Sharmi Banerjee - Academia.edu (original) (raw)

Uploads

Papers by Sharmi Banerjee

In the production of pharmaceutical tablets and capsules, a lubricant such as magnesium stearate ... more In the production of pharmaceutical tablets and capsules, a lubricant such as magnesium stearate is often added to the powder blend or granulation in order to reduce friction and, in some cases, minimize sticking to the manufacturing equipment surfaces. If the lubricated blend is exposed to excessive shear during the processing steps prior to tableting or encapsulation, adverse effects on quality attributes of the final dosage form may be observed. These could include an increase in wetting contact angle, a slowdown in disintegration and/or dissolution, and a reduction in the tensile strength of compacts. In this work, two different computational modeling approaches are proposed to predict the effect of shear during the processing of lubricated pharmaceutical blends on drug product quality attributes. In this case study, an agitated powder feed system is examined, but the approach could be readily extended to other powder processing operations. First, a framework for the prediction of lubrication-based tensile strength reduction using the discrete element method (DEM) is proposed. This approach utilizes a companion study in a lab-scale, high-shear mixer to map the DEM predictions of extent of shear to an experimentally relevant tensile strength prediction. Second, a compartment model (CM) approach is proposed to model the powder flow and lubrication in the feed system. The tensile strength predictions from these two different approaches both compare favorably to experimental measurements of tensile strength.

ROC curve of the classification of patients in METABRIC validation data.</p

Means and standard deviations of accuracy for phenotype prediction and AUC for network i... more Means and standard deviations of accuracy for phenotype prediction and AUC for network identification on simulation data with different SNR.</p

ROC curve of the classification of patients in Loi et al. data.</p

An overview of the CyNetSVM app.</p

Functional enrichment of genes identified from Loi et al. data in sig... more Functional enrichment of genes identified from Loi et al. data in signaling pathways and associated p-values.</p

Functional enrichment of genes identified from the discovery dataset in signaling pathwa... more Functional enrichment of genes identified from the discovery dataset in signaling pathways and associated p-values.</p

Network identified from METABRIC discovery data.</p

Screenshot of the CyNetSVM app.</p

Network identified from Loi et al. data.</p

Additional file 6: Table S5. Summary of WGCNA clustering results.

Additional file 5: Table S4. Summary of â omicsâ dataset included in this study.

Additional file 3: Table S2. Summary of eight dLGN RNAseq libraries and differentially expressed ... more Additional file 3: Table S2. Summary of eight dLGN RNAseq libraries and differentially expressed genes.

Additional file 2: Fig. S1. Distribution of read depth for CpG sites determined in four dLGN WGBS... more Additional file 2: Fig. S1. Distribution of read depth for CpG sites determined in four dLGN WGBS libraries. Fig. S2. Distribution of CpG methylation levels determined for four dLGN WGBS libraries. Fig. S3. Venn diagram of DMS lists identified from four pairwise comparisons. Fig. S4. Relationships between mCH and gene expression. The mCH profiles for (A) P6 WT, (B) P6 Math5KO, (C) P23 WT and (D) P23 Math5KO. Red line denote the group of genes with the top one-third expression; green line denote the group of genes with the median one-third expression; blue line denote the group of genes with the bottom one-third expression; and black line show the group of genes not expressed. The average expression levels at P3 and P7 were shown for P6. Fig. S5. Pairwise comparisons identified common sets of 463 upregulated (A) and 554 downregulated (B) genes from P3 to P23 were identified in both WT and Math5KO. No gene was identified to be overlapped for upregulated (C) or downregulated (D) in Mat...

2014 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems (BioWireleSS), 2014

This paper presents a novel noncontact vital sign detection (VSD) system, which integrates an ent... more This paper presents a novel noncontact vital sign detection (VSD) system, which integrates an entire radar transceiver along with advanced signal processing algorithms in one compact PXI (Peripheral Component Interconnect eXtensions for Instrumentation) system. Realtime signal processing has been implemented using automatic DC calibration based on compressed sensing algorithm and 'differentiate and cross-multiply' (DACM) demodulation algorithm. Experimental results show that the proposed compact instrument based radar is able to measure respiration motions with simple hardware configuration. This facilitates many proof-of-concept studies for noncontact vital sign detection.

PloS one, 2017

One of the important tasks in cancer research is to identify biomarkers and build classification ... more One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store a...

Additional file 4: Table S3. Summary of DMRs and GO enrichment analysis for genes associated with... more Additional file 4: Table S3. Summary of DMRs and GO enrichment analysis for genes associated with DMRs.

Additional file 1: Table S1. Summary statistics for four dLGN WGBS libraries.

Chromatin immunoprecipitation (ChIP), followed by high-throughput DNA sequencing (ChIP-seq), enab... more Chromatin immunoprecipitation (ChIP), followed by high-throughput DNA sequencing (ChIP-seq), enables genome-wide mapping of transcription-factor binding sites (TFBS). Several transcription factors (TFs) have been known to be able to differentiate tumor sub-types in diseases like cancer. For instance, the Luminal A and Luminal B sub-types of breast cancer tumors are high in estrogen receptor (ER) while human epidermal growth factor receptor 2 (HER2) tumors are high in HER2 protein. The accurate mapping of the DNAprotein loci is important in determining the causality of epigenetic regulation of gene expression under both normal and disease conditions in order to promote the development of targeted drug therapy. In this paper, we leverage the popular variational Bayes framework for Gaussian mixture models to demonstrate its effectiveness in identifying transcription-factor binding sites (TFBS) and common regions co-regulated by multiple TFs. We show that our method performs favorably w...