Underwater ghost imaging with detection distance up to 9.3 attenuation lengths.

Underwater ghost imaging LiDAR is an effective method of underwater detection. In this research, theoretical and experimental investigations were conducted on underwater ghost imaging, combining the underwater optical field transmission model with the inherent optical parameters of a water body. In addition, the Wells model and the approximate Sahu-Shanmugam scattering phase function were used to create a model for underwater optical transmission. The second-order Glauber function of the optical field was then employed to analyze the scattering field degradation during the transmission process. The simulation and experimental results verified that the proposed underwater model could better reveal the degrading effect of a water body on ghost imaging. A further series of experiments comparing underwater ghost imaging at different detection distances was also conducted. In the experimental system, gated photomultiplier tube (PMT) was used to filter out the peak of backscattering, allowing a larger gain to be set for longer-range detection of the target. The laser with a central wavelength of 532 nm was operated at a frequency of 2 KHz, with a single pulse energy of 2 mJ, a pulse width of 10 ns. High-reflective targets were imaged up to 65.2 m (9.3 attenuation lengths (ALs), attenuation coefficient c = 0.1426 m-1, and scattering coefficient b = 0.052 m-1) and diffuse-reflection targets up to 41.2 m (6.4 ALs, c = 0.1569 m-1, and b = 0.081 m-1). For the Jerlov-I (c = 0.048 m-1 and b = 0.002 m-1) water body, the experimentally obtained maximum detection distance of 9.3 ALs can be equivalent to 193.7 m under the same optical system conditions.

Satellite-derived bathymetry based on machine learning models and an updated quasi-analytical algorithm approach.

Retrieving the water depth by satellite is a rapid and effective method for obtaining underwater terrain. In the optical shallow waters, the bottom signal has a great impact on the radiation from the water which related to water depth. In the optical shallow waters, the spatial distribution characteristic of water quality parameters derived by the updated quasi analysis algorithm (UQAA) is highly correlated with the bottom brightness. Because the bottom reflection signal is strongly correlated with the spatial distribution of water depth, the derived water quality parameters may helpful and applicable for optical remote sensing based satellite derived bathymetry. Therefore, the influence on bathymetry retrieval of the UQAA IOPs is worth discussing. In this article, different machine learning algorithms using a UQAA were tested and remote sensing reflectance at water depth in situ points and their detection accuracy were evaluated by using Worldwiew-2 multispectral remote sensing images and laser measurement data. A backpropagation (BP) neural network, extreme value learning machine (ELM), random forest (RF), Adaboost, and support vector regression (SVR) machine models were utilized to compute the water depth retrieval of Ganquan Island in the South China Sea. According to the obtained results, bathymetry using the UQAA and remote sensing reflectance is better than that computed using only remote sensing reflectance, in which the overall improvements in the root mean square error (RMSE) were 1 cm to 5 cm and the overall improvement in the mean relative error (MRE) was 1% to 5%. The results showed that the results of the UQAA could be used as a main water depth estimation eigenvalue to increase water depth estimation accuracy.

Precise detection of water surface through the analysis of a single green waveform from bathymetry LiDAR.

Determination of the correct water surface height (WSH) from green laser (532 nm) echoes alone in bathymetry LiDAR is challenging, as the green laser return near the water surface involves both specular reflection from the air-water interface and backscattered return from the water volume. In this paper, a low-complexity method based on linear approximation of the leading edge (LLE) is proposed. The results of this LLE method were compared with those of three common algorithms of peak detection, half peak power, and surface-volume-bottom implemented on airborne datasets with various surface roughness conditions. In addition, the method was evaluated in waters with a wide range of optical properties through a controllable tank experiment. The uncertainties in the WSHs of all algorithms were greater when the water volume backscattering dominated the surface return; they were sensitive to variations in the optical properties of water, and increased exponentially with decreasing LiDAR attenuation coefficient (KLiDAR). Comparatively, the LLE algorithm had the fastest computational speed and demonstrated the best performance in situations where specular reflection or volume backscatter return was dominant, with average and maximum errors of less than 0.06 and 0.13 m, respectively.

Remote sensing of seawater optical properties and the subsurface phytoplankton layer in coastal waters using an airborne multiwavelength polarimetric ocean lidar.

