Physics Commons^™

The Structural Information Filtered Features Potential For Machine Learning Calculations Of Energies And Forces Of Atomic Systems., Jorge Arturo Hernandez Zeledon

Graduate Theses, Dissertations, and Problem Reports

In the last ten years, machine learning potentials have been successfully applied to the study of crystals, and molecules. However, more complex materials like clusters, macro-molecules, and glasses are out reach of current methods. The input of any machine learning system is a tensor of features (the most universal type are rank 1 tensors or vectors of features), the quality of any machine learning system is directly related to how well the feature space describes the original physical system. So far, the feature engineering process for machine learning potentials can not describe complex material. The current methods are highly inefficient …

Three-Dimensional Imaging Of Kndy Neurons In The Mammalian Brain Using Optical Tissue Clearing And Multiple-Label Immunocytochemistry, Aleisha M. Moore, Kathryn A. Lucas, Robert L. Goodman, Lique M. Coolen, Michael N. Lehman

Faculty & Staff Scholarship

Kisspeptin/Neurokinin B/Dynorphin (KNDy) neurons of the arcuate nucleus (ARC) play a key role in the regulation of fertility. The ability to detect features of KNDy neurons that are essential for fertility may require three-dimensional (3D) imaging of the complete population. Recently developed protocols for optical tissue clearing permits 3D imaging of neuronal populations in un-sectioned brains. However, these techniques have largely been described in the mouse brain. We report 3D imaging of the KNDy cell population in the whole rat brain and sheep hypothalamus using immunolabelling and modification of a solvent-based clearing protocol, iDISCO. This study expands the use of …

Macrophage Sensing Of Single- Walled Carbon Nanotubes Via Toll- Like Receptors, Sourav P. Mukherjee, Olesja Bondarenko, Pekka Kohonen, Fernando T. Andon, Tana Brzicova, Isabel Gessner, Sanjay Mathur, Massimo Bottini, Paolo Calligari, Lorenzo Stella, Elena Kisin, Anna Shvedova, Reija Autio, Heli Salminen-Mankonen, Ritta Lahesmaa, Bengt Fadeel

Faculty & Staff Scholarship

Carbon-based nanomaterials including carbon nanotubes (CNTs) have been shown to trigger

inflammation. However, how these materials are ‘sensed’ by immune cells is not known. Here we compared the effects of two carbon-based nanomaterials, single-walled CNTs (SWCNTs) and graphene oxide (GO), on primary human monocyte-derived macrophages. Genome-wide transcriptomics assessment was performed at sub-cytotoxic doses. Pathway analysis of the microarray data revealed pronounced effects on chemokine-encoding genes in macrophages exposed to SWCNTs, but not in response to GO, and these results were validated by multiplex array-based cytokine and chemokine profiling. Conditioned medium from SWCNT-exposed cells acted as a chemoattractant for dendritic cells. …