Computer simulation of blood flow in the human arm

http://www.worldcat.org/oclc/1306856

Overconfident, resentful, and misinformed: How racial animus motivates confidence in false beliefs

Many Americans not only hold misinformed beliefs about policy-relevant topics (e.g., climate change, public health) but hold those views with high degrees of confidence in their factual accuracy. Epistemic overconfidence – an application of the Dunning Kruger Effect (DKE, or “ignorance of one’s own ignorance” – is politically consequential, as misinformed individuals who hold those views with high degrees of confidence may be especially likely to oppose evidence-based policies and resist attitude change. Yet, its psychological origins – particularly in application to misinformation endorsement – are not well understood. In this paper, we propose that racial animus plays a key psychological role in motivating Americans to express confidence in misinformed beliefs. Using nationally representative survey data from the American National Election Study, we find that racial resentment plays a strong role in leading Americans to hold confidently misinformed views about racialized policy issues (e.g., the causes of anthropogenic climate change, the origins of the COVID-19 pandemic), but not on less-racialized issues (e.g., MMR vaccine safety). We conclude by discussing how our work underscores the often-overlooked importance of intergroup attitudes in shaping DKE, and helps resolve theoretical tensions in the study of misinformation acceptance. Objective We examine the role of racial resentment in motivating Americans to express confidence in misinformed beliefs on racialized scientific issues. Methods We study survey data from the 2020 American National Election Study. We examine respondents’ endorsement of misinformation on different scientific issues and their reported confidence in these views. Results We find that racial resentment plays a strong role in leading Americans to hold confidently misinformed views about highly racialized policy issues (e.g., the occurrence of anthropogenic climate change or the origins of the COVID-19 pandemic), but not on less racialized issues (e.g., childhood vaccine safety). Conclusions Our work underscores the often-overlooked importance of intergroup attitudes in shaping overconfidence and helps resolve theoretical tensions in the study of misinformation acceptance

The site of synthesis of the iron-sulfur subunits of the flavoprotein and iron-protein fractions of human NADH dehydrogenase

The site of synthesis of the iron-sulfur subunits of the flavoprotein and iron-protein fractions of the human respiratory chain NADH dehydrogenase has been investigated to test the possibility that any of them is synthesized in mitochondria. For this purpose, antibodies specific for individual subunits of the bovine enzyme, which cross- reacted with the homologous human subunits in immunoblot assays, were tested against HeLa cell mitochondrial proteins labeled in vivo with [35S]methionine in the absence or presence of inhibitors of mitochondrial or cytoplasmic protein synthesis. The results clearly indicated that all the iron-sulfur subunits of the flavoprotein and iron-protein fractions of human complex I are synthesized in the cytosol and are, therefore, encoded in nuclear genes

Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions

In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios. Current deep learning techniques to address multiple-output problems are based on two main methodologies: (1) mixture density networks, which suffer from poor stability at high dimensions, or (2) multiple choice learning (MCL), an approach that uses $M$ single-output functions, each only producing a point estimate hypothesis. This paper presents a Mixture of Multiple-Output functions (MoM) approach using a novel variant of dropout, Multiple Hypothesis Dropout. Unlike traditional MCL-based approaches, each multiple-output function not only estimates the mean but also the variance for its hypothesis. This is achieved through a novel stochastic winner-take-all loss which allows each multiple-output function to estimate variance through the spread of its subnetwork predictions. Experiments on supervised learning problems illustrate that our approach outperforms existing solutions for reconstructing multimodal output distributions. Additional studies on unsupervised learning problems show that estimating the parameters of latent posterior distributions within a discrete autoencoder significantly improves codebook efficiency, sample quality, precision and recall.Comment: To appear in Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24). 13 pages (9 main, 4 appendix

Wireless Technologies for Implantable Devices

Wireless technologies are incorporated in implantable devices since at least the 1950s. With remote data collection and control of implantable devices, these wireless technologies help researchers and clinicians to better understand diseases and to improve medical treatments. Today, wireless technologies are still more commonly used for research, with limited applications in a number of clinical implantable devices. Recent development and standardization of wireless technologies present a good opportunity for their wider use in other types of implantable devices, which will significantly improve the outcomes of many diseases or injuries. This review briefly describes some common wireless technologies and modern advancements, as well as their strengths and suitability for use in implantable medical devices. The applications of these wireless technologies in treatments of orthopedic and cardiovascular injuries and disorders are described. This review then concludes with a discussion on the technical challenges and potential solutions of implementing wireless technologies in implantable devices

Comment on "Local accumulation times for source, diffusion, and degradation models in two and three dimensions" [J. Chem. Phys. 138, 104121 (2013)]

In a recent paper, Gordon, Muratov, and Shvartsman studied a partial differential equation (PDE) model describing radially symmetric diffusion and degradation in two and three dimensions. They paid particular attention to the local accumulation time (LAT), also known in the literature as the mean action time, which is a spatially dependent timescale that can be used to provide an estimate of the time required for the transient solution to effectively reach steady state. They presented exact results for three-dimensional applications and gave approximate results for the two-dimensional analogue. Here we make two generalizations of Gordon, Muratov, and Shvartsman’s work: (i) we present an exact expression for the LAT in any dimension and (ii) we present an exact expression for the variance of the distribution. The variance provides useful information regarding the spread about the mean that is not captured by the LAT. We conclude by describing further extensions of the model that were not considered by Gordon,Muratov, and Shvartsman. We have found that exact expressions for the LAT can also be derived for these important extensions..

Rescue of Pressure Overload-Induced Heart Failure by Estrogen Therapy.

BackgroundEstrogen pretreatment has been shown to attenuate the development of heart hypertrophy, but it is not known whether estrogen could also rescue heart failure (HF). Furthermore, the heart has all the machinery to locally biosynthesize estrogen via aromatase, but the role of local cardiac estrogen synthesis in HF has not yet been studied. Here we hypothesized that cardiac estrogen is reduced in HF and examined whether exogenous estrogen therapy can rescue HF.Methods and resultsHF was induced by transaortic constriction in mice, and once mice reached an ejection fraction (EF) of ≈35%, they were treated with estrogen for 10 days. Cardiac structure and function, angiogenesis, and fibrosis were assessed, and estrogen was measured in plasma and in heart. Cardiac estrogen concentrations (6.18±1.12 pg/160 mg heart in HF versus 17.79±1.28 pg/mL in control) and aromatase transcripts (0.19±0.04, normalized to control, P<0.05) were significantly reduced in HF. Estrogen therapy increased cardiac estrogen 3-fold and restored aromatase transcripts. Estrogen also rescued HF by restoring ejection fraction to 53.1±1.3% (P<0.001) and improving cardiac hemodynamics both in male and female mice. Estrogen therapy stimulated angiogenesis as capillary density increased from 0.66±0.07 in HF to 2.83±0.14 (P<0.001, normalized to control) and reversed the fibrotic scarring observed in HF (45.5±2.8% in HF versus 5.3±1.0%, P<0.001). Stimulation of angiogenesis by estrogen seems to be one of the key mechanisms, since in the presence of an angiogenesis inhibitor estrogen failed to rescue HF (ejection fraction=29.3±2.1%, P<0.001 versus E2).ConclusionsEstrogen rescues pre-existing HF by restoring cardiac estrogen and aromatase, stimulating angiogenesis, and suppressing fibrosis