Lower Estimates Research Articles

Mortality attributable to ambient PM2.5 exposure across regions in China from 2005 to 2020

BackgroundChina has implemented rigorous clean air policies since 2013 to address PM2.5 pollution particularly affecting economically developed regions. The Fusion relative risk model was recently developed to better capture the association between long-term PM2.5 exposure and mortality risk, but it has not been used in China. MethodsWe estimated cause-specific PM2.5 attributable deaths among adults (≥ 25 years) across China over 2005–2020 at a 0.1° × 0.1° spatial resolution using the Fusion model, and also adopted the Global Exposure Mortality Model (GEMM) for comparison. We investigated 31 provinces and targeted five mega city clusters: Beijing–Tianjin–Hebei cluster (BTH), Yangtze River Delta cluster (YRD), Pearl River Delta cluster (PRD), Cheng–Yu cluster (CY) and Middle reaches of the Yangtze River cluster (MYR). ResultsThe Fusion model estimated PM2.5 attributable deaths increased from 2.38 million (95 % UI: 2.06-2.64) in 2005 to 2.68 million (95 % UI: 2.33-2.95) in 2013 (12.6 %), then declined by 11.6 % to 2.37 million (95 % UI: 2.04-2.65) in 2020. Ischemic Heart Disease (IHD) and Stroke contributed most to the total attributable deaths (33.4 % and 35.0 % in 2020). Compared with the GEMM, the Fusion model generated higher attributable mortality estimates for IHD, Stroke and Chronic Obstructive Pulmonary Disease, but lower estimates for Lung Cancer and Lower Respiratory Infections. PM2.5 attributable deaths were clustered in densely populated and highly polluted regions, with Henan and Shandong bearing the highest mortality burden among the 31 provinces. PM2.5 attributable deaths in BTH, YRD, PRD, CY and MYR declined by 5.3 %, 11.5 %, 18.0 %, 25.4 % and 18.6 % respectively over 2013–2020, with greater declines in attributable mortality rates (18.9 %, 23.8 %, 33.8 %, 27.3 % and 24.1 %). ConclusionsFuture clean air policies in China should consider regional disparities and continue prioritizing highly polluted and densely populated urban areas, including the five mega city clusters.

Lower estimates of myocardial perfusion are associated with greater aortic perivascular adipose tissue density in humans independent of aortic stiffness.

Aortic perivascular adipose tissue (aPVAT) density is associated with age-related aortic stiffness in humans and therefore, may contribute to cardiovascular dysfunction. A lower subendocardial viability ratio (SEVR), an estimate of myocardial perfusion, indicates greater cardiovascular disease (CVD) risk and is associated with aortic stiffness in clinical populations. However, the influence of aortic stiffness on the relation between aPVAT density and SEVR/cardiovascular (CV) hemodynamics in apparently healthy adults is unknown. We hypothesize that greater aPVAT density will be associated with lower SEVR and higher CV hemodynamics independent of aortic stiffness. Fourteen (6 males/8 females; mean age, 55.4 ± 5.6 yr; body mass index, 25.5 ± 0.6 kg/m2) adults completed resting measures of myocardial perfusion (SEVR), CV hemodynamics (pulse wave analysis), aortic stiffness [carotid-femoral pulse wave velocity (cfPWV)], and a computed tomography scan to acquire aPVAT and visceral adipose tissue (VAT) density. Greater aPVAT density (i.e., higher density) was associated with lower SEVR (r = -0.78, P < 0.001) and a higher systolic pressure time integral (r = 0.49, P = 0.03), forward pulse height (r = 0.49, P = 0.03), reflected pulse height (r = 0.55, P = 0.02), ejection duration (r = 0.56, P = 0.02), and augmentation pressure (r = 0.69, P = 0.003), but not with the diastolic pressure time integral (r = -0.22, P = 0.22). VAT density was not associated with SEVR or any CV hemodynamic endpoints (all, P > 0.05). Furthermore, the relation between aPVAT density and SEVR remained after adjusting for aortic stiffness (r = -0.66, P = 0.01) but not age (r = -0.24, P > 0.05). These data provide initial evidence for aPVAT as a novel yet understudied local fat depot contributing to lower myocardial perfusion in apparently healthy adults with aging.NEW & NOTEWORTHY Aortic perivascular adipose tissue (aPVAT) density is associated with aging and aortic stiffness in humans and, therefore, may contribute to lower myocardial perfusion. We demonstrate that greater aPVAT, but not visceral adipose tissue density is associated with lower myocardial perfusion and augmentation pressure independent of aortic stiffness, but not independent of age. These data provide novel evidence for aPVAT as a potential therapeutic target to improve myocardial perfusion and cardiovascular function in humans with aging.

