This article presents a framework to efficiently manage a sizable fleet of diverse distributed energy resources (DERs) operating within distribution systems to optimize the operations of local energy communities (LECs) and improve grid services. First, we develop aggregation modeling for DERs to evaluate cumulative flexibility that considers their preferences, spatial placement, and temporal behavior. Subsequently, we employ a hierarchical control framework (HCF) to put these aggregated flexibility of DERs for their effective dispatching. The HCF involves three key entities: an electric utility (EU) operator, community aggregators (CAs), and individual DERs. CAs harness the flexibility obtained from the aggregated DERs within their respective LECs to minimize operational costs while also considering the distribution network constraints. On the other hand, the EU operator coordinates dispatch setpoints received from CAs along with the disaggregated DERs to regulate distribution system node voltages and reduce power losses to enhance grid services. Numerical simulations conducted on a modified IEEE-123 bus radial distribution network demonstrate the efficacy of our approach in effectively managing DERs for cost-efficient operations and improving grid services.

Load flow analysis seeks the initial conditions for power system networks and is crucial for planning, operation, and control. The classical methods of load flow analysis mostly fail to analyze radial distribution networks because of the wide range of network parameters and many more. The present article evaluates and compares the computational efficiency of three load flow models that best suits the radial distribution network. In the second stage, a novel approach is proposed for the optimum placement of capacitors, based on the Data Envelopment Analysis. A common solution methodology is currently unavailable to find the optimum location for capacitor placement. For example, the loss sensitivity index only locates the node for power loss reduction, or the voltage sensitivity index only discerns the node for voltage profile improvement. The novel approach identifies the potential node for capacitors placement, whether for power loss reduction, voltage profile improvement, or other objectives. The capacitors sizes are evaluated using the curve fitting technique. The proposed approach is easy to implement and time-efficient. The technical and economic analysis is carried out on the IEEE-33 bus to show the efficacy of the proposed approach.

MOTIVATION: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples. RESULTS: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of ∼150 × coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples. AVAILABILITY AND IMPLEMENTATION: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OBJECTIVE: Currently, there is a disconnect between finding a patient's relevant molecular profile and predicting actionable therapeutics. Here we develop and implement the Integrating Molecular Profiles with Actionable Therapeutics (IMPACT) analysis pipeline, linking variants detected from whole-exome sequencing (WES) to actionable therapeutics. METHODS AND MATERIALS: The IMPACT pipeline contains 4 analytical modules: detecting somatic variants, calling copy number alterations, predicting drugs against deleterious variants, and analyzing tumor heterogeneity. We tested the IMPACT pipeline on whole-exome sequencing data in The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples with known EGFR mutations. We also used IMPACT to analyze melanoma patient tumor samples before treatment, after BRAF-inhibitor treatment, and after BRAF- and MEK-inhibitor treatment. RESULTS: IMPACT Food and Drug Administration (FDA) correctly identified known EGFR mutations in the TCGA lung adenocarcinoma samples. IMPACT linked these EGFR mutations to the appropriate FDA-approved EGFR inhibitors. For the melanoma patient samples, we identified NRAS p.Q61K as an acquired resistance mutation to BRAF-inhibitor treatment. We also identified CDKN2A deletion as a novel acquired resistance mutation to BRAFi/MEKi inhibition. The IMPACT analysis pipeline predicts these somatic variants to actionable therapeutics. We observed the clonal dynamic in the tumor samples after various treatments. We showed that IMPACT not only helped in successful prioritization of clinically relevant variants but also linked these variations to possible targeted therapies. CONCLUSION: IMPACT provides a new bioinformatics strategy to delineate candidate somatic variants and actionable therapies. This approach can be applied to other patient tumor samples to discover effective drug targets for personalized medicine.IMPACT is publicly available at http://tanlab.ucdenver.edu/IMPACT.

Solid tumor samples typically contain multiple distinct clonal populations of cancer cells, and also stromal and immune cell contamination. A majority of the cancer genomics and transcriptomics studies do not explicitly consider genetic heterogeneity and impurity, and draw inferences based on mixed populations of cells. Deconvolution of genomic data from heterogeneous samples provides a powerful tool to address this limitation. We discuss several computational tools, which enable deconvolution of genomic and transcriptomic data from heterogeneous samples. We also performed a systematic comparative assessment of these tools. If properly used, these tools have potentials to complement single-cell genomics and immunoFISH analyses, and provide novel insights into tumor heterogeneity.