Asteroid Spin-Rate Study using the Intermediate Palomar Transient Factory

Two dedicated asteroid rotation-period surveys have been carried out using data taken on January 6-9 and February 20-23 of 2014 by the Intermediate Palomar Transient Factory (iPTF) in the $R$~band with $\sim 20$-min cadence. The total survey area covered 174~deg$^2$ in the ecliptic plane. Reliable rotation periods for 1,438 asteroids are obtained from a larger data set of 6,551 mostly main-belt asteroids, each with $\geq 10$~detections. Analysis of 1751, PTF based, reliable rotation periods clearly shows the "spin barrier" at $\sim 2$~hours for "rubble-pile" asteroids. We also found a new large-sized super-fast rotator, 2005 UW163 (Chang et al., 2014), and other five candidates as well. Our spin-rate distributions of asteroids with $3 < D < 15$~km shows number decrease when frequency greater than 5 rev/day, which is consistent to that of the Asteroid Light Curve Database (LCDB, Warner et al., 2009) and the result of (Masiero et al., 2009). We found the discrepancy in the spin-rate distribution between our result and (Pravec et al., 2008, update 2014-04-20) is mainly from asteroids with $\Delta m < 0.2$ mag that might be primarily due to different survey strategies. For asteroids with $D \leq 3$~km, we found a significant number drop at $f = 6$ rev/day. The YORP effect timescale for small-sized asteroid is shorter that makes more elongate objets spun up to reach their spin-rate limit and results in break-up. The K-S test suggests a possible difference in the spin-rate distributions of C- and S-type asteroids. We also find that C-type asteroids have a smaller spin-rate limit than the S-type, which agrees with the general sense that the C-type has lower bulk density than the S-type.Comment: Submitted to ApJ (Jan, 2015). Accepted by ApJ (June, 2015). The whole set of the folded lightcurves will be available on the published articl

313 new asteroid rotation periods from Palomar Transient Factory observations

A new asteroid rotation period survey have been carried out by using the Palomar Transient Factory (PTF). Twelve consecutive PTF fields, which covered an area of 87 deg$^2$ in the ecliptic plane, were observed in $R$ band with a cadence of $\sim$20 min during February 15--18, 2013. We detected 2500 known asteroids with a diameter range of 0.5 km $\leq D \leq$ 200 km. Of these, 313 objects had highly reliable rotation periods and exhibited the "spin barrier" at $\sim2$ hours. In contrast to the flat spin rate distribution of the asteroids with 3 km $\leq D \leq$ 15 km shown by Pravec et al. (2008), our results deviated somewhat from a Maxwellian distribution and showed a decrease at the spin rate greater than 5 rev/day. One super-fast-rotator candidate and two possible binary asteroids were also found in this work.Comment: 18 pages, 20 figures and 2 very long table

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

The Enhanced metastatic potential of hepatocellular carcinoma (HCC) cells with sorafenib resistance

Acquired resistance towards sorafenib treatment was found in HCC patients, which results in poor prognosis. To investigate the enhanced metastatic potential of sorafenib resistance cells, sorafenib-resistant (SorR) cell lines were established by long-term exposure of the HCC cells to the maximum tolerated dose of sorafenib. Cell proliferation assay and qPCR of ABC transporter genes (ABCC1-3) were first performed to confirm the resistance of cells. Migration and invasion assays, and immunoblotting analysis on the expression of epithelial to mesenchymal transition (EMT) regulatory proteins were performed to study the metastatic potential of SorR cells. The expression of CD44 and CD133 were studied by flow cytometry and the gene expressions of pluripotency factors were studied by qPCR to demonstrate the enrichment of cancer stem cells (CSCs) in SorR cells. Control (CTL) and SorR cells were also injected orthotopically to the livers of NOD-SCID mice to investigate the development of lung metastasis. Increased expressions of ABCC1-3 were found in SorR cells. Enhanced migratory and invasive abilities of SorR cells were observed. The changes in expression of EMT regulatory proteins demonstrated an activation of the EMT process in SorR cells. Enriched proportion of CD44+ and CD44+CD133 + cells were also observed in SorR cells. All (8/8) mice injected with SorR cells demonstrated lung metastasis whereas only 1/8 mouse injected with CTL cells showed lung metastasis. HCC cells with sorafenib resistance demonstrated a higher metastatic potential, which may be due to the activated EMT process. Enriched CSCs were also demonstrated in the sorafenib resistant cells. This study suggests that advanced HCC patients with acquired sorafenib resistance may have enhanced tumor growth or distant metastasis, which raises the concern of long-term sorafenib treatment in advanced HCC patients who have developed resistance of sorafenib. © 2013 Chow et al.published_or_final_versio