Joint estimation of SOH and RUL for lithium batteries based on variable frequency and model integration

The safe and stable operation of lithium-ion batteries in electric vehicles is crucial. Aiming at the problems of a large workload of online estimation and prediction and inability to weigh the whole life cycle of the battery in the joint estimation of SOH (State of Health) and RUL (Remaining Useful Life) for traditional lithium-ion batteries, this paper proposes an optimization scheme for the joint analysis of SOH-RUL. First, the article extracts suitable health features. It utilizes an integrated Extreme Learning Machine (ELM) to estimate the SOH of the battery and inputs the estimates into a Regression Vector Machine (RVM) model for RUL prediction. To better cope with the phenomenon of "sudden capacity change" in batteries, this paper divides the entire life cycle into the early and late stages. A lower estimation frequency and model integration are employed in the early stage.In contrast, the estimation frequency and model integration are increased later to balance speed and safety in total life cycle estimation. Finally, validation was performed on the NASA and CALCE datasets, comparing the effect of constant estimated frequency and model integration with that of varying estimated frequency and model integration at different aging stages. The RMSE of SOH estimation error for both datasets is within 2% during the early and late stages, and the SOH estimation error for the entire lifecycle is within 2%. The absolute RUL prediction error for the NASA dataset is below five times; for the CALCE dataset, it is below 20 times in most cases.

The inter-rater and intra-rater reliability in scoring writing tests in UTAS

Rater reliability, the consistency of marking across different raters and times, is one important component of reliability regarding the quality of test scores. It is essential regarding performance assessment, such as writing, when the fairness of assessment results can come into question due to the subjectivity when scoring. The present study, part of a larger-scale funded research project, aimed to study this overlooked area in the Omani context, i.e., the reliability in scoring the writing section of the final exams in the University of Technology and Applied Sciences (UTAS). More specifically, the study investigated the estimates of inter-rater and intra-rater reliability among 10 writing markers assessing 286 and 156 students' writing scripts belonging to four levels of proficiency at three different levels of analysis: the whole writing tests, tasks 1 and tasks 2, and the constituent criteria of both tasks. The results indicated a rather high value of inter-rater reliability and a moderate one for intra-rater reliability in general. However, when interpreted regarding raters' personal and background information, some low estimates shed light on the importance of factors influencing scoring consistency across different assessors and times.

