Evaluating New Liquid Storable Bipropellants: Safety and Performance Assessments

Conventional storable bipropellants make use of hydrazine and its derivatives as fuels and nitrogen tetroxide as an oxidizer. In recent years, the toxicity character of these chemicals pushed the propulsion community towards “green” alternatives. Several candidates have been proposed among existing and newly developed chemicals, highlighting the need for a common and robust selection methodology. This paper aims at reviewing the most important selection criteria in the field of toxicity and discusses how to objectively define a green propellant, considering both the health and environmental hazards caused by the chemicals. Additionally, consistent figures of merit in the field of safety and handling operations and performance are proposed. In particular, operating temperatures, flammability and stability issues are discussed in the framework of physical hazards and storage requirements, while vacuum impulses, adiabatic flame temperature and sooting occurrence of the investigated couples are compared to the UDMH/NTO benchmark case. Hydrogen peroxide and nitrous oxide, and light hydrocarbons, alcohols and kerosene are selected from the open literature as promising green oxidizers and fuels, respectively. The identified methodology highlights merits and limitations of each chemical, as well as the fact that the identification of a universally best suited green couple is quite impractical. On the contrary, the characteristics of each propellant lead to a scenario of several “sub-optimal” couples, each of them opportunely fitting into a specific mission class

Monoethanolamine-Based Fuels Hypergolic with Rocket-Grade Hydrogen Peroxide

The object of this work is to study the ignition performances of promising catalytically promoted amino-based green fuels to be used in combination with rocket-grade hydrogen peroxide. The main hypergolic parameter, the ignition delay time, was experimentally determined with an automated drop test setup. Additionally, the kinematic viscosity was experimentally measured, while the CEA2 software was used to determine the ideal rocket performances of the propellants. Three inorganic copper salts were selected as catalysts to be used in combination with monoethanolamine in concentrations ranging from 0.5 wt% to 20 wt%. Then, N,N-dimethylethylenediamine was introduced as part of a blend with monoethanolamine to target the high viscosity and low gravimetric specific impulse of the fuel for the pure monoethanolamine case. Due to the reduced monoethanolamine and low additive content, some formulations were observed to be characterized by gravimetric specific impulse higher than 320 s, and kinematic viscosity lower than 5 cSt while retaining ignition delay times shorter than 30 ms with 98 wt% HTP. Finally, the addition of ethanol to the blend was preliminarily investigated to improve the additive solubility. The effects on the ignition delay time were found to depend on the concentration ratio of the two amines

Role of Pressure and Aluminum Size in Solid Propellant CCP Generation

Aluminum combustion in solid propellants generates condensed products leaving the burning surface. The population of this particles is quite wide, spanning from smoke-oxide to molten metal drops. Their properties depend upon both intrinsic propellant features and combustion conditions (e.g. composition, microstructure, combustion pressure, and propellant burning rate). In propellants, aluminum is typically used in the shape of a micrometric powder. This class of energetic materials produces spherical agglomerates having the size between some tens to few hundreds of micrometers. When the metal fuel turns to nanometric, flake-kind aggregates emerge from the burning surface. Some macroscopic properties, such as the burning rate, are affected. This paper presents some results obtained from a set of aluminized propellants based on inert binder (hydroxylterminated polybutadiene) and ammonium perchlorate. The effect of both powder size and pressure is explored in terms of ballistics and condensed combustion residues. A nonstraightforward trend with pressure emerges when the condensed combustion products of propellants containing micro-aluminum and nano-aluminum are compared

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation