Publications

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue, benign and malignant tumors from each patient. We demonstrated human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We demonstrated that multiple-histological-samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

We study the many-body electronic structure of the stoichiometric and electron-doped trilayer nickelate Pr

A classical problem in fluid mechanics is the motion of an axisymmetric vor-tex sheet evolving under the action of surface tension, surrounded by an invis-cid fluid. Lagrangian descriptions of these dynamics are well-known, involv-ing complex nonlocal expressions for the radial and longitudinal velocities interms of elliptic integrals. Here we use these prior results to arrive at a remark-ably compact and exact Eulerian evolution equation for the sheet radius r.´; t/in an explicit flux form associated with the conservation of enclosed volume.The flux appears as an integral involving the pairwise mutual induction formulafor vortex loop pairs first derived by Helmholtz and Maxwell. We show howthe well-known linear stability results for cylindrical vortex sheets in the pres-ence of surface tension and streaming flows [A. M. Sterling and C. A. Sleicher,J. Fluid Mech. 68, 477 (1975)] can be obtained directly from this formulation.Furthermore, the inviscid limit of the empirical model of Eggers and Dupont[J. Fluid Mech. 262 205 (1994); SIAM J. Appl. Math. 60, 1997 (2000)], whichhas served as the basis for understanding singularity formation in droplet pin-choff, is derived within the present formalism as the leading-order term in anasymptotic analysis for long slender axisymmetric vortex sheets and should pro-vide the starting point for a rigorous analysis of singularity formation.

With the rise of commercial spaceflight and prospective human missions to Mars, a wider health range of humans will enter space for longer spans and at higher exposure to environmental stressors than ever before. Numerous adverse health effects have been observed in space, including bone demineralization and skeletal muscle atrophy, among others. Scientists across the world are conducting space omics studies to develop countermeasures for safe and effective crewed space missions. However, optimal extraction of scientific insight from such data is contingent on improved standardization. In response, we founded ISSOP (International Standards for Space Omics Processing), an international consortium of scientists who aim to enhance guidelines between space biologists globally. This paper informs scientists and data scientists from many fields about the challenges and future avenues of space omics and can serve as an introductory reference for new members in the space biology discipline.

We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of K-means applied in the image space while addressing its clustering performance issues. Specifically, we develop a measure of similarity between images and centroids that encompasses a general class of deformations: diffeomorphisms, rendering the clustering invariant to them. Our work leverages the Thin-Plate Spline interpolation technique to efficiently learn diffeomorphisms best characterizing the image manifolds. Extensive numerical simulations show that our approach competes with state-of-the-art methods on various datasets.

Hyperbolic Cooper-pair polaritons (HCP) in cuprate superconductors are of fundamental interest due to their potential for providing insights into the nature of unconventional superconductivity. Here, we critically assess an experimental approach using near-field imaging to probe HCP in Bi2Sr2CaCu2O8+x (Bi-2212) in the presence of graphene surface plasmon polaritons (SPP). Our simulations show that inherently weak HCP features in the near-field can be strongly enhanced when coupled to graphene SPP in layered graphene/hexagonal boron nitride (hBN)/Bi-2212 heterostructures. This enhancement arises from our multilayered structures effectively acting as plasmonic cavities capable of altering collective modes of a layered superconductor by modifying its electromagnetic environment. The degree of enhancement can be selectively controlled by tuning the insulating spacer thickness with atomic precision. Finally, we verify the expected renormalization of room-temperature graphene SPP using near-field infrared imaging. Our modeling, augmented with data, attests to the validity of our approach for probing HCP modes in cuprate superconductors.

Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe. Large N-body simulations have been built to obtain predictions in the non-linear regime. However, N-body simulations are computationally expensive and generate large amount of data, putting burdens on storage. These data are snapshots of the simulated Universe at different times, and fine sampling is necessary to accurately save its whole history. We employ a deep neural network model to predict the nonlinear N-body simulation at an intermediate time step given two widely separated snapshots. Our results outperform the cubic Hermite interpolation benchmark method in interpolating N-body simulations. This work can greatly reduce the storage requirement and allow us to reconstruct the cosmic history from far fewer snapshots of the universe.

The enrichment of late‐onset Alzheimer’s disease (LOAD) GWAS variants in noncoding regions of the genome reveals new potential for modeling disease risk. Yet, identifying noncoding causal variants and the cell types in which they are functional remains challenging. Translating noncoding variants into novel mouse models can elucidate phenotypic effects of those variants through specific perturbations of gene enhancers associated with LOAD risk.

How bulk cytoplasm generates forces to separate post-anaphase microtubule (MT) asters in Xenopus laevis and other large eggs remains unclear. Previous models proposed that dynein-based, inward organelle transport generates length-dependent pulling forces that move centrosomes and MTs outwards, while other components of cytoplasm are static. We imaged aster movement by dynein and actomyosin forces in Xenopus egg extracts and observed outward co-movement of MTs, endoplasmic reticulum (ER), mitochondria, acidic organelles, F-actin, keratin, and soluble fluorescein. Organelles exhibited a burst of dynein-dependent inward movement at the growing aster periphery, then mostly halted inside the aster, while dynein-coated beads moved to the aster center at a constant rate, suggesting organelle movement is limited by brake proteins or other sources of drag. These observations call for new models in which all components of the cytoplasm comprise a mechanically integrated aster gel that moves collectively in response to dynein and actomyosin forces.