The vertical profiles of the seawater optical properties and subsurface phytoplankton layer observed during an airborne lidar flight experiment carried out on 29 January 2021 in the coastal waters near Qionghai city were studied. We employed a hybrid inversion model combining the Klett and perturbation retrieval methods to estimate the seawater optical properties, while the vertical subsurface phytoplankton layer profiles were obtained by an adaptive evaluation. The airborne lidar data preprocessing scheme and inversion of the seawater optical properties were described in detail, and the effects of water environment parameters on the airborne lidar detection performance in coastal waters were discussed. The obtained seawater optical properties and phytoplankton layer profiles exhibit characteristic spatiotemporal distributions. The vertical stratification of seawater optical properties along a flight track from 19.19°N to 19.27°N is more pronounced than that from 19.27°N to 19.31°N. The subsurface phytoplankton layer appears along the flight track at water depths of 5-14 m with a thickness of 2-8.3 m. The high concentrations of chlorophyll, colored dissolved organic matter (CDOM), and suspended particulate matter (SPM) in coastal waters are the main factors leading to the shallower detection depth for airborne lidar. A 532 nm laser emission wavelength is more suitable than 486 nm for investigating coastal waters. The 532 nm receiving channel with 25 mrad receiving field of view achieves a better detection performance than that with 6 mrad. These results indicate that lidar technology has great potential for the wide-range and long-term monitoring of coastal waters.

Forward volume scattering function (0.03°-60°) measured using an oblique-incidence particle sizer.

The forward volume scattering function (VSF) is an inherent optical property important in ocean lidar and underwater imaging and communication. The scattered power within 60° contains >90% of total scattered power, making it essential for determining the asymmetry parameter g. Thus, the new oblique-incidence-design Bettersize BT-3000 particle sizer was utilized to measure forward VSF (0.03°-60°) synchronously. A double-exponential model was then used to construct the full-angle-range VSF (0°-180°). The g value calculated therefrom had an uncertainty of <1%. Calibration was implemented using 11-µm beads alone, and the BT-3000's performance was validated.

Dissolved Organic Carbon Source Attribution in the Changjiang Outflow Region of the East China Sea.

The variable optical properties of chromophoric dissolved organic matter (CDOM) under the complicated dynamic marine environment make it difficult to establish a robust inversion algorithm for quantifying the dissolved organic carbon (DOC). To better understand the main factors affecting the relationship between the DOC and the CDOM when the Changjiang diluted water (CDW) interacts with the marine currents on the wide continental shelf, we measured the DOC concentration, the absorption, and the fluorescence spectra of the CDOM along the main axis and the northern boundary of the CDW. The sources of DOC and their impacts on the relationship between the optical properties of the DOC and CDOM are discussed. We reached the following conclusions: There are strong positive correlations between the absorptive and fluorescent properties of the DOC and the CDOM as a whole. The dilution of the terrestrial DOC carried by the CDW through mixing with saline sea water is the dominant mechanism controlling the characteristics of the optical properties of the CDOM. CDOM optical properties can be adopted to establish inversion models in retrieving DOC in Changjiang River Estuary. It is concluded that the introduction of extra DOC from different sources is the main factor causing the regional optical complexity leading to the bias of DOC estimation rather than removal mechanism. As whole, the input of polluted water from Huangpujiang River with abnormally high a(355) and Fs(355) will induce the overestimation of DOC. In the main axis of CDW, the impact from autochthonous DOC input to the correlation between DOC and CDOM can be neglected in comparison with conservative dilution procedure. The relationship between the DOC and the CDOM on the northern boundary of the CDW is more complicated, which can be attributed to the continuous input of terrestrial material from the Old Huanghe Delta by the Subei Coastal Current, the input of materials from the Yellow sea by the Yellow Sea Warm Western Coastal Current, and the input of materials from the Changjiang Basin by the CDW. The results of this study suggest that long-term observations of the regional variations in the DOM inputs from multiple sources in the interior of the CDW are essential, which is conducive to assess the degree of impact to the DOC estimation through the CDOM in the East China Sea.

Ocean mixed layer depth estimation using airborne Brillouin scattering lidar: simulation and model.

The potential of Brillouin scattering lidar for detecting the mixed layer depth (MLD) was studied. We simulated the Brillouin scattering lidar signal in various water environmental parameters and developed an MLD retrieval model for Brillouin scattering lidar data. We first analyzed the theoretical maximum detectable depth for Brillouin scattering lidar in low-latitude sea regions based on the multiple scattering lidar equations. Subsequently, a theoretical method for calculating the Brillouin scattering frequency shift and linewidth was derived based on the international thermodynamic equation of seawater-2010 and the coupled wave equations. Then we used the theoretical method and the temperature-salinity (T-S) profile of the global Argo data in low-latitude regions to simulate the vertical profile distribution of the Brillouin scattering frequency shift and linewidth. Furthermore, we used a maximum angle method to estimate the ocean MLD in low-latitude regions based on the vertical profile distribution of the Brillouin scattering frequency shift and density in seawater. They are well correlated, which indicates that the frequency-shift component of the Brillouin scattering lidar signal for estimating ocean MLD is feasible and reliable. It appears that airborne or spaceborne Brillouin scattering lidar technology provides great potential for high-efficiency, large-area, and long-term monitoring of the global ocean MLD and upper-ocean water bodies.

