What are the perceptions and concerns of people living with diabetes and National Health Service staff around the potential implementation of AI-assisted screening for diabetic eye disease? Development and validation of a survey for use in a secondary care screening setting.

INTRODUCTION: The English National Health Service (NHS) Diabetic Eye Screening Programme (DESP) performs around 2.3 million eye screening appointments annually, generating approximately 13 million retinal images that are graded by humans for the presence or severity of diabetic retinopathy. Previous research has shown that automated retinal image analysis systems, including artificial intelligence (AI), can identify images with no disease from those with diabetic retinopathy as safely and effectively as human graders, and could significantly reduce the workload for human graders. Some algorithms can also determine the level of severity of the retinopathy with similar performance to humans. There is a need to examine perceptions and concerns surrounding AI-assisted eye-screening among people living with diabetes and NHS staff, if AI was to be introduced into the DESP, to identify factors that may influence acceptance of this technology. METHODS AND ANALYSIS: People living with diabetes and staff from the North East London (NEL) NHS DESP were invited to participate in two respective focus groups to codesign two online surveys exploring their perceptions and concerns around the potential introduction of AI-assisted screening.Focus group participants were representative of the local population in terms of ages and ethnicity. Participants' feedback was taken into consideration to update surveys which were circulated for further feedback. Surveys will be piloted at the NEL DESP and followed by semistructured interviews to assess accessibility, usability and to validate the surveys.Validated surveys will be distributed by other NHS DESP sites, and also via patient groups on social media, relevant charities and the British Association of Retinal Screeners. Post-survey evaluative interviews will be undertaken among those who consent to participate in further research. ETHICS AND DISSEMINATION: Ethical approval has been obtained by the NHS Research Ethics Committee (IRAS ID: 316631). Survey results will be shared and discussed with focus groups to facilitate preparation of findings for publication and to inform codesign of outreach activities to address concerns and perceptions identified.

3D high spatial resolution visualisation and quantification of interconnectivity in polymer films.

A porous network acts as transport paths for drugs through films for controlled drug release. The interconnectivity of the network strongly influences the transport properties. It is therefore important to quantify the interconnectivity and correlate it to transport properties for control and design of new films. This work presents a novel method for 3D visualisation and analysis of interconnectivity. High spatial resolution 3D data on porous polymer films for controlled drug release has been acquired using a focused ion beam (FIB) combined with a scanning electron microscope (SEM). The data analysis method enables visualisation of pore paths starting at a chosen inlet pore, dividing them into groups by length, enabling a more detailed quantification and visualisation. The method also enables identification of central features of the porous network by quantification of channels where pore paths coincide. The method was applied to FIB-SEM data of three leached ethyl cellulose (EC)/hydroxypropyl cellulose (HPC) films with different weight percentages. The results from the analysis were consistent with the experimentally measured release properties of the films. The interconnectivity and porosity increase with increasing amount of HPC. The bottleneck effect was strong in the leached film with lowest porosity.

Trimetallic Pd@Au@Pt nanocomposites platform on -COOH terminated reduced graphene oxide for highly sensitive CEA and PSA biomarkers detection.

In this paper, a trimetallic Pd@Au@Pt nanocomposites platform on -COOH terminated reduced graphene oxide (COOH-rGO) was newly developed for sensing carcinoembryonic antigen (CEA) and prostate specific antigen (PSA) biomarkers. Trimetallic electro-catalytic surfaces were prepared by the electrodeposition of noble metals (Pd@Au@Pt) nanoparticles on COOH- rGO. After EDC/NHS treatment, the anti-CEA and anti-PSA were immobilized separately on two different platforms. Under optimized conditions, the platforms were analyzed by cyclic voltammetry and differential pulse voltammetry (DPV). The platform shows good electro catalytic activity, high sensitivity, and acceptable stability for sensing CEA and PSA biomarkers. For CEA, we obtained sensitivity of 0.099 ± 0.007µAng-1ml, wide linear range from 12pgml-1 to 85ngml-1 and a limit of detection (LOD) of 8pgml-1, while for PSA sensitivity is 0.267 ± 0.02µAng -1 ml, wide linear range from 3pgml-1 to 60ngml-1 and LOD of 2pgml-1. The validation of the platform was observed through standard addition method. Thus, the sensing platform could be used for the point of care detection of CEA and PSA.

