Early Developing Pig Embryos Mediate Their Own Environment in the Maternal Tract

The maternal tract plays a critical role in the success of early embryonic development providing an optimal environment for establishment and maintenance of pregnancy. Preparation of this environment requires an intimate dialogue between the embryo and her mother. However, many intriguing aspects remain unknown in this unique communication system. To advance our understanding of the process by which a blastocyst is accepted by the endometrium and better address the clinical challenges of infertility and pregnancy failure, it is imperative to decipher this complex molecular dialogue. The objective of the present work is to define the local response of the maternal tract towards the embryo during the earliest stages of pregnancy. We used a novel in vivo experimental model that eliminated genetic variability and individual differences, followed by Affymetrix microarray to identify the signals involved in this embryo-maternal dialogue. Using laparoscopic insemination one oviduct of a sow was inseminated with spermatozoa and the contralateral oviduct was injected with diluent. This model allowed us to obtain samples from the oviduct and the tip of the uterine horn containing either embryos or oocytes from the same sow. Microarray analysis showed that most of the transcripts differentially expressed were down-regulated in the uterine horn in response to blastocysts when compared to oocytes. Many of the transcripts altered in response to the embryo in the uterine horn were related to the immune system. We used an in silico mathematical model to demonstrate the role of the embryo as a modulator of the immune system. This model revealed that relatively modest changes induced by the presence of the embryo could modulate the maternal immune response. These findings suggested that the presence of the embryo might regulate the immune system in the maternal tract to allow the refractory uterus to tolerate the embryo and support its development

Unsupervised Learning for Improved Gamma-Ray Spectrometry in Pixelated Cadmium Zinc Telluride (CZT) Detectors

Machine learning has been found to be ubiquitously useful across many industries, presenting an opportunity to improve radiation detection performance using data-driven algorithms. Improved detector resolution can aid in the detection, identification, and quantification of radionuclides. In this work, a novel, data-driven, unsupervised learning approach is developed to improve detector spectral characteristics by learning, and subsequently rejecting, poorly performing regions of the pixelated detector. Feature engineering is used to fit individual characteristic photo peaks to a Doniach lineshape with a linear background model. Then, principal component analysis is used to learn a lower-dimension latent space representation of each photo peak where the pixels are clustered, and subsequently ranked, based on the cluster mean distance to an optimal point. Pixels within the worst cluster(s) are rejected to improve the full-width at half-maximum (FWHM) by 10% to 15% (relative to the bulk detector) at 50% net efficiency when applied to training data obtained from measurements of a 100 μCi 154Eu source using a H3D M400i pixelated cadmium zinc telluride detector. These results compare well with, but do not outperform, a greedy algorithm that accumulates pixels in order of FWHM from lowest to highest used as a benchmark. In the future, this approach can be extended to include the detector energy and angular response. Finally, the model is applied to newly seen natural and enriched uranium spectra relevant for nuclear safeguards applications

Exploration of flavor familiarity effect in Korean and US consumers’ hot sauces perceptions

The present work explored how consumers' product perceptions differ when flavor familiarity with the product set varied. Half of the samples used in this study contained fermented ingredients (fermented red pepper or gochujang, a traditional Korean fermented soybean/red pepper paste), and the others were top selling hot sauce products in the US market. Free-choice profiling was performed by Korean and US consumers and was analyzed using GPA. Descriptive analysis was conducted and analyzed using PCA. While Korean and US consumers perceived product similarly along the first principal dimension which described distinctive sensory differences among the products, in the next principal dimension, it was found that these consumers perceived the products differently. Observations indicated that this discrepancy seemed to be originated from differences in flavor familiarity. This study showed flavor familiarity not only influences one's preference but also may influence perception of foods such as hot sauces