Genetic diversity and phylogenetic characteristics of viruses in lily plants in Beijing

Lily (Lilium) is an important bulbous perennial herb that is frequently infected by one or more viruses. To investigate the diversity of lily viruses, lilies with virus-like symptoms in Beijing were collected to perform small RNA deep sequencing. Then, the 12 complete and six nearly full-length viral genomes, including six known viruses and two novel viruses were determined. Based on sequence and phylogenetic analyses, two novel viruses were considered to be members of the genera Alphaendornavirus (Endornaviridae) and Polerovirus (Solemoviridae). These two novel viruses were provisionally named lily-associated alphaendornavirus 1 (LaEV-1) and lily-associated polerovirus 1 (LaPV-1). Based on sequence, phylogenetic and recombination analyses, strawberry latent ringspot virus (SLRSV) in the genus Stralarivirus (Secoviridae) was identified for the first time in China, and shown to exhibit the highest nucleotide (nt) diversity among the available full-length SLRSV genome sequences, with the highest identities of 79.5% for RNA1 and 80.9% for RNA2. Interestingly, the protease cofactor region in RNA1 was 752 aa in length, whereas those of the other 27 characterized isolates ranged from 700–719 aa in length. The genome sequences of lily virus A (Potyvirus), lily virus X (Potexvirus), and plantago asiatica mosaic virus (Potexvirus) exhibited varying degrees of sequence diversity at the nucleotide level compared with their corresponding characterized isolates. In addition, plantago asiatica mosaic virus (PlAMV) tended to cluster on a host species-basis. One identified lily mottle virus (Potyvirus) isolate was detected as a recombinant, and which clustered in a different group with four other isolates. Seven identified lily symptomless virus (Carlavirus) isolates, including one recombinant, were clustered into three clades. Our results revealed the genetic diversity of lily-infecting viruses, and sequence insertion, host species and recombination are factors that likely contribute to this diversity. Collectively, our results provide useful information regarding the control of viral disease in lily

Particle scattering induced orbital angular momentum spectrum change of vector Bessel–Gaussian vortex beam

In this paper, we obtain the intensity and phase distributions of the scattering and external fields of a vector Bessel–Gaussian vortex beam in the far-field region after being scattered by a particle. In our analysis, we use the Generalized Lorenz–Mie theory (GLMT) and the angular spectrum decomposition method (ASDM). The orbital angular momentum (OAM) spectra of the fields are analyzed by using the spiral spectrum expansion method, which is a frequently used tool for studying the propagation of vortex beams in turbulent atmospheres. Both scattered and external fields show a significant difference in spiral spectra for particles with different characteristic parameters, such as the size and complex refractive index. We also examine sampling the phase along with a circle and show that it is unable to fully express the information of the fields. This study can provide a theoretical basis for the inversion of characteristic parameters of the Bessel–Gaussian vortex beam and spherical particle by OAM spectra with applications in remote sensing engineering

Experimental study on LBL beams

Six specimens were made and tested to study the mechanical properties of LBL beams. The mean ultimate loading value is 68.39 MPa with a standard deviation of 6.37 MPa, giving a characteristic strength (expected to be exceeded by 95% of specimens) of 57.91 MPa, and the mean ultimate deflection is 53.3 mm with a standard deviation of 5.5 mm, giving the characteristic elastic modulus of 44.3 mm. The mean ultimate bending moment is 20.18 kN.m with a standard deviation of 1.88 kN.m, giving the characteristic elastic modulus of 17.08 kN.m. The mean elastic modulus is 9688 MPa with a standard deviation of 1765 MPa, giving the characteristic elastic modulus of 6785 MPa, and the mean modulus of rupture is 93.3 MPa with a standard deviation of 8.6 MPa, giving the characteristic elastic modulus of 79.2 MPa. The strain across the cross-section for all LBL beams is basically linear throughout the loading process, following standard beam theory

Infrared spectroscopy : a tool for protein characterization

Infrared (IR) spectroscopy, which belongs to vibrational spectroscopy, detects the vibrations of molecules, for example, proteins. The absorption of the peptide group gives rise to 9 characteristic bands in the infrared region, named A, B, I-VII, with a decreasing energy or wavenumber (cm-1). Among the 9 bands, amide I, which is mainly caused by C=O stretching vibration, is most sensitive to backbone structure and environment, and therefore can be used for structural analysis. In this thesis, a membrane protein sarcoplasmic reticulum Ca2+-ATPase (SERCA1a) and a self-assembling peptide was studied with IR spectroscopy.   In the first two papers, IR spectroscopy was used to assess the quality of a recombinant SERCA1a. A yeast-based expression system was applied to express recombinant SERCA1a, and the reaction cycle as well as the structure was analysed with IR spectroscopy. Different reaction intermediates were accumulated under different buffer conditions upon the release of ATP. The results showed that the recombinant protein shared similar IR features compared to the native protein. However, two SERCA1a preparations showed a difference around 1640 cm-1 in the amide I region. Using curve fitting, the band was assigned to β structure, and further investigation indicated that the difference in this region originates from protein aggregation. In the third paper, a co-fitting approach was tested and showed to be a more reliable method for structural analysis, and it can be applied in the biological IR spectroscopy. In the fourth paper, a peptide was computational designed and was predicted to self-assemble to amyloid fibrils, the formation of the fibril was confirmed with both electron microscopy and X-ray diffraction. IR spectroscopy was used to analyze further the structural details and the results support our structural predication.

