Adverse Events in a Cohort of HIV Infected Pregnant and Non-Pregnant Women Treated with Nevirapine versus Non-Nevirapine Antiretroviral Medication

BACKGROUND: Predictors of adverse events (AE) associated with nevirapine use are needed to better understand reports of severe rash or liver enzyme elevation (LEE) in HIV+ women. METHODOLOGY: AE rates following ART initiation were retrospectively assessed in a multi-site cohort of 612 women. Predictors of onset of rash or LEE were determined using univariate and multivariate analyses. PRINCIPAL FINDINGS: Of 612 subjects, 152 (24.8%) initiated NVP-based regimens with 86 (56.6%) pregnant; 460 (75.2%) initiated non-NVP regimens with 67 (14.6%) pregnant. LEE: No significant difference was found between regimens in the development of new grade ≥2 LEE (p  =  0.885). Multivariate logistic regression demonstrated an increased likelihood of LEE with HCV co-infection (OR 2.502, 95% CI: 1.04 to 6, p =  0.040); pregnancy, NVP-based regimen, and baseline CD4 >250 cells/mm(3) were not associated with this toxicity. RASH: NVP initiation was associated with rash after controlling for CD4 and pregnancy (OR 2.78; 95%CI: 1.14-6.76), as was baseline CD4 >250 cells/mm(3) when controlling for pregnancy and type of regimen (OR 2.68; 95% CI: 1.19-6.02 p  =  0.017). CONCLUSIONS: CD4 at initiation of therapy was a predictor of rash but not LEE with NVP use in HIV+ women. Pregnancy was not an independent risk factor for the development of AEs assessed. The findings from this study have significant implications for women of child-bearing age initiating NVP-based ART particularly in resource limited settings. This study sheds more confidence on the lack of LEE risk and the need to monitor rash with the use of this medication

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Simulating Instructional Roles through Pedagogical Agents

This paper describes the design and empirical validation of three distinct pedagogical agent roles (Expert, Motivator, and Mentor) for college students within the MIMIC (Multiple Intelligent Mentors Instructing Collaboratively) agent-based research environment. The pedagogical agent roles were operationalized by image, animation, affect, voice and script, and were developed in Poser 4 and implemented via Microsoft Agent. Two controlled experiments validated the instantiation of the three roles according to learner perception (N=78) and actual impact on motivation and learning (N=71). The results confirmed that the agent roles were not only perceived by the students to reflect their intended purposes but also led to significant changes in learning and motivation, as designed. Specifically, the Expert agent led to increased information acquisition, the Motivator led to increased self-efficacy, and the Mentor led to overall improved learning and motivation. The implications for intelligent tutoring and multi-agent system design and development is discussed

Thematic Awareness and Recall of Information From Text

In a repeated-measures experimental design, 28 college students read and later answered questions about specific pieces of information in 2 sets of 4 fictitious biographical passages that differed in terms of the presence of thematic connections (with theme or without theme). As hypothesized, the students recalled more items from the theme set than the nontheme set. The positive effect of thematic connections was strongest for the students who exhibited some awareness of theme. In other words, the students who selected the theme set as the easiest recalled more than those who chose the non-theme set as easiest. The students who picked the theme set as easiest were also more accurate in their prediction estimates of their theme set recall. Student explanations for why one story set was easier than the other were predominantly Story explanations (referring to characteristics of the story) and Order explanations (referring to factors influenced by set order). Students gave different explanations depending on which story set they picked as easiest. As expected, the students who picked the non-theme set as easiest gave relatively few Story explanations. The students reported different patterns of strategy use depending upon whether or not they switched strategies for the different story sets