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 ± 5.33 kg BW) were selected and randomly divided into four groups. A 2 × 2 factorial design was adopted for the experiment, with the factors of the oat hay feeding method (fed as free-choice or 16.7% in the diet) and compound probiotics (CMP) inclusion (0.15% or 0%) in the pelleted starter. The results showed that, compared with giving oat hay as free-choice, feeding a diet of 16.7% oat hay increased the pelleted starter intake at 1-84 d (p < 0.05), with an average daily gain (ADG) at 61-84 d (p = 0.02); adding CMP to the pelleted starter did not significantly affect body weight, and reduced the fecal index (p < 0.05). Feeding 16.7% oat hay increased the concentration of IgA, IgG, and IgM (p < 0.01), while adding CMP increased the catalase (p < 0.01) and decreased the concentration of malondialdehyde (p < 0.01) in serum. Feeding 16.7% oat hay increased the ruminal concentration of propionic acid (p < 0.05) and isobutyric acid (p = 0.08), and decreased the ruminal pH (p = 0.08), the concentration of acetic acid (p < 0.05), and the ratio of acetic acid to propionic acid (p < 0.01). Feeding 16.7% oat hay reduced the relative abundance of ruminal Firmicutes, Unidentified-Bacteria, Actinobacteria, Prevotella, NK4A214-group, Olsenella, and Actinobacteriota (p < 0.05); adding CMP increased the relative abundance of ruminal Prevotella, Rikenellaceae-RC9-gut-group, Ruminococcus, NK4A214-group, and Ruminococcus (p < 0.05), and decreased the abundance of Desulfobacterora, Prevotella-7, and Erysipelotricaceae-UCG-002 (p < 0.05). In conclusion, feeding a diet of 16.7% oat hay increased the pelleted starter intake and average daily gain, while slightly reducing the ruminal pH values; adding CMP to the pelleted starter resulted in reduced diarrhea incidence, increased serum antioxidant capacity and immunity, as well as ruminal richness and diversity of microorganisms in dairy calves from 3 to 5 months of age.

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). The results showed that, compared to when the cows were fed the CON diet, when cows were fed the IS or IO diet, they had lower milk fat concentrations, energy corrected milk, 3.5% fat-corrected milk yield, feed efficiency and apparent digestibility of NDF and ADF. However, cows fed the IO diet had a lower apparent digestibility of ether extracts. In addition, we observed that when cows were fed the high-starch (IS) or high-fat (IO) diet, they had a higher fecal concentration of propionate and acetate, and a lower NH3-N. Compared to when the cows were fed the CON diet, cows fed the IS diet had a lower pH, and cows fed the IO diet had a lower concentration of valerate in feces. In the hindgut microbiota, the relative abundance of Oscillospiraceae_UCG-005 was increased, while the Verrucomicrobiota and Lachnospiraceae_AC2044_group were decreased when cows were fed the IO diet. The relative abundance of Prevotellaceae_UCG-003 was increased, while the Alistipes and Verrucomicrobiota decreased, and the Treponema, Spirochaetota and Lachnospiraceae_AC2044_group showed a decreasing trend when cows were fed the IS diet. In summary, this study suggested that high-starch or high-fat feeding could induce MFD in dairy cows, and the high-fat diet had the greatest effect on milk fat; the high-starch or high-fat diet affected hindgut fermentation and apparent fiber digestibility. The changes in hindgut flora suggested that hindgut microbiota may be associated with MFD in cows.

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. Secondly, the continuous attention mechanism makes the prediction results more accurate than those of previous studies. Thirdly, we developed continuous spatial self-attention, temporal self-attention, and transformation attention mechanisms to create a bridge between effective spatial information and future temporal evolution information. Fourthly, we show that the STNN model can reconstruct the phase space of the dynamical system, which is explored in the time-series prediction. The experimental results demonstrate that the STNN significantly outperforms the existing methods on various benchmarks and real-world systems in the multi-step-ahead prediction of a short-term time-series.

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. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

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. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.