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Background Cardiometabolic diseases are highly comorbid, but their relationship with female‐specific or overwhelmingly female‐predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross‐trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross‐trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score–based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross‐trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female‐specific health conditions.

Slurry polymerization processes using Zeigler–Natta catalysts are most widely used for the production of polyethylene due to their several advantages over other processes. Optimal operating conditions are required to obtain the maximum productivity of the polymer at minimal cost while ensuring operational safety in the slurry phase ethylene polymerization reactors. The main focus of this multi-objective optimization study is to obtain the optimal operating conditions corresponding to the maximization of productivity and yield at a minimal operating cost. The tuned reactor model has been optimized. The single objective optimization (SOO) and multi-objective optimization (MOO) problems are solved using non-dominating sorting genetic algorithm-II (NSGA-II). A complete range of Pareto optimal solutions are obtained to obtain the maximum productivity and polymer yield at different input costs.

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders which includes autistic disorders, Asperger’s Syndrome and Pervasive Developmental Disorders not otherwise-specified according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSMV). ASD is primarily characterised by impaired reciprocal-social interaction, repetitive-stereotypic behaviours and deficits in communication skills. It has a striking male bias in the ratio of about 4.5:1. Its current prevalence rate is about 1 in 59 among 8 years old children in the USA and its prevalence is increasing at an alarming rate throughout the world, including India. Although the exact aetiology of ASD is yet to be deciphered, number of evidences show that it is influenced by both genetic and epigenetic contributions. Twin and family studies confirm the heritable nature of such disorders. Aberrant DNA methylation and post translational modifications to histone play a major role in its aetiology. Rett Syndrome and Fragile X syndrome are single gene disorders associated with ASD, while Prader Willi and Angelman’s syndrome are genomic imprinted disorders where epigenetic regulatory mechanisms play pivotal role in the disease pathogenesis. In this work, our aim was to give an overview to the DNA methylation signatures in ASD and closely associated neuropsychiatric conditions. We conclude with the discussion of the probable therapeutics that might be developed in future.

The supercritical antisolvent (SAS) recrystallization process is one of the most promising recrystallization techniques for the particle formation of pharmaceutical compounds. In this process, a solution of active pharmaceutical ingredient (API) is sprayed into the supercritical carbon dioxide (SC CO2) environment. The mass transport of both the solvent and the antisolvent results in supersaturation followed by the crystallization of the API. In this work, a model is developed to estimate the supersaturation profile of solute in a droplet falling in the SC CO2 environment. The droplet consists of paracetamol as a solute and ethanol as a solvent. It moves down in the antisolvent (supercritical CO2) environment. Interestingly, the present model predicts a rise in supersaturation followed by a fall for a while and then a sharp increase. The competing phenomena of nucleation and growth mechanisms are used to justify this variation in the supersaturation.

In the current scenario of energy requirement and the commercialization aspect of algal biofuel and biomass, it is important that means of predicting the production be available. In this paper, the mathematical models are developed for the tubular, bubble column and airlift photobioreactors to predict the productivity of the algal biomass. A modified Monod kinetic equation, incorporating the effect of nutrient and CO2 concentrations, light availability and oxygen built-up, is used to the estimate specific growth rate of the biomass. The light availability inside the reactor is defined in terms of the modified Beer–Lambert’s law as a function of distance from the surface where light is incident and the cell mass concentration. This allows a more accurate measurement of the shading effect. The equations are solved for different reactor types and their estimated productivities are successfully validated against values available in published literature. The model predicts comparatively better productivity for the tubular reactor (1.5 g/L day) than the bubble column and airlift reactor (1.42 and 1.35 g/L day respectively) because tubular reactor has shorter light/dark cycles and better light availability. The analysis is also done to identify the effect of nutrient, carbon dioxide, light and hydrodynamics on the overall productivity.

