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Hepcidin-25 was identified as themain iron regulator in the human body, and it by binds to the sole iron-exporter ferroportin. Studies showed that the N-terminus of hepcidin is responsible for this interaction, the same N-terminus that encompasses a small copper(II) binding site known as the ATCUN (amino-terminal Cu(II)- and Ni(II)-binding) motif. Interestingly, this copper-binding property is largely ignored in most papers dealing with hepcidin-25. In this context, detailed investigations of the complex formed between hepcidin-25 and copper could reveal insight into its biological role. The present work focuses on metal-bound hepcidin-25 that can be considered the biologically active form. The first part is devoted to the reversed-phase chromatographic separation of copper-bound and copper-free hepcidin-25 achieved by applying basic mobile phases containing 0.1% ammonia. Further, mass spectrometry (tandemmass spectrometry (MS/MS), high-resolutionmass spectrometry (HRMS)) and nuclear magnetic resonance (NMR) spectroscopy were employed to characterize the copper-peptide. Lastly, a three-dimensional (3D)model of hepcidin-25with bound copper(II) is presented. The identification of metal complexes and potential isoforms and isomers, from which the latter usually are left undetected by mass spectrometry, led to the conclusion that complementary analytical methods are needed to characterize a peptide calibrant or referencematerial comprehensively. Quantitative nuclear magnetic resonance (qNMR), inductively-coupled plasma mass spectrometry (ICP-MS), ion-mobility spectrometry (IMS) and chiral amino acid analysis (AAA) should be considered among others.
Gamma-ray bursts (GRBs) are brief flashes of gamma-rays and are considered to be the most energetic explosive phenomena in the Universe(1). The emission from GRBs comprises a short (typically tens of seconds) and bright prompt emission, followed by a much longer afterglow phase. During the afterglow phase, the shocked outflow-produced by the interaction between the ejected matter and the circumburst medium-slows down, and a gradual decrease in brightness is observed(2). GRBs typically emit most of their energy via.-rays with energies in the kiloelectronvolt-to-megaelectronvolt range, but a few photons with energies of tens of gigaelectronvolts have been detected by space-based instruments(3). However, the origins of such high-energy (above one gigaelectronvolt) photons and the presence of very-high-energy (more than 100 gigaelectronvolts) emission have remained elusive(4). Here we report observations of very-high-energy emission in the bright GRB 180720B deep in the GRB afterglow-ten hours after the end of the prompt emission phase, when the X-ray flux had already decayed by four orders of magnitude. Two possible explanations exist for the observed radiation: inverse Compton emission and synchrotron emission of ultrarelativistic electrons. Our observations show that the energy fluxes in the X-ray and gamma-ray range and their photon indices remain comparable to each other throughout the afterglow. This discovery places distinct constraints on the GRB environment for both emission mechanisms, with the inverse Compton explanation alleviating the particle energy requirements for the emission observed at late times. The late timing of this detection has consequences for the future observations of GRBs at the highest energies.
The flat spectrum radio quasar 3C 279 is known to exhibit pronounced variability in the high-energy (100MeV < E < 100 GeV) gamma-ray band, which is continuously monitored with Fermi-LAT. During two periods of high activity in April 2014 and June 2015 target-of-opportunity observations were undertaken with the High Energy Stereoscopic System (H.E.S.S.) in the very-high-energy (VHE, E > 100 GeV) gamma-ray domain. While the observation in 2014 provides an upper limit, the observation in 2015 results in a signal with 8 : 7 sigma significance above an energy threshold of 66 GeV. No VHE variability was detected during the 2015 observations. The VHE photon spectrum is soft and described by a power-law index of 4.2 +/- 0.3. The H.E.S.S. data along with a detailed and contemporaneous multiwavelength data set provide constraints on the physical parameters of the emission region. The minimum distance of the emission region from the central black hole was estimated using two plausible geometries of the broad-line region and three potential intrinsic spectra. The emission region is confidently placed at r greater than or similar to 1 : 7 X 1017 cm from the black hole, that is beyond the assumed distance of the broad-line region. Time-dependent leptonic and lepto-hadronic one-zone models were used to describe the evolution of the 2015 flare. Neither model can fully reproduce the observations, despite testing various parameter sets. Furthermore, the H.E.S.S. data were used to derive constraints on Lorentz invariance violation given the large redshift of 3C 279.
