Experimental Analysis of Proton-Induced Displacement and Ionization Damage Using Gate-Controlled Lateral PNP Bipolar Transistors

The electrical characteristics of proton-irradiated bipolar transistors are affected by ionization damage to the insulating oxide and displacement damage to the semiconductor bulk. While both types of damage degrade the transistor, it is important to understand the mechanisms individually and to be able to analyze them separately. In this paper, a method for analyzing the effects of ionization and displacement damage using gate-controlled lateral PNP bipolar junction transistors is described. This technique allows the effects of oxide charge, surface recombination velocity, and bulk traps to be measured independently

Clinically Actionable Insights into Initial and Matched Recurrent Glioblastomas to Inform Novel Treatment Approaches

© 2019 H. P. Ellis et al. Glioblastoma is the most common primary adult brain tumour, and despite optimal treatment, the median survival is 12-15 months. Patients with matched recurrent glioblastomas were investigated to try to find actionable mutations. Tumours were profiled using a validated DNA-based gene panel. Copy number variations (CNVs) and single nucleotide variants (SNVs) were examined, and potentially pathogenic variants and clinically actionable mutations were identified. The results revealed that glioblastomas were IDH-wildtype (IDHWT; n = 38) and IDH-mutant (IDHMUT; n = 3). SNVs in TSC2, MSH6, TP53, CREBBP, and IDH1 were variants of unknown significance (VUS) that were predicted to be pathogenic in both subtypes. IDHWT tumours had SNVs that impacted RTK/Ras/PI(3)K, p53, WNT, SHH, NOTCH, Rb, and G-protein pathways. Many tumours had BRCA1/2 (18%) variants, including confirmed somatic mutations in haemangioblastoma. IDHWT recurrent tumours had fewer pathways impacted (RTK/Ras/PI(3)K, p53, WNT, and G-protein) and CNV gains (BRCA2, GNAS, and EGFR) and losses (TERT and SMARCA4). IDHMUT tumours had SNVs that impacted RTK/Ras/PI(3)K, p53, and WNT pathways. VUS in KLK1 was possibly pathogenic in IDHMUT. Recurrent tumours also had fewer pathways (p53, WNT, and G-protein) impacted by genetic alterations. Public datasets (TCGA and GDC) confirmed the clinical significance of findings in both subtypes. Overall in this cohort, potentially actionable variation was most often identified in EGFR, PTEN, BRCA1/2, and ATM. This study underlines the need for detailed molecular profiling to identify individual GBM patients who may be eligible for novel treatment approaches. This information is also crucial for patient recruitment to clinical trials

Default Risk and Equity Returns: A Comparison of the Bank-Based German and the U.S. Financial System

In this paper, we address the question whether the impact of default risk on equity returns depends on the financial system firms operate in. Using an implementation of Merton's option-pricing model for the value of equity to estimate firms' default risk, we construct a factor that measures the excess return of firms with low default risk over firms with high default risk. We then compare results from asset pricing tests for the German and the U.S. stock markets. Since Germany is the prime example of a bank-based financial system, where debt is supposedly a major instrument of corporate governance, we expect that a systematic default risk effect on equity returns should be more pronounced for German rather than U.S. firms. Our evidence suggests that a higher firm default risk systematically leads to lower returns in both capital markets. This contradicts some previous results for the U.S. by Vassalou/Xing (2004), but we show that their default risk factor looses its explanatory power if one includes a default risk factor measured as a factor mimicking portfolio. It further turns out that the composition of corporate debt affects equity returns in Germany. Firms' default risk sensitivities are attenuated the more a firm depends on bank debt financing

Connecting Mission Profiles and Radiation Vulnerability Assessment

Radiation vulnerability assessment early in spacecraft development is cheaper and faster than in late development phases. RGENTIC and SEAM are two software platforms that can be coupled to provide this type of early assessment. Specifically, RGENTIC is a tool that outputs descriptions of radiation risks based on a selected mission environment and the system’s electronic part portfolio, while SEAM models how radiation-induced faults in electronic parts propagate through a system. In this work, we propose a spacecraft evaluation flow where RGENTIC’s outputs, which are radiation vulnerabilities of electronic parts for a given mission, become inputs to SEAM, resulting in an automatic part-type template palette presented to users so that they can easily begin modeling the occurrence and propagation of radiation-induced faults in their spacecraft. In this context, fault propagation modeling shows how radiation effects impact the spacecraft’s electronics. The interface between these platforms can be streamlined through the creation of a SEAM global part-type library with templates based on radiation effects in part-type families such as sensors, processors, voltage regulators, and so forth. Several of the part-types defined in RGENTIC have been integrated into SEAM templates. Ultimately, all 66+ part-types from the RGENTIC look-up table will be included in the SEAM global part library. Once accomplished, the part templates can be used to populate each project-specific part library in SEAM, ensuring all RGENTIC’s part-types are represented, and the radiation effects are consistent between the two. The harmonization process between RGENTIC and SEAM begins as follows: designers input a detailed knowledge of their system and mission into RGENTIC, which then outputs a generic part-type list that associates each part-type with potential radiation concerns. The list is then downloaded in a SEAM-readable file, which SEAM uses to populate the initially blank project with the part templates that correspond to RGENTIC’s output. The final product is a system fault model using a project-specific radiation effect part library. The radiation effects considered in the part library are associated with three categories of radiation-environment issues: single event effects (SEE), total ionizing dose (TID), and displacement damage dose (DDD). An example part-type is the discrete LED, which has been functionally decomposed into input power and output light. It has a single possible radiation-induced fault that is associated with DDD, which causes degraded brightness and is observed on the output. Overall, designers will benefit from a coordination of these two tools because it simplifies the initial definition of the project in SEAM. This is especially the case for new users, since the necessary radiation models for their parts are available before modeling commences. Furthermore, starting from a duplicate of an existing project decreases the amount of time and effort required to develop project-specific models. Incorporating RGENTIC’s table of part-types resolves these issues and provides a streamlined process for creating system radiation fault models. Consequently, spacecraft designers can identify radiation problems early in the design cycle and fix them with lower cost and less effort than in later design stages

