SGLSA: Sphygmus gated laser speckle angiography for microcirculation hemodynamics imaging.

Hemodynamics imaging of the retinal microcirculation has been demonstrated to be potential access to evaluating ophthalmic diseases, cardio-cerebrovascular diseases, and metabolic diseases. However, existing structural and functional imaging techniques are insufficient in spatial or temporal resolution. The sphygmus gated laser speckle angiography (SGLSA) is proposed for structural and functional imaging with high spatiotemporal resolution. Compared with classic LSCI algorithms, SGLSA presents a much clearer perfusion image and higher signal-to-noise ratio pulsatility. The SGLSA algorithm also shows better performance on patients than traditional LSCI methods. The high spatiotemporal resolution provided by the SGLSA algorithm greatly enhances the ability of retinal microcirculation analysis, which makes up for the deficiency of the LSCI technology, and attaches great significance to retinal hemodynamic imaging, biomarker research, and clinical diagnosis.

Spatio-temporal distribution of vector borne diseases in Australia and Papua New Guinea vis-à-vis climatic factors.

BACKGROUND & OBJECTIVES: Weather and climate are directly linked to human health including the distribution and occurrence of vector-borne diseases which are of significant concern for public health. METHODS: In this review, studies on spatiotemporal distribution of dengue, Barmah Forest Virus (BFV) and Ross River Virus (RRV) in Australia and malaria in Papua New Guinea (PNG) under the influence of climate change and/ or human society conducted in the past two decades were analysed and summarised. Environmental factors such as temperature, rainfall, relative humidity and tides were the main contributors from climate. RESULTS: The Socio-Economic Indexes for Areas (SEIFA) index (a product from the Australian Bureau of Statistics that ranks areas in Australia according to relative socio-economic advantage and disadvantage) was important in evaluating contribution from human society. INTERPRETATION & CONCLUSION: For future studies, more emphasis on evaluation of impact of the El Niño-Southern Oscillation (ENSO) and human society on spatio-temporal distribution of vector borne diseases is recommended to highlight importance of the environmental factors in spreading mosquito-borne diseases in Australia and PNG.

Portable boom-type ultrahigh-resolution OCT with an integrated imaging probe for supine position retinal imaging.

To expand the clinical applications and improve the ease of use of ultrahigh-resolution optical coherence tomography (UHR-OCT), we developed a portable boom-type ophthalmic UHR-OCT operating in supine position that can be used for pediatric subjects, bedridden patients and perioperative conditions. By integrating the OCT sample arm probe with real-time iris display and automatic focusing electric lens for easy alignment, coupling the probe on a self-locking multi-directional manipulator to reduce motion artifacts and operator fatigue, and installing the OCT module on a moveable cart for system mobility, our customized portable boom-type UHR-OCT enables non-contact, high-resolution and high-stability retinal examinations to be performed on subjects in supine position. The spectral-domain UHR-OCT operates at a wavelength of 845 nm with 130 nm FWHM (full width at half maximum) bandwidth, achieving an axial resolution of ≈2.3µm in tissue with an A-line acquisition rate up to 128 kHz. A high-definition two-dimensional (2D) raster protocol was used for high-quality cross-sectional imaging while a cube volume three-dimensional (3D) scan was used for three-dimensional imaging and en-face reconstruction, resolving major layer structures of the retina. The feasibility of the system was demonstrated by performing supine position 2D/3D retinal imaging on healthy human subjects, sedated infants, and non-sedated awake neonates.

The impact of healthcare reform on the dynamic changes in health service utilization and equity: a 10-year follow-up study.

In the past decade, the government of China has implemented healthcare reforms to provide universal access to healthcare by 2020. We aimed to systematically analyse the dynamic changes in health services and equity during the past 10 years to understand the correlation between health services and social-economic status. We performed a longitudinal study in which we extracted aggregated data mainly from a project (2009, 2011, 2012, 2015, 2019). A multi-stage stratified cluster randomized design was used to obtain a representative sample in each county. Concentration indexes were used to analyse the equity of the changes in utilization. We built multivariate random-effects generalized least squares regression models with the panel data to test whether the rate of receiving a medical consultation in the last 2 weeks or the rate of hospital admission or the prevalence of chronic illness was associated with social-economic status including education level and rural disposable income per capita. We found declines in both the rate of not receiving a medical consultation during the last 2 weeks (P < 0.05 intervention group) and the rate of hospital avoidance (P < 0.05) from 2009 to 2019. The equity in residents' health service utilization has improved constantly. We additionally found that rural disposable income per capita is a protective factor for the rate of a receiving a medical consultation during the last 2 weeks and the rate of hospital admission. China's 2009 healthcare reform have positively influenced utilization rates and equity in health service utilization in the past decade, a range of health service-targeted strategies are needed including strengthen the prevention and treatment of chronic diseases, focus attention on the health status of elderly residents and improve social-economic status, especially the level of education.

