Point Process Analysis of Noise in Early Invertebrate Vision

Noise is a prevalent and sometimes even dominant aspect of many biological processes. While many natural systems have adapted to attenuate or even usefully integrate noise, the variability it introduces often still delimits the achievable precision across biological functions. This is particularly so for visual phototransduction, the process responsible for converting photons of light into usable electrical signals (quantum bumps). Here, randomness of both the photon inputs (regarded as extrinsic noise) and the conversion process (intrinsic noise) are seen as two distinct, independent and significant limitations on visual reliability. Past research has attempted to quantify the relative effects of these noise sources by using approximate methods that do not fully account for the discrete, point process and time ordered nature of the problem. As a result the conclusions drawn from these different approaches have led to inconsistent expositions of phototransduction noise performance. This paper provides a fresh and complete analysis of the relative impact of intrinsic and extrinsic noise in invertebrate phototransduction using minimum mean squared error reconstruction techniques based on Bayesian point process (Snyder) filters. An integrate-fire based algorithm is developed to reliably estimate photon times from quantum bumps and Snyder filters are then used to causally estimate random light intensities both at the front and back end of the phototransduction cascade. Comparison of these estimates reveals that the dominant noise source transitions from extrinsic to intrinsic as light intensity increases. By extending the filtering techniques to account for delays, it is further found that among the intrinsic noise components, which include bump latency (mean delay and jitter) and shape (amplitude and width) variance, it is the mean delay that is critical to noise performance. Consequently, if one wants to increase visual fidelity, reducing the photoconversion lag is much more important than improving the regularity of the electrical signal.This work was supported by the Gates Cambridge Trust (PhD studentship for research) https://www.gatescambridge.org/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

QTL mapping result for plant height in barley.

<p>Mapping QTL for plant height was conducted in the DH population of AC Metcalfe and Baudin in MapQTL5.0. In the chart, the red curve indicated LOD value at any genetic position. The black dash line indicated that the LOD value was 3.0.</p

Alignment of genetic maps derived from de novo and POPSEQ.

<p>A total of 12,998 SNP markers were dotted in the figure. X-axis indicates the accumulative genetic position in the <i>de novo</i> genetic map; Y-axis indicates the accumulative genetic position in the POPSEQ genetic map.</p

Additional file 3: Table S2. of Development of genome-wide InDel markers and their integration with SSR, DArT and SNP markers in single barley map

Fifty five InDel markers used for HRM analysis between Morex and Barke. The fifty five InDel markers were polymorphic between Morex and Barke, and each marker had single amplicon. HRM analysis of these markers was conducted between Morex and Barke. (DOCX 15 kb

QTL mapping result for plant height in barley.

<p>Mapping QTL for plant height was conducted in the DH population of AC Metcalfe and Baudin in MapQTL5.0. In the chart, the red curve indicated LOD value at any genetic position. The black dash line indicated that the LOD value was 3.0.</p

SNP markers details across each chromosome.

<p>SNP markers details across each chromosome.</p

Alignment of BAC contig positions derived from de novo and POPSEQ.

<p>A total of 1,355 BAC contigs were dotted in the figure. X-axis indicates the accumulative genetic position in the <i>de novo</i> genetic map; Y-axis indicates the accumulative genetic position in the POPSEQ genetic map.</p

Number of AS transcripts commonly occurring in at least three out of four barley varieties.

<p>Number of AS transcripts commonly occurring in at least three out of four barley varieties.</p

Co-expression of AS hormone responsive genes with genes responsible for protein synthesis and energy metabolism.

<p>Co-expression of AS hormone responsive genes with genes responsible for protein synthesis and energy metabolism.</p

Alternative splicing events and genes.

<p>Alternative splicing events and genes.</p