Online Optimization Methods for the Quantification Problem

The estimation of class prevalence, i.e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc. For example, in sentiment analysis, the objective is often not to estimate whether a specific text conveys a positive or a negative sentiment, but rather estimate the overall distribution of positive and negative sentiments during an event window. A popular way of performing the above task, often dubbed quantification, is to use supervised learning to train a prevalence estimator from labeled data. Contemporary literature cites several performance measures used to measure the success of such prevalence estimators. In this paper we propose the first online stochastic algorithms for directly optimizing these quantification-specific performance measures. We also provide algorithms that optimize hybrid performance measures that seek to balance quantification and classification performance. Our algorithms present a significant advancement in the theory of multivariate optimization and we show, by a rigorous theoretical analysis, that they exhibit optimal convergence. We also report extensive experiments on benchmark and real data sets which demonstrate that our methods significantly outperform existing optimization techniques used for these performance measures.Comment: 26 pages, 6 figures. A short version of this manuscript will appear in the proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 201

Cancer impacts microRNA expression, release and function in cardiac and skeletal muscle

Circulating microRNAs are emerging as important biomarkers of various diseases including cancer. Intriguingly, circulating levels of several microRNAs are lower in cancer patients compared with healthy individuals. In this study, we tested the hypothesis that a circulating microRNA might serve as a surrogate of the effects of cancer on microRNA expression or release in distant organs. Here we report that circulating levels of the muscle-enriched miR-486 is lower in breast cancer patients compared with healthy individuals, and that this difference is replicated faithfully in MMTV-PyMT and MMTV-Her2 transgenic mouse models of breast cancer. In tumor-bearing mice, levels of miR-486 were relatively reduced in muscle, where there was elevated expression of the miR-486 target genes PTEN and FOXO1A and dampened signaling through the PI3K/AKT pathway. Skeletal muscle expressed lower levels of the transcription factor MyoD which controls miR-486 expression. Conditioned media (CM) obtained from MMTV-PyMT and MMTV-Her2/Neu tumor cells cultured in vitro was sufficient to elicit reduced levels of miR-486 and increased PTEN and FOXO1A expression in C2C12 murine myoblasts. Cytokine analysis implicated TNFα and four additional cytokines as mediators of miR-486 expression in CM-treated cells. Since miR-486 is a potent modulator of PI3K/AKT signaling and the muscle-enriched transcription factor network in cardiac/skeletal muscle, our findings implicated TNFα-dependent miRNA circuitry in muscle differentiation and survival pathways in cancer

Organ-specific adaptive signaling pathway activation in metastatic breast cancer cells

Breast cancer metastasizes to bone, visceral organs, and/or brain depending on the subtype, which may involve activation of a host organ-specific signaling network in metastatic cells. To test this possibility, we determined gene expression patterns in MDA-MB-231 cells and its mammary fat pad tumor (TMD-231), lung-metastasis (LMD-231), bone-metastasis (BMD-231), adrenal-metastasis (ADMD-231) and brain-metastasis (231-BR) variants. When gene expression between metastases was compared, 231-BR cells showed the highest gene expression difference followed by ADMD-231, LMD-231, and BMD-231 cells. Neuronal transmembrane proteins SLITRK2, TMEM47, and LYPD1 were specifically overexpressed in 231-BR cells. Pathway-analyses revealed activation of signaling networks that would enable cancer cells to adapt to organs of metastasis such as drug detoxification/oxidative stress response/semaphorin neuronal pathway in 231-BR, Notch/orphan nuclear receptor signals involved in steroidogenesis in ADMD-231, acute phase response in LMD-231, and cytokine/hematopoietic stem cell signaling in BMD-231 cells. Only NF-κB signaling pathway activation was common to all except BMD-231 cells. We confirmed NF-κB activation in 231-BR and in a brain metastatic variant of 4T1 cells (4T1-BR). Dimethylaminoparthenolide inhibited NF-κB activity, LYPD1 expression, and proliferation of 231-BR and 4T1-BR cells. Thus, transcriptome change enabling adaptation to host organs is likely one of the mechanisms associated with organ-specific metastasis and could potentially be targeted therapeutically