Young core-collapse supernovae with dense-wind progenitors may be able to accelerate cosmic-ray hadrons beyond the knee of the cosmic-ray spectrum, and this may result in measurable gamma-ray emission. We searched for gamma-ray emission from ten super- novae observed with the High Energy Stereoscopic System (H.E.S.S.) within a year of the supernova event. Nine supernovae were observed serendipitously in the H.E.S.S. data collected between December 2003 and December 2014, with exposure times ranging from 1.4 to 53 h. In addition we observed SN 2016adj as a target of opportunity in February 2016 for 13 h. No significant gamma-ray emission has been detected for any of the objects, and upper limits on the >1 TeV gamma-ray flux of the order of similar to 10(-13) cm(-)(2)s(-1) are established, corresponding to upper limits on the luminosities in the range similar to 2 x 10(39) to similar to 1 x 10(42) erg s(-1). These values are used to place model-dependent constraints on the mass-loss rates of the progenitor stars, implying upper limits between similar to 2 x 10(-5) and similar to 2 x 10(-3) M-circle dot yr(-1) under reasonable assumptions on the particle acceleration parameters.
The blazar Mrk 501 (z = 0.034) was observed at very-high-energy (VHE, E greater than or similar to 100 GeV) gamma-ray wavelengths during a bright flare on the night of 2014 June 23-24 (MJD 56832) with the H.E.S.S. phase-II array of Cherenkov telescopes. Data taken that night by H.E.S.S. at large zenith angle reveal an exceptional number of gamma-ray photons at multi-TeV energies, with rapid flux variability and an energy coverage extending significantly up to 20 TeV. This data set is used to constrain Lorentz invariance violation (LIV) using two independent channels: a temporal approach considers the possibility of an energy dependence in the arrival time of gamma-rays, whereas a spectral approach considers the possibility of modifications to the interaction of VHE gamma-rays with extragalactic background light (EBL) photons. The non-detection of energy-dependent time delays and the non-observation of deviations between the measured spectrum and that of a supposed power-law intrinsic spectrum with standard EBL attenuation are used independently to derive strong constraints on the energy scale of LIV (E-QG) in the subluminal scenario for linear and quadratic perturbations in the dispersion relation of photons. For the case of linear perturbations, the 95% confidence level limits obtained are E-QG,E-1 > 3.6 x 10(17) GeV using the temporal approach and E-QG,E-1 > 2.6 x 10(19) GeV using the spectral approach. For the case of quadratic perturbations, the limits obtained are E-QG,E-2 > 8.5 x 10(10) GeV using the temporal approach and E-QG,E-2 > 7.8 x 10(11) GeV using the spectral approach.
PKS 1830-211 is a known macrolensed quasar located at a redshift of z = 2.5. Its highenergy gamma-ray emission has been detected with the Fermi-Large Area Telescope (LAT) instrument and evidence for lensing was obtained by several authors from its high-energy data. Observations of PKS 1830-211 were taken with the High Energy Stereoscopic System (H.E.S.S.) array of Imaging Atmospheric Cherenkov Telescopes in 2014 August, following a flare alert by the Fermi-LAT Collaboration. The H.E.S.S observations were aimed at detecting a gamma-ray flare delayed by 20-27 d from the alert flare, as expected from observations at other wavelengths. More than 12 h of good-quality data were taken with an analysis threshold of similar to 67 GeV. The significance of a potential signal is computed as a function of the date and the average significance over the whole period. Data are compared to simultaneous observations by Fermi-LAT. No photon excess or significant signal is detected. An upper limit on PKS 1830-211 flux above 67 GeV is computed and compared to the extrapolation of the Fermi-LAT flare spectrum.
