Search

As one of the most economically significant Oleaceae family members, Jasminum sambac is renowned for its distinct sweet, heady fragrance. Using Illumina reads, Nanopore long reads, and HiC-sequencing, we efficiently assembled and annotated the J. sambac genome. The high-quality genome assembly consisted of a total of 507 Mb sequence (contig N50 = 17.6 Mb) with 13 pseudomolecules. A total of 21,143 protein-coding genes and 303 Mb repeat sequences were predicted. An ancient whole-genome triplication event at the base of Oleaceae (~66 million years ago [Ma], Late Cretaceous) was identified and this may have contributed to the diversification of the Oleaceae ancestor and its divergence from the Lamiales. Stress-related (e.g., WRKY) and flowering-related (e.g., MADS-box) genes were located in the triplicated regions, suggesting that the polyploidy event might have contributed adaptive potential. Genes related to terpenoid biosynthesis, for example, FTA and TPS, were observed to be duplicated to a great extent in the J. sambac genome, perhaps explaining the strong fragrance of the flowers. Copy number changes in distinct phylogenetic clades of the MADS-box family were observed in J. sambac genome, for example, AGL6- and Mα- were lost and SOC- expanded, features that might underlie the long flowering period of J. sambac. The structural genes implicated in anthocyanin biosynthesis were depleted and this may explain the absence of vivid colours in jasmine. Collectively, assembling the J. sambac genome provides new insights into the genome evolution of the Oleaceae family and provides mechanistic insights into floral properties.

It was acknowledged long ago that microorganisms have played critical roles in animal evolution. Tibetan wild asses (TWA,Equus kiang) are the only wild perissodactyls on the Qinghai-Tibet Plateau (QTP) and the first national protected animals; however, knowledge about the relationships between their gut microbiota and the host's adaptability remains poorly understood. Herein, 16S rRNA and meta-genomic sequencing approaches were employed to investigate the gut microbiota–host associations in TWA and were compared against those of the co-resident livestock of yak (Bos grunnies) and Tibetan sheep (Ovis aries). Results revealed that the gut microbiota of yak and Tibetan sheep underwent convergent evolution. By contrast, the intestinal microflora of TWA diverged in a direction enabling the host to subsist on sparse and low-quality forage. Meanwhile, high microbial diversity (Shannon and Chao1 indices), cellulolytic activity, and abundant indicator species such as Spirochaetes, Bacteroidetes,Prevotella_1, andTreponema_2supported forage digestion and short-chain fatty acid production in the gut of TWA. Meanwhile, the enterotype identification analysis showed that TWA shifted their enterotype in response to low-quality forage for a better utilization of forage nitrogen and short-chain fatty acid production. Metagenomic analysis revealed that plant biomass degrading microbial consortia, genes, and enzymes like the cellulolytic strains (Prevotella ruminicola, Ruminococcus flavefaciens, Ruminococcus albus, Butyrivibrio fibrisolvens, andRuminobacter amylophilus), as well as carbohydrate metabolism genes (GH43, GH3, GH31, GH5, and GH10) and enzymes (β-glucosidase, xylanase, and β-xylosidase, etc.) had a significantly higher enrichment in TWA. Our results indicate that gut microbiota can improve the adaptability of TWA through plant biomass degradation and energy maintenance by the functions of gut microbiota in the face of nutritional deficiencies and also provide a strong rationale for understanding the roles of gut microbiota in the adaptation of QTP wildlife when facing harsh feeding environments.

This study aimed to investigate the forage yield and quality and extract factors affecting them and provide scientific support for ecological protection of alpine grassland and sustainable development of animal husbandry in the Qinghai-Tibetan Plateau. Samples were collected in late August (peak of the growing season) 2014 in two mountain transects (Laji mountain, 36° 21′ N, 101° 27′ E, 3389–3876 m a.s.l. and Heitu mountain, 34° 22′ N, 100° 30′ E, 4121–4268 m a.s.l.) of Sanjiangyuan. Forage biomass and nutrients were measured by the national standard method, and influencing factors were extracted by RDA. Results showed that the forage yield decreased significantly with the increasing altitude, no matter in the mountain > 4000 m a.s.l. or 4000 m a.s.l., fiber and lignin were mainly affected by soil pH and SOC and crude protein (CP) by soil pH (p

To examine the combined effects of planting models with nutrient management strategies on grain filling rates, productivity, soil water storage, water-use efficiency and economic benefit of rain-fed corn, there for a field research was carried out during 2015–2016 in a semi-arid region of China. Three planting models were tested: ridge furrow system with plastic mulching (M1), furrow planting with soil crust ridges (M2), and flat planting without plastic mulching (M3); in combination with four nitrogen levels: 0 kg ha–1 (N0); 100 kg ha–1 (N1); 200 kg ha–1 (N2), and 300 kg ha–1 (N3). We found that M1 planting model with higher 300 kg-1 N ha-1 could significantly increase soil water availability at different growth stages, as a result significantly increased WUE (56.6%), RUE (71.2%), ET (8.8%), soil water storage, biomass (35.5%), and grain yield (70.8%) of rain-fed maize than that of M3N0 treatment. Increasing the nitrogen level as a result significantly increased the economic benefit, yields and yield components of rain-fed maize under different planting models, but AEN shows a significantly decreasing trend. The M1N3 treatment significantly promoted the LAI and grains filling of the basal, middle and upper seeds than that of M2 and M3 cultivation models with the highest nitrogen level. The M1N3 treatment obtained the highest (12,775 CNY ha-1) net profit, improved the SWS throughout the grain-filling duration, which resulted in higher productivity. Thus, M1N3 treatment can be used to improving productivity, economic benefit, and water use in semi-arid areas.

BACKGROUND: Parkinson’s disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson’s disease based on deep recurrent neural network of EEG signal METHODS: The EEG signals of 20 patients with Parkinson’s disease and 20 healthy people in Henan Provincial People’s Hospital (People’s Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step. RESULTS: The suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class. Also, 11.49% percent of Parkinson cases are classified wrongly in the healthy class. CONCLUSIONS: The experimental model has high efficiency and can be used as a reliable tool for clinical PD detection. In future research, more cases should be used to test and develop the proposed model.

Background Parkinson's disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson's disease based on deep recurrent neural network of EEG signal. Methods The EEG signals of 20 patients with Parkinson's disease and 20 healthy people in Henan Provincial People's Hospital (People's Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step. Results The suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class. Also, 11.49% percent of Parkinson cases are classified wrongly in the healthy class. Conclusions The experimental model has high efficiency and can be used as a reliable tool for clinical PD detection. In future research, more cases should be used to test and develop the proposed model.