A three-dimensional statistical model for imaged microstructures of porous polymer films.

A thresholded Gaussian random field model is developed for the microstructure of porous materials. Defining the random field as a solution to stochastic partial differential equation allows for flexible modelling of nonstationarities in the material and facilitates computationally efficient methods for simulation and model fitting. A Markov Chain Monte Carlo algorithm is developed and used to fit the model to three-dimensional confocal laser scanning microscopy images. The methods are applied to study a porous ethylcellulose/hydroxypropylcellulose polymer blend that is used as a coating to control drug release from pharmaceutical tablets. The aim is to investigate how mass transport through the material depends on the microstructure. We derive a number of goodness-of-fit measures based on numerically calculated diffusion through the material. These are used in combination with measures that characterize the geometry of the pore structure to assess model fit. The model is found to fit stationary parts of the material well.

Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort.

The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these associations. This requires automated systems that extract quantitative measures of vessel morphology from large numbers of retinal images. Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules. Therefore, the accurate detection of the vessel type is an important element in such automated systems. This paper presents a deep learning approach for the automatic classification of arterioles and venules across the entire retinal image, including vessels located at the optic disc. This comprises of a convolutional neural network whose architecture contains six learned layers: three convolutional and three fully-connected. Complex patterns are automatically learnt from the data, which avoids the use of hand crafted features. The method is developed and evaluated using 835,914 centreline pixels derived from 100 retinal images selected from the 135,867 retinal images obtained at the UK Biobank (large population-based cohort study of middle aged and older adults) baseline examination. This is a challenging dataset in respect to image quality and hence arteriole/venule classification is required to be highly robust. The method achieves a significant increase in accuracy of 8.1% when compared to the baseline method, resulting in an arteriole/venule classification accuracy of 86.97% (per pixel basis) over the entire retinal image.

Airborne particulate in Varanasi over middle Indo-Gangetic Plain: variation in particulate types and meteorological influences.

The variation in particulate mass and particulate types (PM2.5 and PM10) with respect to local/regional meteorology was analyzed from January to December 2014 (n = 104) for an urban location over the middle Indo-Gangetic Plain (IGP). Both coarser (mean ± SD; PM10 161.3 ± 110.4 µg m-3, n = 104) and finer particulates (PM2.5 81.78 ± 66.4 µg m-3) revealed enormous mass loading with distinct seasonal effects (range: PM10 12-535 µg m-3; PM2.5 8-362 µg m-3). Further, 56% (for PM2.5) to 81% (for PM10) of monitoring events revealed non-attainment national air quality standard especially during winter months. Particulate types (in terms of PM2.5/PM10 0.49 ± 0.19) also exhibited temporal variations with high PM2.5 loading particularly during winter (0.62) compared to summer months (0.38). Local meteorology has clear distinguishing trends in terms of dry summer (March to June), wet winter (December to February), and monsoon (July to September). Among all the meteorological variables (average temperature, rainfall, relative humidity (RH), wind speed (WS)), temperature was found to be inversely related with particulate loading (rPM10 -0.79; rPM2.5 -0.87) while RH only resulted a significant association with PM2.5 during summer (rPM10 0.07; rPM2.5 0.55) and with PM10 during winter (rPM10 0.53; rPM2.5 0.24). Temperature, atmospheric boundary layer (ABL), and RH were cumulatively recognized as the dominant factors regulating particulate concentration as days with high particulate loading (PM2.5 >150 µg m-3; PM10 >260 µg m-3) appeared to have lower ABL (mean 660 m), minimum temperature (<22.6 °C), and high RH (∼79%). The diurnal variations of particulate ratio were mostly insignificant except minor increases during night having a high wintertime ratio (0.58 ± 0.07) over monsoon (0.34 ± 0.05) and summer (0.30 ± 0.07). Across the region, atmospheric visibility appeared to be inversely associated with particulate (rPM2.5 -0.84; rPM10 -0.79) for all humid conditions, while at RH ≥80%, RH appeared as the most dominant factor in regulating visibility compared to particulate loading. The Lagrangian particle dispersion model was further used to identify possible regions contributing particulate loading through regional/transboundary movement.

Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model.

