Genetic inter-relationships among Chinese wild grapes based on SRAP marker analyses

Sequence-Related Amplified Polymorphism (SRAP) markers were used to assess genetic inter-relationships among 39 grape genotypes. These included 22 indigenous Chinese grape species/varieties, the north American V. riparia and the European V. vinifera L. 'Thompson seedless' and 'Pinot noir'. Of the 72 SRAP primer combinations tested, 25 primers generated 135 reliable bands, with an average of 5.52 bands per primer pair. Further analysis shows that 106 of 135 bands were generated by 25 polymorphic primer pairs, with a polymorphism efficiency of 79 %. The similarity coefficients of SRAP polymorphism varied from 0.463 to 0.981 among the genotypes analysed. A dendrogram analysis divided the 39 Vitis accessions into 21 groups with similarity coefficients of 0.83. It reveals broadly similar genetic relationships among the genotypes examined to those previously determined using classical taxonomic methods. Our results define V. heyneana subsp. ficifolia and V. baihensis as subspecies of V. heyneana and V. bashanica, respectively. We question the placement of V. davidii var. cyanocarpa and V. davidii var. ningqiangensis as varieties in V. davidii

COVID-19 in Austin, Texas: Epidemiological Assessment of Grocery Shopping

There are an estimated 24,000 grocery store workers in the Austin-Round Rock metropolitan area (MSA) representing 2% of the labor force [1]. The Austin Stay Home - Work Safe order that was issued on March 24, 2020 and extended on April 13, 2020 restricts non-essential work, but permits work in grocery stores and public grocery shopping [2,3]. Daily interactions between grocery workers and the general population may undermine efforts to reduce person-to-person contact, and exacerbate the individual and city-wide risks associated with COVID-19 transmission. In response to a request from the city of Austin, we projected the epidemiological impacts of grocery work under different assumptions regarding the effectiveness of precautionary measures taken by workers and shoppers in grocery stores. To do so, we modified the Austin-Round Rock module of our US COVID-19 Pandemic Model to explicitly include a population subgroup representing grocery workers and contacts that occur between members of the general public and grocery workers in stores. As a base case scenario, we assumed that grocery workers would maintain typical workforce contact rates, estimated as twice the average workplace contacts for 18-49 year olds in the general population. Our analysis suggests that grocery shopping can considerably increase the community-wide risk of COVID-19 and that both shoppers and workers can and should do their part to protect themselves and others from transmission in stores. Furthermore, the risk of COVID-19 hospitalizations within the population of grocery workers is expected to be much higher than that in the non-working 18-49 year old population.Integrative Biolog

COVID-19 in Austin, Texas: Relaxing Social Distancing Measures

To support planning by the city of Austin and Travis County, we analyzed the Austin-Round Rock module of our US COVID-19 Pandemic Model to project the number of hospitalizations under different scenarios for relaxing social distancing measures following the March 24th Stay Home-Work Safe order. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of social distancing measures in the Austin-Round Rock Metropolitan Area. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in the Austin-Round Rock MSA between March 13 and April 19, 2020. The data suggest that social distancing following the March 24th Stay Home-Work Safe order has resulted in a 94% reduction in COVID-19 transmission, with our uncertainty in this estimate ranging from 70% and 100%. The data also suggest that approximately 13.6% of symptomatic cases are detected (i.e., reported as confirmed cases). We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the threat of COVID-19 and the extent to which social distancing measures can mitigate that threat. Our projections indicate that the Stay Home-Work Safe has likely prevented a COVID-19 healthcare crisis in the region during the first wave of the pandemic. When current measures are relaxed, we may see more COVID-19 transmission in the area leading to a second pandemic wave. Whether or not and how quickly COVID-19 cases and hospitalizations rise in the second wave will critically depend on the extent to which individuals and communities continue to take steps to reduce the risks of transmission.Integrative Biolog

Online Clustering of Bandits with Misspecified User Models

The contextual linear bandit is an important online learning problem where given arm features, a learning agent selects an arm at each round to maximize the cumulative rewards in the long run. A line of works, called the clustering of bandits (CB), utilize the collaborative effect over user preferences and have shown significant improvements over classic linear bandit algorithms. However, existing CB algorithms require well-specified linear user models and can fail when this critical assumption does not hold. Whether robust CB algorithms can be designed for more practical scenarios with misspecified user models remains an open problem. In this paper, we are the first to present the important problem of clustering of bandits with misspecified user models (CBMUM), where the expected rewards in user models can be perturbed away from perfect linear models. We devise two robust CB algorithms, RCLUMB and RSCLUMB (representing the learned clustering structure with dynamic graph and sets, respectively), that can accommodate the inaccurate user preference estimations and erroneous clustering caused by model misspecifications. We prove regret upper bounds of $O(\epsilon_*T\sqrt{md\log T} + d\sqrt{mT}\log T)$ for our algorithms under milder assumptions than previous CB works (notably, we move past a restrictive technical assumption on the distribution of the arms), which match the lower bound asymptotically in $T$ up to logarithmic factors, and also match the state-of-the-art results in several degenerate cases. The techniques in proving the regret caused by misclustering users are quite general and may be of independent interest. Experiments on both synthetic and real-world data show our outperformance over previous algorithms

COVID-19 Healthcare Demand Projections: Austin, Texas

To support planning by the city of Austin and Travis County, we analyzed the Austin-Round Rock module of our US COVID-19 Pandemic Model to project the number of hospitalizations under different social distancing scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of social distancing measures in the Austin-Round Rock Metropolitan Area. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in the Austin-Round Rock MSA between March 13 and April 19, 2020. The data suggest that social distancing following the March 24th Stay Home-Work Safe order has resulted in a 94% reduction in COVID-19 transmission, with our uncertainty in this estimate ranging from 55% and 100%. The data also suggest that approximately 13.6% of symptomatic cases are detected (i.e., reported as confirmed cases). We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which early social distancing measures are mitigating that threat. Our projections indicate that the Stay Home-Work Safe has delayed and possibly even prevented a COVID-19 healthcare crisis in the region.Integrative Biolog

COVID-19 Healthcare Demand Projections: 22 Texas Cities

To support planning by cities across Texas, we analyzed all 22 Texas city modules of our US COVID-19 Pandemic Model​ to project the number of hospitalizations under four different social distancing scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. We update our model inputs on a daily basis, as our understanding of the virus improves. Appendix 1 below provides our current estimates. These results are not forecasts and do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of social distancing measures in Texas cities. We are sharing these results prior to peer review to provide intuition for policy makers regarding the immediate threat of COVID-19, the risks of medical surges, and the extent to which early social distancing measures can mitigate the threat. Our projections indicate that COVID-19 may quickly exceed healthcare capacity across Texas cities and that extensive social distancing measures can both delay and diminish pandemic surges.Integrative Biolog

The effectiveness of COVID-19 testing and contact tracing in a US city

Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries world-wide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.This research was supported by NIH grant R01 AI151176 (to X.W., Z.D., S.J.F., and L.A.M.), CDC grant U01 IP001136 (to X.W.,Z.D., S.J.F., and L.A.M.), and a donation from Love, Tito’s (the philanthropic arm of Tito’s Homemade Vodka, Austin, TX) to the University of Texas to support the modeling of COVID-19 mitigation strategies (to X.W., Z.D., M.L., L.A.M., and D.B.). D.B.’s effort on this project was also supported by core funds of the Dell Medical School at UT.Dell Medical SchoolIntegrative Biolog

COVID-19 Healthcare Demand Projections: Austin, Texas

To support planning by the city of Austin, we analyzed the Austin-Round Rock module of our US COVID-19 Pandemic Model​ to project the number of hospitalizations under four different social distancing scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. We update our model inputs on a daily basis, as our understanding of the virus improves. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of social distancing measures in the Austin-Round Rock Metropolitan Area. We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which early social distancing measures can mitigate that threat. Our projections indicate that without extensive social distancing measures, the emerging outbreak will quickly surpass healthcare capacity in the region. Although these analyses are specific to the Austin-Round Rock metropolitan area, we expect that the impacts of the mitigation strategies will be qualitatively similar for cities throughout the US.Integrative Biolog

On-Demand Communication for Asynchronous Multi-Agent Bandits

This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where agents operate asynchronously -- agent pull times and rates are unknown, irregular, and heterogeneous -- and face the same instance of a K-armed bandit problem. Agents can share reward information to speed up the learning process at additional communication costs. We propose ODC, an on-demand communication protocol that tailors the communication of each pair of agents based on their empirical pull times. ODC is efficient when the pull times of agents are highly heterogeneous, and its communication complexity depends on the empirical pull times of agents. ODC is a generic protocol that can be integrated into most cooperative bandit algorithms without degrading their performance. We then incorporate ODC into the natural extensions of UCB and AAE algorithms and propose two communication-efficient cooperative algorithms. Our analysis shows that both algorithms are near-optimal in regret.Comment: Accepted by AISTATS 202