A Structural Model of the Genome Packaging Process in a Membrane-Containing Double Stranded DNA Virus

Peer reviewe

Zernike Phase Contrast Cryo-Electron Microscopy and Tomography for Structure Determination at Nanometer and Subnanometer Resolutions

Zernike phase contrast cryo-electron microscopy (ZPC-cryoEM) is an emerging technique that is capable of producing higher image contrast than conventional cryoEM. By combining this technique with advanced image processing methods, we achieved subnanometer resolution for two biological specimens: 2D bacteriorhodopsin crystal and epsilon15 bacteriophage. For an asymmetric reconstruction of epsilon15 bacteriophage, ZPC-cryoEM can reduce the required amount of data by a factor of ~3, compared with conventional cryoEM. The reconstruction was carried out to 13 Å resolution without the need to correct the contrast transfer function. New structural features at the portal vertex of the epsilon15 bacteriophage are revealed in this reconstruction. Using ZPC cryo-electron tomography (ZPC-cryoET), a similar level of data reduction and higher resolution structures of epsilon15 bacteriophage can be obtained relative to conventional cryoET. These results show quantitatively the benefits of ZPC-cryoEM and ZPC-cryoET for structural determinations of macromolecular machines at nanometer and subnanometer resolutions.National Institutes of Health (U.S.) (Grant P41RR002250)National Institutes of Health (U.S.) (Grant R01AI0175208)National Institutes of Health (U.S.) (Grant PN1EY016525)Robert Welch Foundation (Q1242

4.4 Å cryo-EM structure of an enveloped alphavirus Venezuelan equine encephalitis virus

This study uses high-resolution cryo-electron microscopy to provide a complete structural model of the VEEV alphavirus, bridging the gap between incomplete crystal structures and lower resolution electron microscopy analyses

Effect of sources of sulphur on yield and disease incidence in crops in Jiangxi Province, China

The Jiangxi Academy of Agricultural Sciences (JAAS) has conducted five experiments to compare elemental S and sulphate S containing fertilizers. The elemental S containing fertilizer used was a sulphur enhanced diammonium phosphate (SEF12) and the sulphate S was supplied from single superphosphate (SSP). A diammonium phosphate (DAP) control was used. In a cabbage experiment conducted in 2003 there was a significant response to S and a lower incidence of soft rot where SEF12 was applied (18-19% reduction compared to DAP, c.f. 5% with SSP) and leaf disease (20-31% reduction, c.f. 19% with SSP) where elemental S was applied. In two rice experiments there was a greater response to SEF12 compared to SSP and this was associated with a lower incidence of disease and insects (rice leaf roller and brown plant hopper) where SEF12 was applied. Incidences of rice leaf blight and rice blast were also observed on the DAP and SSP treatments. Two plot trials with rapeseed were established in 2006 in which the fertilizers were applied either at rates to deliver the same rate of P as applied in DAP at Maying Shishan or with the same rate of S as applied in SSP at Maying Sequ. At the Maying Shishan site there was a significant response to S in SSP and SEF12 when applied at the same P application rate. At the Maying Sequ site SEF12 out yielded SSP when applied at the same S rate. These trials are the first to report soil applied elemental S having an effect on protecting crops against insects and disease and indicate that the mechanism involved requires further investigation in both upland and flooded crops

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition

<div><p>Humans can easily understand other people’s actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.</p></div

The architecture of the proposed model of visual primary cortex combining visual attention.

<p>It is consisted of four parts: visual perception, visual attention, feature extraction and action recognition. Spatiotemporal information is detected by modeling properties of classical and nonclassical receptive field of cells in V1; motion objects are detected with attention computational model by grouping spatiotemporal information; spike trains of spiking neurons produced by stimulus-driven leaky integrate-and-fire are analyzed to extract action features; the mean motion map as feature sets is constructed for action recognition with SVM classifier.</p

Comparison of Our approach with Others on KTH Dataset.

<p>Comparison of Our approach with Others on KTH Dataset.</p

Histograms representing the average recognition rates obtained by our model with 2 conditions: (1) surround inhibition and (2) no surround inhibition on Weizmann and KTH datasets.

<p><i>A</i>. Weizmann, <i>B</i>. KTH(s1), <i>C</i>. KTH(s2), <i>D</i>. KTH(s3), <i>E</i>. KTH(s4)</p

Example of operation of the attention model with a video subsequence.

<p>From the first to final column: snapshots of origin sequences, surround suppression energy (with <i>v</i> = 0.5<i>ppF</i> and <i>θ</i> = 0°), perceptual grouping feature maps (with <i>v</i> = 0.5<i>ppF</i> and <i>θ</i> = 0°), saliency maps and binary masks of moving objects, and ground truth rectangles after localization of action objects.</p

The control factor of standard deviations of the Gaussian envelopes as a function of normalized surround suppression motion energy used to compute range of perceptual grouping and weight facilitative interaction.

<p>The control factor of standard deviations of the Gaussian envelopes as a function of normalized surround suppression motion energy used to compute range of perceptual grouping and weight facilitative interaction.</p