No abstract

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at a post-transcriptional level by silencing targeted messenger RNA (mRNA). Most studies concerning miRNA expression use solid tissue samples. However, circulating miRNAs from different body fluids have recently emerged as diagnostic and prognostic molecules, given that they hold informative value and have increased stability in cell-free form. Blood sampling of cats can be challenging given their small body size and because they often experience distress when handled. We quantified miR-20a, -192, -365, -15b-5p, and -16-5p from plasma and serum samples of 10 healthy domestic cats. Our RT-rtPCR procedure used 100 µL of either plasma or serum samples as sources of biomarker molecules. However, serum provided higher amounts of miRNA than plasma samples, with a p < 0.0001 for miR-20a and p < 0.0002 for miR-16-5p.

No abstract

The 5G mobile networks will increase the amount of available spectrum by using new licensed and unlicensed bands. However, most of the newly allocated bandwidth is restricted to higher frequencies, that provides smaller coverage. Lower frequencies, that provide higher ranges, are mostly occupied by either radio or television channels. With the move to digital television, some analog TV channels are available in specific regions, opening the possibility of frequency reuse by third parties. Geolocation databases and collaborative sensing techniques are used to locate and use the available TV Whitespaces (TVWS) for mobile transmission. This paper presents the proposal of a cognitive cycle in the ns-3 simulator (LENA/LTE), which includes collaborative sensing and a cognitive radio resource scheduler. The simulations were done considering a rural area and the results show that the implementation may be useful for further research in developing new solutions for the 5G Cognitive MAC Layer.

Cure fraction is not an easy task to be calculated relating probabilistic estimations to an event. For instance, cancer patients may abandon treatment, be cured, or die due to another illness, causing limitations regarding the information about the odds of cancer cure (related to the patient follow-up) and may mislead the researcher's inference. In this paper, we overcame this limitation and proposed a risk assessment tool related to the lifetime of cancer patients to survival functions to help medical decision-making. Moreover, we proposed a new machine learning algorithm, so-called long-term generalized weighted Lindley (LGWL) distribution, solving the inferential limitation caused by the censored information. Regarding the robustness of this distribution, some mathematical properties are shown and inferential procedures discussed, under the maximum likelihood estimators' perspective. Empirical results used TCGA lung cancer data (but not limited to this cancer type) showing the competitiveness of the proposed distribution to the medical field. The cure-rate is dynamic but quantifiable. For instance, after 14 years of development/spread of lung cancer, the group of patients under the age of 70 had a cure fraction of 32%, while the group of elderly patients presented a cure fraction of 22%, whereas those estimations using the traditional (long-term) Weibull distribution is 31% and 17%. The LGWL returned closer curves to the empirical distribution, then were better adjusted to the adopted data, elucidating the importance of cure-rate fraction in survival models.