Current Organizations / Institutes

The organizations/institutes from the scholar's latest article

Previous Organizations / Institutes

The organizations / institutes from the scholar's other articles in the past ten years


Ann V. Basting has a total of 27 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Global-, Regional-, and National-Level Impacts of the COVID-19 Pandemic on Tuberculosis Diagnoses, 2020–2021

    by Jorge R. Ledesma, Ann Basting, Huong T. Chu, Jianing Ma, Meixin Zhang, Avina Vongpradith, Amanda Novotney, Jeremy Dalos, Peng Zheng, Christopher J. L. Murray, et al.

    Evaluating cross-country variability on the impact of the COVID-19 pandemic on tuberculosis (TB) may provide urgent inputs to control programs as countries recover from the pandemic. We compared expected TB notifications, modeled using trends in annual TB notifications from 2013–2019, with observed TB notifications to compute the observed to expected (OE) ratios for 170 countries. We applied the least absolute shrinkage and selection operator (LASSO) method to identify the covariates, out of 27 pandemic- and tuberculosis-relevant variables, that had the strongest explanatory power for log OE ratios. The COVID-19 pandemic was associated with a 1.55 million (95% CI: 1.26–1.85, 21.0% [17.5–24.6%]) decrease in TB diagnoses in 2020 and a 1.28 million (0.90–1.76, 16.6% [12.1–21.2%]) decrease in 2021 at a global level. India, Indonesia, the Philippines, and China contributed the most to the global declines for both years, while sub-Saharan Africa achieved pre-pandemic levels by 2021 (OE ratio = 1.02 [0.99–1.05]). Age-stratified analyses revealed that the ≥ 65-year-old age group experienced greater relative declines in TB diagnoses compared with the under 65-year-old age group in 2020 (RR = 0.88 [0.81–0.96]) and 2021 (RR = 0.88 [0.79–0.98]) globally. Covariates found to be associated with all-age OE ratios in 2020 were age-standardized smoking prevalence in 2019 (β = 0.973 [0.957–990]), school closures (β = 0.988 [0.977–0.998]), stay-at-home orders (β = 0.993 [0.985–1.00]), SARS-CoV-2 infection rate (β = 0.991 [0.987–0.996]), and proportion of population ≥65 years (β = 0.971 [0.944–0.999]). Further research is needed to clarify the extent to which the observed declines in TB diagnoses were attributable to disruptions in health services, decreases in TB transmission, and COVID-19 mortality among TB patients.


  • Top Cited Papers

    A high-resolution protein architecture of the budding yeast genome

    by Matthew J. Rossi, Prashant K. Kuntala, William K. M. Lai, Naomi Yamada, Nitika Badjatia, Chitvan Mittal, Guray Kuzu, Kylie Bocklund, Nina P. Farrell, Thomas R. Blanda, et al.

    The genome-wide architecture of chromatin-associated proteins that maintains chromosome integrity and gene regulation is not well defined. Here we use chromatin immunoprecipitation, exonuclease digestion and DNA sequencing (ChIP–exo/seq)1,2 to define this architecture in Saccharomyces cerevisiae. We identify 21 meta-assemblages consisting of roughly 400 different proteins that are related to DNA replication, centromeres, subtelomeres, transposons and transcription by RNA polymerase (Pol) I, II and III. Replication proteins engulf a nucleosome, centromeres lack a nucleosome, and repressive proteins encompass three nucleosomes at subtelomeric X-elements. We find that most promoters associated with Pol II evolved to lack a regulatory region, having only a core promoter. These constitutive promoters comprise a short nucleosome-free region (NFR) adjacent to a +1 nucleosome, which together bind the transcription-initiation factor TFIID to form a preinitiation complex. Positioned insulators protect core promoters from upstream events. A small fraction of promoters evolved an architecture for inducibility, whereby sequence-specific transcription factors (ssTFs) create a nucleosome-depleted region (NDR) that is distinct from an NFR. We describe structural interactions among ssTFs, their cognate cofactors and the genome. These interactions include the nucleosomal and transcriptional regulators RPD3-L, SAGA, NuA4, Tup1, Mediator and SWI–SNF. Surprisingly, we do not detect interactions between ssTFs and TFIID, suggesting that such interactions do not stably occur. Our model for gene induction involves ssTFs, cofactors and general factors such as TBP and TFIIB, but not TFIID. By contrast, constitutive transcription involves TFIID but not ssTFs engaged with their cofactors. From this, we define a highly integrated network of gene regulation by ssTFs.