Preparation and Characterization of a Novel Amidoxime-Modified Polyacrylonitrile/Fly Ash Composite Adsorbent and Its Application to Metal Wastewater Treatment.

The polyacrylonitrile/fly ash composite was synthesized through solution polymerization and was modified with NH2OH·HCl. The amidoxime-modified polyacrylonitrile/fly ash composite demonstrated excellent adsorption capacity for Zn2+ in an aqueous medium. Fourier transform-Infrared spectroscopy, thermogravimetric analysis, nitrogen adsorption, X-ray diffraction, and scanning electron microscopy were used to characterize the prepared materials. The results showed that the resulting amidoxime-modified polyacrylonitrile/fly ash composite was able to effectively remove Zn2+ at pH 4-6. Adsorption of Zn2+ was hindered by the coexisting cations. The adsorption kinetics of Zn2+ by Zn2+ followed the pseudo-second order kinetic model. The adsorption process also satisfactorily fit the Langmuir model, and the adsorption process was mainly single layer. The Gibbs free energy ΔG0, ΔH0, and ΔS0 were negative, indicating the adsorption was a spontaneous, exothermic, and high degree of order in solution system.

Functional imaging of human retina using integrated multispectral and laser speckle contrast imaging.

A novel integration of retinal multispectral imaging (MSI), retinal oximetry and laser speckle contrast imaging (LSCI) is presented for functional imaging of retinal blood vessels that could potentially allow early detection or monitoring of functional changes. We designed and built a cost-effective, scalable, retinal imaging instrument that integrates structural and functional retinal imaging techniques, including MSI, retinal oximetry and LSCI. Color fundus imaging was performed with 470 nm, 550 nm and 600 nm wavelength light emitting diode (LED) illumination. Retinal oximetry was performed using 550 nm and 600 nm LED illumination. LSCI of blood flow was performed using 850 nm laser diode illumination at 82 frames per second. LSCI can visualize retinal and choroidal vasculature without requiring exogenous contrast agents and can provide time-resolved information on blood flow, generating a cardiac pulse waveform from retinal vasculature. The technology can rapidly acquire structural MSI images, retinal oximetry and LSCI blood flow information in a simplified clinical workflow without requiring patients to move between instruments. Results from multiple modalities can be combined and registered to provide structural as well as functional information on the retina. These advances can reduce barriers for clinical adoption, accelerating research using MSI, retinal oximetry and LSCI of blood flow for diagnosis, monitoring and elucidating disease pathogenesis.

Plant trait-environment trends and their conservation implications for riparian wetlands in the Yellow River.

Determining the relationship between plant functional traits and the environment are key for the protection and sustainable utilization of riparian wetlands. In the middle and lower reaches of the Yellow River, riparian wetlands are divided into seasonal floodplain wetlands (natural) and pond-like wetlands or paddy fields (artificial). Here, species composition differences were catalogued based on plant functional traits including origin, life history, and wetland affinity in natural and artificial wetlands. Wetland physicochemical characteristics and regional socio-economic parameters collected as indicators of environmental variables were used to analyze the plant functional trait-environment relationship. The results reveal that plant functional traits in the seasonal floodplain wetland are impacted by physicochemical characteristics of habitat. The abundance of annual plants tends to decrease with concentration of heavy metals, while species diversity is mainly determined by soil physical and chemical properties, especially soil pH and temperature. Specifically, wetland-obligate species (not in water) are more resistant to heavy metal content in water than species with other types of wetland affinity. Life history strategies of species in artificial sites tend to be significantly associated with animal husbandry and artificial populations, while the wetland affinity of species is mainly determined by regional agriculture, especially the installation of agricultural covered areas. Furthermore, water quality and nutrients in suspended sediments from the Yellow River affected species diversity and life history strategies by affecting water and soil conditions of surrounding wetlands, especially conductivity and phosphorus levels.

