MUC1 oncogene amplification correlates with protein overexpression in invasive breast carcinoma cells

The MUC1 gene is aberrantly overexpressed in approximately 90% of human breast cancers. Several studies have shown that MUC1 overexpression is due to transcriptional regulatory events. However, the importance of gene amplification as a mechanism leading to the increase of MUC1 expression in breast cancer has been poorly characterized. The aim of this study was to evaluate the role of MUC1 gene amplification and protein expression in human breast cancer development. By means of real-time quantitative polymerase chain reaction and immunohistochemical methods, 83 breast tissue samples were analyzed for MUC1 gene amplification and protein expression. This analysis showed MUC1 genomic amplification and a positive association with the histopathological group in 12% (1 out of 8) of benign lesions and 38% (23 out of 60) of primary invasive breast carcinoma samples (P = 0.004). Array-comparative genomic hybridization meta-analysis of 886 primary invasive breast carcinomas obtained from 22 studies showed MUC1 genomic gain in 43.7% (387 out of 886) of the samples. Moreover, we identified a highly statistical significant association between MUC1 gene amplification and MUC1 protein expression assessed by immunohistochemistry and Western blot test (P < 0.0001). In conclusion, this study demonstrated that MUC1 copy number increases from normal breast tissue to primary invasive breast carcinomas in correlation with MUC1 protein expression

Intelligent learning algorithms integrated with feature engineering for sustainable groundwater salinization modelling: Eastern Province of Saudi Arabia

Groundwater (GW) salinization refers to the natural or human-induced process of GW acquiring a higher level of salt content. While geological factors or proximity to the ocean can naturally cause this phenomenon, it can also be attributed to activities like irrigation, changes in land use, and the discharge of industrial or municipal waste. The following research employs the use of various feature extraction methods coupled with novel chemometrics approaches informed by machine learning (ML) techniques including; Gaussian Process Regression (GPR), Support Vector Regression (SVR), Regression tree (RT) and Robust linear regression (RLR). Based on the feature selection methods, the models were classified into three different combinations. The intelligent learning algorithms equally depict higher PCC values ranging from 0.935 to 1.00 in the training and 0.779 to 0.999 in the validation stage respectively, which depicts a higher relation between the experimental and simulated values. The performance results indicate that GPR-Comb-3 showed the highest performance in both the training and validation stages respectively. It is worth mentioning that even the RLR technique equally depicts exceptional prediction skills in both the training and validation steps. In conclusion, the outcomes of the current research depict the significance of these techniques in evaluating GW salinization

NEOTROPICAL XENARTHRANS: a data set of occurrence of xenarthran species in the Neotropics

Xenarthrans—anteaters, sloths, and armadillos—have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard to sloth species, Bradypus variegatus has the most records (n = 962), and Bradypus pygmaeus has the fewest (n = 12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans data set. Please cite this data paper when using its data in publications. We also request that researchers and teachers inform us of how they are using these data