Effects of the Oat Hay Feeding Method and Compound Probiotic Supplementation on the Growth, Antioxidant Capacity, Immunity, and Rumen Bacteria Community of Dairy Calves.

This study aimed to investigate the effects of the oat hay feeding method and compound probiotics (CMP) on the growth, health, serum antioxidant and immune indicators, rumen fermentation, and bacteria community of dairy calves from 3 to 5 months of age. Forty-eight female Holstein calves (80 ± 7 days of age, 93.71 ± 5.

Effect of a High-Starch or a High-Fat Diet on the Milk Performance, Apparent Nutrient Digestibility, Hindgut Fermentation Parameters and Microbiota of Lactating Cows.

In this study, changes in milk performance, nutrient digestibility, hindgut fermentation parameters and microflora were observed by inducing milk fat depression (MFD) in dairy cows fed with a high-starch or a high-fat diet. Eight Holstein cows were paired in a completely randomized cross-over design within two 35 d periods (18 d control period and 17d induction period). During the control period, all cows were fed the low-starch and low-fat diet (CON), and at the induction period, four of the cows were fed a high-starch diet with crushed wheat (IS), and the other cows were fed a high-fat diet with sunflower fat (IO).

Spatiotemporal Transformer Neural Network for Time-Series Forecasting.

Predicting high-dimensional short-term time-series is a difficult task due to the lack of sufficient information and the curse of dimensionality. To overcome these problems, this study proposes a novel spatiotemporal transformer neural network (STNN) for efficient prediction of short-term time-series with three major features. Firstly, the STNN can accurately and robustly predict a high-dimensional short-term time-series in a multi-step-ahead manner by exploiting high-dimensional/spatial information based on the spatiotemporal information (STI) transformation equation.

Artificial intelligence in cancer target identification and drug discovery.

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations.

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research.

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series.