Fine particulates (PM2.5) constitute dominant proportion of airborne particulates and have been often associated with human health disorders, changes in regional climate, hydrological cycle and more recently to food security. Intrinsic properties of particulates are direct function of sources. This initiates the necessity of conducting a comprehensive review on PM2.5 sources over South Asia which in turn may be valuable to develop strategies for emission control. Particulate source apportionment (SA) through receptor models is one of the existing tool to quantify contribution of particulate sources. Review of 51 SA studies were performed of which 48 (94%) were appeared within a span of 2007-2016. Almost half of SA studies (55%) were found concentrated over few typical urban stations (Delhi, Dhaka, Mumbai, Agra and Lahore). Due to lack of local particulate source profile and emission inventory, positive matrix factorization and principal component analysis (62% of studies) were the primary choices, followed by chemical mass balance (CMB, 18%). Metallic species were most regularly used as source tracers while use of organic molecular markers and gas-to-particle conversion were minimum. Among all the SA sites, vehicular emissions (mean ± sd: 37 ± 20%) emerged as most dominating PM2.5 source followed by industrial emissions (23 ± 16%), secondary aerosols (22 ± 12%) and natural sources (20 ± 15%). Vehicular emissions (39 ± 24%) also identified as dominating source for highly polluted sites (PM2.5>100 µgm-3, n = 15) while site specific influence of either or in combination of industrial, secondary aerosols and natural sources were recognized. Source specific trends were considerably varied in terms of region and seasonality. Both natural and industrial sources were most influential over Pakistan and Afghanistan while over Indo-Gangetic plain, vehicular, natural and industrial emissions appeared dominant. Influence of vehicular emission was found single dominating source over southern part while over Bangladesh, both vehicular, biomass burning and industrial sources were significant.

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies.

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis.

Automated retinal vessel recognition and measurements on large datasets.

The characteristics of the retinal vascular network have been prospectively associated with many systemic and vascular diseases. QUARTZ is a fully automated software that has been developed to localize and quantify the morphological characteristics of blood vessels in retinal images for use in epidemiological studies. This software was used to analyse a dataset containing 16,000 retinal images from the EPIC-Norfolk cohort study. The objective of this paper is to both assess the suitability of this dataset for computational analysis and to further evaluate the QUARTZ software.

Temporal variability of MODIS aerosol optical depth and chemical characterization of airborne particulates in Varanasi, India.

Temporal variation of airborne particulate mass concentration was measured in terms of toxic organics, metals and water-soluble ionic components to identify compositional variation of particulates in Varanasi. Information-related fine particulate mass loading and its compositional variation in middle Indo-Gangetic plain were unique and pioneering as no such scientific literature was available. One-year ground monitoring data was further compared to Moderate Resolution Imaging Spectroradiometer (MODIS) Level 3 retrieved aerosol optical depth (AOD) to identify trends in seasonal variation. Observed AOD exhibits spatiotemporal heterogeneity during the entire monitoring period reflecting monsoonal low and summer and winter high. Ground-level particulate mass loading was measured, and annual mean concentration of PM2.5 (100.0 ± 29.6 µg/m(3)) and PM10 (176.1 ± 85.0 µg/m(3)) was found to exceed the annual permissible limit (PM10: 80 %; PM2.5: 84 %) and pose a risk of developing cardiovascular and respiratory diseases. Average PM2.5/PM10 ratio of 0.59 ± 0.18 also indicates contribution of finer particulates to major variability of PM10. Particulate sample was further processed for trace metals, viz. Ca, Fe, Zn, Cu, Pb, Co, Mn, Ni, Cr, Na, K and Cd. Metals originated mostly from soil/earth crust, road dust and re-suspended dust, viz. Ca, Fe, Na and Mg were found to constitute major fractions of particulates (PM2.5: 4.6 %; PM10: 9.7 %). Water-soluble ionic constituents accounted for approximately 27 % (PM10: 26.9 %; PM2.5: 27.5 %) of the particulate mass loading, while sulphate (8.0-9.5 %) was found as most dominant species followed by ammonium (6.0-8.2 %) and nitrate (5.5-7.0 %). The concentration of toxic organics representing both aliphatic and aromatic organics was determined by organic solvent extraction process. Annual mean toxic organic concentration was found to be 27.5 ± 12.3 µg/m(3) (n = 104) which constitutes significant proportion of (PM2.5, 17-19 %; PM10, 11-20 %) particulate mass loading with certain exceptions up to 50 %. Conclusively, compositional variation of both PM2.5 and PM10 was compared to understand association of specific sources with different fractions of particulates.