Characterization of sleep apnea among a sample of adults from Samoa

Sleep apnea is a global public health concern, but little research has examined this issue in low- and middle-income countries, including Samoa. The purpose of this study was to examine the sample prevalence and characteristics of sleep apnea using a validated home sleep apnea device (WatchPAT, Itamar) and explore factors that may influence sleep health in the Samoan setting. This study used data collected through the Soifua Manuia (“Good Health”) study, which investigated the impact of the body mass index (BMI)-associated genetic variant rs373863828 in CREBRF on metabolic traits in Samoan adults (sampled to overrepresent the obesity-risk allele of interest). A total of 330 participants had sleep data available. Participants (53.3 % female) had a mean (SD) age of 52.0 (9.9) years and BMI of 35.5 (7.5) kg/m2, and 36.3 % of the sample had type 2 diabetes. Based on the 3 % and 4 % apnea hypopnea indices (AHI) and the 4 % oxygen desaturation index (ODI), descriptive analyses revealed moderate to severe sleep apnea (defined as ≥15 events/hr) in 54.9 %, 31.5 %, and 34.5 % of the sample, respectively. Sleep apnea was more severe in men (e.g., AHI 3 % ≥15 in 61.7 % of men and 48.9 % of women). Correction for non-representational sampling related to the CREBRF obesity-risk allele resulted in only slightly lower estimates. Multiple linear regression linked a higher number of events/hr to higher age, male sex, higher BMI, higher abdominal-hip circumference ratio, and geographic region of residence. Further research and an increased focus on equitable and affordable diagnosis and access to treatment are crucial to addressing sleep apnea in Samoa and globally.

Bayesian Lower and Upper Estimates for Ether Option Prices with Conditional Heteroscedasticity and Model Uncertainty

This paper aims to leverage Bayesian nonlinear expectations to construct Bayesian lower and upper estimates for prices of Ether options, that is, options written on Ethereum, with conditional heteroscedasticity and model uncertainty. Specifically, a discrete-time generalized conditional autoregressive heteroscedastic (GARCH) model is used to incorporate conditional heteroscedasticity in the logarithmic returns of Ethereum, and Bayesian nonlinear expectations are adopted to introduce model uncertainty, or ambiguity, about the conditional mean and volatility of the logarithmic returns of Ethereum. Extended Girsanov’s principle is employed to change probability measures for introducing a family of alternative GARCH models and their risk-neutral counterparts. The Bayesian credible intervals for “uncertain” drift and volatility parameters obtained from conjugate priors and residuals obtained from the estimated GARCH model are used to construct Bayesian superlinear and sublinear expectations giving the Bayesian lower and upper estimates for the price of an Ether option, respectively. Empirical and simulation studies are provided using real data on Ethereum in AUD. Comparisons with a model incorporating conditional heteroscedasticity only and a model capturing ambiguity only are presented.

Improving Indoor WiFi Localization by Using Machine Learning Techniques

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.

Interval State Estimation of Systems with Metzler Polytopic Models

The paper deals with the design of interval observers for interval-defined strictly Metzler polytopic positive systems. The stability conditions for the proposed structure of the interval observer are formulated using linear matrix inequalities to ensure a positive estimate of the system state. The proposed method makes it possible to calculate time-varying lower and upper estimates of the state vector, assuming that the disturbance is bounded. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Practical non-stationary extreme value analysis of peaks over threshold using the generalised Pareto distribution: Estimating uncertainties in return values

Choice of tuning parameters influences the performance of non-stationary extreme value modelling for peaks over threshold using the generalised Pareto (GP) distribution. We examine the effect of tuning parameter choice on maximum roughness-penalised likelihood estimation of GP models, the shape and scale parameters of which are assumed to vary smoothly on a one-dimensional “directional” covariate domain, under a B-spline representation. We examine the effect of (a) extreme value model parameterisation, (b) relative roughness penalty of GP parameters as a function of covariate, (c) cross-validation strategy for roughness parameter tuning, and (d) estimator for return value, on the estimation of return values corresponding to return periods 1000× the period of a sample of size 1000. Bootstrap resampling is used for thorough uncertainty quantification. We also compare results with those from stationary inference.Results from a large simulation study of 16 cases broadly representative of North Sea conditions for significant wave height with direction, indicate that (i) multiple two-group cross-validation yields lower return value estimates that ten-group cross-validation (leading to negative bias on average, for the case studies considered), (ii) the quantile of the bootstrap predictive estimator yields larger values than the mean over bootstraps of the quantile estimate (leading to reduced omni-directional bias for the case studies considered). Further, (iii) the use of stationary models for non-stationary tails is only reasonable when a high extreme value threshold is set for the stationary analysis. However, (iv) the relative performance of different modelling strategies is sensitive to the specific characteristics of the case study.

