Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision

Chronic disease prevalence and care among the elderly in urban and rural Beijing, China - a 10/66 Dementia Research Group cross-sectional survey

<p>Abstract</p> <p>Background</p> <p>Demographic ageing is occurring at an unprecedented rate in China. Chronic diseases and their disabling consequences will become much more common. Public policy has a strong urban bias, and older people living in rural areas may be especially vulnerable due to limited access to good quality healthcare, and low pension coverage. We aim to compare the sociodemographic and health characteristics, health service utilization, needs for care and informal care arrangements of representative samples of older people in two Beijing communities, urban Xicheng and rural Daxing.</p> <p>Methods</p> <p>A one-phase cross-sectional survey of all those aged 65 years and over was conducted in urban and rural catchment areas in Beijing, China. Assessments included questionnaires, a clinical interview, physical examination, and an informant interview. Prevalence of chronic diseases, self-reported impairments and risk behaviours was calculated adjusting for household clustering. Poisson working models were used to estimate the independent effect of rural versus urban residence, and to explore the predictors of health services utilization.</p> <p>Results</p> <p>We interviewed 1002 participants in rural Daxing, and 1160 in urban Xicheng. Those in Daxing were more likely to be younger, widowed, less educated, not receiving a pension, and reliant on family transfers. Chronic diseases were more common in Xicheng, when based on self-report rather than clinical assessment. Risk exposures were more common in Daxing. Rural older people were much less likely to access health services, controlling for age and health. Community health services were ineffective, particularly in Daxing, where fewer than 3% of those with hypertension were adequately controlled. In Daxing, care was provided by family, who had often given up work to do so. In Xicheng, 45% of those needing care were supported by paid caregivers. Caregiver strain was higher in Xicheng. Dementia was strongly associated with care needs and caregiver strain, but not with medical helpseeking.</p> <p>Conclusion</p> <p>Apparent better health in Daxing might be explained by under-diagnosis, under-reporting or selective mortality. Far-reaching structural reforms may be needed to improve access and strengthen rural healthcare. The impact of social and economic change is already apparent in Xicheng, with important implications for future long-term care.</p

Superchirality induced enhanced circular dichroism spectroscopy via multi-beam superposition

Circular dichroism (CD) spectroscopy is a typical technique to detect the chirality of an object. Due to the mismatch between light wavelength and the size of molecules, CD signal generally tends to be very weak in natural materials. In this work, we demonstrate the generation of superchiral field in free space through multi-beam superposition, without plasmonic/dielectric substrate that required in traditional methods. Through a systematic study of the superposition effect of up to six beams of light, we found that even achiral light fields can obtain optical chirality through superposition, and circularly polarized light fields can most effectively enhance the optical chirality density in the interference field. Generally, a maximal superchiral factor equivalent to the number of superposed beams can be achieved through optimizing the orientation of the illumination. However, when the number of superposed beams is more than three, the initial phase of superposed beams would play a significant role in determining the characteristics of the interference field, which must be carefully treated in modulating both the shape and the optical chiral density of the synthesized subwavelength chiral hotspot. Furthermore, we demonstrate that this technique is suitable to combine optical tweezers and CD spectroscopy. Based on three-beam superposition configuration, we explore the optical force and CD effects from single chiral nanoparticle, which are introduced into the optical standing wave formed by a flat mirror. It is found out that the nanoparticle can be stably trapped by the superchiral hotspot and gives rise to 2.92-fold enhancement of CD signal. This work may serve as a guideline for establishing applications in chirality sorting, chiral imaging, and CD spectroscopy

Automatic plankton image classification combining multiple view features via multiple kernel learning

Abstract Background Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Results Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness. Conclusions This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy

Morphological Changes of Calcium Carbonate and Mechanical Properties of Samples during Microbially Induced Carbonate Precipitation (MICP)