The present work reviews the liquid antisolvent crystallization (LASC) to prepare the nanoparticle of pharmaceutical compounds to enhance their solubility, dissolution rate, and bioavailability. The application of ultrasound and additives is discussed to prepare the particles with narrow size distribution. The use of ionic liquid as an alternative to conventional organic solvent is presented. Herbal compounds, also known for low aqueous solubility and limited clinical application, have been crystalized by LASC and discussed here. The particle characteristics such as particle size and particle size distribution are interpreted in terms of supersaturation, nucleation, and growth phenomena. To overcome the disadvantage of batch crystallization, the scientific literature on continuous flow reactors is also reviewed. LASC in a microfluidic device is emerging as a promising technique. The different design of the microfluidic device and their application in LASC are discussed. The combination of the LASC technique with traditional techniques such as high-pressure homogenization and spray drying is presented. A comparison of product characteristics prepared by LASC and the supercritical CO

In pharmaceutical research and development, various new chemical entities (NCE) are found to be poorly water-soluble. Therefore, solubility enhancement, a key factor for higher bioavailability, is a major challenge in pharmaceutical industries. Particle size reduction is one such method that increases the surface area of the pharmaceutical compounds and subsequently leads to a higher dissolution rate and bioavailability. Conventional processes such as milling, high-pressure homogenization, and spray drying are well established and widely used for particle size reduction. However, a few disadvantages such as a broader particle size distribution (PSD) and thermal and chemical degradation of the product are major concerns for the product quality. Non-conventional processes such as liquid anti-solvent crystallization, supercritical anti-solvent process, rapid expansion of supercritical solutions, particles from gas saturated solutions, and pulsed laser ablation are emerging as potential alternatives to overcome the disadvantages of conventional processes. This review critically summarizes the milling, spray drying, high-pressure homogenization, liquid anti-solvent crystallization, spray freeze-drying, supercritical carbon dioxide $$(\mathrm{SC C}{\mathrm{O}}_{2})$$ –based micronization processes, pulsed laser ablation and combinative techniques. The success of these processes in enhancing the dissolution rate and bioavailability of many active pharmaceutical ingredients (APIs) has been critically examined. The advantages and limitations of these processes are also discussed. Finally, opportunities for future research are also proposed.

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community., Exome sequences from the first 49,960 participants in the UK Biobank highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.

Traditional biomass-burning stoves are used for cooking and heating across the globe. These stoves generate smoke that results in household air pollution, which poses a significant risk to human health. In the past decades, there have been many efforts to promote the adoption of improved cookstove designs, but uptake of improved stoves is often slow due to high costs, inconsistent supply chains, and incompatibility with local cooking practices. This paper presents survey results from rural villages in Uttarakhand, India regarding routines and attitudes on cooking and space heating. Significant findings include the dual use of liquified petroleum gas and biomass fuels, the interconnected and seasonal nature of cooking and space heating, the cultural significance of traditional cookstoves, and the prominence of locally available materials in cookstove construction and maintenance. Comparisons of these surveys’ findings to previous investigations on energy use in the Himalayan region show many common trends, but also reveal regional differences. The paper concludes that due to the significance of culture and context in cookstove design, understanding user needs and behaviors and working with local communities are integral parts the design methodology for clean cookstoves. These results provide a case study which agrees with existing literature on the importance of participatory design in global development.

Arsenic exposure by groundwater contamination is a menace which threatens more than 26 million individuals of West Bengal. Interestingly, with similar levels of arsenic exposure, only 15-20% of the population show arsenic-induced skin lesions, the hallmarks of chronic arsenic toxicity, but the rest do not. In this study, our aim was to identify whether microRNAs (miRNA) have any role to play in causing such arsenic susceptibility. Global plasma miRNA profiling was done in 12 arsenic-exposed individuals with skin lesions and 12 exposed individuals without skin lesions. Two hundred two miRNAs were found to be differentially regulated between the two study groups. Results were validated by quantitative real-time PCR in 30 exposed subjects from each of the groups, which showed that among others miR-21, miR-23a, miR-27a, miR-122, miR-124, miR-126, miR-619, and miR-3613 were significantly upregulated and miR-1282 and miR-4530 were downregulated in the skin lesion group compared with the no skin lesion group. Bioinformatic analyses predicted that these altered miRNAs have targets in 7 different biochemical pathways, including glycerophospholipid metabolism, colorectal cancer, glycosphingolipid biosynthesis, T cell receptor signaling, and neurotrophin signaling pathways; glycerophospholipid metabolism pathway being the most enriched pathway. Association study show that these microRNAs contribute significantly to the increased prevalence of other non-dermatological health effects like conjunctival irritations of the eyes and respiratory distress in the study subjects. To our knowledge, this is the first study of its kind involving miRNA expressions contributing to arsenic susceptibility in the exposed population of West Bengal.