Abstract A148: Perceptions of lung cancer screening among Native American patients: The Tribally Engaged Approaches to Lung Screening (TEALS) study

Abstract Introduction: Uptake of lung cancer screening (LCS) with low dose computed tomography is poor nationwide. The U.S. Native American (NA) population has a high prevalence of smoking and lung cancer mortality but low rates of LCS completion. Awareness and perceptions of LCS among NA men and women have rarely been studied. Methods: Using a community-engaged research approach in partnership with the Choctaw Nation Health Services Authority in Oklahoma, the “Tribally Engaged Approaches to Lung Screening” study developed and implemented a cluster-randomized controlled trial of a coordinated LCS program. In 2022, NA men and women eligible for LCS from six tribal health clinics completed a baseline survey prior to being referred for LCS. Surveys included demographic characteristics, smoking history, use of cancer preventive services, beliefs regarding personal risk of lung cancer, and perceptions toward LCS. Findings: Among 210 participants (57% women, 43% men), mean age was 63 years; 50% reported annual household income below $25,000, 83% completed high school or equivalent, with 40% having some college education. The median pack years of smoking was 46 years. The proportion of participants who currently smoke was 72%. Current cigarette users were slightly younger than former users (median age 61 vs 64 years, respectively) and similarly had lower maximum estimates of pack-years of use (median, 44 vs 52). Most (93%) had more than one primary care visit within the past 12 months, and 76% completed an annual physical or wellness visit in the past year. Overall, 73% had completed colonoscopy and, among women, 80% had a mammogram. Only 62% had heard about LCS. However, 74% agreed with the statement “It is likely that I will get lung cancer sometime in my lifetime”. More than 90% of participants agreed that LCS would help with each of the following: early detection, chances of dying from lung cancer, reduce personal worry about lung cancer, reduce their family’s worries, improve planning for the future and peace of mind. However, 44% worried that LCS would find “something wrong”, 15% endorsed that they would rather not know if they had any lung problems, and 10% were afraid the scan would damage their lungs. About 20% worried about being a social outcast or being blamed for having smoked. Regarding potential barriers, 32% felt that cost would be a problem, 28% felt they did not have the time, and 17% that transportation would be a problem. Overall,14% reported bad experiences with healthcare or a provider, and 8% did not trust the healthcare system. Regarding LCS, 28% felt they did not know enough about the test, 11% felt they were too old to benefit from LCS, and 8% felt LCS was not worth the effort. Conclusions: Perceptions of the potential benefits from LCS were overall favorable in this sample of NA adults. However, up to 30% perceived significant barriers to LCS uptake. Greater awareness of LCS and mitigation of barriers to LCS would help to reduce the marked disparities in lung cancer mortality among NA patients. Citation Format: Dorothy A. Rhoades, Cara Vaught, Ann Chou, Kathleen Dwyer, Mark P Doescher, Michele Gibson, Zsolt Nagykaldi. Perceptions of lung cancer screening among Native American patients: The Tribally Engaged Approaches to Lung Screening (TEALS) study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A148.

The Incorrect Use of Artificial Intelligence Applications among University Students and their Impact on the Credibility of Learning

This study aims to explore the level of using artificial intelligence applications among university students and their impact on the credibility of e-learning. The study used the descriptive method because it is suitable for the study. The study included a sample of 94 Jordanian university students, who were selected by stratified random method. The study used a questionnaire of 40 items as the research instrument to determine the level of the use of artificial intelligence technology and distance education in distorting the contemporary educational landscape. The results showed that universities try to limit the incorrect use of AI applications among students by using specific detection tools. The students showed that their universities have strict rules and regulations regarding the use of AI tools in their studies. Therefore, there were low estimations from the students on the large incorrect usage of AI tools. The results also revealed that gender and the specialization of the student do not affect their views toward the incorrect use of AI tools in their education, whether face-to-face education or online education.