N2NSR-OCT: Simultaneous denoising and super-resolution in optical coherence tomography images using semisupervised deep learning.

Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.

Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography.

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation strategy. Compared with supervised learning approaches, the proposed approach demonstrated similar or even better performance in the OCTA reconstruction task. These investigations indicate that the proposed weakly supervised learning strategy is well capable of performing OCTA reconstruction, and has a certain potential towards clinical applications.

High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning.

SIGNIFICANCE: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead to the degradation of the detection sensitivity, thus reducing the signal-to-noise ratio (SNR) of OCT images. AIM: We propose using deep learning to reconstruct high SNR OCT images from low bit-depth acquisition. APPROACH: The feasibility of our approach is evaluated by applying this approach to the quantized 3- to 8-bit data from native 12-bit interference fringes. We employ a pixel-to-pixel generative adversarial network (pix2pixGAN) architecture in the low-to-high bit-depth OCT image transition. RESULTS: Extensively, qualitative and quantitative results show our method could significantly improve the SNR of the low bit-depth OCT images. The adopted pix2pixGAN is superior to other possible deep learning and compressed sensing solutions. CONCLUSIONS: Our work demonstrates that the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings.

Retinal choroidal vessel imaging based on multi-wavelength fundus imaging with the guidance of optical coherence tomography.

A multispectral fundus camera (MSFC), as a novel noninvasive technology, uses an extensive range of monochromatic light sources that enable the view of different sectional planes of the retinal and choroidal structures. However, MSFC imaging involves complex processes affected by various factors, and the recognized theory based on light absorption above the choroid is not sufficient. In an attempt to supplement the relevant explanations, in this study, we used optical coherence tomography (OCT), a three-dimensional tomography modality, to analyze MSFC results at the retina and choroid. The swept-source OCT system at 1060 nm wavelength with a 200 kHz A-scan rate and an MSFC with 11 bands at 470 to 845 nm are employed. A quantitative evaluation procedure is proposed to compare MSFC and OCT en face images. The comparative study shows that 1) the MSFC images with the illumination wavelength of less than 605 nm could mainly provide the retinal structure information; 2) Relative choroidal layer thickness information could be inferred from the MSFC images, especially the image acquiring under the wavelength more than 605 nm. According to the results, further investigation revealed the contribution of the perivascular tissue and the sclera scattering in the difference of vascular brightness in MSFC images.

Spatial differentiation of carbon emissions from residential energy consumption: A case study in Kaifeng, China.

Effective strategies, policies and measures for carbon emission reduction need to be developed and implemented according to good understanding of both local conditions and spatial differentiation mechanism of energy consumption associated with human activities at high resolution. In the study, we first collected statistical yearbooks, high resolution remotely sensed imageries, and 3895 usable questionnaires for the urban areas of Kaifeng; then measured the carbon emissions from household energy consumption, using the accounting method provided in the IPCC GHG Inventory Guidelines; and finally applied both exploratory and explanatory statistical methods to characterize the spatial pattern of carbon emissions at high resolution, identify key influencing factors, and gain better understanding of the spatial differentiation mechanism of urban residential carbon emissions. Our study reached the following conclusions: (1) Central heating facilities with controllable flow are important for carbon emissions reduction, but its spatial distribution shows unfairness; (2) Spatial clusters of high carbon emission areas were primarily located in the outer suburbs of the city, validated to some extent the hypothesis that urban sprawl has a driving effect on the increasing urban residential carbon emissions; (3) Factors like size of residential area, family structure, life style, personal preference and behavior rather than household income have significant impacts on household carbon emissions, implying that effective control of residential areas, promotion of family life and low-carbon lifestyle, and effective guidance of proper behaviors and preferences will play a crucial role in reducing urban residential carbon emissions; and (4) Most of the identified influencing factors exhibit clear and specific spatial patterns and gradients of impact, implying that measures for urban residential carbon emission reduction should be adapted to location conditions. The study has generated a set of concrete evidences and improved understandings of the spatially differentiated mechanisms upon which the formation and deployment of any effective strategies, policies and measures for reducing urban residential carbon emissions should be based.

