884 research outputs found
Access to Healthy Foods Across America
Research indicates that individuals who have access to healthy food tend to eat healthier. Food environments that do not have access to healthy food have been shown to be a leading cause of obesity in the United States. Major health consequences of obesity include cardiovascular disease, type 2 diabetes, hypertension, stroke, high cholesterol, gallbladder disease, osteoarthritis, and some cancers. The availability of healthy foods can be determined by median household income, with income levels being shown to affect access to healthy foods in local areas. However, no study has shown if this phenomenon is prevalent across the United States. Our cross-sectional study seeks to determine if the socioeconomic level of counties affects access to healthier food options across the United States. The study will include approximately 380 grocery stores nationwide with 190 grocery stores from high-income counties and 190 grocery stores from low-income counties. The counties are separated by the top and bottom 25th percentile of national household income levels and the middle-income household levels are excluded. Data collection will utilize the Nutrition Environment Measures Survey in Stores (NEMS-S). Convenience sampling of the study will be dependent on researchers access and ability to travel their choice of counties from an eligible list of high and low income counties. Researchers will be recruited from an informational flyer posted in professional public health research groups on LinkedIn. The eligibility of researchers is determined by their responses to an eleven question Qualtrics recruitment survey. Qualified researchers recruited from LinkedIn will conduct the NEMS-S to measure and evaluate food availability, price, and quality in grocery stores. Proposed quantitative analyses of NEMS-S scores will utilize SPSS software by comparing high and low income counties using t-tests. This nation-wide analysis should be completed by winter of 2014
Can Machines Learn Morality? The Delphi Experiment
As AI systems become increasingly powerful and pervasive, there are growing
concerns about machines' morality or a lack thereof. Yet, teaching morality to
machines is a formidable task, as morality remains among the most intensely
debated questions in humanity, let alone for AI. Existing AI systems deployed
to millions of users, however, are already making decisions loaded with moral
implications, which poses a seemingly impossible challenge: teaching machines
moral sense, while humanity continues to grapple with it.
To explore this challenge, we introduce Delphi, an experimental framework
based on deep neural networks trained directly to reason about descriptive
ethical judgments, e.g., "helping a friend" is generally good, while "helping a
friend spread fake news" is not. Empirical results shed novel insights on the
promises and limits of machine ethics; Delphi demonstrates strong
generalization capabilities in the face of novel ethical situations, while
off-the-shelf neural network models exhibit markedly poor judgment including
unjust biases, confirming the need for explicitly teaching machines moral
sense.
Yet, Delphi is not perfect, exhibiting susceptibility to pervasive biases and
inconsistencies. Despite that, we demonstrate positive use cases of imperfect
Delphi, including using it as a component model within other imperfect AI
systems. Importantly, we interpret the operationalization of Delphi in light of
prominent ethical theories, which leads us to important future research
questions
The Association Between Ambient Temperatures and Hospital Admissions Due to Respiratory Diseases in the Capital City of Vietnam
This study aimed to examine the short-term effects of ambient temperature on hospital admissions due to respiratory diseases among Hanoi residents. We collected 34,653 hospital admissions for 365 days (November 1, 2017, to November 31, 2018) from two hospitals in Hanoi. A quasi-Poisson regression model with time series analysis was used to explore the temperature-health outcome relationship's overall pattern. The non-linear curve indicated the temperatures with the lowest risk range from 22 degrees (Celcius) to 25 degrees (Celcius). On average, cold temperatures showed a higher risk than hot temperatures across all genders and age groups. Hospital admissions risk was highest at 13 degrees (Celcius) (RR = 1.39; 95% CI = 1.26â1.54) for cold effects and at 33 degrees (Celcius) (RR = 1.21, 95% CI = 1.04â1.39) for the hot effects. Temporal pattern analysis showed that the most effect on respiratory diseases occurred at a lag of 0 days for hot effect and at a lag of 1 day for cold effect. The risk of changing temperature among women and people over 5 years old was higher than other groups. Our results suggest that the risk of respiratory admissions was greatest when the temperature was low. Public health prevention programs should be enhanced to improve public awareness about the health risks of temperature changes, especially respiratory diseases risked by low temperatures
Amilorides inhibit SARS-CoV-2 replication in vitro by targeting RNA structures
The SARS-CoV-2 pandemic, and the likelihood of future coronavirus pandemics, emphasized the urgent need for development of novel antivirals. Small-molecule chemical probes offer both to reveal aspects of virus replication and to serve as leads for antiviral therapeutic development. Here, we report on the identification of amiloride-based small molecules that potently inhibit OC43 and SARS-CoV-2 replication through targeting of conserved structured elements within the viral 5âČ-end. Nuclear magnetic resonanceâbased structural studies revealed specific amiloride interactions with stem loops containing bulge like structures and were predicted to be strongly bound by the lead amilorides in retrospective docking studies. Amilorides represent the first antiviral small molecules that target RNA structures within the 5âČ untranslated regions and proximal region of the CoV genomes. These molecules will serve as chemical probes to further understand CoV RNA biology and can pave the way for the development of specific CoV RNAâtargeted antivirals
A First Search for coincident Gravitational Waves and High Energy Neutrinos using LIGO, Virgo and ANTARES data from 2007
