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  1. Debris-covered glaciers (DCGs) are globally distributed and thought to contain greater microbial diversity than clean surface continental glaciers, but the ecological characteristics of microbial communities on the surface of DCGs have remained underexplored. Here, we investigated bacterial and fungal diversity and co-occurrence networks on the supraglacial debris habitat of two DCGs (Hailuogou and Dagongba Glaciers) in the southeastern Tibetan Plateau. We found that the supraglacial debris harbored abundant microbes with Proteobacteria occupying more than half (51.5%) of the total bacteria operational taxonomic units. The composition, diversity, and co-occurrence networks of both bacterial and fungal communities in the debris were significantly different between Hailuogou Glacier and Dagongba Glacier even though the glaciers are geographically adjacent within the same mountain range. Bacteria were more diverse in the debris of the Dagongba Glacier, where a lower surface velocity and thicker debris layer allowed the supraglacial debris to continuously weather and accumulate nutrients. Fungi were more diverse in the debris of the Hailuogou Glacier, which experiences a wetter monsoonal climate, is richer in calcium, has greater debris instability, and greater ice velocity than the Dagongba Glacier. These factors may provide ideal conditions for the dispersal and propagation of fungi spores on the Hailuogou Glacier. In addition, we found an obvious gradient of bacterial diversity along the supraglacial debris transect on the Hailuogou Glacier. Bacterial diversity was lower where debris cover was thin and scattered and became more diverse near the glacial terminus in thick, slow-moving debris. No such increasing bacterial pattern was detected on the Dagongba Glacier, which implies a positive relationship of debris age, thickness, and weathering on bacterial diversity. Additionally, a highly connected bacterial co-occurrence network with low modularity was found in the debris of the Hailuogou Glacier. In contrast, debris from the Dagongba Glacier exhibited less connected but more modularized co-occurrence networks of both bacterial and fungal communities. These findings indicate that less disturbed supraglacial debris conditions are crucial for microbes to form stable communities on DCGs. 
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    Free, publicly-accessible full text available August 1, 2024
  2. Free, publicly-accessible full text available June 27, 2024
  3. Abstract Background

    Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics.


    Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP.


    The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians.


    Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.

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