Sintered aluminium heat pipe (SAHP)

This work is the product of an ongoing PhD project in the School of the Built and Natural Environment of Northumbria University in collaboration with the University of Liverpool and Thermacore Europe Ltd. The achievements at the end of the first year are summarized. The main objective of the project is to develop an aluminum ammonia heat pipe with a sintered wick structure. Currently available ammonia heat pipes mainly use extruded axially grooved aluminum tubes as a capillary wick. There have been a few attempts of employing porous steel or nickel wicks in steel tubes with ammonia as the working fluid (Bai, Lin et al. 2009)although it is a common practice in loop heat pipes but there is no report of aluminum-ammonia heat pipes porous aluminium wick structures. The main barrier is the difficulty of sintering aluminum powders to manufacture porous wicks. So far during this project promising sintered aluminum heat pipe samples have been manufactured using the Selective Laser Melting (SLM) technique with various wick characteristics. This SLM method has proven to be capable of manufacturing very complicated wick structures with different thickness, porosity, permeability and pore sizes in different regions of a heat pipe. In addition the entire heat pipe including the end cap, outer tube wall, wick and the fill tube can be generated in a single process

A Pretargeted Approach for the Multimodal PET/NIRF Imaging of Colorectal Cancer

The complementary nature of positron emission tomography (PET) and near-infrared fluorescence (NIRF) imaging makes the development of strategies for the multimodal PET/NIRF imaging of cancer a very enticing prospect. Indeed, in the context of colorectal cancer, a single multimodal PET/NIRF imaging agent could be used to stage the disease, identify candidates for surgical intervention, and facilitate the image-guided resection of the disease. While antibodies have proven to be highly effective vectors for the delivery of radioisotopes and fluorophores to malignant tissues, the use of radioimmunoconjugates labeled with long-lived nuclides such as 89Zr poses two important clinical complications: high radiation doses to the patient and the need for significant lag time between imaging and surgery. In vivo pretargeting strategies that decouple the targeting vector from the radioactivity at the time of injection have the potential to circumvent these issues by facilitating the use of positron-emitting radioisotopes with far shorter half-lives. Here, we report the synthesis, characterization, and in vivo validation of a pretargeted strategy for the multimodal PET and NIRF imaging of colorectal carcinoma. This approach is based on the rapid and bioorthogonal ligation between a trans-cyclooctene- and fluorophore-bearing immunoconjugate of the huA33 antibody (huA33-Dye800-TCO) and a 64Cu-labeled tetrazine radioligand (64Cu-Tz-SarAr). In vivo imaging experiments in mice bearing A33 antigen-expressing SW1222 colorectal cancer xenografts clearly demonstrate that this approach enables the non-invasive visualization of tumors and the image-guided resection of malignant tissue, all at only a fraction of the radiation dose created by a directly labeled radioimmunoconjugate. Additional in vivo experiments in peritoneal and patient-derived xenograft models of colorectal carcinoma reinforce the efficacy of this methodology and underscore its potential as an innovative and useful clinical tool

Direct in-gel fluorescence detection and cellular imaging of O-GlcNAc-modified proteins

We report an advanced chemoenzymatic strategy for the direct fluorescence detection, proteomic analysis, and cellular imaging of O-GlcNAc-modified proteins. O-GlcNAc residues are selectively labeled with fluorescent or biotin tags using an engineered galactosyltransferase enzyme and [3 + 2] azide−alkyne cycloaddition chemistry. We demonstrate that this approach can be used for direct in-gel detection and mass spectrometric identification of O-GlcNAc proteins, identifying 146 novel glycoproteins from the mammalian brain. Furthermore, we show that the method can be exploited to quantify dynamic changes in cellular O-GlcNAc levels and to image O-GlcNAc-glycosylated proteins within cells. As such, this strategy enables studies of O-GlcNAc glycosylation that were previously inaccessible and provides a new tool for uncovering the physiological functions of O-GlcNAc

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

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

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

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

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations