Researchers at the University of California, Santa Barbara have developed a method to implement high-conductivity nanometer-scale doped-multilayer-graphene (DMG) interconnects that are compatible with high-volume manufacturing of integrated circuits (ICs). This method can also be applied to the implementation of any multi-layer graphene (MLG) or DMG based structures on any substrate or strata, and it overcomes all previous obstacles preventing it from being a Copper replacement. This innovation presents higher electrical conductivity than Copper, faster speed and lower noise for signal propagation and clock distribution in chips, and significantly lower resistive losses in on-chip power distribution. Compared to all currently available options, the DMG interconnect is shown to be the most reliable interconnect material because it produces negligible electromigration and less than 4% conductivity degradation over 1000 hours at room temperature without any encapsulation or barrier layer needed for Copper. It also has a substantially higher current-carrying capacity, allowing for improved speed through thinner wires, reduced noise-coupling, and lower switching energy or power consumption in integrated circuits. Overall, this helps create faster, smaller, lighter, more flexible, more reliable, and more cost-effective integrated circuits.
DeepSEA and Seqweaver: Deep learning frameworks for the identification of de novo disease-causing mutations in noncoding sequences
Princeton Docket #s 18-3426, 18-3440
Researchers at Princeton University and Rockefeller University have collaborated to establish methods for the identification of de novo mutations in the vast noncoding genomic sequences. These methods, DeepSEA 2.0 and Seqweaver, use a novel deep learning framework to compare large datasets of regulatory sequences compiled from in vivo analyses with datasets of families affected by complex human diseases to pinpoint contributing mutations at the single nucleotide level in transcriptional regulatory sequences and RNA regulatory sequences, respectively. Application of DeepSEA 2.0 and Seqweaver to a database of families affected by simplex Autism Spectrum Disorder (ASD) revealed point mutations in transcriptional regulatory sequences and RNA regulatory sequences that contribute to an estimated 14% and 12% of cases, respectively. This provides more than a 50% increase to cases with the previously identified mutations in coding regions, which contribute to an estimated 30% of cases. DeepSEA 2.0 and Seqweaver can be used to analyze other complex human diseases with large datasets to identify contributing point mutations in noncoding regions.
The human genome is comprised of gene coding regions, which code for the amino acid sequences of proteins, and noncoding regions, which include sequences that regulate gene expression and RNA processing at the DNA and RNA levels. While significant progress has been made in identifying disease-causing mutations in gene coding regions, the impact of noncoding mutations, which make up the majority of the human genome, remains underappreciated. The lack of progress in this area reflects the difficulty in distinguishing rare disease-relevant mutations from biological and technical variations that are common in noncoding sequences.
Genetic testing: Identification of DNA mutations/variants that could cause disease
Drug target identification: Predicts likely causal disease mutations
Development of personalized medicine and clinical diagnosis products
Accurately identifies novel transcriptional and RNA processing mutations.
Accurately predicts RNA binding protein binding sites using RNA sequence features alone
Prioritizes regulatory sequences in the noncoding genome
Intellectual Property & Development Status
Patent protection is pending.
Princeton is currently seeking commercial partners for the further development and commercialization of this opportunity.
Zhou J, Park C, Theesfeld C, Yuan Y, Sawicka K, Darnell J, Scheckel C, Fak J, Tajima Y, Darnell R, Troyanskaya O. 2018. Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism. bioRxiv doi: 10.1101/319681
Olga Troyanskaya is a professor at the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University, where she has been on the faculty since 2003. In 2014 she became the deputy director of Genomics at the Center for Computational Biology at the Flatiron Institute, a part of the Simons Foundation in NYC. She holds a Ph.D. in Biomedical Informatics from Stanford University, has been honored as one of the top young technology innovators by the MIT Technology Review, and is a recipient of the Sloan Research Fellowship, the National Science Foundation CAREER award, the Overton award from the International Society for Computational Biology, and the Ira Herskowitz award from the Genetic Society of America.
Robert Darnell is the Robert and Harriet Heilbrunn Professor of Cancer Biology at Rockefeller University, where he has been on the faculty since 1992. He has been a Howard Hughes Medical Institute Investigator since 2002 and the President, CEO, and Scientific Director of the New York Genome Center since 2012. He holds a Ph.D. in Molecular Biology and M.D. from Washington University School of Medicine and is a recipient of the NINDS Outstanding Investigator award, the NIH Director’s Transformative Research award, the Burroughs Wellcome Fund award, The Derek Denny-Brown Young Neurological Scholar award, and the Irma T. Hirschl/Monique Weill-Caulier Trust Research award.
Jian Zhou is a Flatrion fellow, at the Flatiron Institute at the Simons Foundation that mainly works on understanding chromatin and genome variation. He received a B.S. from Peking University and a Ph.D. from Princeton University.
Chandra Theesfeld is a research scientist in the laboratory of Professor Olga Troyanskaya at Princeton University.
Christopher Park is a research scientist at the Simons Foundation.
Princeton University Office of Technology Licensing
(609) 258-7256 • firstname.lastname@example.org
Princeton University Office of Technology Licensing
University Administrative Fellow
Over the last 50 years, the U.S. Department of Justice has ranked art crime behind only drugs and arms in terms of highest-grossing criminal trades. While many incidents go unreported making it difficult to estimate the volume and total economic value lost, Interpol estimates have put the losses worldwide due to forgery and theft at $4 to 6 billion. Various methods have been implemented to authenticate forgeries such as carbon dating for antiquities, stable isotope signatures in marble pieces, and UV/infrared analysis of paintings. However, these techniques utilize a passive approach to analyze pre-existing conditions of pieces of art and more advanced methods are beneficial for tracking and authenticating valuable items. MSU researchers have developed a method of authentication using uncommon ingredients offering protection against sophisticated forgers and thieves.
Description of Technology
This MSU technology is a method of labeling (tagging) a workpiece with a rare man made isotope, which acts as a unique identifier for anti-counterfeiting purposes. The workpiece would be marked with sub-surface deposition directly into the backside of a canvas or other medium. These rare isotopes can be generated and implanted at advanced accelerator labs such as a cyclotron along with a visual marker and are impossible to generate otherwise. The unique decay signatures can be verified with inexpensive standard detection techniques offering an easy and reliable way to monitor the workpiece. The method may include unique combinations of isotope patterns and/or combinations of isotopes with distinct decay emissions and half-lives.
Licensing Rights Available:
Full licensing rights available
Wolfgang Bauer, Bradley Sherrill
This technology supports robust auto-focusing
while keeping the sample at an optimized focal position over a large field of
Airport travelers, retail shoppers and hospital visitors are a few of the millions of people who could benefit from improved indoor positioning. Yet even after decades of research, a simple and robust positioning system is lacking. Problems include high deployment overhead and inaccuracy.
One promising strategy exploits the unique frequency signatures of light fixtures, which are the result of manufacturing variations. A common cell phone camera may be used to detect these unique signatures and compare them with a database to determine one’s location. However, cameras have significant drawbacks such as limited bandwidth and low dynamic range, making it difficult to measure the signatures of individual light sources on a high ceiling, for example, in a warehouse store.\r\n\r\nUW–Madison researchers have developed an indoor navigation system using a mobile device equipped with two photodetectors. The system is able to determine the angular position of different light fixtures while avoiding the limitations in bandwidth and sensitivity associated with standard camera detectors. It is suitable for facilities with high ceilings more than three or four meters above the floor
Specifically, the new method can be illustrated in three steps: (i) identify multiple light source signals within the field-of-view according to known light source signatures; (ii) determine the angles of the multiple light sources; and (iii) identify the location of the mobile device based on the angle of the multiple light sources and a known mapping of light sources to locations.
Title: Residency Interview Tracker
The residency interview tracker (RIT) allows fourth year medical students and their advisors to track the residency application season real-time. The goal of creating the RIT was to lessen the financial, time and emotional burden the application and interview process requires . This is achieved on a student by student basis by allowing the advisers to know real-time how the students are faring during interview season and allow the advisors to intervene if necessary. This software will make the interview process for all medical school students and student affair advisers easier, more manageable and cost effective. Additionally, over-time data analytics on the student interview process as well as where students end up matching can be collected and analyzed.
Currently, advisors track student interviews through e-mailing and asking them where they are going and ultimately where they end up matching. This process is ineffective and requires a large amount of time.
• Medical students
• Office of Student Affairs
• Clinical faculty
• Graduate education programs
• Increased efficiency
• More manageable
• Cost effective
• Reduced uncertainty
UC San Diego researchers discovered that the current technology for these applications were simply not suitable for the needed purpose, and the systems were over-priced for the functionality they offered. UCSD researchers invented, built, and implemented, an advanced system that actually met their technical needs. This system is operational, highly functional, and helping to protect the incalculable value contained within banks of some of UCSD’s deep-freezers.