Invariant description of static and dynamical Brans-Dicke spherically symmetric models

We investigate spherically symmetric static and dynamical Brans-Dicke theory exact solutions using invariants and, in particular, the Newman Penrose formalism utilizing Cartan scalars. The GR limit of these solutions is examined through the use of Cartan invariants via the Cartan-Karlhede algorithm and is additionally supported by analysis of scalar polynomial invariants. It is determined that the appearance of horizons in these spacetimes depends primarily on one of the parameters, $n$, of the family of solutions. In particular, expansion-free surfaces appear which, for a subset of parameter values, define additional surfaces distinct from the standard surfaces (e.g., apparent horizons) identified in previous work. These surfaces in static spherically symmetric Brans-Dicke solutions was previously shown to correspond to the Schwarzschild horizon in general relativity when an appropriate limit exists between the two theories. We show additionally that other geometrically defined horizons exist for these cases, and identify all solutions for which the corresponding general relativity limit is not a Schwarzschild one, yet still contains horizons. The identification of some of these other surfaces was noted in previous work and is characterized invariantly in this work. In the case of the family of dynamical Brans-Dicke solutions, we identify similar invariantly defined surfaces as in the static case and present an invariant characterization of their geometries. Through the analysis of the Cartan invariants, we determine which members of these families of solutions are locally equivalent, through the use of the Cartan-Karlhede algorithm. In addition, we identify black hole surfaces, naked singularities, and wormholes with the Cartan invariants. The aim of this work is to demonstrate the usefulness of Cartan invariants for describing properties of exact solutions.Comment: 19 page

Restriction Spectrum Imaging Differentiates True Tumor Progression From Immune-Mediated Pseudoprogression: Case Report of a Patient With Glioblastoma.

Immunotherapy is increasingly used in the treatment of glioblastoma (GBM), with immune checkpoint therapy gaining in popularity given favorable outcomes achieved for other tumors. However, immune-mediated (IM)-pseudoprogression is common, remains poorly characterized, and renders conventional imaging of little utility when evaluating for treatment response. We present the case of a 64-year-old man with GBM who developed pathologically proven IM-pseudoprogression after initiation of a checkpoint inhibitor, and who subsequently developed true tumor progression at a distant location. Based on both qualitative and quantitative analysis, we demonstrate that an advanced diffusion-weighted imaging (DWI) technique called restriction spectrum imaging (RSI) can differentiate IM-pseudoprogression from true progression even when conventional imaging, including standard DWI/apparent diffusion coefficient (ADC), is not informative. These data complement existing literature supporting the ability of RSI to estimate tumor cellularity, which may help to resolve complex diagnostic challenges such as the identification of IM-pseudoprogression

Fellowships in Community Pharmacy Research: Experiences of Five Schools and Colleges of Pharmacy

Objective To describe common facilitators, challenges, and lessons learned in 5 schools and colleges of pharmacy in establishing community pharmacy research fellowships. Setting: Five schools and colleges of pharmacy in the United States. Practice description: Schools and colleges of pharmacy with existing community partnerships identified a need and ability to develop opportunities for pharmacists to engage in advanced research training. Practice innovation: Community pharmacy fellowships, each structured as 2 years long and in combination with graduate coursework, have been established at the University of Pittsburgh, Purdue University, East Tennessee State University, University of North Carolina at Chapel Hill, and The Ohio State University. Evaluation: Program directors from each of the 5 community pharmacy research fellowships identified common themes pertaining to program structure, outcomes, and lessons learned to assist others planning similar programs. Results: Common characteristics across the programs include length of training, prerequisites, graduate coursework, mentoring structure, and immersion into a pharmacist patient care practice. Common facilitators have been the existence of strong community pharmacy partnerships, creating a fellowship advisory team, and networking. A common challenge has been recruitment, with many programs experiencing at least one year without filling the fellowship position. All program graduates (n = 4) have been successful in securing pharmacy faculty positions. Conclusion: Five schools and colleges of pharmacy share similar experiences in implementing community pharmacy research fellowships. Early outcomes show promise for this training pathway in growing future pharmacist-scientists focused on community pharmacy practice

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies

Service Members in School: Military Veterans' Experiences Using the Post-9/11 GI Bill and Pursuing Postsecondary Education, Summary

The Post-9/11 GI Bill, which took effect in August of 2009, significantly increased the higher education benefits available to eligible individuals who served on active duty in the U.S. armed forces after September 10, 2001. The result is the most generous education benefit for veterans since the original GI Bill of 1944. However, the new array of benefits is also more complicated to administer than benefits offered under the existing Montgomery GI Bill, resulting in numerous first-year implementation challenges. To better understand these challenges from the perspective of students and higher education institutions, the American Council on Education (ACE) asked RAND to survey and conduct focus groups with veterans and eligible dependents and to interview higher education administrators. This report, which was made possible by ACE and the Lumina Foundation for Education, presents results of the study, describing not only students' and institutions' reported experiences with the new benefits, but also students' experiences transferring military training to academic credit and adapting to life on campus

Military Veterans' Experiences Using the Post-9/11 GI Bill and Pursuing Postsecondary Education

Presents survey findings on the new GI Bill's impact on the choice to pursue higher education and choice of college, students' satisfaction with the bill, and challenges. Offers suggestions for enhancing students' experience and institutional practice