Instrument response effects on the retrieval of oceanic lidar.

Seawater properties can be retrieved from oceanic lidar returns. However, the actual returns include the ideal returns convolved by the instrument response, which inevitably introduces retrieval error. In this paper, instrument response effects on the retrieval of oceanic lidar are analyzed from different aspects. The results demonstrate that the retrieval of the lidar attenuation coefficient near the water surface is affected by the instrument response in homogeneous water. Considering the ratio of the signal distortion region (relative error of attenuation >10%) to the maximum detection depth (three dynamic ranges) is less than 20%, the pulse width of the instrument response should be less than 10-0.042(Kd)-2+0.709(Kd)-1+1.136ns. In addition, an average relative error of 55% will be introduced to the retrieval of phytoplankton layer thickness in the stratified water, which can be reduced to 6% after correcting for the influence of the instrument response. However, a relative error greater than 20% still exists when the instrument response length is two times larger than the layer thickness. These conclusions provide guidelines to a future design of oceanic lidar.

Iterative retrieval method for ocean attenuation profiles measured by airborne lidar.

Lidar remote sensing for ocean optical properties has been increasingly applied because of its ability to provide vertical structure information, which cannot be directly obtained by ocean color remote sensing. However, the application of this technology demands an inversion method to infer two quantities, i.e., attenuation and backscatter, from a single measurement. Here, a new iterative retrieval method is demonstrated to deduce the attenuation coefficient from ocean lidar return signals. One calculates the logarithmic backscatter-to-attenuation ratio k by an iterative solution based on a bio-optical model. Procedural examples of lidar-processing results-from raw data to attenuation-are presented, and the inversion results are compared with in situ measurements. The correlation coefficient R between the lidar-retrieval and in situ measurements is 0.8, and the root mean square error (RMSE) is 0.032. We then map the vertical structure of the lidar-retrieved attenuation along airborne lidar flight tracks and discuss the influences of k, the reference depth zm, and the reference value αm. Consequently, the reference value has little influence on the results for high-optical-thickness water, and k is the main error source in lidar return inversion. Primary results indicate that this method provides a more accurate k and improves the inversion accuracy of the lidar attenuation coefficient.

Active and passive optical remote sensing of the aquatic environment: introduction to the feature issue.

Through decades of efforts and practices, we have achieved great progress in understanding ocean biology and biogeochemistry through satellite measurements of ocean (water) color, or passive remote sensing. These include detailed global maps of the distribution of surface phytoplankton, the production of newly formed particulate organic matter through photosynthesis (i.e., primary production), as well as the change and feedback of phytoplankton in a changing climate, to name a few. However, these results are still far from a full account of ocean biology and biogeochemistry, where we want more detailed information of phytoplankton (e.g., types and sizes), as well as information in the vertical dimension. For such, we are happy to see new developments in ocean optics and ocean color remote sensing. These include, but certainly are not limited to, hyperspectral sensors, measurements via polarized setups, as well as ocean lidar systems. In particular, through pumping laser light into deeper ocean, lidar has demonstrated great potential to fill the gap of passive ocean color remote sensing. These developments in technology are providing exciting new findings where breakthroughs in ocean biogeochemistry are on the horizon. Thus, we organized this feature issue in Applied Optics to summarize a few recent developments and achievements, where readers and the community can easily capture progress on both fronts, as well as the potential and advantages of the fusion of passive and active optical sensing. Specifically, this issue contains 12 papers describing research in both active and passive optical remote sensing of aquatic environment. They are still limited in number and subject, but are expected to stimulate the ocean color community with findings relevant for satellite applications.

Detecting subsurface phytoplankton layer in Qiandao Lake using shipborne lidar.

