Design, Manufacture, and Evaluation of Prototype Telescope Windows for Use as Low Vision Aids

Pixellated Optics, a class of optical devices which preserve phase front continuity only over small sub areas of the device, allow for a range of uses that would not otherwise be possible. One potential use is as Low Vision Aids (LVAs), where they are hoped to combine the function and performance of existing devices with the size and comfort of conventional eyewear. For these devices a Generalised Confocal Lenslet Array (GCLA) is designed to magnify object space, creating the effect of traditional refracting telescope within a thin, planar device. By creating a device that is appreciably thinner than existing LVA telescopes it is hoped that the comfort for the wearer will be increased. We have developed a series of prototype GLCA-based devices to examine their real-world performance, focussing on the resolution, magnification and clarity of image attainable through the devices. It is hoped that these will form the basis for a future LVA devices. This development has required novel manufacturing techniques and a phased development approach centred on maximising performance. Presented here will be an overview of the development so far, alongside the performance of the latest devices

Behavioural and pathomorphological impacts of flash photography on benthic fishes

Millions of people take animal pictures during wildlife interactions, yet the impacts of photographer behaviour and photographic flashes on animals are poorly understood. We investigated the pathomorphological and behavioural impacts of photographer behaviour and photographic flashes on 14 benthic fish species that are important for scuba diving tourism and aquarium displays. We ran a field study to test effects of photography on fish behaviour, and two laboratory studies that tested effects of photographic flashes on seahorse behaviour, and ocular and retinal anatomy. Our study showed that effects of photographic flashes are negligible and do not have stronger impacts than those caused solely by human presence. Photographic flashes did not cause changes in gross ocular and retinal anatomy of seahorses and did not alter feeding success. Physical manipulation of animals by photographing scuba divers, however, elicited strong stress responses. This study provides important new information to help develop efficient management strategies that reduce environmental impacts of wildlife tourism

Non-contact in-process ultrasonic screening of thin fusion welded joints

Joining of metal structures, or welding, plays a critical role in our modern world, where safety critical welds of thin sheet metals are used in pipework, nuclear cannisters and aircraft ducting among others. A potential failure in such welds could prove to be catastrophic, hence the need for thorough inspection and testing. With the ever-increasing automation of welding operations, manually deployed Non-destructive Evaluation (NDE) has become a major bottleneck in the supply chain. This paper introduces for the first time, non-contact gas-coupled ultrasonic sensors deployed in-process during weld deposition, for screening of weld penetration directly at the point of manufacture. 3mm thick mild steel plates were butt-welded together using Gas Tungsten Arc Welding (GTAW) while non-contact air-coupled ultrasonic transducers, generating surface guided Lamb waves, performed inspection screening of the weld seam. Optimised Signal-to-Noise Ratio (SNR) was achieved through a customised acoustic matching layer, low-noise amplifiers and real-time signal processing. By performing multiple trials at varying levels of welding input power (0.59 kJ/mm to 1.03 kJ/mm), it was demonstrated that the amplitude of the through transmission A0 Lamb wave is correlated to the Weld Penetration Depth (WPD) and can be used for on-line weld quality screening. Advantages of the outlined method include higher production rates, reduced levels of scrap and higher production quality in regards to thin metal sheet welded components

Design, manufacture, and evaluation of prototype telescope windows for use in low-vision aids.

Pixellated Optics, a class of optical devices which preserve phase front continuity only over small sub areas of the device, allow for a range of uses that would not otherwise be possible. One potential use is as Low Vision Aids (LVAs), where they are hoped to combine the function and performance of existing devices with the size and comfort of conventional eyewear. For these devices a Generalised Confocal Lenslet Array (GCLA) is designed to magnify object space, creating the effect of traditional refracting telescope within a thin, planar device. By creating a device that is appreciably thinner than existing LVA telescopes it is hoped that the comfort for the wearer will be increased. We have developed a series of prototype GLCA-based devices to examine their real-world performance, focussing on the resolution, magnification and clarity of image attainable through the devices. It is hoped that these will form the basis for a future LVA devices. This development has required novel manufacturing techniques and a phased development approach centred on maximising performance. Presented here will be an overview of the development so far, alongside the performance of the latest devices

Atmospheric Methane : Comparison Between Methane's Record in 2006–2022 and During Glacial Terminations

Atmospheric methane's rapid growth from late 2006 is unprecedented in the observational record. Assessment of atmospheric methane data attributes a large fraction of this atmospheric growth to increased natural emissions over the tropics, which appear to be responding to changes in anthropogenic climate forcing. Isotopically lighter measurements of (Figure presented.) are consistent with the recent atmospheric methane growth being mainly driven by an increase in emissions from microbial sources, particularly wetlands. The global methane budget is currently in disequilibrium and new inputs are as yet poorly quantified. Although microbial emissions from agriculture and waste sources have increased between 2006 and 2022 by perhaps 35 Tg/yr, with wide uncertainty, approximately another 35–45 Tg/yr of the recent net growth in methane emissions may have been driven by natural biogenic processes, especially wetland feedbacks to climate change. A model comparison shows that recent changes may be comparable or greater in scale and speed than methane's growth and isotopic shift during past glacial/interglacial termination events. It remains possible that methane's current growth is within the range of Holocene variability, but it is also possible that methane's recent growth and isotopic shift may indicate a large-scale reorganization of the natural climate and biosphere is under way

Acetylcysteine has No Mechanistic Effect in Patients at Risk of Contrast-Induced Nephropathy - A Failure of Academic Clinical Science

Contrast‐induced nephropathy (CIN) is a major complication of imaging in patients with chronic kidney disease (CKD). The publication of an academic randomized controlled trial (RCT; n = 83) reporting oral (N)‐acetylcysteine (NAC) to reduce CIN led to > 70 clinical trials, 23 systematic reviews, and 2 large RCTs showing no benefit. However, no mechanistic studies were conducted to determine how NAC might work; proposed mechanisms included renal artery vasodilatation and antioxidant boosting. We evaluated the proposed mechanisms of NAC action in participants with healthy and diseased kidneys. Four substudies were performed. Two randomized, double‐blind, placebo‐controlled, three‐period crossover studies (n = 8) assessed the effect of oral and intravenous (i.v.) NAC in healthy kidneys in the presence/absence of iso‐osmolar contrast (iodixanol). A third crossover study in patients with CKD stage III (CKD3) (n = 8) assessed the effect of oral and i.v. NAC without contrast. A three‐arm randomized, double‐blind, placebo‐controlled parallel‐group study, recruiting patients with CKD3 (n = 66) undergoing coronary angiography, assessed the effect of oral and i.v. NAC in the presence of contrast. We recorded systemic (blood pressure and heart rate) and renal (renal blood flow (RBF) and glomerular filtration rate (GFR)) hemodynamics, and antioxidant status, plus biomarkers of renal injury in patients with CKD3 undergoing angiography. Primary outcome for all studies was RBF over 8 hours after the start of i.v. NAC/placebo. NAC at doses used in previous trials of renal prophylaxis was essentially undetectable in plasma after oral administration. In healthy volunteers, i.v. NAC, but not oral NAC, increased blood pressure (mean area under the curve (AUC) mean arterial pressure (MAP): mean difference 29 h⋅mmHg, P = 0.019 vs. placebo), heart rate (28 h⋅bpm, P < 0.001), and RBF (714 h⋅mL/min, 8.0% increase, P = 0.006). Renal vasodilatation also occurred in the presence of contrast (RBF 917 h⋅mL/min, 12% increase, P = 0.005). In patients with CKD3 without contrast, only a rise in heart rate (34 h⋅bpm, P = 0.010) and RBF (288 h⋅mL/min, 6.0% increase, P = 0.001) occurred with i.v. NAC, with no significant effect on blood pressure (MAP rise 26 h⋅mmHg, P = 0.156). Oral NAC showed no effect. In patients with CKD3 receiving contrast, i.v. NAC increased blood pressure (MAP rise 52 h⋅mmHg, P = 0.008) but had no effect on RBF (151 h⋅mL/min, 3.0% increase, P = 0.470), GFR (29 h⋅mL/min/1.73m², P = 0.122), or markers of renal injury. Neither i.v. nor oral NAC affected plasma antioxidant status. We found oral NAC to be poorly absorbed and have no reno‐protective effects. Intravenous, not oral, NAC caused renal artery vasodilatation in healthy volunteers but offered no protection to patients with CKD3 at risk of CIN. These findings emphasize the importance of mechanistic clinical studies before progressing to RCTs for novel interventions. Thousands were recruited to academic clinical trials without the necessary mechanistic studies being performed to confirm the approach had any chance of working

Atmospheric Methane: Comparison Between Methane's Record in 2006–2022 and During Glacial Terminations

Atmospheric methane's rapid growth from late 2006 is unprecedented in the observational record. Assessment of atmospheric methane data attributes a large fraction of this atmospheric growth to increased natural emissions over the tropics, which appear to be responding to changes in anthropogenic climate forcing. Isotopically lighter measurements of d13C-CH4 are consistent with the recent atmospheric methane growth being mainly driven by an increase in emissions from microbial sources, particularly wetlands. The global methane budget is currently in disequilibrium and new inputs are as yet poorly quantified. Although microbial emissions from agriculture and waste sources have increased between 2006 and 2022 by perhaps 35 Tg/yr, with wide uncertainty, approximately another 35–45 Tg/yr of the recent net growth in methane emissions may have been driven by natural biogenic processes, especially wetland feedbacks to climate change. A model comparison shows that recent changes may be comparable or greater in scale and speed than methane's growth and isotopic shift during past glacial/interglacial termination events. It remains possible that methane's current growth is within the range of Holocene variability, but it is also possible that methane's recent growth and isotopic shift may indicate a large-scale reorganization of the natural climate and biosphere is under way

A study of machine learning object detection performance for phased array ultrasonic testing of carbon fibre reinforced plastics

