CFD analysis of acoustofluidic channels and the effects on biologic delivery.

T-cell transformation is an ever-expanding treatment for several types of cancer, with a potential to be adapted to other disorders in which the immune system plays a key role in the pathophysiology. Currently, all FDA approved chimeric antigen receptor (CAR) T-cell cancer therapies rely on transformation via viral transduction. However, viral transduction is plagued by poor consistency and the potential to create adverse immune reactions when T-cells are reintroduced into a patient. Other transformation methods are being explored, with an alternative called acoustofluidic sonoporation showing promise. In these procedures, cells are passed through a channel, of the millimeter scale, while ultrasound (US) is applied. The US causes unstable cavitation of perfluorocarbon microbubbles (MBs) resulting in rupture that reversibly permeabilizes cells, allowing entry of almost any water-soluble biologic (e.g. DNA/RNA, small molecules, etc.). While current research demonstrates that acoustofluidic sonoporation may be better than other transfection methods, there is a limited understanding of the fluid dynamics within the acoustofluidic devices and the physical mechanisms of the alteration in cell permeability. In this thesis, computational fluid dynamic (CFD) modeling was utilized to simulate fluid and particle flow through various acoustofluidic channel geometries and the results were compared with biological delivery experiments to cells. It was found a 1-mm diameter Concentric Spiral channel is an optimal design as it maximizes wall shear stress (WSS) and US exposure, as compared to 1-mm and 2-mm diameter Rectilinear channels. With further refinement of the CFD simulations, optimization of channel geometry, flow rate, and US parameters could be enhanced. This optimization could enable acoustofluidic sonoporation to be translated into manufacturing of CAR T-cell therapies for clinical treatments of cancer and other disorders in the future

Sterile Neutrino Dark Matter

Neutrinos are weakly interacting, electrically neutral particles in the standard model of particle physics. These neutrinos are referred to as left-handed or active neutrinos and are classified into three flavors (electron, mu, and tau). Neutrino oscillation is the phenomenon that involves the oscillation of neutrinos between the three flavors. This phenomenon is also applied the oscillation of left-handed neutrinos into right-handed (sterile) neutrinos. The sterile neutrino is a hypothetical particle that does not interact with the weak force and only interacts through the gravitational force. This characteristic of the sterile neutrino makes it a very good dark matter candidate

On Dirac Factorization, Fractional Calculus, and Polynomial Linearization

We postulate the existence of fractional order derivative operators that satisfy a semi-group property in order to further factor the Klein-Gordon equation in Dirac's fashion. The analog of Dirac's matrices are found and we study the generalization of the Dirac algebra generated by these matrices. In this way, a hierarchy of generalized Clifford algebras is formed. We then apply this procedure to Schr\"odinger's equation, and examine the resulting coefficients before moving to a more general setting in which we study the linearization of polynomials with coefficients that do not commute with the indeterminates. Partial differential equations with non-constant coefficients are the archetypal example in this setting.Comment: 15 page

Implementation of Rare Isotopologues into Machine Learning of the Chemical Inventory of the Solar-Type Protostellar Source IRAS 16293-2422

Machine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. In interstellar sources further along the path of star formation, such as those where a protostar itself has been formed, the chemistry is known to be drastically different from that of largely quiescent dark clouds. To that end, we have tested the ability of various machine learning models to fit the column densities of the molecules detected in source B of the Class 0 protostellar binary IRAS 16293-2422. By including a simple encoding of isotopic composition in our molecular feature vectors, we also examine for the first time how well these models can replicate the isotopic ratios. Finally, we report the predicted column densities of the chemically relevant molecules that may be excellent targets for radioastronomical detection in IRAS 16293-2422B.Comment: Accepted for publication in Digital Discovery. 18 pages, 8 figures, 5 table

Rhodium(II) Proximity-Labeling Identifies a Novel Target Site on STAT3 for Inhibitors with Potent Anti-Leukemia Activity

Nearly 40 % of children with acute myeloid leukemia (AML) suffer relapse arising from chemoresistance, often involving upregulation of the oncoprotein STAT3 (signal transducer and activator of transcription 3). Herein, rhodium(II)-catalyzed, proximity-driven modification identifies the STAT3 coiled-coil domain (CCD) as a novel ligand-binding site, and we describe a new naphthalene sulfonamide inhibitor that targets the CCD, blocks STAT3 function, and halts its disease-promoting effects in vitro, in tumor growth models, and in a leukemia mouse model, validating this new therapeutic target for resistant AML

Differences between the 2018 and 2019 stratospheric polar vortex split events

Two recent occurrences in February 2018 and January 2019 of a dynamic split in the Northern Hemisphere stratospheric polar vortex are compared in terms of their evolution and predictability. The 2018 split vortex was associated with primarily wavenumber‐2 wave forcing that was not well predicted more than 7‐10 days ahead of time, and was followed by persistent coupling to the surface with strong weather impacts. In 2019 the vortex was first displaced by slow wavenumber‐1 amplification into the stratosphere, which was predictable at longer lead times, and then split; the surface impacts following the event were weaker. Here we examine the role of large‐scale climate influences, such as the phase of the El Niño‐Southern Oscillation, the Quasi‐biennial Oscillation and Madden‐Julian Oscillation, on the wave forcing, surface impacts, and predictability of these two events. Linkages between the forecast error in the stratospheric polar vortex winds with the forecast error in the Quasi‐biennial Oscillation and Madden‐Julian Oscillation are examined

Thermal stress induces glycolytic beige fat formation via a myogenic state.

Environmental cues profoundly affect cellular plasticity in multicellular organisms. For instance, exercise promotes a glycolytic-to-oxidative fibre-type switch in skeletal muscle, and cold acclimation induces beige adipocyte biogenesis in adipose tissue. However, the molecular mechanisms by which physiological or pathological cues evoke developmental plasticity remain incompletely understood. Here we report a type of beige adipocyte that has a critical role in chronic cold adaptation in the absence of β-adrenergic receptor signalling. This beige fat is distinct from conventional beige fat with respect to developmental origin and regulation, and displays enhanced glucose oxidation. We therefore refer to it as glycolytic beige fat. Mechanistically, we identify GA-binding protein α as a regulator of glycolytic beige adipocyte differentiation through a myogenic intermediate. Our study reveals a non-canonical adaptive mechanism by which thermal stress induces progenitor cell plasticity and recruits a distinct form of thermogenic cell that is required for energy homeostasis and survival