The Ark

The need for SARS-CoV-2 vaccines with improved potency, lower reactogenicity, broader coverage, and prolonged protection persists. We examined the safety and immunogenicity of two ferritin scaffold-based self-assembling SARS-CoV-2 mRNA virus-like particle (VLP) vaccines. In this, randomized, Phase I, open-label, active-controlled study (www. govNCT06147063) participants received a single 5μg or 10μg intramuscular injection of AZD9838 (BA.4/5 variant) or AZD6563 (XBB.1.5 variant), or 30μg BNT162b2, a licensed mRNA vaccine (XBB.1.5 variant). The primary safety endpoint was the incidence of solicited adverse reactions (ARs) through Day 8, unsolicited adverse events (AEs) through Day 29, and serious AEs (SAEs), medically attended AEs (MAAEs), and AEs of special interest (AESIs) through Day 361. The primary immunogenicity endpoint was to characterize the neutralizing antibody (nAb) responses to the ancestral and Omicron (BA.4/5, XBB.1.5) variants at Day 29; characterization of nAb response to Omicron JN.1 was an exploratory analysis. In total, 166 participants aged 18-64years and 76 participants ≥65years of age were vaccinated. AZD9838 and AZD6563 were well-tolerated at both dosages. Overall, fewer solicited ARs were reported with AZD9838 and AZD6563 versus BNT162b2. Unsolicited AEs were similar between groups; no related SAEs, AESIs, or MAAEs were reported to Day 180. Day 29 nAb GMTs were higher following 10μg AZD6563 versus 5μg and higher than AZD9838 across variants and age groups, remaining above baseline and similar to BNT162b2 at Day 180; 10μg AZD6563 resulted in nAb GMTs similar to BNT162b2 in both age groups. By combining mRNA vaccine technology with VLP-based antigen display, we developed two candidate SARS-CoV-2 vaccines, AZD9838 and AZD6563, that were well tolerated versus a licensed mRNA vaccine, BNT162b2. Furthermore, the variant-matched AZD6563 generated a similar immunogenicity to BNT162b2 but at one third of the dosage (10μg versus 30μg).

Abstract Objective To evaluate the ability of a natural language processing system to automatically reconstruct the SOFA score from unstructured clinical notes in patients with sepsis and validate its applicability in intensive care units. Materials and methods Retrospective study in the MIMIC-III database that included 284 adults with sepsis. The SOFA calculated with structured data was compared with the SOFA reconstructed by free text extraction. Clinical rules were applied for calculation at 24 h and 48 h. Variable completeness, severity reclassification, and association with hospital mortality were evaluated using logistic regression. Results Automated extraction increased the availability of critical variables (respiratory 33% to 100%, vasopressor 12% to 41%). The reconstructed SOFA increased by 3 points at 24 hours, reclassifying patients with high severity (SOFA ≥ 6) from 17% to 48% and SOFA ≥ 10 from 5% to 22%. Reconstructed scores remained associated with mortality at 24 h (OR 1.16, 95% CI 1.09-1.24) and at 48 h (OR 1.23, 95% CI 1.15-1.31), comparable to that based on structured data (p < 0.001). Discussion Automatic reconstruction of the SOFA from free text recovers information missing from structured fields, reducing underestimation of severity. Conclusion NLP approaches supported by large language models provide a more complete and clinically consistent SOFA score in sepsis when structured data are insufficient.

“Edu-choreographing” is a play with the post-qualitative concept <italic>edu-crafting</italic> offered by Carol A. Taylor in 2018. Post-qualitative research challenges and expands qualitative research, especially in destabilizing the subject, making it a relational, becoming, embodied and affective subject that is changing <italic>with</italic> the research phenomena and <italic>with</italic> the world. New concepts are created within post-qualitative research to seek to express this relational ontology, destabilization, and entanglement. Edu-crafting - a combination of “education” and “crafting” - is one such concept which has caught our interest. To craft is to design, to create, to let something take shape. We understand Taylor’s edu-crafting as a way of a) focusing on education as something that can be crafted, and b) intense pedagogical entanglements where the teacher is actively crafting the pedagogies, but also, at the same time, the teacher is being crafted right back from the ongoing flow of the pedagogical event. The students, as well as other aspects like spaces, time, discourses, and materials like arts materials, entangle with and have agency on the ongoing crafting of the teaching pedagogies.

Dance and music performance quality has been a consistently debated aspect of performance that has largely been based on human judgment which is subject to bias and lack of consistency. The recent progress in artificial intelligence, and especially deep learning provides a strong alternative to objective, data-driven assessment. This paper hopes to investigate the application of deep learning models Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Gated Recurrent Units (GRUs) to evaluate the performance of artists in these two areas. The proposed framework uses a mixture of multimodal data including motion capture or audio data and visual data to extract and combine features which determine rhythm and expression, synchronization and technical accuracy. The methodology focuses on the strong training, validation and testing plans to provide the accuracy and generalization to all the performers and genres. One use of this research is in real time feedback systems to learn music and dance, in competitions, automated scoring, and intelligent tutoring systems that adjust to the level of performance of the learner. Moreover, the paper identifies the opportunities of cross-cultural dataset growth, methods of bias mitigation, and explainable AI processes to provide transparency in automated assessments. The outcomes of the experiments prove the effectiveness of deep learning models to capture subtle features of performance, which is better than the traditional and classical approaches to machine learning. This study is part of the emerging convergence of artificial intelligence and performing arts, which will open the door to more equitable, more knowledgeable, and more globally applicable evaluation mechanisms.

Soil contamination by heavy metals endangers the environment, food security, and public health alike. Existing analytical approaches to estimate how metals bind to soil particles typically employ Langmuir and Freundlich isotherms; yet these models cannot adequately represent the intricate, nonlinear interactions among soil chemistries and the dissolved metals. Instead, we applied Artificial Neural Networks (ANNs) to cope with the inherent complexity. A series of feed-forward networks received, as input layers, key physicochemical variables soil pH, cation exchange capacity (CEC), organic matter percentage, clay fraction supplemented with initial concentrations of lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn). Hidden layers captured the multivariate nonlinear spectra, and the output layer quantified adsorbed mass for each species. The network training employed diverse datasets curated from laboratory batch experiments and reached R² coefficients surpassing 0.95 in nearly all validation folds. Imbalanced or distorted data cases, often compromising conventional models, did not derail the ANNs. The speed with which realistic field samples can be processed renders the approach practical for frequent, nationwide screening. By incorporating ANNs into geochemical diagnostics, the research provides authorities and industries with a low-cost, yet robust, forecasting protocol for assessing the binding potential of legacy or emerging contaminants, thus guiding targeted analytical follow-ups and optimizing decontamination design schemes.