Prevalence estimates for COVID-19-related health behaviors based on the cheating detection triangular model

BackgroundSurvey studies in medical and health sciences predominantly apply a conventional direct questioning (DQ) format to gather private and highly personal information. If the topic under investigation is sensitive or even stigmatizing, such as COVID-19-related health behaviors and adherence to non-pharmaceutical interventions in general, DQ surveys can lead to nonresponse and untruthful answers due to the influence of social desirability bias (SDB). These effects seriously threaten the validity of the results obtained, potentially leading to distorted prevalence estimates for behaviors for which the prevalence in the population is unknown. While this issue cannot be completely avoided, indirect questioning techniques (IQTs) offer a means to mitigate the harmful influence of SDB by guaranteeing the confidentiality of individual responses. The present study aims at assessing the validity of a recently proposed IQT, the Cheating Detection Triangular Model (CDTRM), in estimating the prevalence of COVID-19-related health behaviors while accounting for cheaters who disregard the instructions.MethodsIn an online survey of 1,714 participants in Taiwan, we obtained CDTRM prevalence estimates via an Expectation-Maximization algorithm for three COVID-19-related health behaviors with different levels of sensitivity. The CDTRM estimates were compared to DQ estimates and to available official statistics provided by the Taiwan Centers for Disease Control. Additionally, the CDTRM allowed us to estimate the share of cheaters who disregarded the instructions and adjust the prevalence estimates for the COVID-19-related health behaviors accordingly.ResultsFor a behavior with low sensitivity, CDTRM and DQ estimates were expectedly comparable and in line with official statistics. However, for behaviors with medium and high sensitivity, CDTRM estimates were higher and thus presumably more valid than DQ estimates. Analogously, the estimated cheating rate increased with higher sensitivity of the behavior under study.ConclusionsOur findings strongly support the assumption that the CDTRM successfully controlled for the validity-threatening influence of SDB in a survey on three COVID-19-related health behaviors. Consequently, the CDTRM appears to be a promising technique to increase estimation validity compared to conventional DQ for health-related behaviors, and sensitive attributes in general, for which a strong influence of SDB is to be expected.

Tahiti Population (Re)Estimates and Ideologies

The question of Tahiti population at contact was first addressed by assessing the reliability of early and later estimates, and then by a retrodiction based on the first reliable censuses and data on trends. Cook’s famous estimate of 204,000 Tahitians, affected by a wrong number of districts, has been reworked based on his own and the Forsters’ observations, using coast length and the age and sex structure of Tahiti population at contact in relation to infanticide, resulting in a range of 156,000-188,000 persons in 1774, before Boenechea’s flu in 1772 and the 1774 flu epidemics. The discrepancies between Wilson’s 16,050 and other missionaries’ estimate of 50,000 as of 1797 are puzzling, but the 1829-1830 missionary estimates were strongly affected by Tahitians’ resistance to Christianization. The twentieth century estimates are affected by a political context related to colonization - decolonisation, leading to the fact that researchers favour low estimates. McArthur uses missionary data, assuming that baptisms are births, although before the conversion was completed, baptisms included adults and children of all ages. She bases her estimate of 30,000 Tahitians at contact in 1767 on Wilson’s number of 16,050, who applied the size of nuclear households, 6 persons, to clans. Oliver followed her, however not excluding numbers twice as high or more. Our retrodiction on the 1848 and 1881 censuses, based on qualitative missionary reports on sanitary situation and data of Rarotonga that experienced similar events as Tahiti, yield, with conservative assumptions, 180,000 persons at contact, showing consistent results with early navigators’ estimates. My own PhD experience in the late 1980s shows that scholars’ re-estimates were constrained, directly or indirectly, by political considerations related to the guilt syndrome and colonialist ideology. This appears in the frequent contradictions found in their works to support low estimates in order to reduce the decline, despite evidence of dramatic epidemics and a steady decline extending almost throughout the 19th century. The same attitude of denial is found in some missionary, administrative and medical reports, particularly on the Marquesas in the late 19th century. Colonisation was associated with various ideologies, related to anthropology’s early attempts based on theories of evolutionism, functionalism and structuralism, occasionally resulting in fictions such as “primitive thinking”, “ahistorical societies” and “collective unconscious”, that were part of the justification of colonisation and its mistakes.

