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1. Trafficking of mitochondrial double-stranded RNA from mitochondria to the cytosol.

Author

Krieger, Matthew, Krieger, Matthew, Abrahamian, Melania, He, Kevin, Atamdede, Sean, Hakimjavadi, Hesamedin, Momcilovic, Milica, Ostrow, Dejerianne, Maggo, Simran, Tsang, Yik, Gai, Xiaowu, Chanfreau, Guillaume, Shackelford, David, Teitell, Michael, Koehler, Carla, Krieger, Matthew, Krieger, Matthew, Abrahamian, Melania, He, Kevin, Atamdede, Sean, Hakimjavadi, Hesamedin, Momcilovic, Milica, Ostrow, Dejerianne, Maggo, Simran, Tsang, Yik, Gai, Xiaowu, Chanfreau, Guillaume, Shackelford, David, Teitell, Michael, and Koehler, Carla

Abstract

In addition to mitochondrial DNA, mitochondrial double-stranded RNA (mtdsRNA) is exported from mitochondria. However, specific channels for RNA transport have not been demonstrated. Here, we begin to characterize channel candidates for mtdsRNA export from the mitochondrial matrix to the cytosol. Down-regulation of SUV3 resulted in the accumulation of mtdsRNAs in the matrix, whereas down-regulation of PNPase resulted in the export of mtdsRNAs to the cytosol. Targeting experiments show that PNPase functions in both the intermembrane space and matrix. Strand-specific sequencing of the double-stranded RNA confirms the mitochondrial origin. Inhibiting or down-regulating outer membrane proteins VDAC1/2 and BAK/BAX or inner membrane proteins PHB1/2 strongly attenuated the export of mtdsRNAs to the cytosol. The cytosolic mtdsRNAs subsequently localized to large granules containing the stress protein TIA-1 and activated the type 1 interferon stress response pathway. Abundant mtdsRNAs were detected in a subset of non-small-cell lung cancer cell lines that were glycolytic, indicating relevance in cancer biology. Thus, we propose that mtdsRNA is a new damage-associated molecular pattern that is exported from mitochondria in a regulated manner.

Published
2024

2. Nanoblot: an R-package for visualization of RNA isoforms from long-read RNA-sequencing data.

Author

DeMario, Samuel, DeMario, Samuel, Xu, Kevin, He, Kevin, Chanfreau, Guillaume, DeMario, Samuel, DeMario, Samuel, Xu, Kevin, He, Kevin, and Chanfreau, Guillaume

Abstract

RT-PCR and northern blots have long been used to study RNA isoforms usage for single genes. Recent advancements in long-read sequencing have yielded unprecedented information about the usage and abundance of these RNA isoforms. However, visualization of long-read sequencing data remains challenging due to the high information density. To alleviate these issues, we have developed NanoBlot, an open-source R-package that generates northern blot and RT-PCR-like images from long-read sequencing data. NanoBlot requires aligned, positionally sorted and indexed BAM files. Plotting is based around ggplot2 and is easily customizable. Advantages of NanoBlot include a robust system for designing probes to visualize isoforms including excluding reads based on the presence or absence of a specified region, an elegant solution to representing isoforms with continuous variations in length, and the ability to overlay multiple genes in the same plot using different colors. We present examples of nanoblots compared to actual northern blot data. In addition to traditional gel-like images, the NanoBlot package can also output other visualizations such as violin plots and 3-RACE-like plots focused on 3-end isoforms visualization. The use of the NanoBlot package should provide a simple answer to some of the challenges of visualizing long-read RNA-sequencing data.

Published
2023

3. Parking Spaces Occupancy Prediction

Author

Farag, Mohamed, Marcelin, Josh, Hanumaiah, Adi, Lau, Antonio, He, Kevin, Kwon, Eugene, Farag, Mohamed, Marcelin, Josh, Hanumaiah, Adi, Lau, Antonio, He, Kevin, and Kwon, Eugene

Abstract

Across Virginia Tech’s campus, finding parking is consistently a source of frustration for students and faculty. During peak hours, locating free parking spots becomes a challenging task; leading to significant delays and increased traffic around campus. Leveraging modern data-driven technologies such as Smart City infrastructure and Intelligent Transportation, we can alleviate some of the school’s congestion and enhance the parking experience for Virginia Tech residents. The proposed solution is a web app that users can integrate into their daily commute. With the help of live data, the app will give real-time parking recommendations as well various other helpful insights. It will analyze the live data at each of the garages, to predict the occupancy of the garages at a given time of arrival. Machine learning will allow us to estimate the occupancy of each of the garages a given time into the future, depending on the distance to each garage, and provide a recommendation for which garage to target. The application will also allow for more effective collection of data for parking services and could eventually take into account more factors such as schedules and live traffic.

Published
2023

4. Stimulated emission of signal photons from dark matter waves

Author

Agrawal, Ankur, Dixit, Akash V., Roy, Tanay, Chakram, Srivatsan, He, Kevin, Naik, Ravi K., Schuster, David I., Chou, Aaron, Agrawal, Ankur, Dixit, Akash V., Roy, Tanay, Chakram, Srivatsan, He, Kevin, Naik, Ravi K., Schuster, David I., and Chou, Aaron

Abstract

The manipulation of quantum states of light has resulted in significant advancements in both dark matter searches and gravitational wave detectors [1-4]. Current dark matter searches operating in the microwave frequency range use nearly quantum-limited amplifiers [3, 5, 6]. Future high frequency searches will use photon counting techniques [1] to evade the standard quantum limit. We present a signal enhancement technique that utilizes a superconducting qubit to prepare a superconducting microwave cavity in a non-classical Fock state and stimulate the emission of a photon from a dark matter wave. By initializing the cavity in an $|n=4\rangle$ Fock state, we demonstrate a quantum enhancement technique that increases the signal photon rate and hence also the dark matter scan rate each by a factor of 2.78. Using this technique, we conduct a dark photon search in a band around $\mathrm{5.965\, GHz \, (24.67\, \mu eV)}$, where the kinetic mixing angle $\epsilon \geq 4.35 \times 10^{-13}$ is excluded at the $90\%$ confidence level., Comment: 17 pages, 17 figures

Published
2023
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5. Screening p-Hackers: Dissemination Noise as Bait

Author

Echenique, Federico, He, Kevin, Echenique, Federico, and He, Kevin

Abstract

We show that adding noise to data before making data public is effective at screening p-hacked findings: spurious explanations of the outcome variable produced by attempting multiple econometric specifications. Noise creates "baits'' that affect two types of researchers differently. Uninformed p-hackers who engage in data mining with no prior information about the true causal mechanism often fall for baits and report verifiably wrong results when evaluated with the original data. But informed researchers who start with an ex-ante hypothesis about the causal mechanism before seeing any data are minimally affected by noise. We characterize the optimal level of dissemination noise and highlight the relevant trade-offs in a simple theoretical model. Dissemination noise is a tool that statistical agencies (e.g., the US Census Bureau) currently use to protect privacy, and we show this existing practice can be repurposed to improve research credibility.

Published
2022

6. Private Private Information

Author

He, Kevin, Sandomirskiy, Fedor, Tamuz, Omer, He, Kevin, Sandomirskiy, Fedor, and Tamuz, Omer

Abstract

In a private private information structure, agents' signals contain no information about the signals of their peers. We study how informative such structures can be, and characterize those that are on the Pareto frontier, in the sense that it is impossible to give more information to any agent without violating privacy. In our main application, we show how to optimally disclose information about an unknown state under the constraint of not revealing anything about a correlated variable that contains sensitive information.

Published
2022

7. Splicing inactivation generates hybrid mRNA-snoRNA transcripts targeted by cytoplasmic RNA decay.

Author

Liu, Yanru, Liu, Yanru, DeMario, Samuel, He, Kevin, Gibbs, Michelle R, Barr, Keaton W, Chanfreau, Guillaume F, Liu, Yanru, Liu, Yanru, DeMario, Samuel, He, Kevin, Gibbs, Michelle R, Barr, Keaton W, and Chanfreau, Guillaume F

Abstract

Many small nucleolar RNAs (snoRNA)s are processed from introns of host genes, but the importance of splicing for proper biogenesis and the fate of the snoRNAs is not well understood. Here, we show that inactivation of splicing factors or mutation of splicing signals leads to the accumulation of partially processed hybrid messenger RNA-snoRNA (hmsnoRNA) transcripts. hmsnoRNAs are processed to the mature 3' ends of the snoRNAs by the nuclear exosome and bound by small nucleolar ribonucleoproteins. hmsnoRNAs are unaffected by translation-coupled RNA quality-control pathways, but they are degraded by the major cytoplasmic exonuclease Xrn1p, due to their messenger RNA (mRNA)-like 5' extensions. These results show that completion of splicing is required to promote complete and accurate processing of intron-encoded snoRNAs and that splicing defects lead to degradation of hybrid mRNA-snoRNA species by cytoplasmic decay, underscoring the importance of splicing for the biogenesis of intron-encoded snoRNAs.

Published
2022

8. Learning from Viral Content

Author

Dasaratha, Krishna, He, Kevin, Dasaratha, Krishna, and He, Kevin

Abstract

We study learning on social media with an equilibrium model of users interacting with shared news stories. Rational users arrive sequentially, observe an original story (i.e., a private signal) and a sample of predecessors' stories in a news feed, and then decide which stories to share. The observed sample of stories depends on what predecessors share as well as the sampling algorithm generating news feeds. We focus on how often this algorithm selects more viral (i.e., widely shared) stories. Showing users viral stories can increase information aggregation, but it can also generate steady states where most shared stories are wrong. These misleading steady states self-perpetuate, as users who observe wrong stories develop wrong beliefs, and thus rationally continue to share them. Finally, we describe several consequences for platform design and robustness.

Published
2022

9. KL-divergence Based Deep Learning for Discrete Time Model

Author

Liu, Li, Fang, Xiangeng, Wang, Di, Tang, Weijing, He, Kevin, Liu, Li, Fang, Xiangeng, Wang, Di, Tang, Weijing, and He, Kevin

Abstract

Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previous works, training an excellent deep learning model requires a huge amount of data, which may not hold in practice. To address this challenge, we develop a Kullback-Leibler-based (KL) deep learning procedure to integrate external survival prediction models with newly collected time-to-event data. Time-dependent KL discrimination information is utilized to measure the discrepancy between the external and internal data. This is the first work considering using prior information to deal with short data problem in Survival Analysis for deep learning. Simulation and real data results show that the proposed model achieves better performance and higher robustness compared with previous works., Comment: This paper is not complete and the results are not qualified to be public. Therefore we decided to withdraw the paper and plan to submit a newer version in the future

Published
2022

11. Searching for Dark Matter with a Superconducting Qubit

Author

Dixit, Akash V, Dixit, Akash V, Chakram, Srivatsan, He, Kevin, Agrawal, Ankur, Naik, Ravi K, Schuster, David I, Chou, Aaron, Dixit, Akash V, Dixit, Akash V, Chakram, Srivatsan, He, Kevin, Agrawal, Ankur, Naik, Ravi K, Schuster, David I, and Chou, Aaron

Published
2021

12. Natural antisense transcript of Period2, Per2AS, regulates the amplitude of the mouse circadian clock

Author

Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., Kojima, Shihoko, Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., and Kojima, Shihoko

Abstract

In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. We and others recently identified a novel transcript at the Period2 (Per2) locus that is transcribed from the antisense strand of Per2. This transcript, Per2AS, is expressed rhythmically and antiphasic to Per2 mRNA, leading to our hypothesis that Per2AS and Per2 mutually inhibit each other’s expression and form a double negative feedback loop. By perturbing the expression of Per2AS,we found that Per2AS transcription, but not transcript, represses Per2. However, Per2 does not repress Per2AS, as Per2 knockdown led to a decrease in the Per2AS level, indicating that Per2AS forms a single negative feedback loop with Per2 and maintains the level of Per2 within the oscillatory range. Per2AS also regulates the amplitude of the circadian clock, and this function cannot be solely explained through its interaction with Per2, as Per2 knockdown does not recapitulate the phenotypes of Per2AS perturbation. Overall, our data indicate that Per2AS is an important regulatory molecule in the mammalian circadian clock machinery. Our work also supports the idea that antisense transcripts of core clock genes constitute a common feature of circadian clocks, as they are found in other organisms.

Published
2021
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13. Natural antisense transcript of Period2, Per2AS, regulates the amplitude of the mouse circadian clock

Author

Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., Kojima, Shihoko, Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., and Kojima, Shihoko

Abstract

In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. We and others recently identified a novel transcript at the Period2 (Per2) locus that is transcribed from the antisense strand of Per2. This transcript, Per2AS, is expressed rhythmically and antiphasic to Per2 mRNA, leading to our hypothesis that Per2AS and Per2 mutually inhibit each other’s expression and form a double negative feedback loop. By perturbing the expression of Per2AS,we found that Per2AS transcription, but not transcript, represses Per2. However, Per2 does not repress Per2AS, as Per2 knockdown led to a decrease in the Per2AS level, indicating that Per2AS forms a single negative feedback loop with Per2 and maintains the level of Per2 within the oscillatory range. Per2AS also regulates the amplitude of the circadian clock, and this function cannot be solely explained through its interaction with Per2, as Per2 knockdown does not recapitulate the phenotypes of Per2AS perturbation. Overall, our data indicate that Per2AS is an important regulatory molecule in the mammalian circadian clock machinery. Our work also supports the idea that antisense transcripts of core clock genes constitute a common feature of circadian clocks, as they are found in other organisms.

Published
2021
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14. Natural antisense transcript of Period2, Per2AS, regulates the amplitude of the mouse circadian clock

Author

Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., Kojima, Shihoko, Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., and Kojima, Shihoko

Abstract

In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. We and others recently identified a novel transcript at the Period2 (Per2) locus that is transcribed from the antisense strand of Per2. This transcript, Per2AS, is expressed rhythmically and antiphasic to Per2 mRNA, leading to our hypothesis that Per2AS and Per2 mutually inhibit each other’s expression and form a double negative feedback loop. By perturbing the expression of Per2AS,we found that Per2AS transcription, but not transcript, represses Per2. However, Per2 does not repress Per2AS, as Per2 knockdown led to a decrease in the Per2AS level, indicating that Per2AS forms a single negative feedback loop with Per2 and maintains the level of Per2 within the oscillatory range. Per2AS also regulates the amplitude of the circadian clock, and this function cannot be solely explained through its interaction with Per2, as Per2 knockdown does not recapitulate the phenotypes of Per2AS perturbation. Overall, our data indicate that Per2AS is an important regulatory molecule in the mammalian circadian clock machinery. Our work also supports the idea that antisense transcripts of core clock genes constitute a common feature of circadian clocks, as they are found in other organisms.

Published
2021
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15. Natural antisense transcript of Period2, Per2AS, regulates the amplitude of the mouse circadian clock

Author

Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., Kojima, Shihoko, Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., and Kojima, Shihoko

Abstract

In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. We and others recently identified a novel transcript at the Period2 (Per2) locus that is transcribed from the antisense strand of Per2. This transcript, Per2AS, is expressed rhythmically and antiphasic to Per2 mRNA, leading to our hypothesis that Per2AS and Per2 mutually inhibit each other’s expression and form a double negative feedback loop. By perturbing the expression of Per2AS,we found that Per2AS transcription, but not transcript, represses Per2. However, Per2 does not repress Per2AS, as Per2 knockdown led to a decrease in the Per2AS level, indicating that Per2AS forms a single negative feedback loop with Per2 and maintains the level of Per2 within the oscillatory range. Per2AS also regulates the amplitude of the circadian clock, and this function cannot be solely explained through its interaction with Per2, as Per2 knockdown does not recapitulate the phenotypes of Per2AS perturbation. Overall, our data indicate that Per2AS is an important regulatory molecule in the mammalian circadian clock machinery. Our work also supports the idea that antisense transcripts of core clock genes constitute a common feature of circadian clocks, as they are found in other organisms.

