Supplementation of lake extender with cysteamine preserves quality parameters and fertility potential of post-thawed rooster sperm during cryopreservation process.

Background: In the cryopreservation process, rooster spermatozoa are vastly sensitive to reactive oxygen species (ROS). This study aimed to investigate the effects of Lake extender supplemented via Cysteamine (CYS) on the quality and fertility characteristics of rooster semen during the cryopreservation process.

Methods: Semen samples were collected from 10 proved Ross-308 roosters, diluted and cryopreserved in the Lake extender which contained 0, 1, 2, 4, and 8 mM of CYS (C-0, C-1, C-2, C-4, and C-8, respectively).

Role of Oral Yeast in Replenishing Gastric Mucosa with Yeast and Helicobacter pylori.

The relationship between oral and gastric yeasts and their role in the colonization of Helicobacter pylori in the stomach was studied. Four groups of 221, 7, 44, and 10 patients were used for the isolation of H. pylori and oral and gastric yeasts.

Multiple observers ranked set samples for shrinkage estimators.

Ranked set sampling (RSS) is used as a powerful data collection technique for situations where measuring the study variable requires a costly and/or tedious process while the sampling units can be ranked easily (e.g. osteoporosis research).

A Novel Tetravalent Bispecific Immune Cell Engager Activates Natural Killer Cells to Kill Cancer Cells without Mediating Fratricide.

We previously reported the structure, affinity, and anticancer activity of a bivalent bispecific natural killer cell engager (BiKE) composed of one anti-CD16a VHH and one anti-HER2 VHH fused via a linker. In this study, we explored the engineering of a tetravalent BiKE by fusing two anti-CD16a and two anti-HER2 VHHs in tandem, using bivalent BiKE as a template. The tetravalent BiKE was genetically engineered, and its tertiary structure was predicted using in silico modeling.

Unsupervised Liu-type shrinkage estimators for mixture of regression models.

The mixture of probabilistic regression models is one of the most common techniques to incorporate the information of covariates into learning of the population heterogeneity. Despite its flexibility, unreliable estimates can occur due to multicollinearity among covariates. In this paper, we develop Liu-type shrinkage methods through an unsupervised learning approach to estimate the model coefficients in the presence of multicollinearity.