Qiandao Lake is located in the northern edge of subtropics, and its water body is thermally stratified in summer. It is of great scientific significance to study the vertical physical and chemical indexes and phytoplankton characteristics of the Qiandao Lake to reveal the aquatic ecosystem structure of the thermally stratified lake. Conventional observation uses in-situ profile instruments, which is time consuming and labor intensive. In recent years, lidar has shown increasing oceanic applications; however, it has not yet been extensively applied in inland water. There are no studies using lidar for detecting subsurface plankton layer in Qiandao Lake. In this study, we investigated the applicability of this technology for identifying subsurface plankton layer. A simple and fast phytoplankton layer detection method was introduced. The lidar-detected layer was found to well correspond with that of the in-situ measured subsurface chlorophyll maximum layer (SCML) and phycocyanin maximum layer. Primary results show that lidar and our detection method are effective for subsurface phytoplankton layer detection. They can serve as a good monitoring tool for studying inland water stratification.

Remote sensing estimation of colored dissolved organic matter (CDOM) from GOCI measurements in the Bohai Sea and Yellow Sea.

Colored dissolved organic matter (CDOM) is the main constituent of dissolved organic matter (DOM), also a key indicator of water quality conditions. Accurate estimation of CDOM is essential for understanding biogeochemical processes and ecosystems in marine waters. The use of remote sensing to derive the changes in CDOM is vital technology that can be used to dynamically monitor the marine environment and to document the spatiotemporal variations in CDOM over a large scale. In the present study, we develop a simple approach to estimate the CDOM concentrations based on the in situ datasets from four cruise surveys over the Bohai Sea (BS) and Yellow Sea (YS). Eight band combination forms (using Xi as a delegate, where i denotes the numerical order of band combination forms), including single bands, band ratios, and other band combinations by remote sensing reflectance, Rrs(λ), were trained to test the correlations with the CDOM concentrations. The obtained results indicated that X7, i.e., [Rrs(443) + Rrs(555)]/[Rrs(443)/Rrs(555)], was the optimal form, with correlation coefficient (R) values of 0.904 (p < 0.001). The X7-based fitting model was determined as the optimal model by the leave-one-out cross-validation method with relatively low estimation errors (mean relative error, MRE, 20%), and satellite match-up validation with in situ measurements indicated good performance MRE = 20.3%). Moreover, two spatial distribution patterns of CDOM in Jan. 2017 and Apr. 2018 (independent data) retrieved from Geostationary Ocean Color Imager (GOCI) data agreed well with those in situ observations. These results indicate that our proposed algorithm is feasible and robust for retrieving CDOM concentrations in this study region. In addition, we applied this method to GOCI data for the whole 2016 year in the BS and YS and produced the spatial distribution patterns from different temporal scales including monthly, seasonal, and annual scales. Overall, the findings of this study motivate the development and application of a simple but effective method of the CDOM estimation for those optically complex turbid coastal waters, like this study water areas.

Remote sensing of spatial and temporal patterns of phytoplankton assemblages in the Bohai Sea, Yellow Sea, and east China sea.

Marine phytoplankton accounts for roughly half the planetary primary production, and plays significant roles in marine ecosystem functioning, physical and biogeochemical processes, and climate changes. Documenting phytoplankton assemblages' dynamics, particularly their community structure properties, is thus a crucial and also challenging task. A large number of in situ and space-borne observation datasets are collected that cover the marginal seas in the west Pacific, including Bohai Sea, Yellow Sea, and East China Sea. Here, a customized region-specific semi-analytical model is developed in order to detect phytoplankton community structure properties (using phytoplankton size classes, PSCs, as its first-order delegate), and repeatedly tested to assure its reliable performance. Independent in situ validation datasets generate relatively low and acceptable predictive errors (e.g., mean absolute percentage errors, MAPE, are 38.4%, 22.7%, and 34.4% for micro-, nano-, and picophytoplankton estimations, respectively). Satellite synchronization verification also produces comparative predictive errors. By applying this model to long time-series of satellite data, we document the past two-decadal (namely from 1997 to 2017) variation on the PSCs. Satellite-derived records reveal a general spatial distribution rule, namely microphytoplankton accounts for most variation in nearshore regions, when nanophytoplankton dominates offshore water areas, together with a certain high contribution from picophytoplankton. Long time-series of data records indicate a roughly stable tendency during the period of the past twenty years, while there exist periodical changes in a short-term one-year scale. High covariation between marine environment factors and PSCs are further found, with results that underwater light field and sea surface temperature are the two dominant climate variables which exhibit a good ability to multivariate statistically model the PSCs changes in these marginal seas. Specifically, three types of influence induced by underwater light field and sea surface temperature can be generalized to cover different water conditions and regions, and meanwhile a swift response time (approximately < 1 month) of phytoplankton to the changing external environment conditions is found by the wavelet analysis. This study concludes that phytoplankton community structures in the marginal seas remain stable and are year-independent over the past two decades, together with a short-term in-year cycle; this change rule need to be considered in future oceanographic studies.