Published
2021

16. Natural antisense transcript of Period2, Per2AS, regulates the amplitude of the mouse circadian clock

Author

Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., Kojima, Shihoko, Mosig, Rebecca A., Castaneda, Allison N., Deslauriers, Jacob C., Frazier, Landon P., He, Kevin L., Maghzian, Naseem, Pokharel, Aarati, Schrier, Camille T., Zhu, Lily, Koike, Nobuya, Tyson, John J., Green, Carla B., Takahashi, Joseph S., and Kojima, Shihoko

Abstract

In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. In mammals, a set of core clock genes form transcription–translation feedback loops to generate circadian oscillations. We and others recently identified a novel transcript at the Period2 (Per2) locus that is transcribed from the antisense strand of Per2. This transcript, Per2AS, is expressed rhythmically and antiphasic to Per2 mRNA, leading to our hypothesis that Per2AS and Per2 mutually inhibit each other’s expression and form a double negative feedback loop. By perturbing the expression of Per2AS,we found that Per2AS transcription, but not transcript, represses Per2. However, Per2 does not repress Per2AS, as Per2 knockdown led to a decrease in the Per2AS level, indicating that Per2AS forms a single negative feedback loop with Per2 and maintains the level of Per2 within the oscillatory range. Per2AS also regulates the amplitude of the circadian clock, and this function cannot be solely explained through its interaction with Per2, as Per2 knockdown does not recapitulate the phenotypes of Per2AS perturbation. Overall, our data indicate that Per2AS is an important regulatory molecule in the mammalian circadian clock machinery. Our work also supports the idea that antisense transcripts of core clock genes constitute a common feature of circadian clocks, as they are found in other organisms.

Published
2021

17. Private Private Information

Author

He, Kevin, Sandomirskiy, Fedor, Tamuz, Omer, He, Kevin, Sandomirskiy, Fedor, and Tamuz, Omer

Abstract

A private private information structure delivers information about an unknown state while preserving privacy: An agent's signal contains information about the state but remains independent of others' sensitive or private information. We study how informative such structures can be, and characterize those that are optimal in the sense that they cannot be made more informative without violating privacy. We connect our results to fairness in recommendation systems and explore a number of further applications.

Published
2021

18. Multimode photon blockade

Author

Chakram, Srivatsan, Chakram, Srivatsan, He, Kevin, Dixit, Akash V, Oriani, Andrew E, Naik, Ravi K, Leung, Nelson, Kwon, Hyeokshin, Ma, Wen-Long, Jiang, Liang, Schuster, David I, Chakram, Srivatsan, Chakram, Srivatsan, He, Kevin, Dixit, Akash V, Oriani, Andrew E, Naik, Ravi K, Leung, Nelson, Kwon, Hyeokshin, Ma, Wen-Long, Jiang, Liang, and Schuster, David I

Abstract

Interactions are essential for the creation of correlated quantum many-body states. While two-body interactions underlie most natural phenomena, three- and four-body interactions are important for the physics of nuclei [1], exotic few-body states in ultracold quantum gases [2], the fractional quantum Hall effect [3], quantum error correction [4], and holography [5, 6]. Recently, a number of artificial quantum systems have emerged as simulators for many-body physics, featuring the ability to engineer strong interactions. However, the interactions in these systems have largely been limited to the two-body paradigm, and require building up multi-body interactions by combining two-body forces. Here, we demonstrate a pure N-body interaction between microwave photons stored in an arbitrary number of electromagnetic modes of a multimode cavity. The system is dressed such that there is collectively no interaction until a target total photon number is reached across multiple distinct modes, at which point they interact strongly. The microwave cavity features 9 modes with photon lifetimes of $\sim 2$ ms coupled to a superconducting transmon circuit, forming a multimode circuit QED system with single photon cooperativities of $\sim10^9$. We generate multimode interactions by using cavity photon number resolved drives on the transmon circuit to blockade any multiphoton state with a chosen total photon number distributed across the target modes. We harness the interaction for state preparation, preparing Fock states of increasing photon number via quantum optimal control pulses acting only on the cavity modes. We demonstrate multimode interactions by generating entanglement purely with uniform cavity drives and multimode photon blockade, and characterize the resulting two- and three-mode W states using a new protocol for multimode Wigner tomography.

Published
2020

19. Seamless high-Q microwave cavities for multimode circuit QED

Author

Chakram, Srivatsan, Chakram, Srivatsan, Oriani, Andrew E, Naik, Ravi K, Dixit, Akash V, He, Kevin, Agrawal, Ankur, Kwon, Hyeokshin, Schuster, David I, Chakram, Srivatsan, Chakram, Srivatsan, Oriani, Andrew E, Naik, Ravi K, Dixit, Akash V, He, Kevin, Agrawal, Ankur, Kwon, Hyeokshin, and Schuster, David I

Abstract

Multimode cavity quantum electrodynamics ---where a two-level system interacts simultaneously with many cavity modes---provides a versatile framework for quantum information processing and quantum optics. Due to the combination of long coherence times and large interaction strengths, one of the leading experimental platforms for cavity QED involves coupling a superconducting circuit to a 3D microwave cavity. In this work, we realize a 3D multimode circuit QED system with single photon lifetimes of $2$ ms and cooperativities of $0.5-1.5\times10^9$ across 9 modes of a novel seamless cavity. We demonstrate a variety of protocols for universal single-mode quantum control applicable across all cavity modes, using only a single drive line. We achieve this by developing a straightforward flute method for creating monolithic superconducting microwave cavities that reduces loss while simultaneously allowing control of the mode spectrum and mode-qubit interaction. We highlight the flexibility and ease of implementation of this technique by using it to fabricate a variety of 3D cavity geometries, providing a template for engineering multimode quantum systems with exceptionally low dissipation. This work is an important step towards realizing hardware efficient random access quantum memories and processors, and for exploring quantum many-body physics with photons.

Published
2020

20. Seamless high-Q microwave cavities for multimode circuit QED

Author

Chakram, Srivatsan, Chakram, Srivatsan, Oriani, Andrew E, Naik, Ravi K, Dixit, Akash V, He, Kevin, Agrawal, Ankur, Kwon, Hyeokshin, Schuster, David I, Chakram, Srivatsan, Chakram, Srivatsan, Oriani, Andrew E, Naik, Ravi K, Dixit, Akash V, He, Kevin, Agrawal, Ankur, Kwon, Hyeokshin, and Schuster, David I

Abstract

Multimode cavity quantum electrodynamics ---where a two-level system interacts simultaneously with many cavity modes---provides a versatile framework for quantum information processing and quantum optics. Due to the combination of long coherence times and large interaction strengths, one of the leading experimental platforms for cavity QED involves coupling a superconducting circuit to a 3D microwave cavity. In this work, we realize a 3D multimode circuit QED system with single photon lifetimes of $2$ ms and cooperativities of $0.5-1.5\times10^9$ across 9 modes of a novel seamless cavity. We demonstrate a variety of protocols for universal single-mode quantum control applicable across all cavity modes, using only a single drive line. We achieve this by developing a straightforward flute method for creating monolithic superconducting microwave cavities that reduces loss while simultaneously allowing control of the mode spectrum and mode-qubit interaction. We highlight the flexibility and ease of implementation of this technique by using it to fabricate a variety of 3D cavity geometries, providing a template for engineering multimode quantum systems with exceptionally low dissipation. This work is an important step towards realizing hardware efficient random access quantum memories and processors, and for exploring quantum many-body physics with photons.