The novel particle generation processes of Active Pharmaceutical Ingredient (API)/drug have been extensively explored in recent decades due to their wide-range applications in the pharmaceutical industry. The Rapid Expansion of Supercritical Solutions (RESS) is one of the promising techniques to obtain the fine particles (micro to nano-size) of APIs with narrow particle size distribution (PSD). In RESS, supercritical carbon dioxide (SC CO2) and API are used as solvent and solute respectively. In this literature survey, the application of RESS in the formation of fine particles is critically reviewed. Solubility of API in SC CO2 and supersaturation are the key factors in tuning the particle size. The different approaches to model and predict the solubility of API in SC CO2 are discussed. Then, the effect of process parameters on mean particle size and the particle size distribution are interpreted in the context of solubility and supersaturation. Furthermore, the less-explored applications of RESS in preparation of solid-lipid nanoparticles, liposome, polymorphic conversion, cocrystallization and inclusion complexation are compared with traditional processes. The solubility enhancement of API in SC CO2 using co-solvent and its applications in particle generation are explored in published literature. The development and modifications in the conventional RESS process to overcome the limitations of RESS are presented. Finally, the perspective on RESS with special attention to its commercial operation is highlighted.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

In this pilot study, we aim to identify the role of few genetic variants of GABA-receptor type A subunits GABRB3 (rs4906902, rs7171660), GABRG3 (rs208129, rs140679), GABRA5 (rs 140681) in the aetiology of autism spectrum disorders in a population of West Bengal. 192 ASD probands, their parents and 184 ethnically-matched healthy controls were recruited for the study. The rs4906902G and the rs140679T conferred significant risk towards ASD. rs7171660 and rs140679 had transmission bias in the family. Neither alleles of rs 208129 and rs 140681 showed significant over-representation in either groups. All these variants were associated with at least one deficit in ASD-associated phenotypes like ‘relating to people’, ‘Imitation’, ‘emotional response’, ‘body use’, ‘taste, smell, touch response’ and ‘activity levels’.

SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, P=4.5x10-13) that down-regulates ACE2 expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We investigated associations between rare genetic variants and seven COVID-19 outcomes in 543,213 individuals, including 8,248 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 14 interferon pathway genes in which rare deleterious variants have been reported in severe COVID-19 patients; (ii) 167 genes located in COVID-19 GWAS risk loci; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly browsable athttps://rgc-covid19.regeneron.com.

Chemical investigation of root bark of Glycosmis pentaphylla and stem bark of Tabernaemontana coronaria led to the isolation of three carbazole alkaloids glycozoline, glycozolidine and methyl carbazole 3-carboxylate, two furoquinoline alkaloids skimmianine and dictamine, an acridone alkaloid arborinine, three monomeric indole alkaloids coronaridine, 10-methoxy coronaridine and tabernaemontanine, and two dimeric indole alkaloids voacamine and tabernaelegantine B. Their structures were established by detailed spectral analysis. Mutagenic and antimutagenic potential of methanol extract of both plant materials were evaluated by Ames test against known positive mutagens 2-aminofluorine, 4-nitro-O-phenylenediamine and sodium azide using Salmonella typhimurium TA 98 and TA 100 bacterial strains both in the presence and absence of S9. Both the extracts were non-mutagenic in nature. Both the extracts of G. pentaphylla and T. coronaria exhibited significant antimutagenic activity against NPD and sodium azide for S. typhimurium TA98 and TA100 strains. The results indicated that the extracts could counteract the mutagenicity induced by different genotoxic compounds.

Chronic obstructive pulmonary disease (COPD) is a type of obstructive lung disease characterized by long term poor airflow which worsens over time. It is considered to be one of the top five chronic diseases of the world in terms of morbidity and mortality. Genetic variability has been found to contribute to the development of COPD. Although association between gene polymorphisms in EPHX1 and TNF-a genes and chronic obstructive pulmonary disease (COPD) have been found but till date no genetic association studies have been done in the COPD affected Kazakhstan population. The aim of the present work was to investigate the association between the Y113H polymorphism (rs1051740) in EPHX1 gene and -308G/A polymorphism (rs1800629) in TNF-a gene and COPD in Kazakhstan population. A case-control study was conducted in Astana and Akmola regions of Kazakhstan, involving 55 cases with COPD and 52 healthy individuals who served as the controls. The polymorphisms were determined using conventional PCR and Sanger sequencing method. Results show that for the EPHX1 gene Y113H polymorphism, the presence of an "C" allele (TC/CC genotype) was significantly overrepresented in the COPD patients compared to the controls. For the TNF-a gene -308G/A polymorphism, no significant difference was found between the two groups. Thus we found that, Y113H polymorphism in EPHX1 gene contributed to increased susceptibility to COPD in the Kazakhstan population.

