149 research outputs found
Drought Tolerance Dissection and Molecular Breeding in Alfalfa
Drought stress is one of the leading impediments that limit the productivity of global alfalfa (Medicago sativa). The underlying molecular and genetic mechanisms for drought tolerance in alfalfa remain largely unclear. In order to fully reveal the transcriptional changes of alfalfa in response to abiotic stress, the alfalfa transcriptome database under mannitol (simulated drought stress), NaCl (simulated salt stress), or exogenous ABA application was built via various RNA-seq technologies. Through further screening of the transcriptome database, a number of genes significantly induced by drought stress, such as the Nuclear Transport Factor 2-like (MsNTF2L), Drought-Induced Unknown Protein 1 (MsDIUP1), and MsNST1, were identified. These three genes were transferred into alfalfa by overexpression and RNAi techniques, and their physiological characteristics and transcriptional level response were synthetically studied. Alfalfa MsNTF2L-OE plants have been approved by the Ministry of Agriculture of China to carry out the field test in Gansu Province. Furthermore, we constructed a GWAS population and obtained 50 excellent plants with strong drought tolerance and high hay-yield. These studies provide a theoretical foundation for drought-tolerant molecular breeding of alfalfa
Characterization and Tissue-specific Expression of bHLH Genes in Dimocarpus longan
In plants, the basic helix-loop-helix (bHLH) transcription factors (TFs) play pivotal roles in many biological processes including growth, stress response, and secondary metabolite synthesis. To date, many bHLH genes have been identified and characterized in diverse plant species. However, little is known regarding the bHLH genes in Dimocarpus longan Lour. (D. longan). Based on RNA-seq data, we identified 42 putative bHLH genes from D. longan and determined their putative functions using the NCBI Conserved Domain Search Tool and Pfam databases. The physicochemical properties, phylogenetic relationships, conserved motifs, gene ontology (GO) annotations, protein-protein interactions, and tissue-specific expression patterns of these bHLH genes were systematically explored. In total, ten motifs were found in DlbHLH proteins using MEME, among which two were highly conserved. Phylogenetic tree analysis found that DlbHLH proteins can be divided into nine groups, with group 2 being the largest. GO annotation results showed that the DlHLH genes were involved in various molecular functions. RNA-seq and qRT-PCR results revealed important differences in the expression patterns of 17 of the DlbHLH genes. In particular, DlbHLH-9, DlbHLH-19, DlbHLH-25, DlbHLH-26, and DlbHLH-35 were found to show significantly different expression patterns in root and leaf tissues. The results of this study will further enrich our knowledge regarding bHLH transcription factor genes and lay a foundation for enhancing the production of active secondary metabolites by genetic engineering in D. longan
Flavonoid accumulation and identification of flavonoid biosynthesis genes in Dimocarpus longan lour. by transcriptome sequencing
Dimocarpus longan Lour. (D. longan) is widely cultivated and is very popular around the world. Its by-products such as roots and leaves have been used as traditional Chinese medicines due to their content of important secondary metabolites, especially flavonoids. However, the economic value and application of D. longan roots and leaves are limited because they accumulate relatively low levels of flavonoids. Therefore, it is important to find key genes that regulate the accumulation of the predominant flavonoid compounds in D. longan roots and leaves. Here, we have used RNA-sequencing to describe the transcriptome of D. longan. We obtained 75,229,529 raw reads and 15.04 GB of clean data, generating 56,055 unigenes (N50 = 1,583 nt, mean length = 829.61 nt). Next, we annotated these unigenes using the various available bioinformatics databases. By this approach, we identified 6,684 genes differentially expressed between root and leaf tissues, of which thirteen were identified as flavonoid biosynthesis genes. Of these, eight genes were much highly expressed in roots (DlC4H, DlHCT, DlDFR, DlANS, DlANR, DlCHS, DlF3′H, and DlF3H), and two were much highly expressed in leaves (DlLAR and DlFLS). The contents of thirteen flavonoids in D. longan roots and leaves were measured by LC-MS, and epicatechin was found to be the predominant flavonoid in both tissues, which was significantly higher than the other flavonoids measured in the study. Its contents were 213,773.65 ng/g in roots and 22,388.71 ng/g in leaves. Our findings will facilitate efforts to increase the economic value and expand the applications of D. longan roots and leaves by means of genetic engineering
Beam energy distribution influences on density modulation efficiency in seeded free-electron lasers
The beam energy spread at the entrance of undulator system is of paramount
importance for efficient density modulation in high-gain seeded free-electron
lasers (FELs). In this paper, the dependences of high harmonic micro-bunching
in the high-gain harmonic generation (HGHG), echo-enabled harmonic generation
(EEHG) and phase-merging enhanced harmonic generation (PEHG) schemes on the
electron energy spread distribution are studied. Theoretical investigations and
multi-dimensional numerical simulations are applied to the cases of uniform and
saddle beam energy distributions and compared to a traditional Gaussian
distribution. It shows that the uniform and saddle electron energy
distributions significantly enhance the performance of HGHG-FELs, while they
almost have no influence on EEHG and PEHG schemes. A numerical example
demonstrates that, with about 84keV RMS uniform and/or saddle slice energy
spread, the 30th harmonic radiation can be directly generated by a single-stage
seeding scheme for a soft x-ray FEL facility
Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG
Predicting lower limb motion intent is vital for controlling exoskeleton
robots and prosthetic limbs. Surface electromyography (sEMG) attracts
increasing attention in recent years as it enables ahead-of-time prediction of
motion intentions before actual movement. However, the estimation performance
of human joint trajectory remains a challenging problem due to the inter- and
intra-subject variations. The former is related to physiological differences
(such as height and weight) and preferred walking patterns of individuals,
while the latter is mainly caused by irregular and gait-irrelevant muscle
activity. This paper proposes a model integrating two gait cycle-inspired
learning strategies to mitigate the challenge for predicting human knee joint
trajectory. The first strategy is to decouple knee joint angles into motion
patterns and amplitudes former exhibit low variability while latter show high
variability among individuals. By learning through separate network entities,
the model manages to capture both the common and personalized gait features. In
the second, muscle principal activation masks are extracted from gait cycles in
a prolonged walk. These masks are used to filter out components unrelated to
walking from raw sEMG and provide auxiliary guidance to capture more
gait-related features. Experimental results indicate that our model could
predict knee angles with the average root mean square error (RMSE) of
3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best
performance in relevant literatures that has been reported, with reduced RMSE
by at least 9.5%
Radiotherapy Suppresses Bone Cancer Pain through Inhibiting Activation of cAMP Signaling in Rat Dorsal Root Ganglion and Spinal Cord
Mixed plastics waste valorization through tandem chemical oxidation and biological funneling
115 p.-4 fig.-45 fig. supl.-14 tab supl.Mixed plastics waste represents an abundant and largely untapped feedstock for the production of valuable
products. The chemical diversity and complexity of thesematerials, however, present major barriers to realizing this opportunity. In this work, we show that metal-catalyzed autoxidation depolymerizes comingled polymers into a mixture of oxygenated small molecules that are advantaged substrates for biological conversion. We engineer a robust soil bacterium, Pseudomonas putida, to funnel these oxygenated compounds into a single exemplary chemical product, either b-ketoadipate or polyhydroxyalkanoates. This hybrid process establishes a strategy for the selective conversion of mixed plastics waste into useful chemical products.Funding was provided by the US Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office (AMO), and Bioenergy Technologies Office (BETO). This work was performed as part of the BOTTLE Consortium and was supported by AMO and BETO under contract no. DE-AC36-
08GO28308 with the National Renewable Energy Laboratory (NREL),operated by the Alliance for Sustainable Energy, LLC. The BOTTLE Consortium includes members from MIT, funded under contract no.
DE-AC36-08GO28308 with NREL. Contributions by S.S.S. were supported by the US Department of Energy, Office of Basic Energy Sciences, under award no. DEFG02-05ER15690.Peer reviewe
A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution
Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research
Development of Tandem Mass Spectrometric Methods for Characterizing Asphaltenes and Differentiating Small Organic Isomers
High-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) are powerful tools for the characterization of the molecular structures of components of both simple and complex mixtures. MS and MS/MS have played key roles in many fields, including proteomics, metabolomics, and petroleomics. This thesis focuses on the development of tandem mass spectrometric methods for the structural characterization of asphaltenes and small isomeric molecules. In addition, this thesis also presents a method to address a sampling bias in asphaltene analysis. Chapter 2 describes the fundamental aspects of the mass spectrometer used for the research discussed in this thesis. Chapter 3 presents an in-depth study on the determination of the relative abundances of single-core and multicore compounds in asphaltenes. A statistical sampling bias in many earlier asphaltene studies is discussed and a rapid and simple alternative method is introduced to address this sampling bias. Chapter 4 presents an investigation of the sizes of aromatic cores and lengths of alkyl chains in isobaric ions derived from asphaltenes by using an improved data analysis method. Chapter 5 presents a fundamental study on utilization of energy-resolved collision-activated dissociation (CAD) for the differentiation of isomeric ions. Energy-resolved CAD was used to compare the ability of two different activation methods to differentiate four sets of isomeric ions of aromatic hydrocarbons. The comparison suggests that medium-energy CAD (MCAD; commercially known as higher-energy collision dissociation, HCD) is more powerful in differentiation of the four sets of isomers than conventional ion trap CAD (ITCAD)
- …