A Low-Voltage 77-GHz Automotive Radar Chipset

Index Terms -Automotive radar, millimeter-wave receivers and transmitters, millimeter-wave imaging, low-noise amplifiers, power amplifiers, monolithic inductors and transformers

Compendium of Current Total Ionizing Dose and Displacement Damage Results from NASA Goddard Space Flight Center and NASA Electronic Parts and Packaging Program

Total ionizing dose and displacement damage testing was performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include optoelectronics, digital, analog, linear bipolar devices, and hybrid devices. Displacement Damage, Optoelectronics, Proton Damage, Single Event Effects, and Total Ionizing Dose

Compendium of Current Total Ionizing Dose and Displacement Damage Results from NASA Goddard Space Flight Center and Selected NASA Electronic Parts and Packaging Program

Total ionizing dose and displacement damage testing was performed to characterize and determine the suitability of candidate electronics for NASA space utilization. Devices tested include optoelectronics, digital, analog, linear bipolar devices, and hybrid devices

Short-term tissue decomposition alters stable isotope values and C:N ratio, but does not change relationships between lipid content, C:N ratio, and Δδ13C in marine animals

Measures (e.g. δ15N, δ13C, %C, %N and C:N) derived from animal tissues are commonlyused to estimate diets and trophic interactions. Since tissue samples are often exposed toair or kept chilled in ice over a short-term during sample preparation, they may degrade.Herein, we hypothesize that tissue decomposition will cause changes in these measures. Inthis study, we kept marine fish, crustacean and mollusc tissues in air or ice over 120 h (5days). We found that tissue decomposition in air enriched δ15N (range 0.6½ to 1.3½) andδ13C (0.2½ to 0.4½), decreased %N (0.47 to 3.43 percentage points from staring values of~13%) and %C (4.53 to 8.29 percentage points from starting values of ~43%), and subsequentlyincreased C:N ratio (0.14 to 0.75). In air, while such changes to δ13C were relativelyminor and therefore likely tolerable, changes in δ15N, %N, %C and C:N ratio should be interpretedwith caution. Ice effectively reduced the extent to which decomposition enrichedδ15N ( 0.4½) and δ13C ( 0.2½), and eliminated decomposition in C:N ratio, %N and %C.In our second experiment, for fish tissues in either air or ice over 120 h, we observed noeffects of decomposition on relationships between lipid content, C:N ratio, and Δδ13C(change in δ13C after lipid removal), which are employed to correct δ13C for samples containinglipid. We also confirmed that lipid in tissues caused large errors when estimatingδ13C (mean ± standard error = -1.8½ ± 0.1½, range -0.6½ to -3.8½), and showed both lipidextraction and mathematical correction performed equally well to correct for lipids when estimatingδ13C. We, therefore, recommend that specimens of marine animals should be keptin ice during sample preparation for a short-term, as it is an effective means for minimizingchanges of the stable isotope measures in their tissue

Ligand-Activated Peroxisome Proliferator-Activated Receptor- Protects Against Ischemic Cerebral Infarction and Neuronal Apoptosis by 14-3-3 Upregulation

Thiazolidinediones (TZD) were reported to protect against ischemia-reperfusion (I/R) injury. Their protective actions are considered to be PPAR-γ (peroxisome proliferator-activated receptor γ)-dependent. However, it is unclear how PPAR-γ activation confers resistance to I/R

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

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

On systems and control approaches to therapeutic gain

BACKGROUND: Mathematical models of cancer relevant processes are being developed at an increasing rate. Conceptual frameworks are needed to support new treatment designs based on such models. METHODS: A modern control perspective is used to formulate two therapeutic gain strategies. RESULTS: Two conceptually distinct therapeutic gain strategies are provided. The first is direct in that its goal is to kill cancer cells more so than normal cells, the second is indirect in that its goal is to achieve implicit therapeutic gains by transferring states of cancer cells of non-curable cases to a target state defined by the cancer cells of curable cases. The direct strategy requires models that connect anti-cancer agents to an endpoint that is modulated by the cause of the cancer and that correlates with cell death. It is an abstraction of a strategy for treating mismatch repair (MMR) deficient cancers with iodinated uridine (IUdR); IU-DNA correlates with radiation induced cell killing and MMR modulates the relationship between IUdR and IU-DNA because loss of MMR decreases the removal of IU from the DNA. The second strategy is indirect. It assumes that non-curable patient outcomes will improve if the states of their malignant cells are first transferred toward a state that is similar to that of a curable patient. This strategy is difficult to employ because it requires a model that relates drugs to determinants of differences in patient survival times. It is an abstraction of a strategy for treating BCR-ABL pro-B cell childhood leukemia patients using curable cases as the guides. CONCLUSION: Cancer therapeutic gain problem formulations define the purpose, and thus the scope, of cancer process modeling. Their abstractions facilitate considerations of alternative treatment strategies and support syntheses of learning experiences across different cancers