Mycorrhiza and Lichens as Two Models of Fungal Symbiosis

Fungi have evolved many symbioses including different eukaryotes and prokaryotes. Mutualism is one of the symbioses and here both symbionts benefit from the interaction. The most common mutualistic relationships involving fungi are mycorrhiza and lichens. A mycorrhiza is a symbiotic relationship between a roots of a plant and a fungus while lichen associates between a fungus and an algae. Many studies have performed to investigate these symbiotic relationships in depth, however, still have some debates on them, though many taxonomists rely on genetic analyses besides with traditional morphological data. In our study, it highlights the nature, importance, nutritional and pharmaceutical uses, and applications of these mysterious dual between fungi and plant and/or algae

One Decade of Development and Evolution of MicroRNA Target Prediction Algorithms

Nearly two decades have passed since the publication of the first study reporting the discovery of microRNAs (miRNAs). The key role of miRNAs in post-transcriptional gene regulation led to the performance of an increasing number of studies focusing on origins, mechanisms of action and functionality of miRNAs. In order to associate each miRNA to a specific functionality it is essential to unveil the rules that govern miRNA action. Despite the fact that there has been significant improvement exposing structural characteristics of the miRNA-mRNA interaction, the entire physical mechanism is not yet fully understood. In this respect, the development of computational algorithms for miRNA target prediction becomes increasingly important. This manuscript summarizes the research done on miRNA target prediction. It describes the experimental data currently available and used in the field and presents three lines of computational approaches for target prediction. Finally, the authors put forward a number of considerations regarding current challenges and future direction

Leveraging genomic annotations and pleiotropic enrichment for improved replication rates in schizophrenia GWAS

Most of the genetic architecture of schizophrenia (SCZ) has not yet been identified. Here, we apply a novel statistical algorithm called Covariate-Modulated Mixture Modeling (CM3), which incorporates auxiliary information (heterozygosity, total linkage disequilibrium, genomic annotations, pleiotropy) for each single nucleotide polymorphism (SNP) to enable more accurate estimation of replication probabilities, conditional on the observed test statistic (“z-score”) of the SNP. We use a multiple logistic regression on z-scores to combine information from auxiliary information to derive a “relative enrichment score” for each SNP. For each stratum of these relative enrichment scores, we obtain nonparametric estimates of posterior expected test statistics and replication probabilities as a function of discovery z-scores, using a resampling-based approach that repeatedly and randomly partitions meta-analysis sub-studies into training and replication samples. We fit a scale mixture of two Gaussians model to each stratum, obtaining parameter estimates that minimize the sum of squared differences of the scale-mixture model with the stratified nonparametric estimates. We apply this approach to the recent genome-wide association study (GWAS) of SCZ (n = 82,315), obtaining a good fit between the model-based and observed effect sizes and replication probabilities. We observed that SNPs with low enrichment scores replicate with a lower probability than SNPs with high enrichment scores even when both they are genome-wide significant (p < 5x10-8). There were 693 and 219 independent loci with model-based replication rates ≥80% and ≥90%, respectively. Compared to analyses not incorporating relative enrichment scores, CM3 increased out-of-sample yield for SNPs that replicate at a given rate. This demonstrates that replication probabilities can be more accurately estimated using prior enrichment information with CM3

Waste prevention for sustainable resource and waste management

Although the 2Rs (reduce and reuse) are considered high-priority approaches, there has not been enough quantitative research on effective 2R management. The purpose of this paper is to provide information obtained through the International Workshop in Kyoto, Japan, on 11–13 November 2015, which included invited experts and researchers in several countries who were in charge of 3R policies, and an additional review of 245 previous studies. It was found that, regarding policy development, the decoupling between environmental pressures and economy growth was recognized as an essential step towards a sustainable society. 3R and resource management policies, including waste prevention, will play a crucial role. Approaches using material/substance flow analyses have become sophisticated enough to describe the fate of resources and/or hazardous substances based on human activity and the environment, including the final sink. Life-cycle assessment has also been developed to evaluate waste prevention activities. Regarding target products for waste prevention, food loss is one of the waste fractions with the highest priority because its countermeasures have significant upstream and downstream effects. Persistent organic pollutants and hazardous compounds should also be taken into account in the situation where recycling activities are globally widespread for the promotion of a material-cycling society

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels