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 S4×Z5S_4 \times Z_5 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 $S_4 \times Z_5$ 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 θ13\theta_{13} and Neutrino Masses from Modified Neutrino Mixing Matrix

The nonzero and relatively large $\theta_{13}$ have been reported by Daya Bay, T2K, MINOS, and Double Chooz Collaborations. In order to accommodate the nonzero $\theta_{13}$, 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 $\theta_{13}$ which is compatible with the result of the Daya Bay and T2K experiments. The modified TB neutrino mixing matrix predicts the value of $\theta_{13}$ 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 $(M_{\nu})_{22}=(M_{\nu})_{33}=0$ for the neutrino mass matrix from the modified TB neutrino mixing matrix and $(M_{\nu})_{11}=(M_{\nu})_{13}=0$ 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 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

Crystal structure of the DNA-binding domain of Myelin-gene Regulatory Factor

Myelin-gene Regulatory Factor (MyRF) is one of the master transcription factors controlling myelin formation and development in oligodendrocytes which is crucial for the powerful brain functions. The N-terminal of MyRF, which contains a proline-rich region and a DNA binding domain (DBD), is auto-cleaved from the ER membrane, and then enters the nucleus to participate in transcription regulation of the myelin genes. Here we report the crystal structure of MyRF DBD. It shows an Ig-fold like architecture which consists of two antiparallel β-sheets with 7 main strands, packing against each other, forming a β-sandwich. Compared to its homolog, Ndt80, MyRF has a smaller and less complex DBD lacking the helices and the big loops outside the core. Structural alignment reveals that MyRF DBD possess less interaction sites with DNA than Ndt80 and may bind only at the major groove of DNA. Moreover, the structure reveals a trimeric assembly, agreeing with the previous report that MyRF DBD functions as a trimer. The mutant that we designed based on the structure disturbed trimer formation, but didn't affect the auto-cleavage reaction. It demonstrates that the activation of self-cleavage reaction of MyRF is independent of the presence of its N-terminal DBD homotrimer. The structure reported here will help to understand the molecular mechanism underlying the important roles of MyRF in myelin formation and development