Recently, microbially induced carbonate precipitation (MICP) has shown potent potential in the field of civil engineering. The calcium carbonate crystals produced by bacteria during the MICP process play a central role in sticking the soil. However, the morphological changes of calcium carbonate crystals in this process and the mechanical performance of soil in the corresponding stages have not been clearly explored. In this paper, the alterations in the morphology of calcium carbonate crystals were continuously observed via scanning electron microscopy during the MICP process in one week, and the mechanical changes of the samples were monitored every day, so as to reveal the relationship between the morphology of calcium carbonate crystals and the mechanical performance of the samples. The results show that the calcium carbonate crystals undergo a gradual change from ellipsoid to rhombic at the 72nd hour. The mechanical properties of both were greatly improved, among which the compressive strength was increased by 2.78 times compared with the previous time point, and the flexure strength was increased by 2.57 times; this time point was also the time when calcite appears. In addition, we found direct evidence on the first day that bacteria act as the nucleation site of calcium carbonate formation. The above findings have certain guiding significance for the in-depth understanding of the internal microscopic changes of MICP and the influence of calcium carbonate morphology on sample mechanics

The Formation Mechanism of (001) Facet Dominated α‐FAPbI3 Film by Pseudohalide Ions for High‐Performance Perovskite Solar Cells

Abstract Formamidinium lead triiodide (α‐FAPbI3) has been widely used in high‐efficiency perovskite solar cells due to its small band gap and excellent charge‐transport properties. Recently, some additives show facet selectivity to generate a (001) facet‐dominant film during crystallization. However, the mechanism to realize such (001) facet selectivity is not fully understood. Here, the authors attempted to use three ammonia salts NH4X (X are pseudohalide anions) to achieve better (001) facet selectivity in perovskite crystallization and improved crystallinity. After addition, the (001) facet dominance is generally increased with the best effect from SCN− anions. The theoretical calculation revealed three mechanisms of such improvements. First, pseudohalide anions have larger binding energy than the iodine ion to bind the facets including (110), (210), and (111), slowing down the growth of these facets. The large binding energy also reduces nucleation density and improves crystallinity. Second, pseudohalide ions improve phase purity by increasing the formation energies of the δ‐phase and other hexagonal polytypes, retarding the α‐ to δ‐phase transition. Third, the strong binding of these anions can also effectively passivate the iodine vacancies and suppress nonradiative recombination. As a result, the devices show a power conversion efficiency of 24.11% with a Voc of 1.181 V

The Origin, Function, Distribution, Quantification, and Research Advances of Extracellular DNA

In nature, DNA is ubiquitous, existing not only inside but also outside of the cells of organisms. Intracellular DNA (iDNA) plays an essential role in different stages of biological growth, and it is defined as the carrier of genetic information. In addition, extracellular DNA (eDNA) is not enclosed in living cells, accounting for a large proportion of total DNA in the environment. Both the lysis-dependent and lysis-independent pathways are involved in eDNA release, and the released DNA has diverse environmental functions. This review provides an insight into the origin as well as the multiple ecological functions of eDNA. Furthermore, the main research advancements of eDNA in the various ecological environments and the various model microorganisms are summarized. Furthermore, the major methods for eDNA extraction and quantification are evaluated

Pharmacokinetic Comparisons of Different Combinations of Yigan Jiangzhi Formula in Rats: Simultaneous Determination of Fourteen Components by UPLC-MS/MS

A rapid, specific, and sensitive analysis for simultaneous determination of fourteen components (daidzein, fermononetin, apigenin, luteolin, puerarin, ononin, calycosin-7-O-β-D-glucoside, tanshinol, rosmarinic acid, alkanoic acid, salvianolic acid B, berberine, jatrorrhizin, and palmatine) of Yigan Jiangzhi formula (YGJZF, a clinical experienced formula for damp-heat syndrome) in rat plasma was developed and validated using ultraperformance liquid chromatography coupled with mass spectrometry. Lower limit of quantitation ranged from 0.2–10.0 ng/mL, and the calibration curves showed good linearity over 500 times of measuring range. The validated method was successfully applied to the pharmacokinetics investigation of the fourteen compounds in rat plasma after oral administration of two different doses of YGJZF. Compared with the low-dose group of YGJZF, the high-dose group showed significant increase (P<0.01 or P<0.05) in maximum plasma concentration, maximum concentration time, and area under the plasma concentration-time curve and decrease (P<0.01 or P<0.05) in clearance of most of the fourteen analytes, which suggested that the bioavailability of these components could be enhanced by increasing dosage. The above results may provide useful information for cognizing the relationship between in vitro and in vivo data of the fourteen bioactive ingredients of YGJZF and further guiding rational clinical drug prescription