5,389 research outputs found
Non-standard interaction effects on astrophysical neutrino fluxes
We investigate new physics effects in the production and detection of high
energy neutrinos at neutrino telescopes. Analysing the flavor ratios
\phi_\mu/\phi_\tau and \phi_\mu/(\phi_\tau+\phi_e), we find that the Standard
Model predictions for them can be sensibly altered by new physics effects.Comment: 21 pages, 9 figures, REVTeX
Using Artificial Intelligence for COVID-19 Detection in Blood Exams: A Comparative Analysis
COVID-19 is an infectious disease that was declared a pandemic by the World Health Organization (WHO) in early March 2020. Since its early development, it has challenged health systems around the world. Although more than 12 billion vaccines have been administered, at the time of writing, it has more than 623 million confirmed cases and more than 6 million deaths reported to the WHO. These numbers continue to grow, soliciting further research efforts to reduce the impacts of such a pandemic. In particular, artificial intelligence techniques have shown great potential in supporting the early diagnosis, detection, and monitoring of COVID-19 infections from disparate data sources. In this work, we aim to make a contribution to this field by analyzing a high-dimensional dataset containing blood sample data from over forty thousand individuals recognized as infected or not with COVID-19. Encompassing a wide range of methods, including traditional machine learning algorithms, dimensionality reduction techniques, and deep learning strategies, our analysis investigates the performance of different classification models, showing that accurate detection of blood infections can be obtained. In particular, an F-score of 84% was achieved by the artificial neural network model we designed for this task, with a rate of 87% correct predictions on the positive class. Furthermore, our study shows that the dimensionality of the original data, i.e. the number of features involved, can be significantly reduced to gain efficiency without compromising the final prediction performance. These results pave the way for further research in this field, confirming that artificial intelligence techniques may play an important role in supporting medical decision-making
How to identify different new neutrino oscillation physics scenarios at DUNE
Next generation neutrino oscillation experiments are expected to measure the remaining oscillation parameters with very good precision. They will have unprecedented capabilities to search for new physics that modify oscillations. DUNE, with its broad band beam, good particle identification, and relatively high energies will provide an excellent environment to search for new physics. If deviations from the standard three-flavor oscillation picture are seen however, it is crucial to know which new physics scenario is found so that it can be verified elsewhere and theoretically understood. We investigate several benchmark new physics scenarios by looking at existing long-baseline accelerator neutrino data from NOvA and T2K and determine at what sensitivity DUNE can differentiate among them. We consider sterile neutrinos and both vector and scalar non-standard neutrino interactions, all with new complex phases, the latter of which could conceivably provide absolute neutrino mass scale information. We find that, in many interesting cases, DUNE will have good model discrimination. We also perform a new fit to NOvA and T2K data with scalar NSI
Dark Matter interactions in an flavor symmetry framework
The interactions of dark matter (DM) with the visible sector are often
phenomenologically described in the framework of simplified models where the
couplings of quarks to the new particles are generally assumed to be universal
or have a simple structure motivated by observational benchmarks. They should,
however, a priori be treated as free parameters. In this work we discuss one
particular realization of the structure of DM couplings based on an flavor symmetry, which has been shown to account reasonably well for
fermion masses and mixing, and compare their effect on observational signals to
universal as well as Yukawa-like couplings, which are motivated by minimal
flavor violation. We will also comment on how these structures could be
constrained in UV complete theories of DM and how DM observables, such as,
e.g., relic density and direct detection, can potentially be used as a smoking
gun for the underlying flavor symmetries.Comment: 24 pages, 6 figure
Nonzero and Neutrino Masses from Modified Neutrino Mixing Matrix
The nonzero and relatively large have been reported by Daya
Bay, T2K, MINOS, and Double Chooz Collaborations. In order to accommodate the
nonzero , we modified the tribimaximal (TB), bimaxima (BM), and
democratic (DC) neutrino mixing matrices. From three modified neutrino mixing
matrices, two of them (the modified BM and DC mixing matrices) can give nonzero
which is compatible with the result of the Daya Bay and T2K
experiments. The modified TB neutrino mixing matrix predicts the value of
greater than the upper bound value of the latest experimental
results. By using the modified neutrino mixing matrices and impose an
additional assumption that neutrino mass matrices have two zeros texture, we
then obtain the neutrino mass in normal hierarchy when
for the neutrino mass matrix from the
modified TB neutrino mixing matrix and for
the neutrino mass matrix from the modified DC neutrino mixing matrix. For these
two patterns of neutrino mass matrices, either the atmospheric mass squared
difference or the solar mass squared difference can be obtained, but not both
of them simultaneously. From four patterns of two zeros texture to be
considered on the obtained neutrino mass matrix from the modified BM neutrino
mixing matrix, none of them can predict correctly neutrino mass spectrum
(normal or inverted hierarchy).Comment: 13 pages, no figure, some references added, and slight revision due
to reviewer(s) comments, to be published in IJMP
Trends Prediction Using Social Diffusion Models
The importance of the ability of predict trends in social media has been
growing rapidly in the past few years with the growing dominance of social
media in our everyday's life. Whereas many works focus on the detection of
anomalies in networks, there exist little theoretical work on the prediction of
the likelihood of anomalous network pattern to globally spread and become