Published
2020

21. Searching for Dark Matter with a Superconducting Qubit

Author

Dixit, Akash V., Chakram, Srivatsan, He, Kevin, Agrawal, Ankur, Naik, Ravi K., Schuster, David I., Chou, Aaron, Dixit, Akash V., Chakram, Srivatsan, He, Kevin, Agrawal, Ankur, Naik, Ravi K., Schuster, David I., and Chou, Aaron

Abstract

Detection mechanisms for low mass bosonic dark matter candidates, such the axion or hidden photon, leverage potential interactions with electromagnetic fields, whereby the dark matter (of unknown mass) on rare occasion converts into a single photon. Current dark matter searches operating at microwave frequencies use a resonant cavity to coherently accumulate the field sourced by the dark matter and a near standard quantum limited (SQL) linear amplifier to read out the cavity signal. To further increase sensitivity to the dark matter signal, sub-SQL detection techniques are required. Here we report the development of a novel microwave photon counting technique and a new exclusion limit on hidden photon dark matter. We operate a superconducting qubit to make repeated quantum non-demolition measurements of cavity photons and apply a hidden Markov model analysis to reduce the noise to 15.7 dB below the quantum limit, with overall detector performance limited by a residual background of real photons. With the present device, we perform a hidden photon search and constrain the kinetic mixing angle to $\epsilon \leq 1.68 \times 10^{-15}$ in a band around 6.011 GHz (24.86 $\mu$eV) with an integration time of 8.33 s. This demonstrated noise reduction technique enables future dark matter searches to be sped up by a factor of 1300. By coupling a qubit to an arbitrary quantum sensor, more general sub-SQL metrology is possible with the techniques presented in this work., Comment: 15 pages, 14 figures, 2 table. Dark matter exclusion analysis modified to include experimental systematics. Discussion of background calibration and detector compatibility with tunable cavity added to conclusion. Future optimizations and integration into axion search sections moved to Supplemental Material. References updated

Published
2020
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22. Searching for Dark Matter with a Superconducting Qubit

Author

Dixit, Akash V., Chakram, Srivatsan, He, Kevin, Agrawal, Ankur, Naik, Ravi K., Schuster, David I., Chou, Aaron, Dixit, Akash V., Chakram, Srivatsan, He, Kevin, Agrawal, Ankur, Naik, Ravi K., Schuster, David I., and Chou, Aaron

Abstract

Detection mechanisms for low mass bosonic dark matter candidates, such the axion or hidden photon, leverage potential interactions with electromagnetic fields, whereby the dark matter (of unknown mass) on rare occasion converts into a single photon. Current dark matter searches operating at microwave frequencies use a resonant cavity to coherently accumulate the field sourced by the dark matter and a near standard quantum limited (SQL) linear amplifier to read out the cavity signal. To further increase sensitivity to the dark matter signal, sub-SQL detection techniques are required. Here we report the development of a novel microwave photon counting technique and a new exclusion limit on hidden photon dark matter. We operate a superconducting qubit to make repeated quantum non-demolition measurements of cavity photons and apply a hidden Markov model analysis to reduce the noise to 15.7 dB below the quantum limit, with overall detector performance limited by a residual background of real photons. With the present device, we perform a hidden photon search and constrain the kinetic mixing angle to $\epsilon \leq 1.68 \times 10^{-15}$ in a band around 6.011 GHz (24.86 $\mu$eV) with an integration time of 8.33 s. This demonstrated noise reduction technique enables future dark matter searches to be sped up by a factor of 1300. By coupling a qubit to an arbitrary quantum sensor, more general sub-SQL metrology is possible with the techniques presented in this work., Comment: 15 pages, 14 figures, 2 table. Dark matter exclusion analysis modified to include experimental systematics. Discussion of background calibration and detector compatibility with tunable cavity added to conclusion. Future optimizations and integration into axion search sections moved to Supplemental Material. References updated

Published
2020
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23. The Implications of Paraspinal Muscle Atrophy in Low Back Pain, Thoracolumbar Pathology, and Clinical Outcomes After Spine Surgery: A Review of the Literature.

Author

He, Kevin, Head, Jeffery, Mouchtouris, Nikolaos, Hines, Kevin, Shea, Phelan, Schmidt, Richard, Hoelscher, Christian, Stricsek, Geoffrey, Harrop, James, Sharan, Ashwini, He, Kevin, Head, Jeffery, Mouchtouris, Nikolaos, Hines, Kevin, Shea, Phelan, Schmidt, Richard, Hoelscher, Christian, Stricsek, Geoffrey, Harrop, James, and Sharan, Ashwini

Abstract

STUDY DESIGN: Literature review. OBJECTIVES: Paraspinal muscle integrity is believed to play a critical role in low back pain (LBP) and numerous spinal deformity diseases and other pain pathologies. The influence of paraspinal muscle atrophy (PMA) on the clinical and radiographic success of spinal surgery has not been established. We aim to survey the literature in order to evaluate the impact of paraspinal muscle atrophy on low back pain, spine pathologies, and postoperative outcomes of spinal surgery. METHODS: A review of the literature was conducted using a total of 267 articles identified from a search of the PubMed database and additional resources. A full-text review was conducted of 180 articles, which were assessed based on criteria that included an objective assessment of PMA in addition to measuring its relationship to LBP, thoracolumbar pathology, or surgical outcomes. RESULTS: A total of 34 studies were included in this review. The literature on PMA illustrates an association between LBP and both decreased cross-sectional area and increased fatty infiltration of paraspinal musculature. Atrophy of the erector spinae and psoas muscles have been associated with spinal stenosis, isthmic spondylolisthesis, facet arthropathy, degenerative lumbar kyphosis. A number of studies have also demonstrated an association between PMA and worse postoperative outcomes. CONCLUSIONS: PMA is linked to several spinal pathologies and some studies demonstrate an association with worse postoperative outcomes following spinal surgery. There is a need for further research to establish a relationship between preoperative paraspinal muscle integrity and postoperative success, with the potential for guiding surgical decision making.

Published
2020

24. Learning and Type Compatibility in Signaling Games

Author

Massachusetts Institute of Technology. Department of Economics, Fudenberg, Drew, He, Kevin, Massachusetts Institute of Technology. Department of Economics, Fudenberg, Drew, and He, Kevin

Abstract

Which equilibria will arise in signaling games depends on how the receiver interprets deviations from the path of play. We develop a micro-foundation for these off-path beliefs, and an associated equilibrium refinement, in a model where equilibrium arises through non-equilibrium learning by populations of patient and long-lived senders and receivers. In our model, young senders are uncertain about the prevailing distribution of play, so they rationally send out-of-equilibrium signals as experiments to learn about the behavior of the population of receivers. Differences in the payoff functions of the types of senders generate different incentives for these experiments. Using the Gittins index (Gittins (1979)), we characterize which sender types use each signal more often, leading to a constraint on the receiver's off-path beliefs based on “type compatibility” and hence a learning-based equilibrium selection. ©2018 The Econometric Society, NSF grant (SES 1643517)

Published
2020

25. US Renal Data System 2018 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Kurella Tamura, Manjula, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Kurella Tamura, Manjula, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, and Shahinian, Vahakn

Published
2019

26. US Renal Data System 2018 Annual Data Report Epidemiology of Kidney Disease in the United States

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula Kurella, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula Kurella, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, and Shahinian, Vahakn

Published
2019

27. Outcomes associated with apixaban use in end-stage kidney disease patients with atrial fibrillation in the United States

Author

National Institute of Digestive and Diabetes and Kidney Diseases (NIDDK), Konstantinos, Siontis C., Zhang, Xiaosong, Eckard, Ashley, Bhave, Nicole, Schaubel, Douglas E., He, Kevin, Tilea, Anca, Stack, Austin G., Balkrishnan, Rajesh, Yao, Xiaoxi, Noseworthy, Peter A., Shah, Nilay D., Saran, Rajiv, Nallamothu, Brahmajee K., National Institute of Digestive and Diabetes and Kidney Diseases (NIDDK), Konstantinos, Siontis C., Zhang, Xiaosong, Eckard, Ashley, Bhave, Nicole, Schaubel, Douglas E., He, Kevin, Tilea, Anca, Stack, Austin G., Balkrishnan, Rajesh, Yao, Xiaoxi, Noseworthy, Peter A., Shah, Nilay D., Saran, Rajiv, and Nallamothu, Brahmajee K.

Abstract

peer-reviewed, Background: Patients with end-stage kidney disease (ESKD) on dialysis were excluded from clinical trials of direct oral anticoagulants for atrial fibrillation (AF). Recent data have raised concerns regarding the safety of dabigatran and rivaroxaban, but apixaban has not been evaluated despite current labeling supporting its use in this population. The goal of this study was to determine patterns of apixaban use and its associated outcomes in dialysis-dependent patients with ESKD and AF. Methods: We performed a retrospective cohort study of Medicare beneficiaries included in the United States Renal Data System (October 2010 to December 2015). Eligible patients were those with ESKD and AF undergoing dialysis who initiated treatment with an oral anticoagulant. Because of the small number of dabigatran and rivaroxaban users, outcomes were only assessed in patients treated with apixaban or warfarin. Apixaban and warfarin patients were matched (1:3) based on prognostic score. Differences between groups in survival free of stroke or systemic embolism, major bleeding, gastrointestinal bleeding, intracranial bleeding, and death were assessed using Kaplan–Meier analyses. Hazard ratios (HRs) and 95% CIs were derived from Cox regression analyses. Results: The study population consisted of 25 523 patients (45.7% women; 68.2±11.9 years of age), including 2351 patients on apixaban and 23 172 patients on warfarin. An annual increase in apixaban prescriptions was observed after its marketing approval at the end of 2012, such that 26.6% of new anticoagulant prescriptions in 2015 were for apixaban. In matched cohorts, there was no difference in the risks of stroke/systemic embolism between apixaban and warfarin (HR, 0.88; 95% CI, 0.69–1.12; P=0.29), but apixaban was associated with a significantly lower risk of major bleeding (HR, 0.72; 95% CI, 0.59–0.87; P<0.001). In sensitivity analyses, standard-dose apixaban (5 mg twice a day; n=1034) was associated with significantly lower risks, peer-reviewed

Published
2019

28. US Renal Data System 2018 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Kurella Tamura, Manjula, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Kurella Tamura, Manjula, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, and Shahinian, Vahakn

Published
2019

29. US Renal Data System 2018 Annual Data Report Epidemiology of Kidney Disease in the United States

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula Kurella, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Bhave, Nicole, Dietrich, Xue, Ding, Zhechen, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Guro, Paula, Haggerty, Diana, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hsiung, Jui-Ting, Hutton, David, Inoue, Aya, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kleine, Carola-Ellen, Kovesdy, Csaba P, Krueter, William, Kurtz, Vivian, Li, Yiting, Liu, Sai, Marroquin, Maria V, McCullough, Keith, Molnar, Miklos Z, Modi, Zubin, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Repeck, Kaitlyn, Rhee, Connie M, Schaubel, Douglas E, Schrager, Jillian, Selewski, David T, Shamraj, Ruth, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula Kurella, Tilea, Anca, Turf, Megan, Wang, Dongyu, Weng, Wenjing, Woodside, Kenneth J, Wyncott, April, Xiang, Jie, Xin, Xin, Yin, Maggie, You, Amy S, Zhang, Xiaosong, Zhou, Hui, and Shahinian, Vahakn

Published
2019

30. Aggregative Efficiency of Bayesian Learning in Networks

Author

Dasaratha, Krishna, He, Kevin, Dasaratha, Krishna, and He, Kevin

Abstract

When individuals in a social network learn about an unknown state from private signals and neighbors' actions, the network structure often causes information loss. We consider rational agents and Gaussian signals in the canonical sequential social-learning problem and ask how the network changes the efficiency of signal aggregation. Rational actions in our model are log-linear functions of observations and admit a signal-counting interpretation of accuracy. Networks where agents observe multiple neighbors but not their common predecessors confound information, and even a small amount of confounding can lead to much lower accuracy. In a class of networks where agents move in generations and observe the previous generation, we quantify the information loss with an aggregative efficiency index. Aggregative efficiency is a simple function of network parameters: increasing in observations and decreasing in confounding. Later generations contribute little additional information, even with arbitrarily large generations.

Published
2019

31. An Experiment on Network Density and Sequential Learning

Author

Dasaratha, Krishna, He, Kevin, Dasaratha, Krishna, and He, Kevin

Abstract

We conduct a sequential social-learning experiment where subjects each guess a hidden state based on private signals and the guesses of a subset of their predecessors. A network determines the observable predecessors, and we compare subjects' accuracy on sparse and dense networks. Accuracy gains from social learning are twice as large on sparse networks compared to dense networks. Models of naive inference where agents ignore correlation between observations predict this comparative static in network density, while the finding is difficult to reconcile with rational-learning models., Comment: Incorporates the experimental results from a previous version of arXiv:1703.02105

Published
2019
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32. Bayesian posteriors for arbitrarily rare events

Author

Massachusetts Institute of Technology. Department of Economics, Fudenberg, Drew, He, Kevin, Imhof, Lorens A., Massachusetts Institute of Technology. Department of Economics, Fudenberg, Drew, He, Kevin, and Imhof, Lorens A.

Published
2018

33. US Renal Data System 2017 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bhave, Nicole, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kovesdy, Csaba P, Lavallee, Danielle, Leslie, Janet, McCullough, Keith, Modi, Zubin, Molnar, Miklos Z, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Rao, Panduranga, Repeck, Kaitlyn, Rhee, Connie M, Schrager, Jillian, Schaubel, Douglas E, Selewski, David T, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula K, Tilea, Anca, Tong, Lan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth J, Xin, Xin, Yin, Maggie, You, Amy S, Zhou, Hui, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bhave, Nicole, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kovesdy, Csaba P, Lavallee, Danielle, Leslie, Janet, McCullough, Keith, Modi, Zubin, Molnar, Miklos Z, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Rao, Panduranga, Repeck, Kaitlyn, Rhee, Connie M, Schrager, Jillian, Schaubel, Douglas E, Selewski, David T, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula K, Tilea, Anca, Tong, Lan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth J, Xin, Xin, Yin, Maggie, You, Amy S, Zhou, Hui, and Shahinian, Vahakn

Published
2018

34. US Renal Data System 2017 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bhave, Nicole, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kovesdy, Csaba P, Lavallee, Danielle, Leslie, Janet, McCullough, Keith, Modi, Zubin, Molnar, Miklos Z, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Rao, Panduranga, Repeck, Kaitlyn, Rhee, Connie M, Schrager, Jillian, Schaubel, Douglas E, Selewski, David T, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula K, Tilea, Anca, Tong, Lan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth J, Xin, Xin, Yin, Maggie, You, Amy S, Zhou, Hui, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Bhave, Nicole, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaipov, Abduzhappar, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Kovesdy, Csaba P, Lavallee, Danielle, Leslie, Janet, McCullough, Keith, Modi, Zubin, Molnar, Miklos Z, Montez-Rath, Maria, Moradi, Hamid, Morgenstern, Hal, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Park, Christina, Pearson, Jeffrey, Pisoni, Ronald, Potukuchi, Praveen K, Rao, Panduranga, Repeck, Kaitlyn, Rhee, Connie M, Schrager, Jillian, Schaubel, Douglas E, Selewski, David T, Shaw, Sally F, Shi, Jiaxiao M, Shieu, Monica, Sim, John J, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Sumida, Keiichi, Tamura, Manjula K, Tilea, Anca, Tong, Lan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth J, Xin, Xin, Yin, Maggie, You, Amy S, Zhou, Hui, and Shahinian, Vahakn

Published
2018

35. Covariance-Insured Screening

Author

He, Kevin, Kang, Jian, Hong, Hyokyoung Grace, Zhu, Ji, Li, Yanming, Lin, Huazhen, Xu, Han, Li, Yi, He, Kevin, Kang, Jian, Hong, Hyokyoung Grace, Zhu, Ji, Li, Yanming, Lin, Huazhen, Xu, Han, and Li, Yi

Abstract

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size. In order to identify more novel biomarkers and understand biological mechanisms, it is vital to detect signals weakly associated with outcomes among ultrahigh-dimensional predictors. However, existing screening methods, which typically ignore correlation information, are likely to miss these weak signals. By incorporating the inter-feature dependence, we propose a covariance-insured screening methodology to identify predictors that are jointly informative but only marginally weakly associated with outcomes. The validity of the method is examined via extensive simulations and real data studies for selecting potential genetic factors related to the onset of cancer.

Published
2018

36. Mislearning from Censored Data: The Gambler's Fallacy and Other Correlational Mistakes in Optimal-Stopping Problems

Author

He, Kevin and He, Kevin

Abstract

I study endogenous learning dynamics for people who misperceive intertemporal correlations in random sequences. Biased agents face an optimal-stopping problem. They are uncertain about the underlying distribution and learn its parameters from predecessors. Agents stop when early draws are "good enough," so predecessors' experiences contain negative streaks but not positive streaks. When agents wrongly expect systematic reversals (the "gambler's fallacy"), they understate the likelihood of consecutive below-average draws, converge to over-pessimistic beliefs about the distribution's mean, and stop too early. Agents uncertain about the distribution's variance overestimate it to an extent that depends on predecessors' stopping thresholds. I also analyze how other misperceptions of intertemporal correlation interact with endogenous data censoring.

Published
2018
Full Text
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37. US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavalee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula Kurella, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zang, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavalee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula Kurella, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zang, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, and Shahinian, Vahakn

Published
2017

38. US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States (vol 69, pg s7, 2017)

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavallee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula K, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zeng, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavallee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula K, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zeng, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, and Shahinian, Vahakn

Published
2017

39. The Outer Halo of the Milky Way as Probed by RR Lyr Variables from the Palomar Transient Facility

Author

Cohen, Judith, Sesar, Branimir, Bahnolzer, Sophianna, He, Kevin, Kulkarni, Shrinivas R., Prince, Thomas A., Bellm, Eric, Laher, Russ R., Cohen, Judith, Sesar, Branimir, Bahnolzer, Sophianna, He, Kevin, Kulkarni, Shrinivas R., Prince, Thomas A., Bellm, Eric, and Laher, Russ R.

Abstract

RR Lyr stars are ideal massless tracers that can be used to study the total mass and dark matter content of the outer halo of the Milky Way. This is because they are easy to find in the light curve databases of large stellar surveys and their distances can be determined with only knowledge of the light curve. We present here a sample of 112 RR Lyr beyond 50 kpc in the outer halo of the Milky Way, excluding the Sgr streams, for which we have obtained moderate resolution spectra with Deimos on the Keck 2 Telescope. Four of these have distances exceeding 100 kpc. These were selected from a much larger set of 447 candidate RR Lyr which were datamined using machine learning techniques applied to the light curves of variable stars in the Palomar Transient Facility database. The observed radial velocities taken at the phase of the variable corresponding to the time of observation were converted to systemic radial velocities in the Galactic standard of rest. From our sample of 112 RR Lyr we determine the radial velocity dispersion in the outer halo of the Milky Way to be ~90 km/s at 50 kpc falling to about 65 km/s near 100 kpc once a small number of major outliers are removed. With reasonable estimates of the completeness of our sample of 447 candidates and assuming a spherical halo, we find that the stellar density in the outer halo declines as the -4 power of r., Comment: Accepted for publication in the ApJ

Published
2017
Full Text
View/download PDF

40. US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavalee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula Kurella, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zang, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavalee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula Kurella, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zang, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, and Shahinian, Vahakn

Published
2017

41. US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States (vol 69, pg s7, 2017)

Author

Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavallee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula K, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zeng, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, Shahinian, Vahakn, Saran, Rajiv, Saran, Rajiv, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Albertus, Patrick, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Cao, Jie, Chen, Joline LT, Cope, Elizabeth, Dharmarajan, Sai, Dietrich, Xue, Eckard, Ashley, Eggers, Paul W, Gaber, Charles, Gillen, Daniel, Gipson, Debbie, Gu, Haoyu, Hailpern, Susan M, Hall, Yoshio N, Han, Yun, He, Kevin, Hebert, Paul, Helmuth, Margaret, Herman, William, Heung, Michael, Hutton, David, Jacobsen, Steven J, Ji, Nan, Jin, Yan, Kalantar-Zadeh, Kamyar, Kapke, Alissa, Katz, Ronit, Kovesdy, Csaba P, Kurtz, Vivian, Lavallee, Danielle, Li, Yi, Lu, Yee, McCullough, Keith, Molnar, Miklos Z, Montez-Rath, Maria, Morgenstern, Hal, Mu, Qiao, Mukhopadhyay, Purna, Nallamothu, Brahmajee, Nguyen, Danh V, Norris, Keith C, O'Hare, Ann M, Obi, Yoshitsugu, Pearson, Jeffrey, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Potukuchi, Praveen, Rao, Panduranga, Ratkowiak, Kaitlyn, Ravel, Vanessa, Ray, Debabrata, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shaw, Sally, Shi, Jiaxiao, Shieu, Monica, Sim, John J, Song, Peter, Soohoo, Melissa, Steffick, Diane, Streja, Elani, Tamura, Manjula K, Tentori, Francesca, Tilea, Anca, Tong, Lan, Turf, Megan, Wang, Dongyu, Wang, Mia, Woodside, Kenneth, Wyncott, April, Xin, Xin, Zeng, Wei, Zepel, Lindsay, Zhang, Sai, Zho, Hui, Hirth, Richard A, and Shahinian, Vahakn

Published
2017

42. The Outer Halo of the Milky Way as Probed by RR Lyr Variables from the Palomar Transient Facility

Author

Cohen, Judith, Sesar, Branimir, Bahnolzer, Sophianna, He, Kevin, Kulkarni, Shrinivas R., Prince, Thomas A., Bellm, Eric, Laher, Russ R., Cohen, Judith, Sesar, Branimir, Bahnolzer, Sophianna, He, Kevin, Kulkarni, Shrinivas R., Prince, Thomas A., Bellm, Eric, and Laher, Russ R.

Abstract

RR Lyr stars are ideal massless tracers that can be used to study the total mass and dark matter content of the outer halo of the Milky Way. This is because they are easy to find in the light curve databases of large stellar surveys and their distances can be determined with only knowledge of the light curve. We present here a sample of 112 RR Lyr beyond 50 kpc in the outer halo of the Milky Way, excluding the Sgr streams, for which we have obtained moderate resolution spectra with Deimos on the Keck 2 Telescope. Four of these have distances exceeding 100 kpc. These were selected from a much larger set of 447 candidate RR Lyr which were datamined using machine learning techniques applied to the light curves of variable stars in the Palomar Transient Facility database. The observed radial velocities taken at the phase of the variable corresponding to the time of observation were converted to systemic radial velocities in the Galactic standard of rest. From our sample of 112 RR Lyr we determine the radial velocity dispersion in the outer halo of the Milky Way to be ~90 km/s at 50 kpc falling to about 65 km/s near 100 kpc once a small number of major outliers are removed. With reasonable estimates of the completeness of our sample of 447 candidates and assuming a spherical halo, we find that the stellar density in the outer halo declines as the -4 power of r., Comment: Accepted for publication in the ApJ

Published
2017
Full Text
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43. Network Structure and Naive Sequential Learning

Author

Dasaratha, Krishna, He, Kevin, Dasaratha, Krishna, and He, Kevin

Abstract

We study a sequential-learning model featuring a network of naive agents with Gaussian information structures. Agents apply a heuristic rule to aggregate predecessors' actions. They weigh these actions according the strengths of their social connections to different predecessors. We show this rule arises endogenously when agents wrongly believe others act solely on private information and thus neglect redundancies among observations. We provide a simple linear formula expressing agents' actions in terms of network paths and use this formula to characterize the set of networks where naive agents eventually learn correctly. This characterization implies that, on all networks where later agents observe more than one neighbor, there exist disproportionately influential early agents who can cause herding on incorrect actions. Going beyond existing social-learning results, we compute the probability of such mislearning exactly. This allows us to compare likelihoods of incorrect herding, and hence expected welfare losses, across network structures. The probability of mislearning increases when link densities are higher and when networks are more integrated. In partially segregated networks, divergent early signals can lead to persistent disagreement between groups.

Published
2017
Full Text
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44. US Renal Data System 2015 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Ayanian, John, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Chen, Joline LT, Cope, Elizabeth, Eggers, Paul W, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, Nguyen, Danh V, O'Hare, Ann M, Plattner, Brett, Pisoni, Ronald, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Sakhuja, Ankit, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, White, Sarah, Woodside, Kenneth, Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Ayanian, John, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Chen, Joline LT, Cope, Elizabeth, Eggers, Paul W, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, Nguyen, Danh V, O'Hare, Ann M, Plattner, Brett, Pisoni, Ronald, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Sakhuja, Ankit, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, White, Sarah, and Woodside, Kenneth

Published
2016

45. US Renal Data System 2015 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Ayanian, John, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Chen, Joline LT, Cope, Elizabeth, Eggers, Paul W, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, Nguyen, Danh V, O'Hare, Ann M, Plattner, Brett, Pisoni, Ronald, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Sakhuja, Ankit, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, White, Sarah, Woodside, Kenneth, Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Abbott, Kevin C, Agodoa, Lawrence YC, Ayanian, John, Bragg-Gresham, Jennifer, Balkrishnan, Rajesh, Chen, Joline LT, Cope, Elizabeth, Eggers, Paul W, Gillen, Daniel, Gipson, Debbie, Hailpern, Susan M, Hall, Yoshio N, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Hutton, David, Jacobsen, Steven J, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, Nguyen, Danh V, O'Hare, Ann M, Plattner, Brett, Pisoni, Ronald, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Sakhuja, Ankit, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, White, Sarah, and Woodside, Kenneth

Published
2016

46. Classification with Ultrahigh-Dimensional Features

Author

Li, Yanming, Hong, Hyokyoung, Kang, Jian, He, Kevin, Zhu, Ji, Li, Yi, Li, Yanming, Hong, Hyokyoung, Kang, Jian, He, Kevin, Zhu, Ji, and Li, Yi

Abstract

Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work. This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-feature correlations, the proposed method enables detection of marginally weak and sparse signals and recovery of the true informative feature set, and achieves asymptotic optimal misclassification rates. We also show that the proposed procedure provides more powerful discovery boundaries compared to those in \citet{CaiSun:2014} and \citet{JJin:2009}. The performance of the proposed procedure is evaluated using simulation studies and demonstrated via classification of patients with different post-transplantation renal functional types.

Published
2016

47. US Renal Data System 2014 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Chen, Joline TL, Cope, Elizabeth, Gipson, Debbie, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Jacobsen, Steven S, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Leichtman, Alan B, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, O'Hare, Ann M, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, Eggers, Paul W, Agodoa, Lawrence YC, Abbott, Kevin C, Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Chen, Joline TL, Cope, Elizabeth, Gipson, Debbie, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Jacobsen, Steven S, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Leichtman, Alan B, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, O'Hare, Ann M, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, Eggers, Paul W, Agodoa, Lawrence YC, and Abbott, Kevin C

Published
2015

48. US Renal Data System 2014 Annual Data Report: Epidemiology of Kidney Disease in the United States.

Author

Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Chen, Joline TL, Cope, Elizabeth, Gipson, Debbie, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Jacobsen, Steven S, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Leichtman, Alan B, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, O'Hare, Ann M, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, Eggers, Paul W, Agodoa, Lawrence YC, Abbott, Kevin C, Saran, Rajiv, Saran, Rajiv, Li, Yi, Robinson, Bruce, Ayanian, John, Balkrishnan, Rajesh, Bragg-Gresham, Jennifer, Chen, Joline TL, Cope, Elizabeth, Gipson, Debbie, He, Kevin, Herman, William, Heung, Michael, Hirth, Richard A, Jacobsen, Steven S, Kalantar-Zadeh, Kamyar, Kovesdy, Csaba P, Leichtman, Alan B, Lu, Yee, Molnar, Miklos Z, Morgenstern, Hal, Nallamothu, Brahmajee, O'Hare, Ann M, Pisoni, Ronald, Plattner, Brett, Port, Friedrich K, Rao, Panduranga, Rhee, Connie M, Schaubel, Douglas E, Selewski, David T, Shahinian, Vahakn, Sim, John J, Song, Peter, Streja, Elani, Kurella Tamura, Manjula, Tentori, Francesca, Eggers, Paul W, Agodoa, Lawrence YC, and Abbott, Kevin C

Published
2015

49. Expanding the diversity of mycobacteriophages: insights into genome architecture and evolution.

Author

Pope, Welkin H, Pope, Welkin H, Jacobs-Sera, Deborah, Russell, Daniel A, Peebles, Craig L, Al-Atrache, Zein, Alcoser, Turi A, Alexander, Lisa M, Alfano, Matthew B, Alford, Samantha T, Amy, Nichols E, Anderson, Marie D, Anderson, Alexander G, Ang, Andrew AS, Ares, Manuel, Barber, Amanda J, Barker, Lucia P, Barrett, Jonathan M, Barshop, William D, Bauerle, Cynthia M, Bayles, Ian M, Belfield, Katherine L, Best, Aaron A, Borjon, Agustin, Bowman, Charles A, Boyer, Christine A, Bradley, Kevin W, Bradley, Victoria A, Broadway, Lauren N, Budwal, Keshav, Busby, Kayla N, Campbell, Ian W, Campbell, Anne M, Carey, Alyssa, Caruso, Steven M, Chew, Rebekah D, Cockburn, Chelsea L, Cohen, Lianne B, Corajod, Jeffrey M, Cresawn, Steven G, Davis, Kimberly R, Deng, Lisa, Denver, Dee R, Dixon, Breyon R, Ekram, Sahrish, Elgin, Sarah CR, Engelsen, Angela E, English, Belle EV, Erb, Marcella L, Estrada, Crystal, Filliger, Laura Z, Findley, Ann M, Forbes, Lauren, Forsyth, Mark H, Fox, Tyler M, Fritz, Melissa J, Garcia, Roberto, George, Zindzi D, Georges, Anne E, Gissendanner, Christopher R, Goff, Shannon, Goldstein, Rebecca, Gordon, Kobie C, Green, Russell D, Guerra, Stephanie L, Guiney-Olsen, Krysta R, Guiza, Bridget G, Haghighat, Leila, Hagopian, Garrett V, Harmon, Catherine J, Harmson, Jeremy S, Hartzog, Grant A, Harvey, Samuel E, He, Siping, He, Kevin J, Healy, Kaitlin E, Higinbotham, Ellen R, Hildebrandt, Erin N, Ho, Jason H, Hogan, Gina M, Hohenstein, Victoria G, Holz, Nathan A, Huang, Vincent J, Hufford, Ericka L, Hynes, Peter M, Jackson, Arrykka S, Jansen, Erica C, Jarvik, Jonathan, Jasinto, Paul G, Jordan, Tuajuanda C, Kasza, Tomas, Katelyn, Murray A, Kelsey, Jessica S, Kerrigan, Larisa A, Khaw, Daryl, Kim, Junghee, Knutter, Justin Z, Ko, Ching-Chung, Larkin, Gail V, Laroche, Jennifer R, Latif, Asma, Pope, Welkin H, Pope, Welkin H, Jacobs-Sera, Deborah, Russell, Daniel A, Peebles, Craig L, Al-Atrache, Zein, Alcoser, Turi A, Alexander, Lisa M, Alfano, Matthew B, Alford, Samantha T, Amy, Nichols E, Anderson, Marie D, Anderson, Alexander G, Ang, Andrew AS, Ares, Manuel, Barber, Amanda J, Barker, Lucia P, Barrett, Jonathan M, Barshop, William D, Bauerle, Cynthia M, Bayles, Ian M, Belfield, Katherine L, Best, Aaron A, Borjon, Agustin, Bowman, Charles A, Boyer, Christine A, Bradley, Kevin W, Bradley, Victoria A, Broadway, Lauren N, Budwal, Keshav, Busby, Kayla N, Campbell, Ian W, Campbell, Anne M, Carey, Alyssa, Caruso, Steven M, Chew, Rebekah D, Cockburn, Chelsea L, Cohen, Lianne B, Corajod, Jeffrey M, Cresawn, Steven G, Davis, Kimberly R, Deng, Lisa, Denver, Dee R, Dixon, Breyon R, Ekram, Sahrish, Elgin, Sarah CR, Engelsen, Angela E, English, Belle EV, Erb, Marcella L, Estrada, Crystal, Filliger, Laura Z, Findley, Ann M, Forbes, Lauren, Forsyth, Mark H, Fox, Tyler M, Fritz, Melissa J, Garcia, Roberto, George, Zindzi D, Georges, Anne E, Gissendanner, Christopher R, Goff, Shannon, Goldstein, Rebecca, Gordon, Kobie C, Green, Russell D, Guerra, Stephanie L, Guiney-Olsen, Krysta R, Guiza, Bridget G, Haghighat, Leila, Hagopian, Garrett V, Harmon, Catherine J, Harmson, Jeremy S, Hartzog, Grant A, Harvey, Samuel E, He, Siping, He, Kevin J, Healy, Kaitlin E, Higinbotham, Ellen R, Hildebrandt, Erin N, Ho, Jason H, Hogan, Gina M, Hohenstein, Victoria G, Holz, Nathan A, Huang, Vincent J, Hufford, Ericka L, Hynes, Peter M, Jackson, Arrykka S, Jansen, Erica C, Jarvik, Jonathan, Jasinto, Paul G, Jordan, Tuajuanda C, Kasza, Tomas, Katelyn, Murray A, Kelsey, Jessica S, Kerrigan, Larisa A, Khaw, Daryl, Kim, Junghee, Knutter, Justin Z, Ko, Ching-Chung, Larkin, Gail V, Laroche, Jennifer R, and Latif, Asma

