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Over a dozen studies have examined how households who travel to collect water (about one-quarter of humanity) make choices about where and how much to collect. There is little evidence, however, that these studies have informed rural water supply planning in anything but a qualitative way. In this paper, we describe a new web-based decision support tool that planners or community members can use to simulate scenarios such as (1) price, quality, or placement changes of existing sources, (2) the closure of an existing source, or (3) the addition of a new source. We describe the analytical structure of the model and then demonstrate its possibilities using data from a recent study in rural Meru County, Kenya. We discuss some limits of the current model, and encourage readers and practitioners to explore it and suggest ways in which it could be improved or used most effectively.

To expand and maintain water supply infrastructure in rural regions of developing countries, planners and policymakers need better information on the preferences of households who might use the sources. Using data from 387 households in rural Kenya, we model source choice and water demand using a discrete-continuous (linked) demand model. We find that households are sensitive to the price, proximity, taste, and availability in choosing among sources, but are not sensitive to other source qualities including color, health risk, and risk of conflict. Estimates of the value of time implied by our model suggest that households value time spent collecting water at one third of unskilled wages. We use the linked demand framework to estimate own-price elasticities in the rural setting. These estimates range between − 0.13 and − 1.33, with a mean of − 0.56, and are consistent with other elasticity estimates from small and large cities.

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

Pulmonary hypertension (PH) is a progressive disease that causes high patient mortality. With limited hemodynamic reserve, many PH patients require maintenance IV infusion medications to maintain their activities of daily living. One common delivery method for this targeted therapy is through a Remodulin® (treprostinil) pump. When presenting for emergent evaluation, decompensating PH patients have a broad differential diagnosis including pump failure. PH patients are at a high risk of poor patient outcomes given the difficulty in recognizing PH-specific symptoms and unique aspects of their management. Therefore, learners will benefit from participating in an immersive simulation-based PH patient scenario in a safe learning environment. Here, we present a simulated scenario of a decompensating PH patient on a Remodulin® pump.

The implementation of targeted therapies for acute myeloid leukaemia (AML) has been challenging because of the complex mutational patterns within and across patients as well as a dearth of pharmacologic agents for most mutational events. Here we report initial findings from the Beat AML programme on a cohort of 672 tumour specimens collected from 562 patients. We assessed these specimens using whole-exome sequencing, RNA sequencing and analyses of ex vivo drug sensitivity. Our data reveal mutational events that have not previously been detected in AML. We show that the response to drugs is associated with mutational status, including instances of drug sensitivity that are specific to combinatorial mutational events. Integration with RNA sequencing also revealed gene expression signatures, which predict a role for specific gene networks in the drug response. Collectively, we have generated a dataset—accessible through the Beat AML data viewer (Vizome)—that can be leveraged to address clinical, genomic, transcriptomic and functional analyses of the biology of AML. Analyses of samples from patients with acute myeloid leukaemia reveal that drug response is associated with mutational status and gene expression; the generated dataset provides a basis for future clinical and functional studies of this disease.

ImmuneSpace is a data repository and analysis platform of the Human Immunology Project Consortium (HIPC), a multi-center collaborative effort to characterize the status of the immune system in different populations under diverse stimulations and disease states. This ongoing effort has generated large amounts of varied high-throughput, high-dimensional immunological data. ImmuneSpace directly extracts data from ImmPort -- the central NIAID database, and further standardizes it for meta-analysis and reporting, making it ready to be explored using state-of-art visualization and analysis tools. ImmuneSpace will act as a central immunological hub, allowing experimentalists, statisticians, and bioinformaticians to freely retrieve, explore and compare data across assays and across studies generated within and outside of HIPC. We present a selection of multi-cohort analyses using standardized gene expression and flow cytometry data sets across multiple studies identifying novel baseline predictors of vaccine efficacy across a variety of pathogens.

Introduction: The intratumoral heterogeneity of Acute Myeloid Leukemia (AML) and other hematologic malignancies presents a challenge in developing effective single-agent targeted treatments. Furthermore, the emergence of genetically heterogeneous subclones leading to relapse suggests that effective therapies associated with discrete genotypes may require drug combinations, each of which modulates distinct pathways. In addition, microenvironmental rescue signals as well as tumor-intrinsic feedback pathways in AML and other hematologic malignancy subsets will necessitate combinatorial therapy approaches. Towards the goal of identifying new therapeutic combinations for AML and other hematologic malignancies, we assessed the sensitivity of primary patient samples to various drug combinations using an ex vivo functional platform. Methods: We have previously screened over 1000 primary patient specimens against a panel of single-agent small-molecule inhibitors. Using these historical drug sensitivity data, we ranked drugs by their IC50, and used these rankings to assemble an initial panel (1) of 44 drug combinations consisting primarily of kinase inhibitors with non-overlapping pathways. Primary patient samples (n = 74) with various hematologic malignancies were assessed for sensitivities to these combinations by culturing cells in the presence of fixed molar concentrations of the drugs over a dose series. Sensitivity was assessed by a viability assay on day 3 using a tetrazolium reagent. IC50 values for samples sensitive to a combination were sorted according to disease type and compared to those for each single agent to derive an index of effectiveness. Based on data from panel 1, we generated a second panel (2) consisting of 44 drug combinations, including new combinations of kinase inhibitors as well as combinations of drugs from different classes, such as bromodomain inhibitors, BH3 mimetics, proteasome inhibitors, IDH1/2 inhibitors coupled with kinase inhibitors. Primary patient samples (n = 78) were assessed for sensitivities to these combinations. Results: The performance of drug combinations across AML, ALL, CLL, CML or other MDS/MPN specimens are displayed in a heat map (Figure 1) representing the sensitivities of each drug combination relative to either of the single agents comprising that combination (the combination IC50 divided by the lowest single agent IC50 is our combination ratio). For each combination, we then compared the combination ratio of each individual specimen to the median combination ratio across all specimens tested, and cases with a combination ratio value less than 20% of the median were considered hypersensitive to that combination. We calculated the percentage of cases that were sensitive to each combination within the diagnostic subsets of AML, ALL, CLL, CML, and MDS/MPN and subsets with the most frequent sensitivity to a drug combination are indicated on the heat map (80%, white). Combinations of two kinase inhibitors that included the p38MAPK inhibitor, doramapimod, were generally more effective on AML and CLL samples than other diagnostic subsets (panel 1). For CLL sample, combinations including midostaurin and either alisertib, ruxolitinib or sorafenib were particularly effective. Among combinations on panel 2, doramapimod coupled with an apoptosis inducer (ABT-199) exhibited broad efficacy on AML samples. In addition, combinations with the bromodomain inhibitor, JQ1, or the BH3 mimetic, ABT-199, were more broadly effective across diagnostic subsets than many of the kinase-kinase pairs tested. To validate the apparent synergies observed with patient samples, we tested selected combinations on AML-derived cell lines and observed synergies, which were supported with combination indices derived by the Chou-Talalay method. Conclusions: These data suggest that specific drug combinations formed either with two kinase inhibitors or with two compounds from different drug classes are effective in a patient-specific manner with enrichment for certain drug pairs within specific diagnostic subsets. While a secondary evaluation is necessary to validate the initial observation of sensitivity, linking this methodology with genetic attributes for patient samples will identify effective combinations of targeted agents and add therapeutic options for AML treatment. Figure 1. Figure 1. Disclosures Pandya: Microsoft: Employment, Equity Ownership. Bolosky:Microsoft: Employment, Equity Ownership. Druker:Oregon Health & Science University: Patents & Royalties; Henry Stewart Talks: Patents & Royalties; CTI Biosciences: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Novartis Pharmaceuticals: Research Funding; Aptose Therapeutics, Inc (formerly Lorus): Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Roche TCRC, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Millipore: Patents & Royalties; AstraZeneca: Consultancy; Oncotide Pharmaceuticals: Research Funding; Cylene Pharmaceuticals: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; ARIAD: Research Funding; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sage Bionetworks: Research Funding. Tyner:Incyte: Research Funding; Janssen Pharmaceuticals: Research Funding; Constellation Pharmaceuticals: Research Funding; Array Biopharma: Research Funding; Aptose Biosciences: Research Funding.