The present energy scenario of depleting sources of fossil fuel, increasing the cost of processed fuel and geopolitical policies have made it imperative that alternate energy sources and processes that reduce greenhouse gas emissions be identified. Biofuels from algae, in recent times, have emerged as a potential alternative to fossil fuels, as it can be easily produced, stored, and used as transportation fuels. Here, mathematical design models are developed for tubular, bubble column, and airlift photobioreactors to calculate their productivity for the growth of algal biomass. A modified Monod kinetic equation is used to estimate the specific growth rate of the biomass. The model equations are solved for tubular reactor systems and the estimated productivities are successfully validated against values available in published literature. It is found that the tubular reactor has productivity, around 2.1 g/L.day. The analysis is also done to identify the effect of light, nutrient, and carbon dioxide on overall productivity.

SUMMARYThe UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

Increasing evidence of arsenic contamination in ground water and its associated adverse health outcomes affects millions of people worldwide. However, arsenic toxicity studies in children have gained impetus very recently due to the non-prominence of the hallmarks of arsenic toxicity i.e skin lesions. We recognized the need to evaluate the status of genetic damage brought about by early life exposure to arsenic in children as measured by micronucleus (MN) assay for three cell types namely buccal mucosa, urothelial cells and lymphocytes. A thorough health checkup and complete haematogram of the study participants was performed to measure overall health effects and changes in the blood profile in children exposed to arsenic through drinking water in West Bengal, India. Since telomere length alteration has been identified as a good indicator of arsenic toxicity in adults, we measured the telomere length of the arsenic exposed and unexposed children. We found that all the three cell types had significantly higher (P 0.0001) MN frequency in the arsenic exposed children when compared to the unexposed. Blood profiling showed significantly altered neutrophil, eosinophil, lymphocyte and haemoglobin levels in the arsenic exposed children than their unexposed counterparts. Telomere length in the arsenic exposed children was slightly higher than the unexposed. This is a firsthand report of the genetic damage observed in children exposed to arsenic through drinking water in West Bengal, India.

To best define biomarkers of response, and to shed insight on mechanism of action of certain clinically important agents for early breast cancer, we used a brief-exposure paradigm in the preoperative setting to study transcriptional changes in patient tumors that occur with one dose of therapy prior to combination chemotherapy. Tumor biopsies from breast cancer patients enrolled in two preoperative clinical trials were obtained at baseline and after one dose of bevacizumab (HER2-negative), trastuzumab (HER2-positive) or nab-paclitaxel, followed by treatment with combination chemo-biologic therapy. RNA-Sequencing based PAM50 subtyping at baseline of 46 HER2-negative patients revealed a strong association between the basal-like subtype and pathologic complete response (pCR) to chemotherapy plus bevacizumab (p ≤ 0.0027), but did not provide sufficient specificity to predict response. However, a single dose of bevacizumab resulted in down-regulation of a well-characterized TGF-β activity signature in every single breast tumor that achieved pCR (p ≤ 0.004). The TGF-β signature was confirmed to be a tumor-specific read-out of the canonical TGF-β pathway using pSMAD2 (p ≤ 0.04), with predictive power unique to brief-exposure to bevacizumab (p ≤ 0.016), but not trastuzumab or nab-paclitaxel. Down-regulation of TGF-β activity was associated with reduction in tumor hypoxia by transcription and protein levels, suggesting therapy-induced disruption of an autocrine-loop between tumor stroma and malignant cells. Modulation of the TGF-β pathway upon brief-exposure to bevacizumab may provide an early functional readout of pCR to preoperative anti-angiogenic therapy in HER2-negative breast cancer, thus providing additional avenues for exploration in both preclinical and clinical settings with these agents.

We reviewed numerical/analytical models for describing rheological properties and mechanical behaviors of biopolymer networks with a focus on the cytoskeleton, a major component of a living cell. The cytoskeleton models are classified into three categories: the cell-scale continuum-based model, the structure-based model, and the polymerbased model, according to the length scales of the phenomena of interest. The criteria for classification of the models are modified and extended from those used by Mofrad [M. R. K. Mofrad, Annual Rev. Fluid Mech. 41, 433 (2009)]. The main principles and characteristics of each model are summarized and discussed by comparison with each other. Since the stress-deformation relation of cytoskeleton is dependent on the length scale of stress elements, our model classification helps systematic understanding of biopolymer network modeling.

