Electron microscopy study of the central retinal fovea in Pied flycatcher: evidence of a mechanism of light energy transmission through the retina

We present unique ultrastructural data on avian retinal cells. Presently and earlier (Zueva et al., 2016) we explored distribution of intermediate filaments (IFs) in retinal cells of the Pied flycatcher (Ficedula hypoleuca, Passeriformes, Aves) in the central foveolar zone. This retinal zone only contains single and double cone photoreceptors. Previously we found that continuous IFs span Müller cells (MC) lengthwise from the retinal inner limiting membrane (ILM) layer up to the outer limiting membrane (OLM) layer. Here we describe long cylindrical bundles of IFs (IFBs) inside the cone inner segments (CIS) adjoining the cone plasma membrane, with these IFBs following along the cone lengthwise, and surrounding the cone at equal spacing one from the other. Double cones form a combined unit, wherein they are separated by their respective plasma membranes. Double cones thus have a common external ring of IFBs, surrounding both cone components. In the layer of cilia, the IFBs that continue into the cone outer segment (COS) follow on to the cone apical tip along the direction of incident light, with single IFs separating from the IFB, touching, and sometimes passing in-between the light-sensitive lamellae of the COS. These new data support our previous hypothesis on the quantum mechanism of light energy propagation through the vertebrate retina (Zueva et al., 2016, 2019).info:eu-repo/semantics/publishedVersio

Neuron-glial Interactions

Although lagging behind classical computational neuroscience, theoretical and computational approaches are beginning to emerge to characterize different aspects of neuron-glial interactions. This chapter aims to provide essential knowledge on neuron-glial interactions in the mammalian brain, leveraging on computational studies that focus on structure (anatomy) and function (physiology) of such interactions in the healthy brain. Although our understanding of the need of neuron-glial interactions in the brain is still at its infancy, being mostly based on predictions that await for experimental validation, simple general modeling arguments borrowed from control theory are introduced to support the importance of including such interactions in traditional neuron-based modeling paradigms.Junior Leader Fellowship Program by “la Caixa” Banking Foundation (LCF/BQ/LI18/11630006

Neuron-Glial Interactions

Although lagging behind classical computational neuroscience, theoretical and computational approaches are beginning to emerge to characterize different aspects of neuron-glial interactions. This chapter aims to provide essential knowledge on neuron-glial interactions in the mammalian brain, leveraging on computational studies that focus on structure (anatomy) and function (physiology) of such interactions in the healthy brain. Although our understanding of the need of neuron-glial interactions in the brain is still at its infancy, being mostly based on predictions that await for experimental validation, simple general modeling arguments borrowed from control theory are introduced to support the importance of including such interactions in traditional neuron-based modeling paradigms.Comment: 43 pages, 2 figures, 1 table. Accepted for publication in the "Encyclopedia of Computational Neuroscience," D. Jaeger and R. Jung eds., Springer-Verlag New York, 2020 (2nd edition

Processing technology investigation of loquat (Eriobotrya japonica) leaf by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry combined with chemometrics.

Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and multivariate statistical analysis were used to investigate the processing technology of Loquat (Eriobotrya japonica) leaf (pipaye, PPY). The differences in samples processed using different methods were revealed by unsupervised principal component analysis (PCA). In the scores plot of PCA, honey-processed PPY (PPPY), crude PPY (CPPY), and heated PPY (HPPY) were clearly discriminated. Furthermore, samples processed at different temperatures could also be distinguished; indeed, our PCA results demonstrated the importance of temperature during processing. Two unique marker ions were found to discriminate between PPPY and CPPY by orthogonal partial least squares discriminant analysis (OPLS-DA), which could be used as potential chemical markers. The method was further confirmed by a verification test with commercial PPY. The orthogonal array experiment revealed an optimized processing condition with 50% honey at 140°C for 20 min after 4 h of moistening time, a process that provides significant information for standardized production

Chemical and genetic discrimination of Cistanches Herba based on UPLC-QTOF/MS and DNA barcoding.

Cistanches Herba (Rou Cong Rong), known as "Ginseng of the desert", has a striking curative effect on strength and nourishment, especially in kidney reinforcement to strengthen yang. However, the two plant origins of Cistanches Herba, Cistanche deserticola and Cistanche tubulosa, vary in terms of pharmacological action and chemical components. To discriminate the plant origin of Cistanches Herba, a combined method system of chemical and genetic--UPLC-QTOF/MS technology and DNA barcoding--were firstly employed in this study. The results indicated that three potential marker compounds (isomer of campneoside II, cistanoside C, and cistanoside A) were obtained to discriminate the two origins by PCA and OPLS-DA analyses. DNA barcoding enabled to differentiate two origins accurately. NJ tree showed that two origins clustered into two clades. Our findings demonstrate that the two origins of Cistanches Herba possess different chemical compositions and genetic variation. This is the first reported evaluation of two origins of Cistanches Herba, and the finding will facilitate quality control and its clinical application

Tentatively identified compounds from leaves of <i>E. japonica.</i>

<p>Tentatively identified compounds from leaves of <i>E. japonica.</i></p

Ion intensities of markers a and b.

<p>(•, CPPY; ▪, PPPY. A, marker a in experimental samples; B, marker a in test samples; C, marker b in experimental samples; D, marker b in test samples).</p

Determination of OA and UA in CPPY, PPPY, and HPPY (N = 3).

<p>Determination of OA and UA in CPPY, PPPY, and HPPY (N = 3).</p

PCA (scores plot) of CPPY, HPPY, and PPPY.

<p>PCA (scores plot) of CPPY, HPPY, and PPPY.</p

Representative profiling of a PPY sample.

<p>Representative profiling of a PPY sample.</p