Development of a CW Superconducting RF Booster Cryomodule for Future Light Sources

Future light sources based on seeded free electron lasers (FEL) have the potential to increase the soft xray flux by several orders of magnitude with short bunch lengths to probe electron structure and dynamics. A low emittance, high rep-rate radio frequency (RF) photocathode electron gun will generate the electron beam that will require very stringent beam control and manipulation through the superconducting linear accelerator to maintain the high brightness required for an x-ray FEL. The initial or booster cavities of the superconducting radio frequency (SRF) linear accelerator will require stringent control of transverse kicks and higher order modes (HOM) during the beam manipulation and conditioning that is needed for emittance exchange and bunch compression. This SBIR proposal will develop, fabricate and test a continuous-wave SRF booster cryomodule specifically for this application. Phase I demonstrated the technical feasibility of the project by completing the preliminary SRF cavity and cryomodule design and its integration into an R&D test stand for beam studies at Lawrence Berkeley National Laboratory (LBNL). The five-cell bulk niobium cavities operate at 750 MHz, and generate 10 MV each with strong HOM damping and special care to eliminate transverse kicks due to couplers. Due to continuous-wave operation at fairly modest beam currents and accelerating gradients the complexity of the two cavity cryomodule is greatly reduced compared to an ILC type system. Phase II will finalize the design, and fabricate and test the booster cryomodule. The cryomodule consists of two five-cell cavities that will accelerate megahertz bunch trains with nano-coulomb charge. The accelerating gradient is a very modest 10 MV/m with peak surface fields of 20 MV/m and 42.6 mT. The cryogenic system operates at 2 K with a design dynamic load of 20 W and total required cryogenic capacity of 45 W. The average beam current of up to 1 mA corresponds to a beam power of 10 kW per 5- cell cavity and will require 20 kW of RF power for transmission, control and regulation. The RF power will be supplied by a commercial tetrode. Cryogenic tests will be carried out at LBNL to make use of their test facilities, cryogenics and laser systems, and for future use with beam. Demonstration of this new type of booster cryomodule will open many new applications of SRF linear accelerators

Determination of mass transfer resistances of fast reactions in three-phase mechanically agitated slurry reactors

A methodology for the determination of mass transfer resistances of fast reactions in three-phase mechanically agitated slurry reactors under the reaction conditions is presented. The mass transfer resistances affect significantly the overall mass transfer rate, the design equation and consequently the scale up of the reactor. There is not established methodology to separate the mass transfer resistances under reaction conditions by changing catalyst loading and manipulating the process variables, pressure and agitation speed. This allows to avoid the use of different catalyst particles and give the chance to calculate the mass transfer resistances without caring about the type of catalyst. We calculate each mass transfer resistance under conditions which do not allow to neglect any of the resistances. It is shown that the level off of mass transfer rate which is developed in the plot of mass transfer rate against agitation speed plots is not enough to determine the limiting regime. The hydrogenation of styrene over Pd/C (5% catalyst content) is used as case study to demonstrate the methodology

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