The mechanoelectrical transducer channel is not required for regulation of cochlear blood flow during loud sound exposure in mice.

The mammalian cochlea possesses unique acoustic sensitivity due to a mechanoelectrical 'amplifier', which requires the metabolic support of the cochlear lateral wall. Loud sound exposure sufficient to induce permanent hearing damage causes cochlear blood flow reduction, which may contribute to hearing loss. However, sensory epithelium involvement in the cochlear blood flow regulation pathway is not fully described. We hypothesize that genetic manipulation of the mechanoelectrical transducer complex will abolish sound induced cochlear blood flow regulation. We used salsa mice, a Chd23 mutant with no mechanoelectrical transduction, and deafness before p56. Using optical coherence tomography angiography, we measured the cochlear blood flow of salsa and wild-type mice in response to loud sound (120 dB SPL, 30 minutes low-pass filtered noise). An expected sound induced decrease in cochlear blood flow occurred in CBA/CaJ mice, but surprisingly the same sound protocol induced cochlear blood flow increases in salsa mice. Blood flow did not change in the contralateral ear. Disruption of the sympathetic nervous system partially abolished the observed wild-type blood flow decrease but not the salsa increase. Therefore sympathetic activation contributes to sound induced reduction of cochlear blood flow. Additionally a local, non-sensory pathway, potentially therapeutically targetable, must exist for cochlear blood flow regulation.

Comparative study of deep learning models for optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Meanwhile, deep learning has achieved rapid development in image-to-image translation tasks. Some studies have proposed applying deep learning models to OCTA reconstruction and have obtained preliminary results. However, current studies are mostly limited to a few specific deep neural networks. In this paper, we conducted a comparative study to investigate OCTA reconstruction using deep learning models. Four representative network architectures including single-path models, U-shaped models, generative adversarial network (GAN)-based models and multi-path models were investigated on a dataset of OCTA images acquired from rat brains. Three potential solutions were also investigated to study the feasibility of improving performance. The results showed that U-shaped models and multi-path models are two suitable architectures for OCTA reconstruction. Furthermore, merging phase information should be the potential improving direction in further research.

Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function.

Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers' eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with scans occurring in one direction. The results showed that the new approach can outperform other related denoising methods on the aspects of preserving detail structure information of retinal layers and improving the perceptual metrics in the human visual perception.

Remediation of PBDEs-metal co-contaminated soil by the combination of metal stabilization, persulfate oxidation and bioremediation.

Laboratory experiments were performed to investigate the efficiency of a simultaneous metal stabilization, persulfate oxidation and bioremediation for decontaminating polybrominated diphenyl ethers (PBDEs) and toxic metals from an actual soil polluted by the recycling activity of electronic waste. Biochar and bentonite were applied to the soil for immobilizing heavy metals (Cu, Pb, Zn and Ni). It was found that the toxicity level declined most significantly in the case of 20 g/kg biochar +20 g/kg bentonite. A low dose of persulfate (20 mmol/kg soil) was found to be suitable for oxidizing soil PBDEs and enhancing the bioavailability of PBDEs residue. Persulfate oxidation reduced the soil organic matter content, and caused dramatic decrease of bacterial density. Nevertheless, microbial activity and number recovered on the whole during 90 days of bioremediation. Finally, a degradation efficiency of 94.6% and a mineralization efficiency of 60.3% were obtained by the hybrid treatment scheme. The pyrosequencing analysis indicates that soil bacterial community changed obviously during the treatments, and there was an enrichment of PBDE-degrading populations during bioremediation relative to that of oxidized soil.

Remediation of resins-contaminated soil by the combination of electrokinetic and bioremediation processes.