Revisiting Fishery Sustainability Targets

Density-dependent population dynamic models strongly influence many of the world’s most important harvest policies. Nearly all classic models (e.g. Beverton-Holt and Ricker) recommend that managers maintain a population size of roughly 40–50 percent of carrying capacity to maximize sustainable harvest, no matter the species’ population growth rate. Such insights are the foundational logic behind most sustainability targets and biomass reference points for fisheries. However, a simple, less-commonly used model, called the Hockey-Stick model, yields very different recommendations. We show that the optimal population size to maintain in this model, as a proportion of carrying capacity, is one over the population growth rate. This leads to more conservative optimal harvest policies for slow-growing species, compared to other models, if all models use the same growth rate and carrying capacity values. However, parameters typically are not fixed; they are estimated after model-fitting. If the Hockey-Stick model leads to lower estimates of carrying capacity than other models, then the Hockey-Stick policy could yield lower absolute population size targets in practice. Therefore, to better understand the population size targets that may be recommended across real fisheries, we fit the Hockey-Stick, Ricker and Beverton-Holt models to population time series data across 284 fished species from the RAM Stock Assessment database. We found that the Hockey-Stick model usually recommended fisheries maintain population sizes higher than all other models (in 69–81% of the data sets). Furthermore, in 77% of the datasets, the Hockey-Stick model recommended an optimal population target even higher than 60% of carrying capacity (a widely used target, thought to be conservative). However, there was considerable uncertainty in the model fitting. While Beverton-Holt fit several of the data sets best, Hockey-Stick also frequently fit similarly well. In general, the best-fitting model rarely had overwhelming support (a model probability of greater than 95% was achieved in less than five percent of the datasets). A computational experiment, where time series data were simulated from all three models, revealed that Beverton-Holt often fit best even when it was not the true model, suggesting that fisheries data are likely too small and too noisy to resolve uncertainties in the functional forms of density-dependent growth. Therefore, sustainability targets may warrant revisiting, especially for slow-growing species.

U.S. vertically integrated electric utility greenhouse gas emissions and carbon risk premiums around the Paris Accord

We study the pricing of greenhouse gas emissions of vertically integrated producers of electricity around the Paris Accord (PA). We study whether emissions are priced by financial markets, providing a market-based incentive for firms to reduce their carbon footprints and if the heightened attention on climate change post-Paris Accord (PA) impacts the size of the “carbon risk premium.” We focus on electricity generators, because they are responsible for the largest share of emissions and emissions reductions in the U.S. and are highly exposed to regulatory, physical, and stranded asset risks. We find the cost of carbon risk is reflected in the returns of vertically integrated electric utilities. The post-PA period provides the strongest evidence that carbon risk is priced. We find that equity markets provide incentives for power producers to reduce emissions, as reductions in emissions are associated with reductions in required returns on equity (increases in equity market values). The challenge for regulators is how to respond in rate cases. Lowering a utility's regulated return to reflect lower market estimates of the return on equity would dilute the market-based incentive for emissions reductions. Adding a longer-term return incentive for continued investment in emissions reductions would reinforce the market incentive.

