Nanoporous amorphous carbon constitutes a highly relevant material for a multitude of applications ranging from energy to environmental and biomedical systems. In the present work, it is demonstrated experimentally how energetic ions can be utilized to tailor porosity of thin sputter deposited amorphous carbon films. The physical mechanisms underlying self-organized nanoporous morphogenesis are unraveled by employing extensive molecular dynamics and phase field models across different length scales. It is demonstrated that pore formation is a defect induced phenomenon, in which vacancies cluster in a spinodal decomposition type of self-organization process, while interstitials are absorbed by the amorphous matrix, leading to additional volume increase and radiation induced viscous flow. The proposed modeling framework is capable to reproduce and predict the experimental observations from first principles and thus opens the venue for computer assisted design of nanoporous frameworks.

Background: Abnormal lipid metabolism plays an essential role in breast cancer progression and metastasis. Lipid droplets (LD) have multifunctional tasks as they store and transfer lipids and act as molecular messengers. In particular, they are known to be involved in reprogramming tumor cells, invasion, and migration of breast cancer cells. In this study, we aimed to identify lipid droplet-associated genes as prognostic markers in breast cancer. Methods: Established lipid droplet-associated proteins were used to create the research gene lists. Bioinformatics analysis on the GEPIA platform was carried out for the list of the genes to identify differential expression in breast cancer versus healthy breast tissues. Differentially expressed genes were analyzed regarding significant changes during the metastatic transition and detected genes which play a role in breast cancer patients. Changes in lipid composition were monitored by mass spectrometry. In more detail, immunohistochemistry and cell culture studies were performed to understand the LD-related proteins and lipids in the cell lines. Results: 143 genes were identified as lipid droplet-associated factors by literature research. Bioinformatics analysis of 1085 breast cancer samples and 291 normal breast tissue samples identified 48 differentially expressed genes in breast cancer with 3 over-expressed genes (SQLE, FADS2, MUCI) and 45 under-expressed genes. Among 48 differentially expressed genes, only one over-expressed gene (SQLE) and 5 under-expressed genes (FABP7, SAA4, CHKB, RBP4, PLA2G4A) were significantly associated with the overall survival of breast cancer patients. While 26 of these genes were also found in the metastatic transition, the expression of only 13 of them changed in cancer. SELP, FABP4, and PLIN1 were detected as the highest F-value in the transitions of metastatic stages. OSBPL2, CPA4, DGAT1, and FADS6 were effective genes in both overall survival and metastatic transition. Among all these genes, only FABP7 showed a statistically significant rank in all criteria as a prognostic factor. Changes in the lipid compositions, size and radii of lipid droplets were also be monitored and combined with bioinformatics analysis. Conclusions: Through bioinformatics analysis, 29 prognostically relevant differentially expressed genes were identified. 26 genes play a role during the metastatic transition highlighting the role of lipid droplet-associated factors in breast cancer.