In this work, soil contaminated by petroleum resins was remediated by electrokinetic-bioremediation (EK-BIO) technology for 60 days. A microbial consortium, comprising Rhizobium sp., Arthrobacter globiformis, Clavibacter xyli, Curtobacterium flaccumfaciens, Bacillus subtilis, Pseudomonas aeruginosa and Bacillus sp., was used to enhance the treatment performance. The results indicate that resin removal and phytotoxicity reduction were highest in the inoculated EK process, wherein 23.6% resins was removed from the soil and wheat seed germination ratio was increased from 47% to around 90% after treatment. The microbial counts, soil basal respiration and dehydrogenase activity were positively related to resins degradation, and they could be enhanced by direct current electric field. After remediation, the C/H ratio of resins decreased from 8.03 to 6.47. Furthermore, the structure of resins was analyzed by Fourier-transform infrared spectroscopy, elemental analysis, and 1H nuclear magnetic resonance (1H NMR) before and after treatment. It was found that the changes of the structure of resins took place during EK-BIO treatment and finally led to the reduction of aromaticity, aromaticity condensation and phytotoxicity.

Assessing the Spatial Distribution of Soil PAHs and their Relationship with Anthropogenic Activities at a National Scale.

Soil polycyclic aromatic hydrocarbon (PAH) pollution is a major concern due to its negative impact on soil quality around the world. In China, accurate data on soil PAHs and information on the relationship with anthropogenic activities are limited. In this study, about 30,800 samples from 1833 soil sample sites were reviewed from 306 published reports to build a soil PAHs database. Based on the data obtained, the results demonstrated that 24.11% of surface soils in China are heavily contaminated. Meanwhile, the concentration of soil PAHs varied, in the order of independent mining and industrial areas (IMIA) > urban areas > suburban areas > rural areas, and the spatial distribution in China demonstrated a descending trend from north to south. Moreover, the characteristic ratio and PCA-MLR (principal component analysis-multiple linear regression) analysis demonstrated that coal combustion and vehicular exhaust emissions were the main sources of soil PAH pollution in China. On the other hand, provincial total Σ16PAHs in surface soil were significantly correlated with the per square kilometer GDP (gross domestic product) of industrial land, the per capita GDP, as well as the production and consumption of energy. These results indicate that anthropogenic factors have greatly affected the levels of soil PAHs in China. This study improves our understanding on the status and sources of soil PAH contamination in China, thereby facilitating the implementation of strategies of prevention, control, and remediation of soils.

Spatial assessment of farmland soil pollution and its potential human health risks in China.

Soil pollution severely threatens agro-ecosystem stability. It is important to accurately understand the status of farmland pollution in order to protect national food safety and human health. However, information of the combined pollution level of Chinese farmland soil and associated human health risk at the national scale is relatively lacking. In this study, 5597 samples from 1781 farmland soil sites were obtained from 553 reports and combined into pollution databases of heavy metals, organochlorines, and polycyclic aromatic hydrocarbons. Based on the data obtained, this paper demonstrated the current pollution status of farmland soil, and assessed the subsequent human health risk. Results showed that the combined pollution ratio of Chinese farmland soil was 22.10%, with 1.23% of severe pollution level. Moreover, the total non-carcinogenic hazard quotients of farmland soil pollution were within the safety threshold for adults, but there was a slight non-carcinogenic risk for children. For adults, the ratio of total farmland area to total carcinogenic risk quotients above the safety threshold of 1 × 10-5 was only 1.02%, but for children, the ratio was as high as 20.75%. On the other side, food crop and vegetable plantations were the priority control farmland soil compared to other types. Meanwhile, Yunnan, Hunan, Anhui, Henan, and Liaoning were selected as the priority control provinces due to their severe pollutions and high human health risks. This study has provided a comprehensive pollution and health risk assessment. Furthermore, the spatial distribution might provide as the scientific support for accelerating the mapping of soil pollution in China, as well as developing the policy for the contaminated farmland soil management.

A deep learning based pipeline for optical coherence tomography angiography.

Optical coherence tomography angiography (OCTA) is a relatively new imaging modality that generates microvasculature map. Meanwhile, deep learning has been recently attracting considerable attention in image-to-image translation, such as image denoising, super-resolution and prediction. In this paper, we propose a deep learning based pipeline for OCTA. This pipeline consists of three parts: training data preparation, model learning and OCTA predicting using the trained model. To be mentioned, the datasets used in this work were automatically generated by a conventional system setup without any expert labeling. Promising results have been validated by in-vivo animal experiments, which demonstrate that deep learning is able to outperform traditional OCTA methods. The image quality is improved in not only higher signal-to-noise ratio but also better vasculature connectivity by laser speckle eliminating, showing potential in clinical use. Schematic description of the deep learning based optical coherent tomography angiography pipeline.