Influence of Phytase Transgenic Corn on the Intestinal Microflora and the Fate of Transgenic DNA and Protein in Digesta and Tissues of Broilers

An experiment was conducted to investigate the effect of phytase transgenic corn (PTC) on intestinal microflora, and the fate of transgenic DNA and protein in the digesta and tissues of broilers. A total of 160 1-day-old Arbor Acres commercial male broilers were randomly assigned to 20 cages (8 chicks per cage) with 10 cages (replicates) for each treatment. Birds were fed with a diet containing either PTC (54.0% during 1–21 days and 61.0% during 22–42 days) or non-transgenic isogenic control corn (CC) for a duration of 42 days. There were no significant differences (P>0.05) between birds fed with the PTC diets and those fed with the CC diets in the quantities of aerobic bacteria, anaerobic bacteria, colibacillus and lactobacilli, or microbial diversities in the contents of ileum and cecum. Transgenic phyA2 DNA was not detected, but phyA2 protein was detected in the digesta of duodenum and jejunum of broilers fed with the PTC diets. Both transgenic phyA2 DNA and protein fragments were not found in the digesta of the ileum and rectum, heart, liver, kidney, and breast or thigh muscles of broilers fed with the PTC diets. It was concluded that PTC had no adverse effect on the quantity and diversity of gut microorganisms; Transgenic phyA2 DNA or protein was rapidly degraded in the intestinal tract and was not transferred to the tissues of broilers.     

Corrigendum of Zhao et al. (2020): a revised infrageneric classification of Lepisorus (Polypodiaceae)

Here, we publish Lepisorus sect. Paragramma (Blume) C.F. Zhao, R. Wei & X.C. Zhang as a combinatio nova to replace the section name in Zhao et al. (2020), which was published as a status nova and turned out to be an invalid name, because we cited an incorrect basionym. A revised infrageneric classification of Lepisorus also is proposed.     

Multi-model recursive identification for nonlinear systems with non-uniformly sampling

A recursive least squares based on Multi-model is proposed for non-uniformly sampled-data nonlinear (NUSDN) systems. The corresponding state space model of an NUSDN system is derived using lifting technique. Taking advantage of the Fuzzy c-Mean Clustering algorithm, NUSDN is divided into several local models. The basic idea is that the NUSDN system is viewed as a model switching system under a given rule. Once the local models are identified, the global model is determined. A pH neutralization process validate the performance of the proposed algorithm.