The growing adoption of Carbon Fibre Reinforced Plastics (CFRPs) in the aerospace industry has resulted in a significant reliance on Non-Destructive Evaluation (NDE) to ensure the quality and integrity of these materials. The interpretation of large amounts of data acquired from automated robotic ultrasonic scanning by expert operators is often time consuming, tedious, and prone to human error creating a bottleneck in the manufacturing process. However, with ever growing trend of computing power and digitally stored NDE data, intelligent Machine Learning (ML) algorithms have been gaining more traction than before for NDE data analysis. In this study, the performance of ML object detection models, statistical methods for defect detection, and traditional amplitude thresholding approaches for defect detection in CFRPs were compared. A novel augmentation technique was used to enhance synthetically generated datasets used for ML model training. All approaches were tested on real data obtained from an experimental setup mimicking industrial conditions, with ML models showing improvement over amplitude thresholding and statistical thresholding techniques. The advantages and limitations of all methods are reported and discussed

Automated deep learning for defect detection in carbon fibre reinforced plastic composites

Carbon Fibre Reinforced Polymers (CFRPs) are used extensively in the aerospace industry because of their unique physical properties and reduced weight that enables lower fuel consumption. This increase was especially rapid in the past decade, with CFRPs accounting for around 50% of the total material weight used in flagship models by Airbus and Boeing [1,2]. Before shipping, Non-Destructive Testing (NDT) methods are used to validate and control the quality of manufactured parts. Commonly used NDT technologies are radiographic testing, eddy current testing, and Ultrasonic Testing (UT). In the aerospace industry, UT is most prominent due to its flexibility and safety. However, when UT is done manually, reliability issues are often observed due to human inspector errors [3]. In addition to this, manufactured parts that need to be inspected are quite large (e.g., wing covers), resulting in slow inspection times. On the other hand, when NDT robotic inspection is deployed, large amounts of data can be captured in a short period of time. While this accelerates the acquisition of information, data interpretation is still done manually thus creating a bottleneck. Therefore, an automated data interpretation system would greatly improve the NDT process. To overcome these challenges, this project proposes a fully automated Deep Learning (DL) approach that leverages current technological advances in Machine Learning (ML) field for defect localization, sizing, and automatic report generation based on ultrasonic amplitude C-scans. Such an approach could decrease the processing time from approximately 6 hours for a 15-meter wing cover to just minutes, significantly benefiting the process throughput. In this research, a manually annotated semi-analytical simulated dataset in form of C-scans was used for training of "You Only Look Once" family of models for the detection and sizing of back-drilled holes and delamination defects in CFRPs. The purpose of using model-based simulations for training was the scarcity of real-world data, and a novel approach of image augmentation was introduced to ensure that the simulated scans closely mimic the experimental data. For NDT inspection, a force-torque-controlled 6-axis industrial robotic arm was used to deliver a phased array ultrasound roller probe to both defect-free and defective CFRP samples of varying thicknesses. The roller-probe array was connected to an array controller and water-coupled to the surface of the CFRPs. Raster scans were performed while the array was excited in linear-scan mode with a sub-aperture of 4 elements and an operating frequency of 5 MHz. Lastly, amplitude C-scan images of 64 x 64 resolution were extracted and used as an object detection validation dataset. These combined methods result in an accurate and precise deep learning network that enables rapid analysis of image data (with the possibility of real-time analysis)

HDAC4-regulated STAT1 activation mediates platinum resistance in ovarian cancer

Ovarian cancer frequently acquires resistance to platinum chemotherapy, representing a major challenge for improving patient survival. Recent work suggests resistant clones exist within a larger drug sensitive cell-population prior to chemotherapy, implying that resistance is selected for rather than generated by treatment. We sought to compare clinically-derived, intra-patient paired models of initial platinum response and subsequent resistant relapse to define molecular determinants of evolved resistance. Transcriptional analysis of a matched cell-line series from three patients with high-grade serous ovarian cancer before and after development of clinical platinum resistance (PEO1/PEO4/PEO6, PEA1/PEA2, PEO14/PEO23) identified 91 up- and 126 down-regulated genes common to acquired resistance. Significantly enhanced apoptotic response to platinum treatment in resistant cells was observed following knockdown of HDAC4, FOLR2, PIK3R1 or STAT1 (p<0.05). Interestingly, HDAC4 and STAT1 were found to physically interact. Acetyl-STAT1 was detected in platinum sensitive but not HDAC4 over-expressing platinum resistant cells from the same patient. In resistant cells, STAT1 phosphorylation/nuclear translocation was seen following platinum exposure, whereas silencing of HDAC4 increased acetyl-STAT1 levels, prevented platinum induced STAT1 activation and restored cisplatin sensitivity. Conversely, matched sensitive cells were refractory to STAT1 phosphorylation on platinum treatment. Analysis of 16 paired tumor biopsies taken before and after development of clinical platinum resistance showed significantly increased HDAC4 expression in resistant tumors (n=7/16[44%]; p=0.04). Therefore, clinical selection of HDAC4 overexpressing tumor cells upon exposure to chemotherapy promotes STAT1 deacetylation and cancer cell survival. Together, our findings identify HDAC4 as a novel, therapeutically tractable target to counter platinum resistance in ovarian cancer