Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model

Phylogenetic comparative analysis is an approach to inferring evolutionary process from a combination of phylogenetic and phenotypic data. The last few years have seen increasingly sophisticated models employed in the evaluation of more and more detailed evolutionary hypotheses, including adaptive hypotheses with multiple selective optima and hypotheses with rate variation within and across lineages. The statistical performance of these sophisticated models has received relatively little systematic attention, however. We conducted an extensive simulation study to quantify the statistical properties of a class of models toward the simpler end of the spectrum that model phenotypic evolution using Ornstein-Uhlenbeck processes. We focused on identifying where, how, and why these methods break down so that users can apply them with greater understanding of their strengths and weaknesses. Our analysis identifies three key determinants of performance: a discriminability ratio, a signal-to-noise ratio, and the number of taxa sampled. Interestingly, we find that model-selection power can be high even in regions that were previously thought to be difficult, such as when tree size is small. On the other hand, we find that model parameters are in many circumstances difficult to estimate accurately, indicating a relative paucity of information in the data relative to these parameters. Nevertheless, we note that accurate model selection is often possible when parameters are only weakly identified. Our results have implications for more sophisticated methods inasmuch as the latter are generalizations of the case we study.Comment: 38 pages, in press at Systematic Biolog

Developing Guidance for Implementing Border Carbon Adjustments:Lessons, Cautions, and Research Needs from the Literature

Policymakers are often reluctant to implement strong carbon pricing for fear of disadvantaging domestic industries and offshoring emissions-intensive activities. Border carbon adjustment (BCA) would address such carbon leakage concerns by using trade measures to ensure that products from foreign producers facing lower (or no) carbon prices are on equal footing with domestically produced goods. Despite intuitive economic appeal, BCA requires numerous challenging regulatory choices, including its scope of applicability (i.e., which policies, goods, sectors, countries), the methodology for assessing the carbon content of products, the type and price of the adjustment, scenarios requiring modification, and how the resulting revenues will be used. Each of these choices has economic and environmental implications that influence the effectiveness of the BCA, as well as nuanced technical, legal, and political consequences that must be considered. In particular, the design of any BCA must comport with international agreements governing trade and climate policy responsibilities. This article reviews the economic and legal literature on BCA, provides guidance for the design and implementation of BCAs, and identifies research priorities

Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians

OBJECTIVE: Despite artificial intelligence (AI) being used increasingly in healthcare, implementation challenges exist leading to potential biases during the clinical decision process of the practitioner. The interaction of AI with novice clinicians was investigated through an identification task, an important component of diagnosis, in dental radiography. The study evaluated the performance, efficiency, and confidence level of dental students on radiographic identification of furcation involvement (FI), with and without AI assistance. MATERIALS AND METHODS: Twenty-two third- and 19 fourth-year dental students (DS3 and DS4, respectively) completed remotely administered surveys to identify FI lesions on a series of dental radiographs. The control group received radiographs without AI assistance while the test group received the same radiographs and AI-labeled radiographs. Data were appropriately analyzed using the Chi-square, Fischer\u27s exact, analysis of variance, or Kruskal-Wallis tests. RESULTS: Performance between groups with and without AI assistance was not statistically significant except for 1 question where tendency was to err with AI-generated answer ( DISCUSSION: Dental students detecting FI in radiographs with AI assistance had a tendency towards over-reliance on AI. CONCLUSION: AI input impacts clinical decision-making, which might be particularly exaggerated in novice clinicians. As it is integrated into routine clinical practice, caution must be taken to prevent overreliance on AI-generated information

Responses of Problematic Algae to Copper-based Algaecide

Eutrophication is the process by which a body of water acquires a high concentration of nutrients, especially phosphates and nitrates. These typically promote excessive growth of algae. As the algae die and decompose, high levels of organic matter and the decomposing organisms deplete the water of available oxygen, causing the death of other organisms, such as fish. Eutrophication is a natural, slow-aging process for a water body, but human activity greatly speeds up the process (USGS.gov). Golf Courses are often fertilized numerous times a year and can be major problem in fertilizer runoff in ponds surrounding the course. In east Texas where rainfall is more abundant then other parts of the state, golf courses are often more affected by algae in their ponds. Our objective is to clear present algae and prevent future algae blooms caused by fertilizer runoff in the ponds at Woodland Hills Golf Club

Mixture Proportion Estimation Beyond Irreducibility

The task of mixture proportion estimation (MPE) is to estimate the weight of a component distribution in a mixture, given observations from both the component and mixture. Previous work on MPE adopts the irreducibility assumption, which ensures identifiablity of the mixture proportion. In this paper, we propose a more general sufficient condition that accommodates several settings of interest where irreducibility does not hold. We further present a resampling-based meta-algorithm that takes any existing MPE algorithm designed to work under irreducibility and adapts it to work under our more general condition. Our approach empirically exhibits improved estimation performance relative to baseline methods and to a recently proposed regrouping-based algorithm

Ultraviolet C (UVC) Standards and Best Practices for the Swine Industry

Ultraviolet C (UVC) light has been widely used for disinfection for a long time in many industries, including human medicine and food processing. The practical application of this technology in livestock production is a more recent development and is increasingly being used on swine farms as producers look for ways to improve biosecurity in response to endemic diseases and the threat of transboundary and foreign animal diseases, such as African swine fever virus (ASFV). However, many swine producers and veterinarians are unfamiliar with the physics/mechanisms of UVC, the doses required to inactivate swine pathogens, and practical conditions under which UVC can operate effectively and practically on swine farms. Safety and maintenance requirements regarding the application are also not widely known. The pork industry lacks standards and best practices to apply this technology effectively and safely. To address this need, subject matter experts were convened for a one-day workshop to define standards and best practices for the use of UVC in the swine industry. The members of the working group included practicing swine veterinarians as well as academics with expertise in epidemiology, infectious disease, biosecurity, chemistry, and engineering. This white paper is the outcome of the workshop. In addition, the content of the white paper may be used to develop fact sheets, brochures and/or tutorial videos for swine producers and veterinarians