PSVI-6 Genomic determinants of the rumen microbiome and metabolome in Angus steers

Abstract The rumen microbiome provides approximately 70% of energy required for the ruminant host through fermentation byproducts. Due to microbial impacts on host health and productivity, several studies have associated microbiome and metabolome variation with important sustainability phenotypes, such as feed efficiency and methane emissions. Recent studies have indicated the microbial communities present in rumen fluid are under low-to-moderate host genetic control. However, microorganisms present on the rumen wall have been underrepresented in research, negating their unique role in host-microbiome interactions. Current studies investigating heritability of the rumen planktonic microorganisms are plagued by small sample sizes, high management and breed heterogeneity, and low resolution for classifying microbial communities. To determine the heritability of the rumen fluid and epithelium-associated microbiome and metabolome, Angus steers (n = 350) were enrolled in a 70-d feed efficiency trial, where real-time dry matter intake was measured. Body weights were collected throughout the trial to calculate average daily gain and residual feed intake. Mid-trial, a novel surgery method was used to swab the rumen wall to capture epithelium-associated microorganisms. On d 70, 50 mL of rumen fluid was obtained from steers using orogastric tubing for collection of planktonic microbial communities. Metagenomic DNA extracted from both rumen fluid and epimural samples was used for reduced representation sequencing to predict microbial features such as diversity, composition, and putative function. Genomic DNA isolated from whole blood was used to perform low-pass whole genome sequencing for use in downstream heritability estimates and variant association analyses. Many core microbial abundances are under low-to-moderate host genetic control (h2 &amp;gt; 0.15; P &amp;lt; 0.05); however, many microorganisms had low heritability estimates (h2 &amp;lt; 0.10). Preliminary genome-wide associations studies are vastly underpowered to detect host genetic variants associated with microbial features, but data generated using the total number of animals will better refine these polymorphisms. Several key heritable metabolites were identified in amino acid metabolism pathways in rumen fluid (P &amp;lt; 0.05). The rumen microbiome appears to act as a complex polygenic trait and incorporating microbial features into prediction models may improve feed efficiency estimations. Understanding host genetic control of the rumen microbiome and metabolome may empower downstream applications to enhance genomic and phenotypic predictions for feed efficiency and other sustainability traits in beef cattle.

Domesticating Human Capital

This article investigates the consumption of knowledge in early modern Amsterdam. A dataset of seventeenth- and eighteenth-century probate inventories is employed to examine the synergies and inequalities between the skilled and educated middle classes and the intellectual elites. A series of democratisation waves in the ownership of books, writing equipment, and measuring tools confirms the unprecedented levels of basic literacy and numeracy skills in the urban centres of the early modern Low Countries revealed by research on signature proficiency and age heaping. The concentration of secular books and advanced knowledge objects in the hands of a small but growing group of affluent households, on the other hand, corresponds to other research that has fixated instead on the role of upper-tail human capital in scientific, technological, and economic progress. Yet, the relatively low value estimates of libraries and scientific instruments, together with a more qualitative examination of two amateur scientists of middling background, dovetails with the hypothesis that the Dutch Scientific Revolution and Enlightenment were marked by a close interaction and mobility between craftsmen and scientists. An above-average income and enough leisure time to develop intellectual interests could be sufficient for inhabitants of Amsterdam to cross the Rubicon from consuming to (re)producing knowledge.

Parasiticide use and obesity in dogs with sudden acquired retinal degeneration syndrome.

