Melanoma and non-melanoma cutaneous malignancies are some of the leading causes of cancer-related death in the United States. Though melanoma is more known to have a high mortality rate, the total mortality per year is nearly equal for between melanoma and non-melanoma skin cancer. Moreover, the non-melanoma types of cutaneous malignancies have potential to become locally invasive and even metastasize with very little to no treatment options when advanced. The development of these malignancies involves various genetic pathways through the four hallmarks of cancer development: malignant cell growth, apoptosis evasion, the use of supporting stroma and vascularization, and modulating and promoting an inadequate immune response. The genetic signaling pathways of basal cell carcinoma, squamous cell carcinoma, verrucous carcinoma, basosquamous cell carcinoma, melanoma, and cutaneous T-cell lymphoma interact with each other through genetic predisposition as well as with environmental exposures. Furthermore, solar ultraviolet radiation and chronic inflammatory states are found to initiate the progression of many of these cutaneous malignancies. This paper includes validated models of genetic pathways, emerging pathways, and crosstalk between genetic pathways through the four hallmarks of cancer development. Moreover, unlike most reviews addressing oncogenetics of the well-recognized, as well as newly dynamic, interrelated, interactive, complex, and adaptive flux states.

Operando X-ray micro-computed tomography (µCT) provides an opportunity to observe the evolution of Li structures inside pouch cells. Segmentation is an essential step to quantitatively analyzing µCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the datasets. Herein, we report a computational approach, batteryNET, to train an Iterative Residual U-Net-based network to detect Li structures. The resulting semantic segmentation shows singular Li-related component changes, addressing diverse morphologies in the dataset. In addition, visualizations of the dead Li are provided, including calculations about the volume and effective thickness of electrodes, deposited Li, and redeposited Li. We also report discoveries about the spatial relationships between these components. The approach focuses on a method for analyzing battery performance, which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

No abstract

No abstract