We present the results of the first search for gravitational wave bursts
associated with high energy neutrinos. Together, these messengers could reveal
new, hidden sources that are not observed by conventional photon astronomy,
particularly at high energy. Our search uses neutrinos detected by the
underwater neutrino telescope ANTARES in its 5 line configuration during the
period January - September 2007, which coincided with the fifth and first
science runs of LIGO and Virgo, respectively. The LIGO-Virgo data were analysed
for candidate gravitational-wave signals coincident in time and direction with
the neutrino events. No significant coincident events were observed. We place
limits on the density of joint high energy neutrino - gravitational wave
emission events in the local universe, and compare them with densities of
merger and core-collapse events.Comment: 19 pages, 8 figures, science summary page at
http://www.ligo.org/science/Publication-S5LV_ANTARES/index.php. Public access
area to figures, tables at
https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=p120000
Search for gravitational waves associated with the InterPlanetary Network short gamma ray bursts
We outline the scientific motivation behind a search for gravitational waves
associated with short gamma ray bursts detected by the InterPlanetary Network
(IPN) during LIGO's fifth science run and Virgo's first science run. The IPN
localisation of short gamma ray bursts is limited to extended error boxes of
different shapes and sizes and a search on these error boxes poses a series of
challenges for data analysis. We will discuss these challenges and outline the
methods to optimise the search over these error boxes.Comment: Methods paper; Proceedings for Eduardo Amaldi 9 Conference on
Gravitational Waves, July 2011, Cardiff, U
Swift follow-up observations of candidate gravitational-wave transient events
We present the first multi-wavelength follow-up observations of two candidate
gravitational-wave (GW) transient events recorded by LIGO and Virgo in their
2009-2010 science run. The events were selected with low latency by the network
of GW detectors and their candidate sky locations were observed by the Swift
observatory. Image transient detection was used to analyze the collected
electromagnetic data, which were found to be consistent with background.
Off-line analysis of the GW data alone has also established that the selected
GW events show no evidence of an astrophysical origin; one of them is
consistent with background and the other one was a test, part of a "blind
injection challenge". With this work we demonstrate the feasibility of rapid
follow-ups of GW transients and establish the sensitivity improvement joint
electromagnetic and GW observations could bring. This is a first step toward an
electromagnetic follow-up program in the regime of routine detections with the
advanced GW instruments expected within this decade. In that regime
multi-wavelength observations will play a significant role in completing the
astrophysical identification of GW sources. We present the methods and results
from this first combined analysis and discuss its implications in terms of
sensitivity for the present and future instruments.Comment: Submitted for publication 2012 May 25, accepted 2012 October 25,
published 2012 November 21, in ApJS, 203, 28 (
http://stacks.iop.org/0067-0049/203/28 ); 14 pages, 3 figures, 6 tables;
LIGO-P1100038; Science summary at
http://www.ligo.org/science/Publication-S6LVSwift/index.php ; Public access
area to figures, tables at
https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=p110003
MDCK Cystogenesis Driven by Cell Stabilization within Computational Analogues
The study of epithelial morphogenesis is fundamental to increasing our
understanding of organ function and disease. Great progress has been made
through study of culture systems such as Madin-Darby canine kidney (MDCK) cells,
but many aspects of even simple morphogenesis remain unclear. For example, are
specific cell actions tightly coupled to the characteristics of the cell's
environment or are they more often cell state dependent? How does the single
lumen, single cell layer cyst consistently emerge from a variety of cell
actions? To improve insight, we instantiated in silico analogues that used
hypothesized cell behavior mechanisms to mimic MDCK cystogenesis. We tested them
through in vitro experimentation and quantitative validation. We observed novel
growth patterns, including a cell behavior shift that began around day five of
growth. We created agent-oriented analogues that used the cellular Potts model
along with an Iterative Refinement protocol. Following several refinements, we
achieved a degree of validation for two separate mechanisms. Both survived
falsification and achieved prespecified measures of similarity to cell culture
properties. In silico components and mechanisms mapped to in vitro counterparts.
In silico, the axis of cell division significantly affects lumen number without
changing cell number or cyst size. Reducing the amount of in silico luminal cell
death had limited effect on cystogenesis. Simulations provide an observable
theory for cystogenesis based on hypothesized, cell-level operating
principles
A common root for coevolution and substitution rate variability in protein sequence evolution
We introduce a simple model that describes the average occurrence of point variations in a generic protein sequence. This model is based on the idea that mutations are more likely to be fixed at sites in contact with others that have mutated in the recent past. Therefore, we extend the usual assumptions made in protein coevolution by introducing a time dumping on the effect of a substitution on its surrounding and makes correlated substitutions happen in avalanches localized in space and time. The model correctly predicts the average correlation of substitutions as a function of their distance along the sequence. At the same time, it predicts an among-site distribution of the number of substitutions per site highly compatible with a negative binomial, consistently with experimental data. The promising outcomes achieved with this model encourage the application of the same ideas in the field of pairwise and multiple sequence alignment
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