THE INDIANA CENTER FOR BREAST CANCER RESEARCH: PROGRESS REPORT

poster abstractThe mission of IUPUI breast cancer center is to address prevention, early detection, and treatment of breast cancer through translational projects, supportive cores, and synergistic programs. This poster details our efforts improve resources for breast cancer research and efforts to develop multi-PI investigator proposals. The Signature Center Initiative has developed two web resources: the Breast Cancer Prognostics Database (BCDB) to study prognostic implications of genes of interest in publically available breast cancer databases and PROGmiR, a microRNA database. The BCDB can be used to study overall, recurrence free and metastasis free survival in large patient series. PROGmiR allows investigators to study the prognostic importance of microRNAs. PROGmiR has recently been published and has been accessed by investigators from several countries. The signature center has also devoted considerable efforts in developing tumor tissue resource. Tissue Bank includes a total sample of N = 500 cases with 30% non-Caucasian cases from Wishard Memorial Hospital. Currently 237 cases have been assembled into a Tissue Microarray with clinical and follow up data. The breast cancer center has funded three pilot projects. Drs. Clark Wells, S. Badve, and G. Sandusky are collaborating on the project: “Histologic Analysis of the Protein Levels of Amot130, AmotL1 and YAP in Normal, Hyperplastic and Invasive Breast Cancer Tissues”. This project is investigating localized protein expression in paraffin-embedded tissues to associate expression levels with disease subtype and patient outcome. Dr. David Gilley and his group are collaborating on the project: “Luminal mammary progenitors are a unique site of telomere dysfunction”. This project is investigating the relationship between telomere dysfunction and breast cancer tumorigenesis. In the third project, Dr. Theresa Guise will be investigating the mechanisms of cancer-associated cachexia. Several multi-PI proposals are under preparation and one proposal with Drs. Nakshatri and Kathy Miller as PIs is currently under review

The Indiana Center for Breast Cancer Research: Progress towards a SPORE Proposal

poster abstractAbstract The Indiana Center for Breast Cancer Research (ICBCR) was funded under the IUPUI Signature Center Initiative in 2010. Its mission is to address the full range of prevention, early detection, and treatment of breast cancer through translational projects, supportive cores, and synergistic programs. This poster details our efforts to date towards applying for a National Cancer Institute Specialized Program of Research Excellence (SPORE) in January 2013. The proposed IU Breast Cancer SPORE will include 4-5 individual research projects, 3 cores, developmental research and career development programs. The SPORE Biostatistics and Bioinformatics core has developed the Breast Cancer Prognostics Database (BCDB), an online tool to study prognostic implications of genes of interest in publically available breast cancer databases. The BCDB can be used to study overall, recurrence free and metastasis free survival in large patient series. Supporting the SPORE Biospecimen/Pathology core, the IU Breast Cancer Tissue Bank includes a total sample of N = 500 cases with 30% non-Caucasian cases from Wishard Memorial Hospital. Currently there are N = 333 cases with tissue microarray data and complete clinical data with an additional 200 cases pending tissue confirmation. Dr. Clark D. Wells together with S. Badve and G. Sandusky are collaborating on the project: “Histologic Analysis of the Protein Levels of Amot130, AmotL1 and YAP in Normal, Hyperplastic and Invasive Breast Cancer Tissues”, a candidate SPORE individual research project. This project is investigating localized protein expression in paraffin-embedded tissues to associate expression levels with disease subtype and patient outcome. Dr. David P. Gilley together with N. Kannan, N. Huda, L. Tu, R. Droumeva, R. Brinkman, J. Emerman, S. Abe, and C. Eaves, are collaborating on the project: “Luminal mammary progenitors are a unique site of telomere dysfunction”, a candidate SPORE developmental research project. This project is investigating the relationship between telomere dysfunction and breast cancer tumorigenesis. These SPORE projects and cores were discussed at the IUSCC Breast Cancer Program retreat held on 1/13/12. Two additional planning meetings were held on 1/5 and 2/23. A timeline was generated to include final project selection in April, internal review in June, external review in August-September, and draft completion by 12/1, to meet the 1/20/13 NIH receipt deadline

Evaluation of methods and marker systems in genomic selection of oil palm (Elaeis guineensis Jacq.)

Background Genomic selection (GS) uses genome-wide markers as an attempt to accelerate genetic gain in breeding programs of both animals and plants. This approach is particularly useful for perennial crops such as oil palm, which have long breeding cycles, and for which the optimal method for GS is still under debate. In this study, we evaluated the effect of different marker systems and modeling methods for implementing GS in an introgressed dura family derived from a Deli dura x Nigerian dura (Deli x Nigerian) with 112 individuals. This family is an important breeding source for developing new mother palms for superior oil yield and bunch characters. The traits of interest selected for this study were fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per palm (O/P) and oil-to-dry mesocarp (O/DM). The marker systems evaluated were simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits. Results The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods. Conclusion Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation

Impact of climate change on rice production: an empirical study in Kaski and Nawalparasi, Nepal

