Leveraging large-scale biobanks for therapeutic target discovery
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Ferolito, Brian R. (ORCID:0000000332086446)↗
Engineering topics
Selva, Luis E.
Publications and source records attributed to Selva, Luis E..
Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP)
OBJECTIVE To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. RESEARCH DESIGN AND METHODS Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). RESULTS T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. CONCLUSIONS Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.
Yang, Peter K. (ORCID:0000000193796981)↗
Identifying intragenic functional modules of genomic variations associated with cancer phenotypes by learning representation of association networks
Background Genome-wide Association Studies (GWAS) aims to uncover the link between genomic variation and phenotype. They have been actively applied in cancer biology to investigate associations between variations and cancer phenotypes, such as susceptibility to certain types of cancer and predisposed responsiveness to specific treatments. Since GWAS primarily focuses on finding associations between individual genomic variations and cancer phenotypes, there are limitations in understanding the mechanisms by which cancer phenotypes are cooperatively affected by more than one genomic variation. Results This paper proposes a network representation learning approach to learn associations among genomic variations using a prostate cancer cohort. The learned associations are encoded into representations that can be used to identify functional modules of genomic variations within genes associated with early- and late-onset prostate cancer. The proposed method was applied to a prostate cancer cohort provided by the Veterans Administration’s Million Veteran Program to identify candidates for functional modules associated with early-onset prostate cancer. The cohort included 33,159 prostate cancer patients, 3181 early-onset patients, and 29,978 late-onset patients. The reproducibility of the proposed approach clearly showed that the proposed approach can improve the model performance in terms of robustness. Conclusions To our knowledge, this is the first attempt to use a network representation learning approach to learn associations among genomic variations within genes. Associations learned in this way can lead to an understanding of the underlying mechanisms of how genomic variations cooperatively affect each cancer phenotype. This method can reveal unknown knowledge in the field of cancer biology and can be utilized to design more advanced cancer-targeted therapies.
60 APPLIED LIFE SCIENCES↗
Single-Event Gate Rupture and Single-Event Burnout Test Results Performed on Hi-Rel Fuji Power MOSFETs: 2SK4217, 2SK4152, 2SK4155, and 2SK4158
Several newly available Hi-Rel total dose hardened power MOSFETs manufactured by Fuji Electric Device Technology Co. Ltd (FDT) in conjunction with the Japan Aerospace Exploration Agency (JAXA) were tested for single-event gate rupture (SEGR) and single-event burnout (SEB). The safe-operating area (SOA) of the 2SK4217 (a 100 V rated device), 2SK4152 (130 V), 2SK4155 (200 V), and the 2SK4158 (250 V) were tested with silver (Ag) and xenon (Xe) ions having incident LETs of 42.2 and 53.1 MeV cm2/mg, respectively. Test results show these devices are comparable to currently available total dose hardened technology.
Selva, Luis E.↗
Commercial Sensor Survey Fiscal Year 2009 master compendium radiation test report
The NASA Electronic Parts and Packaging (NEPP) Program Sensor Technology Commercial Sensor Survey task is geared toward benefiting future NASA space missions with low-cost, short-duty-cycle, visible-wavelength imaging needs. Such applications could include imaging for educational outreach purposes or short surveys of spacecraft, planetary, or lunar surfaces. Under the task, inexpensive, low-power, commercial grade CMOS sensors were surveyed in fiscal year 2007 (FY07), and three sensors were selected and tested for total ionizing dose (TID) and displacement damage dose (DDD) tolerance in fiscal year 2008 (FY08). The selected sensors had to meet selection criteria chosen to support small, low-mass cameras that produce good resolution color images. The commercial CMOS sensors tested under the task in FY08 showed very promising response to typical outreach and survey camera TID and DDD levels. In fiscal year 2009 (FY09), the survey was broadened to include two additional, similar sensor products. This compendium provides results for all radiation testing performed on the Micron and OmniVision sensors that were selected for radiation tolerance testing in FY08 and FY09.
Thorbourn, Dennis O.↗