Delving deeper into the whorl of flower segmentation

We describe an algorithm for automatically segmenting flowers in colour photographs. This is a challenging problem because of the sheer variety of flower classes, the variability within a class and within a particular flower, and the variability of the imaging conditions – lighting, pose, foreshortening, etc.     

Creation and control of high-dimensional multi-partite classically entangled light

Vector beams, non-separable in spatial mode and polarisation, have emerged as enabling tools in many diverse applications, from communication to imaging. This a...     

The Regulatory Role of H19/miR-181a/ATG5 Signaling in Perinatal Nicotine Exposure-Induced Development of Neonatal Brain Hypoxic-Ischemic Sensitive Phenotype

Nicotine exposure either from maternal cigarette smoking or e-cigarette vaping is one of the most common risk factors for neurodevelopmental disease in offspring. Previous studies revealed that perinatal nicotine exposure programs a sensitive phenotype to neonatal hypoxic-ischemic encephalopathy (HIE) in postnatal life, yet the underlying mechanisms remain undetermined. The goal of the present study was to determine the regulatory role of H19/miR-181a/ATG5 signaling in perinatal nicotine exposure-induced development of neonatal brain hypoxic-ischemic sensitive phenotype. Nicotine was administered to pregnant rats via subcutaneous osmotic minipumps. All experiments were conducted in offspring pups at postnatal day 9 (P9). Perinatal nicotine exposure significantly enhanced expression of miR-181a but attenuated autophagy-related protein 5 (ATG5) mRNA and protein levels in neonatal brains. Of interest, miR-181a mimicking administration in the absence of nicotine exposure also produced dose-dependent increased hypoxia/ischemia (H/I)-induced brain injury associated with a decreased ATG5 expression, closely resembling perinatal nicotine exposure-mediated effects. Locked nucleic acid (LNA)-miR-181a antisense reversed perinatal nicotine-mediated increase in H/I-induced brain injury and normalized aberrant ATG5 expression. In addition, nicotine exposure attenuated a long non-coding RNA (lncRNA) H19 expression level. Knockdown of H19 via siRNA increased the miR-181a level and enhanced H/I-induced neonatal brain injury. In conclusion, the present findings provide a novel mechanism that aberrant alteration of the H19/miR-181a/AGT5 axis plays a vital role in perinatal nicotine exposure-mediated ischemia-sensitive phenotype in offspring and suggests promising molecular targets for intervention and rescuing nicotine-induced adverse programming effects in offspring.     

Development of a Mortality Prediction Model in Hospitalised SARS-CoV-2 Positive Patients Based on Routine Kidney Biomarkers

Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694–0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740–0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.     

The Role of the Choroid in Stargardt Disease

Stargardt disease is the commonest juvenile macular dystrophy. It is caused by genetic mutations in the ABCA4 gene. Diagnosis is not always straightforward, and various phenocopies exist. Late-onset disease can be misdiagnosed with age-related macular disease. A correct diagnosis is particularly critical because of emergent gene therapies. Stargardt disease is known to affect retinal pigment epithelium and photoreceptors. Many studies have also highlighted the importance of the choroid in the diagnosis, pathophysiology, and progression of the disease. The choroid is in an integral relationship with the retinal pigment epithelium and photoreceptors, and its possible involvement during the disease should be considered. The purpose of this review is to analyze the current diagnostic tools for choroidal evaluation and the extrapolation of useful data for ophthalmologists and researchers studying the disease.     

Snakes: Active contour models

A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.     

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.     

Synchronization of diffusively-coupled limit cycle oscillators

We develop analytical and numerical conditions to determine whether limit cycle oscillations synchronize in diffusively coupled systems. We examine two classes of systems: reaction–diffusion PDEs with Neumann boundary conditions, and compartmental ODEs, where compartments are interconnected through diffusion terms with adjacent compartments. In both cases the uncoupled dynamics are governed by a nonlinear system that admits an asymptotically stable limit cycle. We provide two-time scale averaging methods for certifying stability of spatially homogeneous time-periodic trajectories in the presence of sufficiently small or large diffusion and develop methods using the structured singular value for the case of intermediate diffusion. We highlight cases where diffusion stabilizes or destabilizes such trajectories.     

Computational Photography with Panoramic Sensors that Have Uniform Resolution with Respect to Unwarping Transformations

We address the problem of designing a computational photographic system for panoramic imaging, which employs a rotationally symmetric reflector and yet produces uniform resolution images. Our design is a catadiotpric sensor, whose key component is a rotationally symmetric mirror. Catadioptric sensors have already been designed that are equiresolution with respect to solid angle, but such sensors do not take into account the effect of unwarping transformations that are typically applied to the initially recorded images. The family of catadioptric sensors that we describe may be considered as realizing area preserving/scaling cylindrical projections. We present the derivations of the equations that describe such sensors and demonstrate in simulation their properties with respect to unwarping.