Variability of particulate backscattering ratio and its relations to particle intrinsic features in the Bohai Sea, Yellow Sea, and East China Sea.

Using two field cruise observations collected during September and December 2016 in the Bohai Sea (BS), Yellow Sea (YS), and East China Sea (ECS), our study explores the variability of the particulate backscattering ratio (i.e., a ratio of particulate backscattering, bbp in m-1, to particulate scattering, bp in m-1, denoted as b˜bp, dimensionless). A large variation of b˜bp (using 550 nm as a delegate) in magnitude is observed in the study regions, ranging from 0.0004 to 0.043 (with an average of 0.015 ± 0.0082), which implies optically complex water conditions. Spectral variation in b˜bp is analyzed quantitatively by our proposed so-called "spectral dependence index," K, recommended as a standard way to determine quantitatively the spectral dependence of b˜bp in water bodies worldwide. The driving mechanism on the b˜bp variability in the study regions is researched for the first time, based on those synchronous data on particle intrinsic attributes, herein mainly referring to particle concentration (TSM, for the content of total suspended matter), composition (using a ratio of Chla/TSM as a surrogate, where Chla refers to the content of chlorophyll a), mean particle size (DA), and mean apparent density (ρa). The TSM, Chla/TSM, and DA cumulatively contribute most (97.8%) of the b˜bp variability, while other factors, such as the ρa, show a weak influence (0.04%). Meanwhile, we model b˜bp with direct linkages to TSM, Chla/TSM, and DA by using a linear regression method, with low estimation errors (such as mean absolute percentage error, MAPE, of about 14%). In short, our findings promote an understanding on the essence of the b˜bp in the BS, YS, and ECS, and are significantly beneficial to the comprehensive grasp of those complex features on suspended particles and those related to biogeochemical processes in marine waters.

Retrievals of phytoplankton community structures from in situ fluorescence measurements by HS-6P.

Phytoplankton community is an important organism indicator of monitoring water quality, and accurately estimating its composition and biomass is crucial for understanding marine ecosystems and biogeochemical processes. Identifying phytoplankton species remains a challenging task in the field of oceanography. Phytoplankton fluorescence is an important biological property of phytoplankton, whose fluorescence emissions are closely related to its community. However, the existing estimation approaches for phytoplankton communities by fluorescence are inaccurate and complex. In the present study, a new, simple method was developed for determining the Chlorophytes, Chrysophytes, Cryptophytes, Diatoms, Dinoflagellates, and Prymnesiophytes based on the fluorescence emission spectra measured from the HOBI Labs Hydroscat-6P (HS-6P) in the Bohai Sea, Yellow Sea, and East China Sea. This study used single bands, band ratios, and band combinations of the fluorescence signals to test their correlations with the six dominant algal species. The optimal band forms were confirmed, i.e., X1 (i.e., fl(700), which means the fluorescence emission signal at 700 nm band) for Chlorophytes, Cryptophytes, Dinoflagellates, and Prymnesiophytes (R = 0.947, 0.862, 0.911, and 0.918, respectively) and X7 (i.e., [fl(700) + fl(550)]/[fl(550)/fl(700)], where fl(550) denotes the fluorescence emission signal at 550 nm band) for Chrysophytes and Diatoms (R = 0.893 and 0.963, respectively). These established models here show good performances, yielding low estimation errors (i.e., root mean square errors of 0.16, 0.02, 0.06, 0.36, 0.18, and 0.03 for Chlorophytes, Chrysophytes, Cryptophytes, Diatoms, Dinoflagellates, and Prymnesiophytes, respectively) between in situ and modeled phytoplankton communities. Meanwhile, the spatial distributions of phytoplankton communities observed from both in situ and fluorescence-derived results agreed well. These excellent outputs indicate that the proposed method is to a large extent feasible and robust for estimating those dominant algal species in marine waters. In addition, we have applied this method to three vertical sections, and the retrieved vertical spatial distributions by this method can fill the gap of the common optical remote sensing approach, which usually only detects the sea surface information. Overall, our findings indicate that the proposed method by the fluorescence emission spectra is a potentially promising way to estimate phytoplankton communities, in particular enlarging the profiling information.

