Fear from the heart: sensitivity to fear stimuli depends on individual heartbeats

Cognitions and emotions can be influenced by bodily physiology. Here, we investigated whether the processing of brief fear stimuli is selectively gated by their timing in relation to individual heartbeats. Emotional and neutral faces were presented to human volunteers at cardiac systole, when ejection of blood from the heart causes arterial baroreceptors to signal centrally the strength and timing of each heartbeat, and at diastole, the period between heartbeats when baroreceptors are quiescent. Participants performed behavioral and neuroimaging tasks to determine whether these interoceptive signals influence the detection of emotional stimuli at the threshold of conscious awareness and alter judgments of emotionality of fearful and neutral faces. Our results show that fearful faces were detected more easily and were rated as more intense at systole than at diastole. Correspondingly, amygdala responses were greater to fearful faces presented at systole relative to diastole. These novel findings highlight a major channel by which short-term interoceptive fluctuations enhance perceptual and evaluative processes specifically related to the processing of fear and threat and counter the view that baroreceptor afferent signaling is always inhibitory to sensory perception

MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation

Saccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which can be estimated from the data using the mean and standard deviation. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. We propose instead to use the median absolute deviation (MAD), a robust estimator of dispersion that is not influenced by outliers. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. Our modified algorithm shows a significant and marked improvement in saccade detection - showing both more true positives and less false negatives – especially under higher noise levels. We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation

Computable bounds for the discrimination of Gaussian states

By combining the Minkowski inequality and the quantum Chernoff bound, we derive easy-to-compute upper bounds for the error probability affecting the optimal discrimination of Gaussian states. In particular, these bounds are useful when the Gaussian states are unitarily inequivalent, i.e., they differ in their symplectic invariants.Comment: 8 pages, 1 figure. REVTe

Effects of Air Pollution Control on Climate

Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).Urban air pollution and climate are closely connected due to shared generating processes (e.g., combustion) for emissions of the driving gases and aerosols. They are also connected because the atmospheric lifecycles of common air pollutants such as CO, NOx and VOCs, and of the climatically important methane gas (CH4) and sulfate aerosols, both involve the fast photochemistry of the hydroxyl free radical (OH). Thus policies designed to address air pollution may impact climate and vice versa. We present calculations using a model coupling economics, atmospheric chemistry, climate and ecosystems to illustrate some effects of air pollution policy alone on global warming. We consider caps on emissions of NOx, CO, volatile organic carbon, and SOx both individually and combined in two ways. These caps can lower ozone causing less warming, lower sulfate aerosols yielding more warming, lower OH and thus increase CH4 giving more warming, and finally, allow more carbon uptake by ecosystems leading to less warming. Overall, these effects significantly offset each other suggesting that air pollution policy has a relatively small net effect on the global mean surface temperature and sea level rise. However, our study does not account for the effects of air pollution policies on overall demand for fossil fuels and on the choice of fuels (coal, oil, gas), nor have we considered the effects of caps on black carbon or organic carbon aerosols on climate. These effects, if included, could lead to more substantial impacts of capping pollutant emissions on global temperature and sea level than concluded here. Caps on aerosols in general could also yield impacts on other important aspects of climate beyond those addressed here, such as the regional patterns of cloudiness and precipitation.This research was supported by the U.S Department of Energy, U.S. National Science Foundation, and the Industry Sponsors of the MIT Joint Program on the Science and Policy of Global Change: Alstom Power (France), American Electric Power (USA), BP p.l.c. (UK/USA), ChevronTexaco Corporation (USA), DaimlerChrysler AG (Germany), Duke Energy (USA), J-Power (Electric Power Development Co., Ltd.) (Japan), Electric Power Research Institute (USA), Electricité de France, ExxonMobil Corporation (USA), Ford Motor Company (USA), General Motors (USA), Mirant (USA), Murphy Oil Corporation (USA), Oglethorpe Power Corporation (USA), RWE/Rheinbraun (Germany), Shell International Petroleum (Netherlands/UK), Statoil (Norway), Tennessee Valley Authority (USA), Tokyo Electric Power Company (Japan), TotalFinaElf (France), Vetlesen Foundation (USA)

