Refine
Year of publication
Document Type
- Article (71)
- Other (2)
- Postprint (2)
- Working Paper (1)
Language
- English (76)
Is part of the Bibliography
- yes (76)
Keywords
- gamma rays: general (22)
- ISM: supernova remnants (15)
- radiation mechanisms: non-thermal (15)
- cosmic rays (11)
- acceleration of particles (9)
- gamma rays: galaxies (9)
- galaxies: active (8)
- ISM: clouds (6)
- astroparticle physics (6)
- gamma rays: stars (6)
Context. The Tarantula Nebula (a.k.a. 30 Dor) is a spectacular star-forming region in the Large Magellanic Cloud (LMC), seen through gas in the Galactic disc and halo. Diffuse interstellar bands (DIBs) offer a unique probe of the diffuse, cool-warm gas in these regions.
Aims. The aim is to use DIBs as diagnostics of the local interstellar conditions, whilst at the same time deriving properties of the yet-unknown carriers of these enigmatic spectral features.
Methods. Spectra of over 800 early-type stars from the Very Large Telescope Flames Tarantula Survey (VFTS) were analysed. Maps were created, separately, for the Galactic and LMC absorption in the DIBs at 4428 and 6614 angstrom and - in a smaller region near the central cluster R 136 - neutral sodium (the Na ID doublet); we also measured the DIBs at 5780 and 5797 angstrom.
Results. The maps show strong 4428 and 6614 angstrom DIBs in the quiescent cloud complex to the south of 30 Dor but weak absorption in the harsher environments to the north (bubbles) and near the OB associations. The Na maps show at least five kinematic components in the LMC and a shell-like structure surrounding R 136, and small-scale structure in the Milky Way. The strengths of the 4428, 5780, 5797 and 6614 angstrom DIBs are correlated, also with Na absorption and visual extinction. The strong 4428 angstrom DIB is present already at low Na column density but the 6614, 5780 and 5797 angstrom DIBs start to be detectable at subsequently larger Na column densities.
Conclusions. The carriers of the 4428, 6614, 5780 and 5797 angstrom DIBs are increasingly prone to removal from irradiated gas. The relative strength of the 5780 and 5797 angstrom DIBs clearly confirm the Tarantula Nebula as well as Galactic high-latitude gas to represent a harsh radiation environment. The resilience of the 4428 angstrom DIB suggests its carrier is large, compact and neutral. Structure is detected in the distribution of cool-warm gas on scales between one and > 100 pc in the LMC and as little as 0.01 pc in the Sun's vicinity. Stellar winds from the central cluster R 136 have created an expanding shell; some infalling gas is also detected, reminiscent of a galactic "fountain".
Notwithstanding their 3 to 5% mortality, the 2.7 million envenomation-related injuries occurring annually-predominantly across Africa, Asia, and Latin America-are also major causes of morbidity. Venom toxin-damaged tissue will develop infections in some 75% of envenomation victims, with E. faecalis being a common culprit of disease; however, such infections are generally considered to be independent of envenomation.
Animal venoms are considered sterile sources of antimicrobial compounds with strong membrane-disrupting activity against multidrug-resistant bacteria.
However, venomous bite wound infections are common in developing nations. Investigating the envenomation organ and venom microbiota of five snake and two spider species, we observed venom community structures that depend on the host venomous animal species and evidenced recovery of viable microorganisms from black-necked spitting cobra (Naja nigricollis) and Indian ornamental tarantula (Poecilotheria regalis) venoms. Among the bacterial isolates recovered from N. nigricollis, we identified two venom-resistant, novel sequence types of Enterococcus faecalis whose genomes feature 16 virulence genes, indicating infectious potential, and 45 additional genes, nearly half of which improve bacterial membrane integrity.
Our findings challenge the dogma of venom sterility and indicate an increased primary infection risk in the clinical management of venomous animal bite wounds. IMPORTANCE Notwithstanding their 3 to 5% mortality, the 2.7 million envenomation-related injuries occurring annually-predominantly across Africa, Asia, and Latin America-are also major causes of morbidity. Venom toxin-damaged tissue will develop infections in some 75% of envenomation victims, with E. faecalis being a common culprit of disease; however, such infections are generally considered to be independent of envenomation. Here, we provide evidence on venom microbiota across snakes and arachnida and report on the convergent evolution mechanisms that can facilitate adaptation to black-necked cobra venom in two independent E. faecalis strains, easily misidentified by biochemical diagnostics.
Therefore, since inoculation with viable and virulence gene-harboring bacteria can occur during envenomation, acute infection risk management following envenomation is warranted, particularly for immunocompromised and malnourished victims in resource-limited settings.
These results shed light on how bacteria evolve for survival in one of the most extreme environments on Earth and how venomous bites must be also treated for infections.
Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.
Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.
Plain Language Summary Radiation belts of the Earth, which are the zones of charged energetic particles trapped by the geomagnetic field, comprise enormous and dynamic systems. While the inner radiation belt, composed mainly of high-energy protons, is relatively stable, the outer belt, filled with energetic electrons, is highly variable and depends substantially on solar activity. Hence, extended reliable observations and the improved models of the electron intensities in the outer belt depending on solar wind parameters are necessary for prediction of their dynamics. The Cluster mission has been measuring electron flux intensities in the radiation belts since its launch in 2000, thus providing a huge dataset that can be used for radiation belts analysis. Using 16 years of electron measurements by the Cluster mission corrected for background contamination, we derived a uniform linear-logarithmic dependence of electron fluxes in the outer belt on the solar wind dynamic pressure.
This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.