To investigate parasiticide use and describe signalment features in patients with sudden acquired retinal degeneration syndrome (SARDS). Retrospective case-control study of dogs with (n = 71) and without (136) SARDS. Parasiticide use, presentation season, weight, body condition, and signalment were compared between dogs diagnosed with SARDS and the reference population by use of descriptive statistics and logistic regression. Animals with SARDS were at a 5.99 times higher odds of having previously used imidacloprid (95% CI, 1.6 to 22.2; P = .003). However, time of last imidocloprid administration was > 6 years prior to diagnosis in 6 SARDS-affected individuals and 15, 26, or 42 months before diagnosis (n = 1 each). No other class of parasiticide had a significant association with SARDS. Seasonal variation was observed with a negative association identified between incidence of SARDS and tick season (October to January; P < .001). Overweight and obese dogs were 4.42 (95% CI, 1.9 to 10.4) and 4.96 (95% CI, 2.1 to 11.6) times more likely to have SARDS (P ≤ .001). History of polyphagia or weight gain was not associated with an increased likelihood of being overweight or obese within the SARDS-affected population (P > .108). While a statistically significant association was found between imidacloprid use and SARDS, this is unlikely to be clinically significant given the lack of a temporal association, sparse exposure numbers, and low point estimate of the OR. A positive association between being overweight or obese and a diagnosis of SARDS was found independent of polyphagia and weight gain, suggesting that it may be a risk factor for the development of SARDS.

Prevalence, risk factors, and characterisation of extended-spectrum β-lactamase -producing Enterobacterales (ESBL-E) in horses entering an equine hospital and description of longitudinal excretion

BackgroundExtended-spectrum β-lactamase -producing Enterobacterales (ESBL-E) are important zoonotic pathogens that can cause serious clinical infections, also in horses. Preventing the spread of ESBL-E, especially in the equine hospital environment, is key to reducing the number of difficult-to-treat infections. Estimating the local prevalence of ESBL-E in horses is crucial to establish targeted infection control programs at equine hospitals. We conducted a prevalence and risk factor study in equine patients on admission to an equine teaching hospital in Finland through a rectal ESBL-E screening specimen of the horse and a questionnaire.ResultsThe prevalence of ESBL-E in admitted horses was 3% (5/161, 95% CI 1–7%); none of the tested factors remained statistically significant in multivariate analysis, although antimicrobial treatment within three months was borderline significant (p = 0.052). Extended-spectrum β-lactamase -producing Klebsiella pneumoniae ST6179:CTX-M-15 was detected in three horses using whole-genome sequencing, which in combination with patient records suggested nosocomial transmission. Escherichia coli isolates were ST1250:CTX-M-1 (n = 1), ST1079:CTX-M-1 (n = 1), and ST1245:CTX-M-14 (n = 1). Multiple virulence genes were detected in the ESBL-E isolates. In the ESBL-E positive horses enrolled in a one-year follow-up study, ESBL-E were unlikely to be isolated in rectal screening specimens after the initial positive specimen.ConclusionsThe prevalence of ESBL-E in horses visiting a veterinary teaching hospital in Finland is low, indicating an overall low prevalence estimate in the country’s equine population. No statistically significant risk factors were identified, likely due to the low number of cases. The duration of ESBL-E carriage is likely to be very short in horses.

Lower confidence and increased error sensitivity in OCD patients while learning under volatility

A decoupling between confidence and action could relate to compulsive behaviour as seen in obsessive-compulsive disorder (OCD). The link between confidence and action in OCD has been investigated in clinical case-control studies and in the general population with discrepant findings. The generalizability of findings from highly-compulsive general population samples to clinical OCD samples has been questioned. Here, we investigate action-confidence coupling for 38 OCD patients compared to 37 healthy controls (HC), using a predictive inference task. We compared those results to a comparison between matched high and low compulsive individuals from the general population. Action-updating, confidence and their coupling were compared between the groups. Moreover, computational modeling was performed to compare groups on error sensitivity and environmental parameters. OCD patients showed lower confidence and higher learning rates in reaction to (small) prediction errors than HC, signaling hyperactive error signaling and lower confidence estimation. No evidence was found for differences in action-confidence coupling between groups. In contrast high the compulsive group showed higher confidence and stronger decoupling than the low compulsive group, both of which were related to symptoms. The underlying mechanisms of obsessive-compulsive behaviour might differ between clinical and highly-compulsive general population samples, resulting in different (meta)cognitive profiles.