Background: Genomics is expected to transform oncology clinical practice. However, converting genomic data into clinically actionable information is a daunting task. Genomic high-throughput technologies produce massive amounts of raw data, and its complexity presents a formidable challenge for clinical adoption. While algorithms exist to convert genomic data into meaningful biological information, they are geared towards the bioinformatics expert user, lack clinical annotation and interpretation, and are not addressing the needs of clinical experts. Furthermore, the main problem is that sequencing results should not be treated like a test as they are geared towards precision diagnostics which require patient clinical data. Methods: We present PAPAyA, a genome informatics platform that provides an overall solution to the genomic data overload which includes analysis of whole transcriptome and exome data, secure storage and management of this data and the interactive presentation of patient genome information in a contextualized manner. It is a continuum of analytics and user experiences with deep understanding of the clinical questions and the workflow. PAPAyA is a framework for hosting multiple genome informatics applications that bring information that is Connected, Digital and in Real Time. Connected in many dimensions - across hospital systems, across time, across many hospitals and their affiliates. Digital means retrievable (without complicated SQL queries) not just stored bits across modalities (genomics is a single vertical that pulls other information towards precise treatment). Real time means up to date ACTGs get converted into actionable information fast, and get to the right clinical expert without manual actions and printing reports. The framework sits on top of a digital health platform that offers core capabilities such as: persisting data and provisioning service (elastic computing), security, auditing, business workflow engine, reports service, user management service, patient identity service, provider registry service. At its core, PAPAyA provides full pipelines that analyze genomic high-throughput data, and further extract and prioritize clinically-meaningful information from the patient's genome. Furthermore, PAPAyA enables storage and secure management of this complex data which allows clinicians to query the data in a clinical action-oriented framework, as opposed to data aggregation and reporting framework. Results: We demonstrate the usability of PAPAyA using public data from TCGA breast cancer cohort. We show the utility of executing in-silico assays applied on RNAseq data, such as ER/PR/Her2, differential expression of long-noncoding RNAs, gene fusions, subtyping, hypoxia index, and other known signatures from the biomedical literature. In parallel, we annotate full exomes in order to find clinically relevant information for variants of unknown significance as well as variants with known disease and response phenotype using biological, functional, and clinical annotation resources. Conclusion: We have implemented an automated open learning system for processing genomic data to aid in clinical decision making. We already started the pilot phase and clinical evaluation with key academic collaborators. Citation Format: Nilanjana Banerjee, Konstantin Volyanskyy, Vartika Agrawal, Yong Mao, Yee-Him Cheung, Sitharthan Kamalakaran, Laurens Boekhorst, Arjan Claassen, Nevenka Dimitrova. PAPAyA – The genome informatics framework for oncology applications and other clinical domains [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-08-02.

Following recent work [e.g., J. Park et al., J. Rheol. 56, 1057–1082 (2012); T. Yaoita et al., Macromolecules 45, 2773–2782 (2012); and G. Ianniruberto et al., Macromolecules 45, 8058–8066 (2012)], we introduce the idea of a configuration dependent friction coefficient (CDFC) based on the relative orientation of Kuhn bonds of the test and surrounding matrix chains. We incorporate CDFC into the “toy” model of Mead et al. [Macromolecules 31, 7895–7914 (1998)] in a manner akin to Yaoita et al. [Nihon Reoroji Gakkaishi 42, 207–213 (2014)]. Additionally, we incorporate entanglement dynamics (ED) of discrete entanglement pairs into the new Mead–Banerjee–Park (MBP) model in a way similar to Ianniruberto and Marrucci [J. Rheol. 58, 89–102 (2014)]. The MBP model predicts a deformation dependent entanglement microstructure which is physically reflected in a reduced modulus that heals slowly following cessation of deformation. Incorporating ED into the model allows “shear modification” to be qualitatively captured. The MBP model is tested against experimental data in steady and transient extensional and shear flows. The MBP model captures the monotonic thinning of the extensional flow curve of entangled monodisperse polystyrene (PS) melts [A. Bach et al., Macromolecules 36, 5174–5179 (2003)] while simultaneously predicting the extension hardening found in PS semidilute solutions where CDFC is diluted out [P. K. Bhattacharjee et al., Macromolecules 35, 10131–10148 (2002)]. The simulation results also show that the rheological properties in nonlinear extensional flows of PS melts are sensitive to CDFC but not to convective constraint release (CCR) while those for shear flows are influenced more by CCR. The monodisperse MBP toy model is generalized to arbitrary polydispersity.