Abstract

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists.

Published
2011

50. Expanding the diversity of mycobacteriophages: insights into genome architecture and evolution.

Author

Pope, Welkin H, Aziz, Ramy1, Pope, Welkin H, Jacobs-Sera, Deborah, Russell, Daniel A, Peebles, Craig L, Al-Atrache, Zein, Alcoser, Turi A, Alexander, Lisa M, Alfano, Matthew B, Alford, Samantha T, Amy, Nichols E, Anderson, Marie D, Anderson, Alexander G, Ang, Andrew AS, Ares, Manuel, Barber, Amanda J, Barker, Lucia P, Barrett, Jonathan M, Barshop, William D, Bauerle, Cynthia M, Bayles, Ian M, Belfield, Katherine L, Best, Aaron A, Borjon, Agustin, Bowman, Charles A, Boyer, Christine A, Bradley, Kevin W, Bradley, Victoria A, Broadway, Lauren N, Budwal, Keshav, Busby, Kayla N, Campbell, Ian W, Campbell, Anne M, Carey, Alyssa, Caruso, Steven M, Chew, Rebekah D, Cockburn, Chelsea L, Cohen, Lianne B, Corajod, Jeffrey M, Cresawn, Steven G, Davis, Kimberly R, Deng, Lisa, Denver, Dee R, Dixon, Breyon R, Ekram, Sahrish, Elgin, Sarah CR, Engelsen, Angela E, English, Belle EV, Erb, Marcella L, Estrada, Crystal, Filliger, Laura Z, Findley, Ann M, Forbes, Lauren, Forsyth, Mark H, Fox, Tyler M, Fritz, Melissa J, Garcia, Roberto, George, Zindzi D, Georges, Anne E, Gissendanner, Christopher R, Goff, Shannon, Goldstein, Rebecca, Gordon, Kobie C, Green, Russell D, Guerra, Stephanie L, Guiney-Olsen, Krysta R, Guiza, Bridget G, Haghighat, Leila, Hagopian, Garrett V, Harmon, Catherine J, Harmson, Jeremy S, Hartzog, Grant A, Harvey, Samuel E, He, Siping, He, Kevin J, Healy, Kaitlin E, Higinbotham, Ellen R, Hildebrandt, Erin N, Ho, Jason H, Hogan, Gina M, Hohenstein, Victoria G, Holz, Nathan A, Huang, Vincent J, Hufford, Ericka L, Hynes, Peter M, Jackson, Arrykka S, Jansen, Erica C, Jarvik, Jonathan, Jasinto, Paul G, Jordan, Tuajuanda C, Kasza, Tomas, Katelyn, Murray A, Kelsey, Jessica S, Kerrigan, Larisa A, Khaw, Daryl, Kim, Junghee, Knutter, Justin Z, Ko, Ching-Chung, Larkin, Gail V, Laroche, Jennifer R, Latif, Asma, Pope, Welkin H, Aziz, Ramy1, Pope, Welkin H, Jacobs-Sera, Deborah, Russell, Daniel A, Peebles, Craig L, Al-Atrache, Zein, Alcoser, Turi A, Alexander, Lisa M, Alfano, Matthew B, Alford, Samantha T, Amy, Nichols E, Anderson, Marie D, Anderson, Alexander G, Ang, Andrew AS, Ares, Manuel, Barber, Amanda J, Barker, Lucia P, Barrett, Jonathan M, Barshop, William D, Bauerle, Cynthia M, Bayles, Ian M, Belfield, Katherine L, Best, Aaron A, Borjon, Agustin, Bowman, Charles A, Boyer, Christine A, Bradley, Kevin W, Bradley, Victoria A, Broadway, Lauren N, Budwal, Keshav, Busby, Kayla N, Campbell, Ian W, Campbell, Anne M, Carey, Alyssa, Caruso, Steven M, Chew, Rebekah D, Cockburn, Chelsea L, Cohen, Lianne B, Corajod, Jeffrey M, Cresawn, Steven G, Davis, Kimberly R, Deng, Lisa, Denver, Dee R, Dixon, Breyon R, Ekram, Sahrish, Elgin, Sarah CR, Engelsen, Angela E, English, Belle EV, Erb, Marcella L, Estrada, Crystal, Filliger, Laura Z, Findley, Ann M, Forbes, Lauren, Forsyth, Mark H, Fox, Tyler M, Fritz, Melissa J, Garcia, Roberto, George, Zindzi D, Georges, Anne E, Gissendanner, Christopher R, Goff, Shannon, Goldstein, Rebecca, Gordon, Kobie C, Green, Russell D, Guerra, Stephanie L, Guiney-Olsen, Krysta R, Guiza, Bridget G, Haghighat, Leila, Hagopian, Garrett V, Harmon, Catherine J, Harmson, Jeremy S, Hartzog, Grant A, Harvey, Samuel E, He, Siping, He, Kevin J, Healy, Kaitlin E, Higinbotham, Ellen R, Hildebrandt, Erin N, Ho, Jason H, Hogan, Gina M, Hohenstein, Victoria G, Holz, Nathan A, Huang, Vincent J, Hufford, Ericka L, Hynes, Peter M, Jackson, Arrykka S, Jansen, Erica C, Jarvik, Jonathan, Jasinto, Paul G, Jordan, Tuajuanda C, Kasza, Tomas, Katelyn, Murray A, Kelsey, Jessica S, Kerrigan, Larisa A, Khaw, Daryl, Kim, Junghee, Knutter, Justin Z, Ko, Ching-Chung, Larkin, Gail V, Laroche, Jennifer R, and Latif, Asma

Abstract

Mycobacteriophages are viruses that infect mycobacterial hosts such as Mycobacterium smegmatis and Mycobacterium tuberculosis. All mycobacteriophages characterized to date are dsDNA tailed phages, and have either siphoviral or myoviral morphotypes. However, their genetic diversity is considerable, and although sixty-two genomes have been sequenced and comparatively analyzed, these likely represent only a small portion of the diversity of the mycobacteriophage population at large. Here we report the isolation, sequencing and comparative genomic analysis of 18 new mycobacteriophages isolated from geographically distinct locations within the United States. Although no clear correlation between location and genome type can be discerned, these genomes expand our knowledge of mycobacteriophage diversity and enhance our understanding of the roles of mobile elements in viral evolution. Expansion of the number of mycobacteriophages grouped within Cluster A provides insights into the basis of immune specificity in these temperate phages, and we also describe a novel example of apparent immunity theft. The isolation and genomic analysis of bacteriophages by freshman college students provides an example of an authentic research experience for novice scientists.

Published
2011

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