A local model of eye adaptation for high dynamic range images

In the real world, the human eye is confronted with a wide range of luminances from bright sunshine to low night light. Our eyes cope with this vast range of intensities by adaptation; changing their sensitivity to be responsive at di erent illumination levels. This adaptation is highly localized, allowing us to see both dark and bright regions of a high dynamic range environment. In this paper we present a new model of eye adaptation based on physiological data. The model, which can be easily integrated into existing renderers, can function either as a static local tone mapping operator for single high dynamic range image, or as a temporal adaptation model taking into account time elapsed and intensity of preadaptation for a dynamic sequence. We nally validate our technique with a high dynamic range display and a psychophysical study.(undefined

Mixing tone mapping operators on the GPU by differential zone mapping based on psychophysical experiments

© 2016 In this paper, we present a new technique for displaying High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays in an efficient way on the GPU. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as the reference that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO. Finally, we present a GPU version, which is perceptually equal to the standard version but with much improved computational performance

S100B Protein, Brain-Derived Neurotrophic Factor, and Glial Cell Line-Derived Neurotrophic Factor in Human Milk

Human milk contains a wide variety of nutrients that contribute to the fulfillment of its functions, which include the regulation of newborn development. However, few studies have investigated the concentrations of S100B protein, brain-derived neurotrophic factor (BDNF), and glial cell line-derived neurotrophic factor (GDNF) in human milk. The associations of the concentrations of S100B protein, BDNF, and GDNF with maternal factors are not well explored.To investigate the concentrations of S100B protein, BDNF, and GDNF in human milk and characterize the maternal factors associated with their levels in human milk, human milk samples were collected at days 3, 10, 30, and 90 after parturition. Levels of S100B protein, BDNF, and GDNF, and their mRNAs in the samples were detected. Then, these concentrations were compared with lactation and other maternal factors. S100B protein levels in human milk samples collected at 3, 10, 30, and 90 d after parturition were 1249.79±398.10, 1345.05±539.16, 1481.83±573.30, and 1414.39±621.31 ng/L, respectively. On the other hand, the BDNF concentrations in human milk samples were 10.99±4.55, 13.01±5.88, 13.35±6.43, and 2.83±5.47 µg/L, while those of GDNF were 10.90±1.65, 11.38±1., 11.29±3.10, and 11.40±2.21 g/L for the same time periods. Maternal post-pregnancy body mass index was positively associated with S100B levels in human milk (r = 0.335, P = 0.030<0.05). In addition, there was a significant correlation between the levels of S100B protein and BDNF (z = 2.09, P = 0.037<0.05). Delivery modes were negatively associated with the concentration of GDNF in human milk.S100B protein, BDNF, and GDNF are present in all samples of human milk, and they may be responsible for the long term effects of breast feeding

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.

OBJECTIVES: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China. METHODS: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces. RESULTS: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic. CONCLUSIONS: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Tone mapping for high dynamic range imaging

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HDR Displays: a Validation Against Reality

In the real world the contrast between bright areas, directly illuminated by the sun, and dark shadows can be of 6 or 7 orders of magnitude. Although such huge contrast ratio is common in the natural world when these luminance levels are to be displayed on a typical monitor, the range is far too large. Bright areas appear overly saturated and shadows are displayed as black. Until recently, the only approach to solve this problem was to compress the luminance component of a High Dynamic Range (HDR) scene. Such techniques are known as tone mapping. However, even tone mapping operators are not always capable of producing sufficient contrast reduction. In this paper we present the results of a psychophysical investigation to validate a novel HDR display which is capable of contrast ratios similar to what is present in the physical world. Images displayed on this device are an accurate representation of a window on a scene and may not be equivalent as standing in the real scene due to a lack of peripheral information. We describe three perceptual studies with the goal of validating the device against real scenes in terms of peripheral vision