The hearts of large mammals generate higher pressures, are less efficient and use more energy than those of small mammals
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ABSTRACT A prevailing assumption in the cardiovascular field is that the metabolic rate of the heart is a constant proportion of a mammal's whole-body aerobic metabolic rate. In this Commentary, we assemble previously published cardiovascular, metabolic and body mass data from matched terrestrial mammalian species, at rest and during heavy exercise, and reveal scaling relationships that challenge this assumption. Our analyses indicate that the fractional metabolic cost of systemic perfusion compared with whole-body metabolic rate increases significantly with body size among resting mammals, from ∼2.5% in a mouse to ∼10% in an elephant. We propose that two significant body size-dependent effects contribute to this conclusion; namely, that larger species generate higher mean systemic arterial blood pressure and that their myocardium operates with lower external mechanical efficiencies compared with those of smaller species. We discuss potential physiological and mechanical explanations, including the additional energy needed to support the arterial blood column above the heart in larger species, especially those with long necks, as well as the possible sources of greater internal energy losses from the heart of larger species. Thus, we present an updated view of how increasing blood pressure and decreasing efficiency of the myocardium result in an increasing fractional metabolic cost of perfusion as body size increases among resting mammals.Circadian patterns of heart rate, systolic and diastolic blood pressure, and rate-pressure product were compared in elders with heart disease (N = 22, mean age 86 years) and a comparison group (N = 18, mean age 80 years) who did not have a cardiac diagnosis. For 4 consecutive days, automated measures of heart rate, diastolic and systolic blood pressure, and rate-pressure product were taken every 2 hours while subjects were awake. Activity-rest patterns were recorded by an observer, and demographic and medication profiles were obtained. Data were subjected to cosinor analysis, and the groups were compared on rhythmic parameters. Although the cardiac subjects were older, in poorer health, less active, and more prone to daytime napping, they exhibited more rhythms in rate-pressure product than did the comparison subjects. The cardiac group also had more synchronized oscillation of overt heart rate and systolic blood pressure rhythms. These results can be attributed to standardized times of cardiac medication administration. Attention to patterns of heart rate and systolic blood pressure in elders may suggest more appropriate times of day for conducting individual cardiac assessments.
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Scaling up language models has been empirically shown to improve performance on a wide range of downstream tasks. However, if we were to observe worse performance as a function of scale ("inverse scaling") on certain tasks, this would indicate that scaling can also encourage behaviors that are misaligned with human preferences. The Inverse Scaling Prize (McKenzie et al. 2022) identified eleven such inverse scaling tasks, evaluated on models of up to 280B parameters and up to 500 zettaFLOPs of training compute. This paper takes a closer look at these inverse scaling tasks. We evaluate models of up to 540B parameters, trained on five times more compute than those evaluated in the Inverse Scaling Prize. With this increased range of model sizes and training compute, only four out of the eleven tasks remain inverse scaling. Six out of the eleven tasks exhibit "U-shaped scaling", where performance decreases up to a certain size, and then increases again up to the largest model evaluated (the one remaining task displays positive scaling). In addition, we find that 1-shot examples and chain-of-thought can help mitigate undesirable scaling patterns even further. U-shaped scaling suggests that the inverse scaling trend observed in McKenzie et al. (2022) may not continue to hold for larger models, which we attribute to the presence of distractor tasks that only sufficiently large models can avoid.
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Improved approximations or displacements and stresses, achieved by the following types of scaling, are presented: a. scaling of the initial stiffness matrix; b. a new type of scaling of displacements; and c. mixed scaling of stiffness and displacements, where the two types of scaling are combined The geometric interpretation of the various scaling types is illustrated and methods for selecting the scaling multipliers based on geometrical considerations, mathematical criteria and the reduced basis approach, are demonstrated and compared. It is shown that high quality approximations can be achieved for very large changes in cross section and geometrical variables with a small computational effort. The results presented indicate that scaling procedures have high potential in future applications where effective reanalysis is essential.
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Widom scaling
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Though immediate pain is reported by nearly all patients undergoing needle-EMG,little is known about its cardiovascular risk from changes in blood-pressure orheart-rate. This study was thus conducted to investigate if, and to whichdegree, blood-pressure and heart-rate are influenced by needle-EMG. In 50patients, 24 women, 26 men, aged 26–78 years, conventional needle-EMGs from 54muscles were recorded. Pain was assessed on a verbal analogue pain-scale (1–10)and blood-pressure and heart-rate were measured before, during and after EMG.Mean pain-ratings before, during and after EMG were 0.8, 4.1 and 1.0,respectively. Mean systolic/diastolic blood-pressure was 144/87mmHg before,145/86mmHg during and 144/87mmHg after EMG. Mean heart-rate before, during andafter EMG was 77, 77 and 78 beats/min, respectively. Systolic/diastolicblood-pressure increased above 145/85mmHg in only 2/6 patients during EMG. Theweak affection of blood-pressure and heart-rate by pain from needle-EMG wasfound in patients with and without hypertension. Mean blood-pressure, heart-rateand pain-ratings before, during and after EMG were independent of age, sex andmuscle. The correlation between pain-ratings and blood-pressure and heart-ratewas not significant. This study shows that needle-EMG moderately hurts but doesnot increase blood-pressure or heart-rate, irrespective of known arterialhypertension. Based upon these findings, the cardiovascular risk of needle-EMGfrom changes in blood-pressure or heart-rate is regarded negligibly low.
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Abstract The association between temperature and metabolic scaling varies among species, which could be due to variation in the surface area and its scaling. This study aims to examine the effect of temperature on metabolic scaling and to verify the links between metabolic scaling and surface area scaling at both the whole body and the cell levels. The routine metabolic rate (RMR), gill surface area (GSA), ventilation frequency (VF), red blood cell surface area ( S RBC ), and metabolic rate (MR RBC ) were determined in silver carp, and their mass‐scaling exponents were analyzed at 10 and 25°C. These results showed that body mass and temperature independently affected the RMR, GSA, and VF, suggesting constant scaling exponents of RMR (0.772), GSA (0.912), and VF (−0.282) with changing temperature. The RMR at 25°C was 2.29 times higher than that at 10°C, suggesting increased metabolic demand at a higher temperature. The results showed that the RMR increased, while the scaling exponents of RMR, GSA, and VF remained unchanged with increasing temperature. These results support the view that the scaling of oxygen supply capacity importantly affects metabolic scaling. The S RBC did not change with either temperature or body mass. However, the MR RBC increased by 5.48 times from 10 to 25°C but did not change with body mass. As the scaling exponents of RMR did not change between temperatures, the results indicate that no obvious link exists between the scaling of both the cell size and cell metabolic rate and the metabolic scaling of silver carp.
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Language models have been shown to exhibit positive scaling, where performance improves as models are scaled up in terms of size, compute, or data. In this work, we introduce NeQA, a dataset consisting of questions with negation in which language models do not exhibit straightforward positive scaling. We show that this task can exhibit inverse scaling, U-shaped scaling, or positive scaling, and the three scaling trends shift in this order as we use more powerful prompting methods or model families. We hypothesize that solving NeQA depends on two subtasks: question answering (task 1) and negation understanding (task 2). We find that task 1 has linear scaling, while task 2 has sigmoid-shaped scaling with an emergent transition point, and composing these two scaling trends yields the final scaling trend of NeQA. Our work reveals and provides a way to analyze the complex scaling trends of language models.
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