Decision Trees for Predicting the Physiological Responses of Rabbits

Comparative transcriptomic analysis of rabbit interscapular brown adipose tissue whitening under physiological conditions

Interscapular brown adipose tissue (iBAT) of both rabbits and humans exhibits a similar whitening phenomenon under physiological conditions. However, a detailed characterization of iBAT whitening in them is still lacking. Here, we chose rabbits as a model to gain a better understanding of the molecular signature changes during the whitening process of iBAT by transcriptomic analysis of rabbit iBAT at day 1, day 14, 1 month and 4 months after birth. We applied non-invasive MRI imaging to monitor the whitening process and correlated these changes with analysis of morphological, histological and molecular features. Principal component analysis (PCA) of differentially expressed genes delineated three major phases for the whitening process as Brown, Transition and Whitened BAT phases. RNA-sequencing data revealed that whitening of iBAT was an orchestrated process where multiple types of cells and tissues participated in a variety of physiological processes including neovascularization, formation of new nervous networks and immune regulation. Several key metabolic and signalling pathways contributed to whitening of iBAT, and immune cells and immune regulation appeared to play an overarching role.

Effect of the Spatial Distribution of the Temperature and Humidity Index in a New Zealand White Rabbit House on Respiratory Frequency and Ear Surface Temperature

The objective of this study was to characterize and evaluate the temperature and humidity index (THI) of New Zealand white (NZW) rabbits kept in a rabbit house using geostatistical techniques. Furthermore, we sought to evaluate its relationship with respiratory frequency (RF) and ear surface temperature (EST). The experiment was conducted at the Federal University of Lavras, Brazil. A total of 52 NZW rabbits were used. For the characterization of the thermal environment, the dry bulb temperature (tdb, °C), relative humidity (RH, %), and dew point temperature (tdp, °C) were collected at 48 points in the rabbit house at 6:00 a.m., 12:00 p.m., and 6:00 p.m. for seven days. The RF and EST of the animals was monitored. Subsequently, the THI was calculated and the data were analyzed using geostatistical tools and kriging interpolation. In addition, the RF and EST data were superimposed on the rabbit house's THI data maps. The magnitude of the variability and structure of the THI inside the rabbit house were characterized and the heterogeneity was visualized. Critical THI points inside the rabbit house and in locations where animals with high RF and ESTs were housed were identified, thus providing information about improving the production environment.

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.