The JAX Synteny Browser for mouse-human comparative genomics.

Visualizing regions of conserved synteny between two genomes is supported by numerous software applications. However, none of the current applications allow researchers to select genome features to display or highlight in blocks of synteny based on the annotated biological properties of the features (e.g., type, function, and/or phenotype association). To address this usability gap, we developed an interactive web-based conserved synteny browser, The Jackson Laboratory (JAX) Synteny Browser. The browser allows researchers to highlight or selectively display genome features in the reference and/or the comparison genome according to the biological attributes of the features. Although the current implementation for the browser is limited to the reference genomes for the laboratory mouse and human, the software platform is intentionally genome agnostic. The JAX Synteny Browser software can be deployed for any two genomes where genome coordinates for syntenic blocks are defined and for which biological attributes of the features in one or both genomes are available in widely used standard bioinformatics file formats. The JAX Synteny Browser is available at: http://syntenybrowser.jax.org/. The code base is available from GitHub: https://github.com/TheJacksonLaboratory/syntenybrowser and is distributed under the Creative Commons Attribution license (CC BY).

Calibration of Time-Interleaved Errors in Digital Real-Time Oscilloscopes.

A channel mismatch calibration method is proposed for use in time-interleaved digital real-time oscilloscope (DRTO) applications. Linear equations are derived using Fourier transforms of the separated signals from each of the time-interleaved analog-to-digital converters (TIADCs). Thus the errors in the TIADCs can be easily calibrated by inversion of a matrix, as opposed to most previous work where additional filters are employed. The calibration accuracy of the proposed method is limited only by the noise produced after the TIADC circuitry, while other methods depend on the filter design. A transfer function measurement method is then proposed for application to commercially available DRTOs. Two-tone signals are measured using DRTOs from various suppliers to validate the proposed method. The occurrence of signals at spurious frequencies is considerably reduced, as demonstrated by the calibrated results.

The current landscape of immunotherapy for pediatric brain tumors.

Pediatric central nervous system tumors are the most common solid malignancies in childhood, and aggressive therapy often leads to long-term sequelae in survivors, making these tumors challenging to treat. Immunotherapy has revolutionized prospects for many cancer types in adults, but the intrinsic complexity of treating pediatric patients and the scarcity of clinical studies of children to inform effective approaches have hampered the development of effective immunotherapies in pediatric settings. Here, we review recent advances and ongoing challenges in pediatric brain cancer immunotherapy, as well as considerations for efficient clinical translation of efficacious immunotherapies into pediatric settings.

High-accuracy photoreceiver frequency response measurements at 1.55 µm by use of a heterodyne phase-locked loop.

We demonstrate a high-accuracy heterodyne measurement system for characterizing the magnitude of the frequency response of high-speed 1.55 µm photoreceivers from 2 MHz to greater than 50 GHz. At measurement frequencies below 2 GHz, we employ a phase-locked loop with a double-heterodyne detection scheme, which enables precise tuning of the heterodyne beat frequency with an RF synthesizer. At frequencies above 2 GHz the system is operated in free-run mode with thermal tuning of the laser beat frequency. We estimate the measurement uncertainties for the low frequency range and compare the measured high-frequency response of a photoreceiver to a measurement using electro-optic sampling.

Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing.

In the United States, the Federal Communications Commission has adopted rules permitting commercial wireless networks to share spectrum with federal incumbents in the 3.5 GHz Citizens Broadband Radio Service band. These rules require commercial systems to vacate the band when sensors detect radars operated by the U.S. military; a key example being the SPN-43 air traffic control radar. Such sensors require highly-accurate detection algorithms to meet their operating requirements. In this paper, using a library of over 14,000 3.5 GHz band spectrograms collected by a recent measurement campaign, we investigate the performance of thirteen methods for SPN-43 radar detection. Namely, we compare classical methods from signal detection theory and machine learning to several deep learning architectures. We demonstrate that machine learning algorithms appreciably outperform classical signal detection methods. Specifically, we find that a three-layer convolutional neural network offers a superior tradeoff between accuracy and computational complexity. Last, we apply this three-layer network to generate descriptive statistics for the full 3.5 GHz spectrogram library. Our findings highlight potential weaknesses of classical methods and strengths of modern machine learning algorithms for radar detection in the 3.5 GHz band.

Optical Fiber Geometry: Accurate Measurement of Cladding Diameter.

We have developed three instruments for accurate measurement of optieal fiber cladding diameter: a contact micrometer, a scanning confocal microscope, and a white-light interference microscope. Each instrument has an estimated uncertainty (3 standard deviations) of 50 nm or less, but the confocal microscope may display a 20 nm systematic error as well. The micrometer is used to generate Standard Reference Materials that are commercially available.

Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.

BACKGROUND: Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D). However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes. There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology. METHODS: We performed comprehensive integrative analysis of Single Nucleotide Polymorphisms (SNP's) individually curated from T2D GWAS results and mapped them to T2D candidate risk genes. Using protein-protein interaction data, we constructed a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. We then studied the relationship between these T2D genes and curated gene sets. RESULTS: We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20. Using the T2D genetic network, we identified highly-interconnected network "hub" genes. By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories. We found that highly-interconnected T2D disease network "hub" genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1. The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list. Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer. We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport. Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported. CONCLUSIONS: Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors. T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks. The framework developed for T2D studies may serve as a guide for studying other complex diseases.