QTL underlying circadian clock parameters under seasonally variable field settings in Arabidopsis thaliana

The circadian clock facilitates coordination of the internal rhythms of an organism to daily environmental conditions, such as the light-dark cycle of one day. Circadian period length (the duration of one endogenous cycle) and phase (the timing of peak activity) exhibit quantitative variation in natural populations. Here, we measured circadian period and phase in June, July and September in three Arabidopsis thaliana recombinant inbred line populations. Circadian period and phase were estimated from bioluminescence of a genetic construct between a native circadian clock gene (COLD CIRCADIAN RHYTHM RNA BINDING 2) and the reporter gene (LUCIFERASE) after lines were entrained under field settings. Using a Bayesian mapping approach, we estimated the median number and effect size of genomic regions (Quantitative Trait Loci, QTL) underlying circadian parameters and the degree to which these regions overlap across months of the growing season. We also tested for QTL associations between the circadian clock and plant morphology. The genetic architecture of circadian phase was largely independent across months, as evidenced by the fact that QTL determining phase values in one month of the growing season were different from those determining phase in a second month. QTL for circadian parameters were shared with both cauline and rosette branching in at least one mapping population. The results provide the first insights into the QTL architecture of the clock under field settings, and suggest that the circadian clock is highly responsive to changing environments and that selection can act on clock phase in a nuanced manner

Efficient measurement of quantum gate error by interleaved randomized benchmarking

We describe a scalable experimental protocol for obtaining estimates of the error rate of individual quantum computational gates. This protocol, in which random Clifford gates are interleaved between a gate of interest, provides a bounded estimate of the average error of the gate under test so long as the average variation of the noise affecting the full set of Clifford gates is small. This technique takes into account both state preparation and measurement errors and is scalable in the number of qubits. We apply this protocol to a superconducting qubit system and find gate errors that compare favorably with the gate errors extracted via quantum process tomography.Comment: 5 pages, 2 figures, published versio

Past and Future Effects of Ozone on Net Primary Production and Carbon Sequestration Using a Global Biogeochemical Model

Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).Exposure of plants to ozone inhibits photosynthesis and therefore reduces vegetation production and carbon sequestration. Simulations with the Terrestrial Ecosystem Model (TEM) for the historical period (1860-1995) show the largest damages occur in the eastern U.S., Europe, and eastern China, with reductions in Net Primary Production (NPP) of over 70% for some locations. Scenarios through the year 2100 using the MIT Integrated Global Systems Model (IGSM) show potentially greater negative effects in the future. In the worst-case scenario, the current land carbon sink in China could become a carbon source. Reduced crop yields resulting from ozone damage are potentially large but can be mitigated by controlling emissions of ozone precursors. Failure to consider ozone damages to vegetation would by itself raise the costs over the next century of stabilizing atmospheric concentrations of CO2 by 3 to 18%. But, climate policy would also reduce ozone precursor emissions, and ozone, and these additional benefits are estimated to be between 4 and 21% of the cost of the climate policy. Tropospheric ozone effects on terrestrial ecosystems thus produce a surprisingly large feedback in estimating climate policy costs that, heretofore, has not been included in cost estimates.This study was funded by the Biocomplexity Program of the U.S. National Science Foundation (ATM-0120468), the Methods and Models for Integrated Assessment Program of the U.S. National Science Foundation (DEB-9711626) and the Earth Observing System Program of the U.S. National Aeronautics and Space Administration (NAG5-10135). We also received support from the federal and industrial sponsors of the MIT Joint Program on the Science and Policy of Global Change

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

Space missions have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for in-situ intelligent decision making to reduce communication and control limitations. While ground based research on Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center is performing flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors in LEO operations. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested