Genetic variability of spelt factor gene in Triticum and Aegilops species

Background Threshability, rachis fragility and spike shape are critical traits for the domestication and evolution of wheat, determining the crop yield and efficiency of the harvest. Spelt factor gene Q controls a wide range of domestication-related traits in polyploid wheats, including those mentioned above. The main goal of the present study was to characterise the Q gene for uninvestigated accessions of wheats, including four endemics, and Aegilops accessions, and to analyze the species evolution based on differences in Q gene sequences. Results We have studied the spike morphology for 15 accessions of wheat species, including four endemics, namely Triticum macha, T. tibetanum, T. aestivum ssp. petropavlovskyi and T. spelta ssp. yunnanense, and 24 Aegilops accessions, which are donors of B and D genomes for polyploid wheat. The Q-5A, q-5D and q-5S genes were investigated, and a novel allele of the Q-5A gene was found in accessions of T. tibetanum (KU510 and KU515). This allele was similar to the Q allele of T. aestivum cv. Chinese Spring but had an insertion 161 bp in length within exon 5. This insertion led to a frameshift and premature stop codon formation. Thus, the T. tibetanum have spelt spikes, which is probably determined by the gene Tg, rather than Q. We determined the variability within the q-5D genes among hexaploid wheat and their D genome donor Aegilops tauschii. Moreover, we studied the accessions C21–5129, KU-2074, and K-1100 of Ae. tauschii ssp. strangulata, which could be involved in the origin of hexaploid wheats. Conclusions The variability and phylogenetic relationships of the Q gene sequences studied allowed us to clarify the relationships between species of the genus Triticum and to predict the donor of the D genome among the Ae. tauschii accessions. Ae. tauschii ssp. strangulata accessions C21–5129, KU-2074 and K-1100 are the most interesting among the analysed accessions, since their partial sequence of q-5D is identical to the q-5D of T. aestivum cv. Chinese Spring. This result indicates that the donor is Ae. tauschii ssp. strangulata but not Ae. tauschii ssp. tauschii. Our analysis allowed us to clarify the phylogenetic relationships in the genus Triticum.

Physiological characterization of a rare subpopulation of doublet-spiking neurons in the ferret lateral geniculate nucleus

Interest in visual system homologies across species has recently increased. Across species, retinas contain diverse retinal ganglion cells including cells with unusual visual response properties. It is unclear whether rare retinal ganglion cells in carnivores project to and drive similarly unique visual responses in the visual thalamus. We discovered a rare subpopulation of thalamic neurons defined by unique spike shape and visual response properties, suggesting that nonstandard visual computations are common to many species.

Application of Spike Sorting Algorithm to Neuronal Signals Originated from Boron Doped Diamond Micro-Electrode Arrays

In this work we report on the implementation of methods for data processing signals from microelectrode arrays (MEA) and the application of these methods for signals originated from two types of MEAs to detect putative neurons and sort them into subpopulations. We recorded electrical signals from firing neurons using titanium nitride (TiN) and boron doped diamond (BDD) MEAs. In previous research, we have shown that these methods have the capacity to detect neurons using commercially-available TiN-MEAs. We have managed to cultivate and record hippocampal neurons for the first time using a newly developed custom-made multichannel BDD-MEA with 20 recording sites. We have analysed the signals with the algorithms developed and employed them to inspect firing bursts and enable spike sorting. We did not observe any significant difference between BDD- and TiN-MEAs over the parameters, which estimated spike shape variability per each detected neuron. This result supports the hypothesis that we have detected real neurons, rather than noise, in the BDD-MEA signal. BDD materials with suitable mechanical, electrical and biocompatibility properties have a large potential in novel therapies for treatments of neural pathologies, such as deep brain stimulation in Parkinson’s disease.

Fusion pore expansion and contraction during catecholamine release from endocrine cells

ABSTRACTAmperometry recording reveals the exocytosis of catecholamine from individual vesicles as a sequential process, typically beginning slowly with a pre-spike foot, accelerating sharply to initiate a spike, reaching a peak, and then decaying. This complex sequence reflects the interplay between diffusion, flux through a fusion pore, and possibly dissociation from a vesicle’s densecore. In an effort to evaluate the impacts of these factors, a model was developed that combines diffusion with flux through a static pore. This model recapitulated the rapid phases of a spike, but generated relations between spike shape parameters that differed from experimental results. To explore the possibility of fusion pore dynamics, a transformation of amperometry current was introduced that yields fusion pore permeability divided by vesicle volume (g/V). Applying this transform to individual fusion events yielded a highly characteristic time course. g/V initially tracks the pre-spike foot and the start of the spike, increasing ∼15-fold to the peak. However, after the spike peaks, g/V unexpectedly declines and settles to a constant value that indicates the presence of a stable post-spike pore. g/V of the post-spike pore varies greatly between events, and has an average that is ∼3.5-fold below the peak value and ∼4.5-fold above the pre-spike value. The post-spike pore persists and g/V remains flat for tens of milliseconds, as long as catecholamine flux can be detected. Applying the g/V transform to rare events with two peaks revealed a stepwise increase in g/V during the second peak. The g/V transform offers an interpretation of amperometric current in terms of fusion pore dynamics and provides a new framework for analyzing the actions of proteins that alter spike shape. The stable post-spike pore conforms with predictions from lipid bilayer elasticity, and offers an explanation for previous reports of prolonged hormone retention within fusing vesicles.STATEMENT OF SIGNIFICANCEAmperometry recordings of catecholamine release from single vesicles reveal a complex waveform with distinct phases. The role of the fusion pore in this waveform is poorly understood. A model based on a static fusion pore fails to recapitulate important aspects of the waveform. A new transform of amperometric current introduced here renders fusion pore permeability in real time. This transform reveals rich dynamic behavior of the fusion pore as catecholamine leaves a vesicle. This analysis shows that fusion pore permeability rapidly increases and then decreases before settling into a stable post-spike configuration.

Morphometry of the Wheat Spike by Analyzing 2D Images

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.

Morphometry of the Wheat Spike by Analyzing 2D Images

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.

Morphometry of the Wheat Spike by Analyzing 2D Images

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data harvesting and analysis for numerous plants, which is automatable using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Models of sections, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections). The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.