Développement de marqueurs fluorogéniques bioorthogonaux pour l'imagerie biologique

Studying protein activities could help us to understand the complex mechanisms controlling cells and organisms. Our laboratory recently developed Fluorescence-Activating and absorption-Shifting Tag (FAST), a small fluorogen-based reporter enabling to fluorescently label fusion proteins in living cells. My PhD thesis presents the developments of new FAST systems with various properties for multiplexed imaging. We report a collection of fluorogens enabling to tune the fluorescence color of FAST from green-yellow to orange and red. Beyond allowing multicolor imaging of FAST-tagged proteins in live cells, these fluorogens enable dynamic color switching because of FAST’s reversible labeling, opening great prospects for the design of selective imaging methods relying on dynamic systems. In order to further expand the spectral properties of FAST to red, we also designed and developed a library of red fluorogenic dyes, for which we engineered specific protein binders by applying a directed evolution strategy based on the yeast display technology and high-throughput fluorescence activating cell sorting (FACS). We finally developed novel fluorogens able to form fluorescent complexes with FAST, but incapable of crossing the plasma membrane, which makes it possible to selectively detect FAST-tagged cell-surface proteins.L'étude de la dynamique des protéines est essentielle pour comprendre les processus biologiques. Notre laboratoire a développé une nouvelle classe de protéines fluorescentes semi-synthétiques, appelée Fluorescence-Activating and absorption-Shifting Tag (FAST). Cette thèse de doctorat présente le développement de nouveaux systèmes FAST avec diverses propriétés pour l'imagerie multiplexée. Nous avons développé une série de fluorogènes permettant de modifier la couleur de FAST de vert-jaune à orange et rouge. Au delà de l’application de l’imagerie multi-couleurs, ces fluorogènes permettant un échange dynamique des couleurs grâce à la liaison réversible de FAST, ouvrant de nouvelles perspectives pour le développement de méthodes d’imagerie sélective reposant sur la dynamique de systèmes réactifs. Pour étendre davantage les propriétés spectrales de FAST vers le rouge lointain, nous avons développé une nouvelle série de fluorogènes rouges, pour lesquels nous avons sélectionné par une stratégie d'évolution dirigée basée sur le yeast display et la cytométrie en flux de nouveaux tags protéiques capables d’interagir avec ces fluorogènes et d’activer leur fluorescence. Nous avons enfin développé de nouveaux fluorogènes capables de former des complexes fluorescents avec FAST, mais incapables de traverser la membrane plasmique, ce qui permet de détecter sélectivement les protéines membranaires

Fluorogenic Labeling Strategies for Biological Imaging

The spatiotemporal fluorescence imaging of biological processes requires effective tools to label intracellular biomolecules in living systems. This review presents a brief overview of recent labeling strategies that permits one to make protein and RNA strongly fluorescent using synthetic fluorogenic probes. Genetically encoded tags selectively binding the exogenously applied molecules ensure high labeling selectivity, while high imaging contrast is achieved using fluorogenic chromophores that are fluorescent only when bound to their cognate tag, and are otherwise dark. Beyond avoiding the need for removal of unbound synthetic dyes, these approaches allow the development of sophisticated imaging assays, and open exciting prospects for advanced imaging, particularly for multiplexed imaging and super-resolution microscopy

Prediction of Short-Time Cloud Motion Using a Deep-Learning Model

A cloud image can provide significant information, such as precipitation and solar irradiation. Predicting short-time cloud motion from images is the primary means of making intra-hour irradiation forecasts for solar-energy production and is also important for precipitation forecasts. However, it is very challenging to predict cloud motion (especially nonlinear motion) accurately. Traditional methods of cloud-motion prediction are based on block matching and the linear extrapolation of cloud features; they largely ignore nonstationary processes, such as inversion and deformation, and the boundary conditions of the prediction region. In this paper, the prediction of cloud motion is regarded as a spatiotemporal sequence-forecasting problem, for which an end-to-end deep-learning model is established; both the input and output are spatiotemporal sequences. The model is based on gated recurrent unit (GRU)- recurrent convolutional network (RCN), a variant of the gated recurrent unit (GRU), which has convolutional structures to deal with spatiotemporal features. We further introduce surrounding context into the prediction task. We apply our proposed Multi-GRU-RCN model to FengYun-2G satellite infrared data and compare the results to those of the state-of-the-art method of cloud-motion prediction, the variational optical flow (VOF) method, and two well-known deep-learning models, namely, the convolutional long short-term memory (ConvLSTM) and GRU. The Multi-GRU-RCN model predicts intra-hour cloud motion better than the other methods, with the largest peak signal-to-noise ratio and structural similarity index. The results prove the applicability of the GRU-RCN method for solving the spatiotemporal data prediction problem and indicate the advantages of our model for further applications