that support global food security and agro-environmental sustainability.

A key component of sustainable transitions is the shift to renewable energy (RE) sources. Much scholarly work has focused on technical challenges of RE production or on social, political, and economic barriers to RE adoption. However, less research has focused on the geopolitical dynamics that could affect also inhibit RE transitions. This is especially true given China’s growing leverage over the production and trade of materials critical to RE production, distribution, and storage. Understanding the mechanisms, objectives, and implications of China’s dominance in the RE market requires a thorough overview. To analyse China’s strategic positioning in the global RE market, we apply a geoeconomics framework, highlighting the interplay between economic power, territorial strategies, and institutional mechanisms. China resembles the function of geopolitical chokepoints by imposing control over important components of the RE market and addressing trade and investment bottlenecks through institutional mechanisms. The study draws attention to the geoeconomic paradox that cooperation and competition coexist and promote market integration despite interdependencies. Being the second-largest producer of RE, the European Union both benefits from and faces difficulties as a result of China’s market power. We show how China’s geoeconomic strategies increase its influence globally and foster intricate interdependencies, which both facilitate market growth and exacerbate tensions with other strong powers.

Machine learning models find significant importance in Geographic Information Systems relating to agriculture. UAV-derived orthomosaics, digital height maps, and vegetative indices provide valuable data for predicting crop yields across regions. These insights can support informed decision-making for farmers and stakeholders. While machine learning excels at analyzing large datasets, training and validating models on randomly distributed spatial data may introduce uncertainty and bias due to spatial autocorrelation. This research evaluates the effectiveness of random and spatial cross-validation (CV) techniques for crop yield prediction using machine learning models. The study utilizes dry yield data from hybrid maize collected over two years across two fields in Waseca, MN. Feature selection was employed to reduce model complexity when analyzing multiple vegetative indices. Results indicate that random CV outperforms spatial CV, and among the models tested, Extreme Gradient Boosting achieved the highest predictive performance.

In the face of escalating climate challenges, the energy sector is increasingly investing in renewable projects. However, the implementation of utility-scale renewable energy initiatives is often hindered by public opposition. While previous research has offered detailed insights into the determinants of public acceptance generally, there remains scope for examining the impact of strategies that developers can employ to coalesce support for success at the local level. This study contributes with a comparative case study of two U.S. utility-scale projects: the Empire Wind offshore wind farm, which achieved success through proactive conflict resolution, and the Northern Pass hydroelectric transmission project, which was halted due to sustained local resistance. Our findings suggest the critical importance of community-centered conflict resolution strategies in garnering public support and facilitating the successful deployment of large-scale renewable energy projects.

BackgroundDuring the COVID-19 pandemic, rapid, at-home testing for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was inconsistently available. Consequently, for some patients, emergency departments (ED) became the preferred site to access COVID-19 testing. To improve operational efficiency, our ED implemented a “COVID-19 Test” chief concern (CC). Our primary objective in this analysis was to broadly assess the utilization of the new “COVID-19 Test” CC and associated clinical care.MethodsWe conducted a retrospective analysis of ED encounters from an academic ED and an affiliated, community-based ED of all patients after the establishment of a CC of “COVID-19 Test” from October 11, 2021–July 31, 2022. The data were extracted from the electronic health record. We calculated descriptive demographic statistics and ran a univariate and multivariate logistic regression with additional diagnostic or therapeutic interventions (binary) as the outcome variable to generate odds ratios (OR) and 95% confidence intervals (CI).ResultsA total of 320 patients were assigned a “COVID-19 Test” CC by a triage nurse. This was 0.5% of all ED encounters in this time frame. Of those, 45% were found to be SARS-CoV-2 positive. Admission or repeat ED visit at 72 hours occurred in 5.3% of patients. Nearly half (46.9%) of patients assigned a “COVID-19 Test” CC underwent additional ED interventions. Patients on Medicaid and those who self-identified as Black or Hispanic/Latino were disproportionately represented in the “COVID-19 Test” CC group as compared to the overall ED population. In multivariate analysis, an Emergency Severity Index of 1, 2 or 3 was associated with significantly higher odds of receiving additional interventions compared to ESI of 4 or 5 (adjusted OR: 46.85; 95% CI 13.28–165.26; P <0.001).ConclusionPatients assigned a chief concern of “COVID-19 Test” had a high COVID-19 positivity rate, often underwent additional ED interventions, and were at low risk of return ED visits or admission. Minoritized and low-income patients were disproportionately represented in the “COVID-19 Test” CC group, highlighting potential disparities in access to at-home COVID-19 testing and implementation of this CC.