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Alfalfa (Medicago sativa L.) is one of the most widely cultivated perennial forage legumes worldwide. Fall dormancy is an adaptive character related to the biomass production and winter survival in alfalfa. The physiological, biochemical and molecular mechanisms causing fall dormancy and the related genes have not been well studied. In this study, we sequenced two standard varieties of alfalfa (dormant and non-dormant) at two time points and generated approximately 160 million high quality paired-end sequence reads using sequencing by synthesis (SBS) technology. The de novo transcriptome assembly generated a set of 192,875 transcripts with an average length of 856 bp representing about 165.1 Mb of the alfalfa leaf transcriptome. After assembly, 111,062 (57.6%) transcripts were annotated against the NCBI non-redundant database. A total of 30,165 (15.6%) transcripts were mapped to 323 Kyoto Encyclopedia of Genes and Genomes pathways. We also identified 41,973 simple sequence repeats, which can be used to generate markers for alfalfa, and 1,541 transcription factors were identified across 1,350 transcripts. Gene expression between dormant and non-dormant alfalfa at different time points were performed, and we identified several differentially expressed genes potentially related to fall dormancy. The Gene Ontology and pathways information were also identified. We sequenced and assembled the leaf transcriptome of alfalfa related to fall dormancy, and also identified some genes of interest involved in the fall dormancy mechanism. Thus, our research focused on studying fall dormancy in alfalfa through transcriptome sequencing. The sequencing and gene expression data generated in this study may be used further to elucidate the complete mechanisms governing fall dormancy in alfalfa.

To investigate the cholesterol-lowering effects of alfalfa saponin extract (ASE) and its regulation mechanism on some key genes involved in cholesterol metabolism, 40 healthy 7 weeks old male Sprague Dawley (SD) rats were randomly divided into four groups with 10 rats in each group: control group, hyperlipidemic group, ASE treatment group, ASE prevention group. The body weight gain, relative liver weight and serum lipid 1evels of rats were determined. Total cholesterol (TC) and total bile acids (TBA) levels in liver and feces were also measured. Furthermore, the activity and mRNA expressions of Hmgcr, Acat2, Cyp7a1 and Ldlr were investigated. The results showed the following: (1) The abnormal serum lipid levels in hyperlipidemic rats were ameliorated by ASE administration (both ASE prevention group and treatment group) (P<0.05). (2) Both ASE administration to hyperlipidemic rats significantly reduced liver TC and increased liver TBA level (P<0.05). TC and TBA levels in feces of hyperlipidemic rats were remarkably elevated by both ASE administration (P<0.05). (3) mRNA expressions of Hmgcr and Acat2 in the liver of hyperlipidemic rats were remarkably down-regulated (P<0.05), as well as mRNA expressions of Cyp7a1 and Ldlr were dramatically up-regulated by both ASE administration (P<0.05). The activities of these enzymes also paralleled the observed changes in mRNA levels. (4) There was no significant difference between ASE treatment and ASE prevention group for most parameters evaluated. Our present study indicated that ASE had cholesterol-lowering effects. The possible mechanism could be attributed to (1) the down-regulation of Hmgcr and Acat2, as well as up-regulation of Cyp7a1 and Ldlr in the liver of hyperlipidemic rats, which was involved in cholesterol biosynthesis, uptake, and efflux pathway; (2) the increase in excretion of cholesterol. The findings in our study suggested ASE had great potential usefulness as a natural agent for treating hyperlipidemia.

No abstract

BackgroundMicroRNAs (miRNAs) are a class of regulatory small RNAs (sRNAs) that regulate gene post-transcriptional expression in plants and animals. High-throughput sequencing technology is capable of identifying small RNAs in plant species. Alfalfa (Medicago sativa L.) is one of the most widely cultivated perennial forage legumes worldwide, and fall dormancy is an adaptive characteristic related to the biomass production and winter survival in alfalfa. Here, we applied high-throughput sRNA sequencing to identify some miRNAs that were responsive to fall dormancy in standard variety (Maverick and CUF101) of alfalfa.ResultsFour sRNA libraries were generated and sequenced from alfalfa leaves in two typical varieties at distinct seasons. Through integrative analysis, we identified 51 novel miRNA candidates of 206 families. Additionally, we identified 28 miRNAs associated with fall dormancy in standard variety (Maverick and CUF101), including 20 known miRNAs and eight novel miRNAs. Both high-throughput sequencing and RT-qPCR confirmed that eight known miRNA members were up-regulated and six known miRNA members were down-regulated in response to fall dormancy in standard variety (Maverick and CUF101). Among the 51 novel miRNA candidates, five miRNAs were up-regulated and three miRNAs were down-regulated in response to fall dormancy in standard variety (Maverick and CUF101), and five of them were confirmed by Northern blot analysis.ConclusionWe identified 20 known miRNAs and eight new miRNA candidates that were responsive to fall dormancy in standard variety (Maverick and CUF101) by high-throughput sequencing of small RNAs from Medicago sativa. Our data provide a useful resource for investigating miRNA-mediated regulatory mechanisms of fall dormancy in alfalfa, and these findings are important for our understanding of the roles played by miRNAs in the response of plants to abiotic stress in general and fall dormancy in alfalfa.

As the key component of wireless data transmission and powering, stretchable antennas play an indispensable role in flexible/stretchable electronics. However, they often suffer from frequency detuning upon mechanical deformations; thus, their applications are limited to wireless sensing with wireless transmission capabilities remaining elusive. Here, a hierarchically structured stretchable microstrip antenna with meshed patterns arranged in an arched shape showcases tunable resonance frequency upon deformations with improved overall stretchability. The almost unchanged resonance frequency during deformations enables robust on-body wireless communication and RF energy harvesting, whereas the rapid changing resonance frequency with deformations allows for wireless sensing. The proposed stretchable microstrip antenna was demonstrated to communicate wirelessly with a transmitter (input power of − 3 dBm) efficiently (i.e., the receiving power higher than − 100 dBm over a distance of 100 m) on human bodies even upon 25% stretching. The flexibility in structural engineering combined with the coupled mechanical–electromagnetic simulations, provides a versatile engineering toolkit to design stretchable microstrip antennas and other potential wireless devices for stretchable electronics.

The development of wearable/stretchable electronics could largely benefit from advanced stretchable antennas with excellent on-body performance upon mechanical deformations.Despite the recent developments of stretchable antennas based on intrinsically stretchable

Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenous methods, advantages, limitations, and challenges are discussed and proposed. In addition, the paper discusses the communication between the various brain-computer interface modes used between severely paralyzed and locked patients and the surrounding environment, particularly the brain-computer interaction system utilizing exogenous (induced) EEG signals (such as P300 and SSVEP). This discussion reveals with an examination of the interface for collecting EEG signals, EEG components, and signal postprocessing. Furthermore, the paper describes the development of natural interaction strategies, with a focus on signal acquisition, data processing, pattern recognition algorithms, and control techniques.