Serum overexpression of miR-301a and miR-23a in patients with colorectal cancer

BACKGROUND: Extracellular vesicles (EVs) are a heterogeneous group of membrane-bound vesicles with complex cargoes including proteins, lipids, and nucleic acids. EVs have received significant attention due to their specific features including stability under harsh conditions and involvement in cell-to-cell communication. Circulating EVs and the molecules associated with them are important in the diagnosis and prognosis of cancers. MicroRNAs (miRNAs) are a group of small noncoding RNAs that have a role in regulating gene expression. Current literature shows that circulating miRNAs can be used as noninvasive biomarkers for early detection of cancers. The present study was set to investigate the potential role of serum exosomal miRNA expression levels in colorectal cancer (CRC) patients and evaluate their correlation with clinicopathologic features. METHODS: Exosome-enriched fractions were isolated from the serum of 25 CRC patients and 13 age- and sex-matched healthy controls using a polymer-based precipitation method. During the pilot phase, real-time polymerase chain reaction (RT-PCR) was carried out on 12 CRC patients and eight healthy participants to evaluate the expression difference of 11 candidate miRNAs between CRC patients and tumor free subjects. Finally, the results were validated in a separate group, which was similar in size to the pilot group. The clinicopathologic data were also collected and the relationship between aberrant miRNA expression and clinicopathological parameters were investigated. RESULTS: There were high expressions of exosomal miR-23a and miR-301a in serum samples of CRC patients compared to normal controls in training and validation phases; these differences were not significantly correlated with clinicopathologic features. Receiver operating characteristic curve analysis showed that miR-301a and miR-23a were able to discriminate CRC patients from normal subjects. CONCLUSION: The findings provide evidence on the roles of miR-301a and miR-23a in CRC development and their potential roles as noninvasive biomarkers for early detection of CRC

Snowballs in Euclid and WFIRST Detectors

Snowballs are transient events observed in HgCdTe detectors with a sudden increase of charge in a few pixels. They appear between consecutive reads of the detector, after which the affected pixels return to their normal behavior. The origin of the snowballs is unknown, but it was speculated that they could be the result of alpha decay of naturally radioactive contaminants in the detectors, but a cosmic ray origin cannot be ruled out. Even though previous studies predicted a low rate of occurrence of these events, and consequently, a minimal impact on science, it is interesting to investigate the cause or causes that may generate snowballs and their impact in detectors designed for future missions. We searched for the presence of snowballs in the dark current data in Euclid and Wide Field Infrared Survey Telescope (WFIRST) detectors tested in the Detector Characterization Laboratory at Goddard Space Flight Center. Our investigation shows that for Euclid and WFIRST detectors, there are snowballs that appear only one time, and others that repeat in the same spatial localization. For Euclid detectors, there is a correlation between the snowballs that repeat and bad pixels in the operational masks (pixels that do not fulfill the requirements to pass spectroscopy noise, photometry noise, quantum efficiency, and/or linearity). The rate of occurrence for a snowball event is about 0.9 snowballs/hr. in Euclid detectors (for the ones that do not have associated bad pixels in the mask), and about 0.7 snowballs/hr. in PV3 Full Array Lot WFIRST detectors

Network Maximal Correlation

We introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association among random variables. NMC is defined via an optimization that infers transformations of variables by maximizing aggregate inner products between transformed variables. For finite discrete and jointly Gaussian random variables, we characterize a solution of the NMC optimization using basis expansion of functions over appropriate basis functions. For finite discrete variables, we propose an algorithm based on alternating conditional expectation to determine NMC. Moreover we propose a distributed algorithm to compute an approximation of NMC for large and dense graphs using graph partitioning. For finite discrete variables, we show that the probability of discrepancy greater than any given level between NMC and NMC computed using empirical distributions decays exponentially fast as the sample size grows. For jointly Gaussian variables, we show that under some conditions the NMC optimization is an instance of the Max-Cut problem. We then illustrate an application of NMC in inference of graphical model for bijective functions of jointly Gaussian variables. Finally, we show NMC's utility in a data application of learning nonlinear dependencies among genes in a cancer dataset

The political and social significance of court festivals in Lorraine 1563-1624

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