Detection of Resistance Mutations to Antivirals Oseltamivir and Zanamivir in Avian Influenza A Viruses Isolated from Wild Birds

The neuraminidase (NA) inhibitors oseltamivir and zanamivir are the first-line of defense against potentially fatal variants of influenza A pandemic strains. However, if resistant virus strains start to arise easily or at a high frequency, a new anti-influenza strategy will be necessary. This study aimed to investigate if and to what extent NA inhibitor–resistant mutants exist in the wild population of influenza A viruses that inhabit wild birds. NA sequences of all NA subtypes available from 5490 avian, 379 swine and 122 environmental isolates were extracted from NCBI databases. In addition, a dataset containing 230 virus isolates from mallard collected at Ottenby Bird Observatory (Öland, Sweden) was analyzed. Isolated NA RNA fragments from Ottenby were transformed to cDNA by RT-PCR, which was followed by sequencing. The analysis of genotypic profiles for NAs from both data sets in regard to antiviral resistance mutations was performed using bioinformatics tools. All 6221 sequences were scanned for oseltamivir- (I117V, E119V, D198N, I222V, H274Y, R292K, N294S and I314V) and zanamivir-related mutations (V116A, R118K, E119G/A/D, Q136K, D151E, R152K, R224K, E276D, R292K and R371K). Of the sequences from the avian NCBI dataset, 132 (2.4%) carried at least one, or in two cases even two and three, NA inhibitor resistance mutations. Swine and environmental isolates from the same data set had 18 (4.75%) and one (0.82%) mutant, respectively, with at least one mutation. The Ottenby sequences carried at least one mutation in 15 cases (6.52%). Therefore, resistant strains were more frequently found in Ottenby samples than in NCBI data sets. However, it is still uncertain if these mutations are the result of natural variations in the viruses or if they are induced by the selective pressure of xenobiotics (e.g., oseltamivir, zanamivir)

Horizontal muon track identification with neural networks in HAWC

Nowadays the implementation of artificial neural networks in high-energyphysics has obtained excellent results on improving signal detection. In thiswork we propose to use neural networks (NNs) for event discrimination in HAWC.This observatory is a water Cherenkov gamma-ray detector that in recent yearshas implemented algorithms to identify horizontal muon tracks. However, thesealgorithms are not very efficient. In this work we describe the implementationof three NNs: two based on image classification and one based on objectdetection. Using these algorithms we obtain an increase in the number ofidentified tracks. The results of this study could be used in the future toimprove the performance of the Earth-skimming technique for the indirectmeasurement of neutrinos with HAWC.<br

A Contribution of the HAWC Observatory to the TeV era in the High Energy Gamma-Ray Astrophysics: The case of the TeV-Halos

We present a short overview of the TeV-Halos objects as a discovery and a relevant contribution of the High Altitude Water \v{C}erenkov (HAWC) observatory to TeV astrophysics. We discuss history, discovery, knowledge, and the next step through a new and more detailed analysis than the original study in 2017. TeV-Halos will contribute to resolving the problem of the local positron excess observed on the Earth. To clarify the latter, understanding the diffusion process is mandatory.Comment: Work presented in the 21st International Symposium on Very High Energy Cosmic Ray Interactions(ISVHECRI 2022) as part of the Ph. D. Thesis of Ramiro Torres-Escobedo (SJTU, Shanghai, China). Accepted for publication in SciPost Physics Proceedings (ISSN 2666-4003). 11 pages, 3 Figures. Short overview of HAWC and TeV Halos objects until 202

Combined dark matter searches towards dwarf spheroidal galaxies with Fermi-LAT, HAWC, H.E.S.S., MAGIC, and VERITAS

Cosmological and astrophysical observations suggest that 85% of the total matter of the Universe is made of Dark Matter (DM). However, its nature remains one of the most challenging and fundamental open questions of particle physics. Assuming particle DM, this exotic form of matter cannot consist of Standard Model (SM) particles. Many models have been developed to attempt unraveling the nature of DM such as Weakly Interacting Massive Particles (WIMPs), the most favored particle candidates. WIMP annihilations and decay could produce SM particles which in turn hadronize and decay to give SM secondaries such as high energy \u1d6fe rays. In the framework of indirect DM search, observations of promising targets are used to search for signatures of DM annihilation. Among these, the dwarf spheroidal galaxies (dSphs) are commonly favored owing to their expected high DM content and negligible astrophysical background. In this work, we present the very first combination of 20 dSph observations, performed by the Fermi-LAT, HAWC, H.E.S.S., MAGIC, and VERITAS collaborations in order to maximize the sensitivity of DM searches and improve the current results. We use a joint maximum likelihood approach combining each experiment’s individual analysis to derive more constraining upper limits on the WIMP DM self-annihilation cross-section as a function of DM particle mass. We present new DM constraints over the widest mass range ever reported, extending from 5 GeV to 100 TeV thanks to the combination of these five different \u1d6fe-ray instruments

Multimessenger NuEM Alerts with AMON

The Astrophysical Multimessenger Observatory Network (AMON), has developed a real-time multi-messenger alert system. The system performs coincidence analyses of datasets from gamma-ray and neutrino detectors, making the Neutrino-Electromagnetic (NuEM) alert channel. For these analyses, AMON takes advantage of sub-threshold events, i.e., events that by themselves are not significant in the individual detectors. The main purpose of this channel is to search for gamma-ray counterparts of neutrino events. We will describe the different analyses that make-up this channel and present a selection of recent results