There is probably little value in attempting to retreat patients with cutaneous reactions, even with the alternative agent, except in patients with limited treatment options.

The seroprevalence of measles (rubeola) antibody in 619 human immunodeficiency virus (HIV)-infected adults was determined by a standard ELISA. Risk factors for a lack of antibody and presumed susceptibility to measles were examined. Whereas overall, 9.8% of patients (60) were found to lack antibody, 17.8% of those born within the United States in 1957 or later were antibody-negative. Multivariate analysis showed that absence of measles antibody was significantly associated with younger age (born in 1957 or later) (odds ratio [OR], 8.15; 95% confidence interval [CI], 3.7-21.5; P < .0001) and birth within the United States (OR, 4.72; 95% CI, 1.7-19.7; P = .0045). Neither minority status, stage of HIV infection, CD4 cell count, nor a history of opportunistic infection bore any relationship to the presence of antibody. While progression of HIV disease does not affect measles serostatus, younger HIV-infected patients, especially those born in the United States in 1957 or later, are at the greatest risk for measles.

Cotopaxi Volcano showed an increased activity since April 2015 and evolved into its eventual mild eruption in August 2015. In this work we use records from a broadband seismic station located at less than 4 km from the vent that encompass data from April to December of 2015, to detect and study low-frequency seismic events. We applied unsupervised learning schemes to group and identify possible premonitory low-frequency seismic families. To find these families we applied a two-stage process in which the events were first separated by their frequency content by applying the k-means algorithm to the spectral density vector of the signals and then were further separated by their waveform by applying Correntropy and Dynamic Time Warping. As a result, we found a particular family related to the volcano's state of activity by exploring its time distribution and estimating its events' locations.

&lt;p&gt;The recently identified Prompt Elasto-Gravity Signals (PEGS), generated by large earthquakes, propagate at the speed of light and are sensitive to the earthquake magnitude and focal mechanism. These characteristics make PEGS potentially more accurate than P-wave based early warning algorithms (which produce saturated magnitude estimations) and faster than Global Navigation Satellite Systems (GNSS)-based systems. We use a deep learning model called PEGSNet, originally developed for application in Japan, to track the temporal evolution of the magnitude of the 2010 M&lt;sub&gt;w&lt;/sub&gt; 8.8 Maule earthquake. The model is a Convolutional Neural Network (CNN), trained on a database of synthetic PEGS -- simulated for an exhaustive set of possible earthquakes distributed along the Chilean subduction zone -- augmented with empirical noise. The approach is multi-station and leverages the information recorded on all the available stations to estimate as fast as possible the magnitude and location of an on-going earthquake. Our results indicate that PEGSNet could have estimated an &amp;#160;M&lt;sub&gt;w&lt;/sub&gt; &gt; 8.7 earthquake after 100 seconds in the Maule case. Our synthetic tests using real data and noise recordings further support the instantaneous tracking of the source time function of the earthquake.&lt;/p&gt;

The recently identified Prompt Elasto-Gravity Signals (PEGS), generated by large earthquakes, propagate at the speed of light and are sensitive to the earthquake magnitude and focal mechanism. These characteristics make PEGS potentially very advantageous for earthquake and tsunami early warning. PEGS-based early warning does not suffer from the saturation of magnitude estimations problem that P-wave based early warning algorithms have, and could be faster than Global Navigation Satellite Systems (GNSS)-based systems while not requiring a priori assumptions on slip distribution. We use a deep learning model called PEGSNet to track the temporal evolution of the magnitude of the 2010 $\textrm{M}_{\textrm{w}}$ 8.8 Maule, Chile earthquake. The model is a Convolutional Neural Network (CNN) trained on a database of synthetic PEGS – simulated for an exhaustive set of possible earthquakes distributed along the Chilean subduction zone – augmented with empirical noise. The approach is multi-station and leverages the information recorded by the seismic network to estimate as fast as possible the magnitude and location of an ongoing earthquake. Our results indicate that PEGSNet could have estimated that the magnitude of the Maule earthquake was above 8.7, 90 seconds after origin time. Our offline simulations using real data and noise recordings further support the instantaneous tracking of the source time function of the earthquake and show that deploying seismic stations in optimal locations could improve the performance of the algorithm.