2,557 research outputs found
Functional Training and Blood Flow Restriction: A Perspective View on the Integration of Techniques
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A shared neural ensemble links distinct contextual memories encoded close in time.
Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time. According to the memory allocation hypothesis, learning triggers a temporary increase in neuronal excitability that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Here we show in mice that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Several findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two contexts are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability, do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged mice, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by ageing could affect the temporal structure of memories, thus impairing efficient recall of related information
Unsuspected Dengue as a Cause of Acute Febrile Illness in Children and Adults in Western Nicaragua
Dengue is an emerging infectious disease of global significance. Suspected dengue, especially in children in Nicaragua’s heavily-urbanized capital of Managua, has been well documented, but unsuspected dengue among children and adults with undifferentitated fever has not
Neural-based Compression Scheme for Solar Image Data
Studying the solar system and especially the Sun relies on the data gathered
daily from space missions. These missions are data-intensive and compressing
this data to make them efficiently transferable to the ground station is a
twofold decision to make. Stronger compression methods, by distorting the data,
can increase data throughput at the cost of accuracy which could affect
scientific analysis of the data. On the other hand, preserving subtle details
in the compressed data requires a high amount of data to be transferred,
reducing the desired gains from compression. In this work, we propose a neural
network-based lossy compression method to be used in NASA's data-intensive
imagery missions. We chose NASA's SDO mission which transmits 1.4 terabytes of
data each day as a proof of concept for the proposed algorithm. In this work,
we propose an adversarially trained neural network, equipped with local and
non-local attention modules to capture both the local and global structure of
the image resulting in a better trade-off in rate-distortion (RD) compared to
conventional hand-engineered codecs. The RD variational autoencoder used in
this work is jointly trained with a channel-dependent entropy model as a shared
prior between the analysis and synthesis transforms to make the entropy coding
of the latent code more effective. Our neural image compression algorithm
outperforms currently-in-use and state-of-the-art codecs such as JPEG and
JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet
(EUV) data. As a proof of concept for use of this algorithm in SDO data
analysis, we have performed coronal hole (CH) detection using our compressed
images, and generated consistent segmentations, even at a compression rate of
bits per pixel (compared to 8 bits per pixel on the original data)
using EUV data from SDO.Comment: Accepted for publication in IEEE Transactions on Aerospace and
Electronic Systems (TAES). arXiv admin note: text overlap with
arXiv:2210.0647
Altered dietary behaviour during pregnancy impacts systemic metabolic phenotypes
RationaleEvidence suggests consumption of a Mediterranean diet (MD) can positively impact both maternal and offspring health, potentially mediated by a beneficial effect on inflammatory pathways. We aimed to apply metabolic profiling of serum and urine samples to assess differences between women who were stratified into high and low alignment to a MD throughout pregnancy and investigate the relationship of the diet to inflammatory markers.MethodsFrom the ORIGINS cohort, 51 pregnant women were stratified for persistent high and low alignment to a MD, based on validated MD questionnaires. 1H Nuclear Magnetic Resonance (NMR) spectroscopy was used to investigate the urine and serum metabolite profiles of these women at 36 weeks of pregnancy. The relationship between diet, metabolite profile and inflammatory status was investigated.ResultsThere were clear differences in both the food choice and metabolic profiles of women who self-reported concordance to a high (HMDA) and low (LMDA) Mediterranean diet, indicating that alignment with the MD was associated with a specific metabolic phenotype during pregnancy. Reduced meat intake and higher vegetable intake in the HMDA group was supported by increased levels of urinary hippurate (p = 0.044) and lower creatine (p = 0.047) levels. Serum concentrations of the NMR spectroscopic inflammatory biomarkers GlycA (p = 0.020) and GlycB (p = 0.016) were significantly lower in the HDMA group and were negatively associated with serum acetate, histidine and isoleucine (p < 0.05) suggesting a greater level of plant-based nutrients in the diet. Serum branched chain and aromatic amino acids were positively associated with the HMDA group while both urinary and serum creatine, urine creatinine and dimethylamine were positively associated with the LMDA group.ConclusionMetabolic phenotypes of pregnant women who had a high alignment with the MD were significantly different from pregnant women who had a poor alignment with the MD. The metabolite profiles aligned with reported food intake. Differences were most significant biomarkers of systemic inflammation and selected gut-microbial metabolites. This research expands our understanding of the mechanisms driving health outcomes during the perinatal period and provides additional biomarkers for investigation in pregnant women to assess potential health risks
Experimental Quantum Hamiltonian Learning
Efficiently characterising quantum systems, verifying operations of quantum
devices and validating underpinning physical models, are central challenges for
the development of quantum technologies and for our continued understanding of
foundational physics. Machine-learning enhanced by quantum simulators has been
proposed as a route to improve the computational cost of performing these
studies. Here we interface two different quantum systems through a classical
channel - a silicon-photonics quantum simulator and an electron spin in a
diamond nitrogen-vacancy centre - and use the former to learn the latter's
Hamiltonian via Bayesian inference. We learn the salient Hamiltonian parameter
with an uncertainty of approximately . Furthermore, an observed
saturation in the learning algorithm suggests deficiencies in the underlying
Hamiltonian model, which we exploit to further improve the model itself. We go
on to implement an interactive version of the protocol and experimentally show
its ability to characterise the operation of the quantum photonic device. This
work demonstrates powerful new quantum-enhanced techniques for investigating
foundational physical models and characterising quantum technologies
Assessment of patients with lower urinary tract symptoms where an undiagnosed neurological disease is suspected:report from an International Continence Society consensus working group
Planetary Candidates Observed by Kepler VI: Planet Sample from Q1-Q16 (47 Months)
\We present the sixth catalog of Kepler candidate planets based on nearly 4
years of high precision photometry. This catalog builds on the legacy of
previous catalogs released by the Kepler project and includes 1493 new Kepler
Objects of Interest (KOIs) of which 554 are planet candidates, and 131 of these
candidates have best fit radii <1.5 R_earth. This brings the total number of
KOIs and planet candidates to 7305 and 4173 respectively. We suspect that many
of these new candidates at the low signal-to-noise limit may be false alarms
created by instrumental noise, and discuss our efforts to identify such
objects. We re-evaluate all previously published KOIs with orbital periods of
>50 days to provide a consistently vetted sample that can be used to improve
planet occurrence rate calculations. We discuss the performance of our planet
detection algorithms, and the consistency of our vetting products. The full
catalog is publicly available at the NASA Exoplanet Archive.Comment: 18 pages, to be published in the Astrophysical Journal Supplement
Serie
Controlo integrado da Lagarta das Pastagens Mythimna unipuncta (Haworth)
II Jornadas Agronómicas Açorianas, Outubro, 1991, Ponta Delgada, Açores.No Arquipélago dos Açores, desde 1970 e com maior incidência na ilha de S. Miguel, devido ao aumento da área de pastagem, com vista ao desenvolvimento da bovinocultura, uma das espécies de Noctuídeos, Mythimna unipuncta (Haworth) (Lepidoptera, Noctuidae), assinalada na região desde 1810
por GODMAN, tornou-se num grave problema económico, estimando-se os seus prejuízos em cerça de 8% da produção anual das pastagens (TAVARES, 1989)
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