Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Detecting Small Size and Minimal Thermal Signature Targets in Infrared Imagery Using Biologically Inspired Vision

Thermal infrared imaging provides an effective sensing modality for detecting small moving objects at long range. Typical challenges that limit the efficiency and robustness of the detection performance include sensor noise, minimal target contrast and cluttered backgrounds. These issues become more challenging when the targets are of small physical size and present minimal thermal signatures. In this paper, we experimentally show that a four-stage biologically inspired vision (BIV) model of the flying insect visual system have an excellent ability to overcome these challenges simultaneously. The early two stages of the model suppress spatio-temporal clutter and enhance spatial target contrast while compressing the signal in a computationally manageable bandwidth. The later two stages provide target motion enhancement and sub-pixel motion detection capabilities. To show the superiority of the BIV target detector over existing traditional detection methods, we perform extensive experiments and performance comparisons using high bit-depth, real-world infrared image sequences of small size and minimal thermal signature targets at long ranges. Our results show that the BIV target detector significantly outperformed 10 conventional spatial-only and spatiotemporal methods for infrared small target detection. The BIV target detector resulted in over 25 dB improvement in the median signal-to-clutter-ratio over the raw input and achieved 43% better detection rate than the best performing existing method

Response of human jaw muscles to axial stimulation of the incisor

The role of periodontal mechanoreceptors (PMRs) in the reflex control of the jaw muscles has thus far been mainly derived from animal studies. To date, the work that has been done on humans has been limited and confined to orthogonal stimulation of the labial surface of the tooth. The purpose of this study was to investigate the response of the masseter and digastric muscles in humans to controlled axial stimulation of the upper left central incisor, both before and during a local anaesthetic block of the PMRs. Ten neurologically normal young adult females were tested, each on two separate occasions to confirm the reproducibility of the results. It was found that the reflex response in the masseter was modulated by the rate of rise of the stimulus used and, to a lesser degree, the level of background muscle activity. There was little detectable change in the activity of the digastric muscle under the tested conditions and what was found could be attributed to cross-talk with the masseter. The reflex responses obtained were significantly different between subjects; however retesting the same subject on a different occasion yielded similar results. The results indicate that the most common response of the masseter muscle to brisk axial stimulation of the incisor is a reflex inhibition at 20 ms, followed by a late excitation at 44 ms. However, it is possible that this late excitation could be due to delayed action potentials and hence be artefactual. As the application of a local anaesthetic block removed or significantly reduced both of these responses, it was concluded that they originated from the PMRs. Unlike during orthogonal stimulation, slowly rising stimuli did not produce any excitatory reflex activity. This indicated a difference in jaw reflexes to forces applied in different directions, possibly due to the activation of different receptor types when stimulating the tooth in either the orthogonal or axial directions

Motion processing model.

<p>A) Schematic of a basic correlator elementary motion detector (EMD) used as the fundamental motion detection algorithm in this paper. B) Diagrammatic representation of the fully elaborated motion processing model used in this study. C) Legend describing the symbolic representations used in B). Each stage of the model represents the processing occurring on a pixel-wise basis within the insect visual system. Connections between near-by processing columns (nearest or next-nearest neighbors) in the 2D network occur between stages, mostly in the form of spatial high-pass filtering, with the only global stage a final spatial summation at the start of stage 5. Each stage is further divided into smaller processing steps involving operations such as 1st order low-pass filtering, centre-surround antagonism, non-linear gains or divisive feedback. Further detail is presented in the text.</p

Image Statistics.

<p>Image Statistics.</p

Model responses after various processing stages.

<p>A) Steady-state responses, integrated over the entire image, of the model to all 14 input images over the range of velocities tested after inclusion of various modeling stages as depicted in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000555#pcbi-1000555-g002" target="_blank">Figure 2</a>. Lines are color coded to the images as shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000555#pcbi-1000555-g001" target="_blank">Figure 1</a>. B) Summary statistics of model performance after each stage of processing. All data are given as mean of responses over the range 0.1–100 degrees/s. Error bars represent 95% confidence intervals. The inclusion of each of the stages improved the ability of the model to reliably encode velocity by reducing the variability in the response between images. Σ illustrated that the responses were summed over all space, EMD stands for correlational elementary motion detector and was the fundamental motion estimation operation. For a detailed description of the processing of each stage, and the exact effects on velocity consistency between images, see main text.</p