The following sections are included: Introduction, Understanding and Predicting Molecular Networks, Understanding and Predicting Molecular Networks, Making Use of Family Structure, Applying Traditional Graph Algorithms to Novel Tasks, Representing Uncertainty in Networks, Conclusion, References.
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Visibility, Physical Work Environment, and Stress in ICU Nurses.
J Nurs Adm
December 2024
Author Affiliations: Research Associate (Dr Keys), The Center for Health Design, Concord, California; National Senior Director (Dr Fineout-Overholt), Evidence-Based Practice and Implementation Science, at Ascension in St. Louis, MO.
Objective: Relationships among coworker and patient visibility, reactions to physical work environment, and work stress in ICU nurses are explored.
Background: Millions of dollars are invested annually in the building or remodeling of ICUs, yet there is a gap in understanding relationships between the physical layout of nursing units and work stress.
Methods: Using a cross-sectional, correlational, exploratory, predictive design, relationships among variables were studied in a diverse sample of ICU nurses.
Learning the language of antibody hypervariability.
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFMetastable state preceding shear zone instability: Implications for earthquake-accelerated landslides and dynamic triggering.
Proc Natl Acad Sci U S A
January 2025
Institut Langevin, École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris, Université Paris Sciences & Lettres, CNRS, Paris 7587, France.
Understanding the dynamic response of granular shear zones under cyclic loading is fundamental to elucidating the mechanisms triggering earthquake-induced landslides, with implications for broader fields such as seismology and granular physics. Existing prediction methods struggle to accurately predict many experimental and in situ landslide observations due to inadequate consideration of the underlying physical mechanisms. The mechanisms that influence landslide dynamic triggering, a transition from static (or extremely slow creeping) to rapid runout, remain elusive.
View Article and Find Full Text PDFIn utero progression of cephaloceles: prenatal to postnatal analysis.
J Neurosurg Pediatr
January 2025
1Division of Neurosurgery, Department of Surgery, Children's Hospital of Philadelphia.
Objective: The natural history of cephaloceles is not well understood. The goal of this study was to better understand the natural history of fetal cephaloceles from prenatal diagnosis to the postnatal period.
Methods: Between January 2013 and April 2023, all patients evaluated with a cephalocele at the Center for Fetal Diagnosis and Treatment were identified.
Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities.
PLoS One
January 2025
Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO2), fine particulate matter (PM2.5), and coarse particulate matter (PM10).
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