Toxicity assessment of effluent from flash light manufacturing industry by bioassay tests in Trigonella foenumgracum.

A rapid bioassay test was conducted to study heavy metal accumulation and biochemical changes in Trigonella foenumgracum (methi) irrigated with 25, 50, 75 and 100% of effluent from flash light manufacturing industry at 60 days after sowing. Total metal concentration in effluent samples was: Cr = 0.12 < Cd = 0.18 < Pb = 0.24 < Cu = 2.68 mg l(-1) whereas, metals were not detected in control. An increase in photosynthetic pigments of exposed plant was noticed up to 50% concentrations of the effluent followed by a decrease at higher concentration as compared to their respective control.An enhanced lipid peroxidation in the treated plants was observed, which was evident by increased level of antioxidants: proline, cysteine, malondialdehyde and ascorbic acid content. The treated plants accumulated metals in the following order: Cu > Pb > Cr > Cd in the roots and shoots.

The role of protein content on the steady and oscillatory shear rheology of model synovial fluids.

Recent studies have debated the role of protein content on the bulk rheology of synovial fluid; in particular, it has been questioned if proteins aggregate or interact with hyaluronic acid in synovial fluid to enhance bulk rheology, or if observed effects were due to systematic measurement error caused by interfacial rheology, stemming from protein adsorption to the interface. Utilizing several techniques to ensure results reflect only bulk rheology, an examination of the role of bovine serum albumin and γ-globulin on model synovial fluid rheology has been undertaken. When interfacial rheology caused by protein adsorption to the interface is abrogated, the bulk rheology of a model synovial fluid composed of bovine serum albumin, γ-globulin, and hyaluronic acid is found to be dominated solely by the hyaluronic acid over a wide range of shear rates, strains and frequencies. These results show that the previously reported enhanced rheological properties of model synovial fluids are solely due to interfacial rheology and not from any type of protein aggregation/interaction in bulk solution.

Vermicomposting of Tea Factory Coal Ash: metal accumulation and metallothionein response in Eisenia fetida (Savigny) and Lampito mauritii (Kinberg).

Earthworms can accumulate heavy metals in their intestines to a great extent. Impact of feed materials and duration of metal exposure on natural activity of earthworms are rather unclear; this investigation therefore addresses the impact of metal rich Tea Factory Coal Ash (TFCA) on reproduction, composting and metal accumulation ability of Eisenia fetida and Lampito mauritii. Earthworm count and cocoon production increased significantly during vermicomposting. pH of the vermicomposted mixtures shifted toward neutrality, total organic C decreased substantially and total N enhanced significantly compared to composting. High heavy metal (Mn, Zn, Cu, As) accumulation was recorded in the intestine of both the earthworm species. Moreover, gradual increase in the metal-inducible metallothionein concentration indicated the causal mechanism of metal accumulation in these species. TFCA+cow dung (CD) (1:1) were most favorable feed mixture for E. fetida and TFCA+CD (1:2) were good for L. mauritii in regard to metal accumulation and compost quality.

Visible light catalysis of rhodamine B using nanostructured Fe(2)O(3), TiO(2) and TiO(2)/Fe(2)O(3) thin films.

The Fe(2)O(3), TiO(2) and TiO(2)/Fe(2)O(3) composite films are deposited using spray pyrolysis method onto glass and FTO coated substrates. The structural, morphological, optical and photocatalytic properties of Fe(2)O(3), TiO(2) and TiO(2)/Fe(2)O(3) thin films are studied. XRD analysis confirms that films are polycrystalline with rhombohedral and tetragonal crystal structures for Fe2O3 and TiO(2) respectively. The photocatalytic activity was tested for the degradation of Rhrodamine B (Rh B) in aqueous medium. The rate constant (-k) was evaluated as a function of the initial concentration of species. Substantial reduction in concentrations of organic species was observed from COD and TOC analysis. Photocatalytic degradation effect is relatively higher in case of the TiO(2)/Fe(2)O(3) than TiO(2) and Fe(2)O(3) thin film photoelectrodes in the degradation of Rh B and 98% removal efficiency of Rh B is obtained after 20min. The photocatalytic experimental results indicate that TiO(2)/α-Fe(2)O(3) photoelectrode is promising material for removing of water pollutants.

Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification.

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images. Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges. Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis.

Magnetic vortex based transistor operations.

Transistors constitute the backbone of modern day electronics. Since their advent, researchers have been seeking ways to make smaller and more efficient transistors. Here, we demonstrate a sustained amplification of magnetic vortex core gyration in coupled two and three vortices by controlling their relative core polarities. This amplification is mediated by a cascade of antivortex solitons travelling through the dynamic stray field. We further demonstrated that the amplification can be controlled by switching the polarity of the middle vortex in a three vortex sequence and the gain can be controlled by the input signal amplitude. An attempt to show fan-out operation yielded gain for one of the symmetrically placed branches which can be reversed by switching the core polarity of all the vortices in the network. The above observations promote the magnetic vortices as suitable candidates to work as stable bipolar junction transistors (BJT).

Does elevating the fetal head prior to delivery using a fetal pillow reduce maternal and fetal complications in a full dilatation caesarean section? A prospective study with historical controls.

A caesarean section at full dilatation (CSFD) can be technically demanding and has consistent association with increased intraoperative trauma. There is evidence that the incidence of caesarean sections at full dilation is on the rise. We report on a prospective study of 50 women undergoing CSFD using a fetal pillow (FP) to elevate the fetal head. Data were compared with historical controls of 124 women without FP use on uterine extensions, uterine incision delivery interval, blood loss, need for transfusion, operating time, length of stay, intensive care unit admission. The FP elevated the fetal head in all 50 women (p < 0.001). We found that patients in the FP group had a lower incidence of extensions (p = 0.03), shorter operating time (p < 0.001), uterine incision to delivery interval (p < 0.001) and shorter length of hospital stay (p < 0.001). Blood loss > 1,000 ml and admission to ICU was also lower but were not statistically significant. There were no significant differences in the fetal complications studied, APGAR scores, admission to neonatal intensive care unit, seizures, neonatal injury or death.

(3 + 1)D superspace description of the incommensurate modulation in the premartensite phase of Ni2MnGa: a high resolution synchrotron x-ray powder diffraction study.

Le Bail and Rietveld analysis of high resolution synchrotron x-ray powder diffraction (SXRPD) data shows unambiguous signatures of the failure of the commensurate 3M modulation model. Using (3 + 1) dimensional superspace group formalism, we have not only confirmed the incommensurate modulation in the premartensite phase with a modulation wavevector of q = 0.337 61(5)c* but also determined the superspace group (Immm(00γ)s00), atomic positions and amplitude of modulations for the incommensurate premartensite phase of Ni2MnGa for the first time. Our results may have important implications in the understanding of the martensitic transition and hence the magnetic field induced strains.

Hyperhomocysteinemia is detrimental to pregnancy in mice and is associated with preterm birth.

Elevated levels of homocysteine produce detrimental effects in humans but its role in preterm birth is not known. Here we used a mouse model of hyperhomocysteinemia to examine the relevance of homocysteine to preterm birth. The mouse carries a heterozygous deletion of cystathionine ß-synthase (Cbs(+/-)). Gestational period was monitored in wild type and Cbs(+/-) female mice. Mouse uterine and placental tissues, human primary trophoblast cells, and human myometrial and placental cell lines were used to determine the influence of homocysteine on expression of specific genes in vitro. The activity of BKCa channel in the myometrial cell line was monitored using the patch-clamp technique. We found that hyperhomocysteinemia had detrimental effects on pregnancy and induced preterm birth in mice. Homocysteine increased the expression of oxytocin receptor and Cox-2 as well as PGE2 production in uterus and placenta, and initiated premature uterine contraction. A Cox-2 inhibitor reversed these effects. Gpr109a, a receptor for niacin, induced Cox-2 in uterus. Homocysteine upregulated GPR109A and suppressed BKCa channel activity in human myometrial cells. Deletion of Gpr109a in Cbs(+/-) mice reversed premature birth. We conclude that hyperhomocysteinemia causes preterm birth in mice through upregulation of the Gpr109a/Cox-2/PGE2 axis and that pharmacological blockade of Gpr109a may have potential in prevention of preterm birth.