This study explores the relationship between climate variables to rice production in Kaski and Nawalparasi district of Nepal. The study was conducted in the year 2016. This study captured the time series data ranging from 1995 to 2014 on rice production, temperature and rainfall of two different districts and analyzed through panel data regression. Regarding primary data collection, a total of 120 sampled households were surveyed by using simple random sampling to understand the perception of farmers to change in climatic parameters using a semi-structured pre-tested questionnaire and Focus Group Discussions. The secondary information was collected from the Ministry of Agriculture and Livestock Development, Department of Hydrology and Meteorology and Centre Bureau of Statistics. The regression model revealed that seasonal rainfall had a linear relation on rice production (p<0.05). Respondents from both districts perceived that temperature, rainfall and hailstone had increased or varied than before. The major problems faced by the farmers of the study area due to climate change were prioritized as drought, flood hailstone, extreme hot and extreme cold. This necessitates the promotion and use of climate-smart technologies for better rice adaptation to climate change

The Efficacy of Molecular Markers Analysis with Integration of Sensory Methods in Detection of Aroma in Rice

Allele Specific Amplification with four primers (External Antisense Primer, External Sense Primer, Internal Nonfragrant Sense Primer, and Internal Fragrant Antisense Primer) and sensory evaluation with leaves and grains were executed to identify aromatic rice genotypes and their F1 individuals derived from different crosses of 2 Malaysian varieties with 4 popular land races and 3 advance lines. Homozygous aromatic (fgr/fgr) F1 individuals demonstrated better aroma scores compared to both heterozygous nonaromatic (FGR/fgr) and homozygous nonaromatic (FGR/FGR) individuals, while, some F1 individuals expressed aroma in both leaf and grain aromatic tests without possessing the fgr allele. Genotypic analysis of F1 individuals for the fgr gene represented homozygous aromatic, heterozygous nonaromatic and homozygous nonaromatic genotypes in the ratio 20 : 19 : 3. Genotypic and phenotypic analysis revealed that aroma in F1 individuals was successfully inherited from the parents, but either molecular analysis or sensory evaluation alone could not determine aromatic condition completely. The integration of molecular analysis with sensory methods was observed as rapid and reliable for the screening of aromatic genotypes because molecular analysis could distinguish aromatic homozygous, nonaromatic homozygous and nonaromatic heterozygous individuals, whilst the sensory method facilitated the evaluation of aroma emitted from leaf and grain during flowering to maturity stages

Polarizability in Substituted Oxazoles: A PC-Model Data Analysis

A systematic investigation of various Oxazole derivatives has been subjected to a Polarized Continuums Model (PCM) for evaluation of their physico-chemical parameters. The data thus obtained have been used to discuss the effect of substituents on polarizability of the oxazole aromatic ring and its possible dependence on the lipophilicity. In view of their biological activity a quantitative dependence of the physico-chemical parameters of the oxazole derivatives with the π– lipophilicity distributive parameter [8] with respect to substituents on the oxazole aromatic ring, have been attempted. These physico-chemical parameters showed a dependence with respect to their X1, X2 and X3 substituents as H, H, H < H, H,Ph < H,Ph < Ph, Ph, Ph The variations thuas obtained have been discussed in light of substitutions and their effect on the polarizability of the oxale ring

Synthesis, gene silencing, and molecular modeling studies of 4 '-C-aminomethyl-2 '-O-methyl modified small interfering RNAs.

The linear syntheses of 4′-C-aminomethyl-2′-O-methyl uridine and cytidine nucleoside phosphoramidites were achieved using glucose as the starting material. The modified RNA building blocks were incorporated into small interfering RNAs (siRNAs) by employing solid phase RNA synthesis. Thermal melting studies showed that the modified siRNA duplexes exhibited slightly lower Tm (1 °C/modification) compared to the unmodified duplex. Molecular dynamics simulations revealed that the 4′-C-aminomethyl-2′-O-methyl modified nucleotides adopt South-type conformation in a siRNA duplex, thereby altering the stacking and hydrogen-bonding interactions. These modified siRNAs were also evaluated for their gene silencing efficiency in HeLa cells using a luciferase-based reporter assay. The results indicate that the modifications are well tolerated in various positions of the passenger strand and at the 3′ end of the guide strand but are less tolerated in the seed region of the guide strand. The modified siRNAs exhibited prolonged stability in human serum compared to unmodified siRNA. This work has implications for the use of 4′-C-aminomethyl-2′-O-methyl modified nucleotides to overcome some of the challenges associated with the therapeutic utilities of siRNAs