Context. We present a detailed view of the pulsar wind nebula (PWN) HESS J1825-137. We aim to constrain the mechanisms dominating the particle transport within the nebula, accounting for its anomalously large size and spectral characteristics. Aims. The nebula was studied using a deep exposure from over 12 years of H.E.S.S. I operation, together with data from H.E.S.S. II that improve the low-energy sensitivity. Enhanced energy-dependent morphological and spatially resolved spectral analyses probe the very high energy (VHE, E > 0.1 TeV) gamma-ray properties of the nebula. Methods. The nebula emission is revealed to extend out to 1.5 degrees from the pulsar, similar to 1.5 times farther than previously seen, making HESS J1825-137, with an intrinsic diameter of similar to 100 pc, potentially the largest gamma-ray PWN currently known. Characterising the strongly energy-dependent morphology of the nebula enables us to constrain the particle transport mechanisms. A dependence of the nebula extent with energy of R proportional to E alpha with alpha = -0.29 +/- 0.04(stat) +/- 0.05(sys) disfavours a pure diffusion scenario for particle transport within the nebula. The total gamma-ray flux of the nebula above 1 TeV is found to be (1.12 +/- 0.03(stat) +/- 0.25(sys)) +/- 10(-11) cm(-2) s(-1), corresponding to similar to 64% of the flux of the Crab nebula. Results. HESS J1825-137 is a PWN with clearly energy-dependent morphology at VHE gamma-ray energies. This source is used as a laboratory to investigate particle transport within intermediate-age PWNe. Based on deep observations of this highly spatially extended PWN, we produce a spectral map of the region that provides insights into the spectral variation within the nebula.
Context. Pulsar wind nebulae (PWNe) represent the most prominent population of Galactic very-high-energy gamma-ray sources and are thought to be an efficient source of leptonic cosmic rays. Vela X is a nearby middle-aged PWN, which shows bright X-ray and TeV gamma-ray emission towards an elongated structure called the cocoon. Aims. Since TeV emission is likely inverse-Compton emission of electrons, predominantly from interactions with the cosmic microwave background, while X-ray emission is synchrotron radiation of the same electrons, we aim to derive the properties of the relativistic particles and of magnetic fields with minimal modelling. Methods. We used data from the Suzaku XIS to derive the spectra from three compact regions in Vela X covering distances from 0.3 to 4 pc from the pulsar along the cocoon. We obtained gamma-ray spectra of the same regions from H.E.S.S. observations and fitted a radiative model to the multi-wavelength spectra. Results. The TeV electron spectra and magnetic field strengths are consistent within the uncertainties for the three regions, with energy densities of the order 10(-12) erg cm(-3). The data indicate the presence of a cutoff in the electron spectrum at energies of similar to 100 TeV and a magnetic field strength of similar to 6 mu G. Constraints on the presence of turbulent magnetic fields are weak. Conclusions. The pressure of TeV electrons and magnetic fields in the cocoon is dynamically negligible, requiring the presence of another dominant pressure component to balance the pulsar wind at the termination shock. Sub-TeV electrons cannot completely account for the missing pressure, which may be provided either by relativistic ions or from mixing of the ejecta with the pulsar wind. The electron spectra are consistent with expectations from transport scenarios dominated either by advection via the reverse shock or by diffusion, but for the latter the role of radiative losses near the termination shock needs to be further investigated in the light of the measured cutoff energies. Constraints on turbulent magnetic fields and the shape of the electron cutoff can be improved by spectral measurements in the energy range greater than or similar to 10 keV.
A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.
Background There is scant information on the breastmilk vitamin A (BMVA) concentration of lactating women in developing countries, partly due to lack of methods applicable in-field. Objective To assess BMVA concentrations of samples collected from lactating women of children aged 6-23 months, in Mecha district, Ethiopia. Subjects/methods Data on socio-demographic and anthropometric characteristics were collected from randomly selected lactating women (n = 104). Breast milk samples were collected and vitamin A concentrations were analyzed using HPLC and iCheck FLUORO then the two measurements were compared. Results The prevalence of underweight (BMI < 18.5 kg/m(2)) among lactating women was 17%. Seventy six percent of the BMVA values were < 1.05 mu mol/l and 81% were < 8 mu g/g fat. The mean BMVA concentration accounted to 41% of the estimated average value for mothers in developing countries. The BMVA values from HPLC and iCheck were correlated (r = 0.59, p = < 0.001), but it was not strong. Conclusions The result indicates the low vitamin A status of the lactating women and their children. It further indicates that intake assessments should not use average BMVA composition. The possibility of using iCheck for monitoring interventions designed to improve vitamin A status of lactating women with low BMVA requires further investigation.