Characterizing SRAM Single Event Upset in Terms of Single and Double Node Charge Collection

A well-collapse source-injection mode for SRAM SEU is demonstrated through TCAD modeling. The recovery of the SRAM s state is shown to be based upon the resistive path from the p+-sources in the SRAM to the well. Multiple cell upset patterns for direct charge collection and the well-collapse source-injection mechanisms are then predicted and compared to recent SRAM test data

DNA methylation-based profiling of bone and soft tissue tumours: a validation study of the 'DKFZ Sarcoma Classifier'

Diagnosing bone and soft tissue neoplasms remains challenging because of the large number of subtypes, many of which lack diagnostic biomarkers. DNA methylation profiles have proven to be a reliable basis for the classification of brain tumours and, following this success, a DNA methylation-based sarcoma classification tool from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has been developed. In this study, we assessed the performance of their classifier on DNA methylation profiles of an independent data set of 986 bone and soft tissue tumours and controls. We found that the 'DKFZ Sarcoma Classifier' was able to produce a diagnostic prediction for 55% of the 986 samples, with 83% of these predictions concordant with the histological diagnosis. On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper. The classifier performed best when diagnosing mesenchymal chondrosarcomas (CHSs, 88% sensitivity), chordomas (85% sensitivity), and fibrous dysplasia (83% sensitivity). Amongst the subtypes least often classified correctly were clear cell CHSs (14% sensitivity), malignant peripheral nerve sheath tumours (27% sensitivity), and pleomorphic liposarcomas (29% sensitivity). The classifier predictions resulted in revision of the histological diagnosis in six of our cases. We observed that, although a higher tumour purity resulted in a greater likelihood of a prediction being made, it did not correlate with classifier accuracy. Our results show that the DKFZ Classifier represents a powerful research tool for exploring the pathogenesis of sarcoma; with refinement, it has the potential to be a valuable diagnostic tool

Methodology for Correlating Historical Degradation Data to Radiation-Induced Degradation System Effects in Small Satellites

When constructing a system-level fault tree to demonstrate device-to-system level radiation degradation, reliability engineers need relevant, device-level failure probabilities to incorporate into reliability models. Deriving probabilities from testing can be expensive and time-consuming, especially if the system is complex. This methodology offers an alternative means of deriving device-level failure probabilities. It uses Bayesian analysis to establish links between historical radiation datasets and failure probabilities. A demonstration system for this methodology is provided, which is a TID response of a linear voltage regulator at 100 krad(SiO2). Data fed into the Bayesian model is derived from literature on the components found within a linear voltage regulator. An example is presented with data pertaining to the device’s bipolar junction transistor (BJT)’s gain degradation factor (GDF). Kernel density estimation is used to provide insight into the dataset’s general distribution shape. This guides the engineer into picking the appropriate distribution for device-level Bayesian analysis. Failure probabilities generated from the Bayesian analysis are incorporated into a LTspice model to derive a system failure probability (using Monte Carlo) of the regulator’s output. In our demonstration system, a 96.5% likelihood of system degradation was found in the assumed environment

The effects of emitter-tied field plates on lateral PNP ionizing radiation response

Radiation response comparisons of lateral PNP bipolar technologies reveal that device hardening may be achieved by extending the emitter contact over the active base. The emitter-tied field plate suppresses recombination of carriers with interface traps

Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes

The nematode Caenorhabditis elegans is a popular model system in genetics, not least because a majority of human disease genes are conserved in C. elegans. To generate a comprehensive inventory of its expressed proteome, we performed extensive shotgun proteomics and identified more than half of all predicted C. elegans proteins. This allowed us to confirm and extend genome annotations, characterize the role of operons in C. elegans, and semiquantitatively infer abundance levels for thousands of proteins. Furthermore, for the first time to our knowledge, we were able to compare two animal proteomes (C. elegans and Drosophila melanogaster). We found that the abundances of orthologous proteins in metazoans correlate remarkably well, better than protein abundance versus transcript abundance within each organism or transcript abundances across organisms; this suggests that changes in transcript abundance may have been partially offset during evolution by opposing changes in protein abundance