"trends". In this work we present an analytic model the social diffusion
dynamics of spreading network patterns. Our proposed method is based on
information diffusion models, and is capable of predicting future trends based
on the analysis of past social interactions between the community's members. We
present an analytic lower bound for the probability that emerging trends would
successful spread through the network. We demonstrate our model using two
comprehensive social datasets - the "Friends and Family" experiment that was
held in MIT for over a year, where the complete activity of 140 users was
analyzed, and a financial dataset containing the complete activities of over
1.5 million members of the "eToro" social trading community.Comment: 6 Pages + Appendi
The interplay between residential location and cycling choice: the case of two metropolitan areas in Sardinia, Italy
The current paper aims to enrich the current understanding of the link between the choice of residential location, the propensity to cycle to work and the propensity to cycle for non-commuting purposes. To highlight the relationship among these choice dimensions we used a composite econometric model that allows for the joint modelling of multiple outcomes. Residential location and cycling propensities are modelled as a function of socio-demographic and level-of-service variables. The inclusion of common error terms allows us to control for self-selection and unobserved effects that can simultaneously influence the underlying propensities. The data for this study is drawn from a survey conducted in the metropolitan areas of Cagliari and Sassari (Sardinia, Italy) in 2016 among a sample of local employees. The sample comprises 2,128 observations. Our results indicate that a significant portion of unobserved variance between the residential location choice and the propensity to cycle for non-commuting reasons exists, suggesting the presence of a self-selection effect
The R18 polyarginine peptide is more effective than the TAT-NR2B9c (NA-1) peptide when administered 60 minutes after permanent middle cerebral artery occlusion in the rat
We examined the dose responsiveness of polyarginine R18 (100, 300, and 1000 nmol/kg) when administered 60 minutes after permanent middle cerebral artery occlusion (MCAO).The TAT-NR2B9c peptide, which is known to be neuroprotective in rodent and nonhuman primate stroke models, served as a positive control. At 24 hours afterMCAO, there was reduced total infarct volume in R18 treated animals at all doses, but this reduction only reached statistical significance at doses of 100 and 1000 nmol/kg. The TAT-NR2B9c peptide reduced infarct volume at doses of 300 and 1000 nmol/kg, but not to a statistically significant extent, while the 100 nmol/kg dose was ineffective.The reduction in infarct volume with R18 and TAT-NR2B9c peptide treatments was mirrored by improvements in one or more functional outcomes (namely, neurological score, adhesive tape removal, and rota-rod), but not to a statistically significant extent. These findings further confirm the neuroprotective properties of polyarginine peptides and for R18 extend its therapeutic time window and dose range, as well as demonstrating its greater efficacy compared to TAT-NR2B9c in a severe stroke model.The superior neuroprotective efficacy of R18 over TAT-NR2B9c highlights the potential of this polyarginine peptide as a lead candidate for studies in human stroke
Exact and Approximate Formulas for Neutrino Mixing and Oscillations with Non-Standard Interactions
We present, both exactly and approximately, a complete set of mappings
between the vacuum (or fundamental) leptonic mixing parameters and the
effective ones in matter with non-standard neutrino interaction (NSI) effects
included. Within the three-flavor neutrino framework and a constant matter
density profile, a full set of sum rules is established, which enables us to
reconstruct the moduli of the effective leptonic mixing matrix elements, in
terms of the vacuum mixing parameters in order to reproduce the neutrino
oscillation probabilities for future long-baseline experiments. Very compact,
but quite accurate, approximate mappings are obtained based on series
expansions in the neutrino mass hierarchy parameter \eta \equiv \Delta
m^2_{21}/\Delta m^2_{31}, the vacuum leptonic mixing parameter s_{13} \equiv
\sin\theta_{13}, and the NSI parameters \epsilon_{\alpha\beta}. A detailed
numerical analysis about how the NSIs affect the smallest leptonic mixing angle
\theta_{13}, the deviation of the leptonic mixing angle \theta_{23} from its
maximal mixing value, and the transition probabilities useful for future
experiments are performed using our analytical results.Comment: 29 pages, 8 figures, final version published in J. High Energy Phy
Poly-arginine peptides reduce infarct volume in a permanent middle cerebral artery rat stroke model
Background: We recently reported that poly-arginine peptides have neuroprotective properties both in vitro and in vivo. In cultured cortical neurons exposed to glutamic acid excitotoxicity, we demonstrated that neuroprotective potency increases with polymer length plateauing at R15 to R18 (R = arginine resides). In an in vivo study in rats, we also demonstrated that R9D (R9 peptide synthesised with D-isoform amino acids) administered intravenously at a dose of 1000 nmol/kg 30 min after permanent middle cerebral artery occlusion (MCAO) reduces infarct volume. Based on these positive in vitro and in vivo findings, we decided to examine the neuroprotective efficacy of the L-isoform poly-arginine peptides, R12, R15 and R18 when administered at a dose of 1000 nmol/kg 30 min after permanent MCAO in the rat.
Results: At 24 h post-MCAO, there was reduced total infarct volume for R12 (12.8 % reduction) and R18 (20.5 % reduction), but this reduction only reached statistical significance for R18. Brain slice analysis revealed significantly reduced injury in coronal slices 4 and 5 for R18, and slice 5 for R12. The R15 peptide had no effect on infarct volume. Peptide treatment did not reveal any statistical significant improvement in functional outcomes.
Conclusion: While these findings confirm the in vivo neuroprotective properties of poly-arginine peptides, additional dose studies are required particularly in less severe transient MCAO models so as to further assess the potential of these agents as a stroke therapy
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