Subsurface plankton layers observed from airborne lidar in Sanya Bay, South China Sea.

In recent years, airborne lidar has been used in a wide range of oceanic applications, including detection of bathymetry, bubbles, internal waves, and schools of fish. However, it has not yet been extensively applied in Chinese seas. For example, there have been no studies to detect subsurface plankton layers in the South China Sea (SCS) by airborne lidar. In this study, we investigated this technology's applicability for identifying subsurface plankton layers in Sanya Bay, SCS. Three airborne lidar flight experiments were carried out in March 2018 and in September 2017. Shipboard synchronous measurements were carried out in March 2018 to validate the lidar measurements. The method that is presented here can be used to detect a subsurface plankton layer, which is characterized by depth, thickness, and intensity. Compared with chlorophyll-a profile synchronously measured by shipborne fluorometer, there was a consistent relationship. The subsurface plankton layer depth error was less than 0.7 m. Next, the spatial distribution and seasonal variation of lidar measured subsurface plankton layers in Sanya Bay, SCS, was analyzed. The results showed that airborne lidar can potentially detect subsurface plankton layer within 50 meters deep in relatively clear water. This will enhance our understanding of biogeochemical processes in these optically complex aquatic systems.

A missing link in the estuarine nitrogen cycle?: Coupled nitrification-denitrification mediated by suspended particulate matter.

In estuarine and coastal ecosystems, the majority of previous studies have considered coupled nitrification-denitrification (CND) processes to be exclusively sediment based, with little focus on suspended particulate matter (SPM) in the water column. Here, we present evidence of CND processes in the water column of Hangzhou Bay, one of the largest macrotidal embayments in the world. Spearman's correlation analysis showed that SPM was negatively correlated with nitrate (rho = -0.372, P = 0.018) and marker genes for nitrification and denitrification in the water column were detected by quantitative PCR analysis. The results showed that amoA and nir gene abundances strongly correlated with SPM (all P < 0.01) and the ratio of amoA/nir strongly correlated with nitrate (rho = -0.454, P = 0.003). Furthermore, aggregates consisting of nitrifiers and denitrifiers on SPM were also detected by fluorescence in situ hybridization. Illumina MiSeq sequencing further showed that ammonia oxidizers mainly belonged to the genus Nitrosomonas, while the potential denitrifying genera Bradyrhizobium, Comamonas, Thauera, Stenotrophomonas, Acinetobacter, Anaeromyxobacter, Sulfurimonas, Paenibacillus and Sphingobacterium showed significant correlations with SPM (all P < 0.01). This study suggests that SPM may provide a niche for CND processes to occur, which has largely been missing from our understanding of nitrogen cycling in estuarine waters.

Ultrasensitive and high specific detection of non-small-cell lung cancer cells in human serum and clinical pleural effusion by aptamer-based fluorescence spectroscopy.

Lung cancer is the leading cause of cancer-related deaths worldwide, and approximately 85% are diagnosed as non-small-cell lung cancer (NSCLC). However, efficient detection and diagnosis of NSCLC at early stage is still challenging. In this work, we developed a simple, ultrasensitive and high selective strategy for A549 human NSCLC cells detection based on combining the reorganization property of a novel cyanine dye 3,3'-di(3-sulfopropyl)-4,5,4',5'-dibenzo-9-methyl-thiacarbocyanine triethylammonium salt (cy-M) to aptamer S6 G-quadruplex structure having specific affinity to NSCLC cells, which induced a dramatic fluorescence enhancement (~ 104 times). Moreover, this strategy was successfully used for detecting of A549 cancer cells in complex media such as human serum and clinical pleural effusion, which strong indicated that the proposed method could be applied for the early diagnosis of lung cancer.

Refined use of AISA band-differences for oil slick identification beyond brightness contrast reversal under sunglint.

Marine oil slicks show brighter or darker than surrounding oil-free seawater under different sunglint, which can be observed by satellite optical sensors. Although this has been interpreted using a critical angle concept and simulated using the Cox-Munk model, it has not been demonstrated in high spatial resolution images from airborne sensors. In this study, an AISA (airborne imaging spectrometer for applications) image was used to determine the characteristics of non-emulsion oil slicks under sunglint in high spatial resolution images. Although a similar positive or negative contrast between non-emulsion oil slicks and oil-free seawater can be observed, it is difficult to directly model sunglint reflectance due to the different remote sensing scale effect. There are many sun glitter pepper noise produced by various micro-mirror facets of ocean surface in high spatial resolution images. Based on the optical image characteristics, a normalized noise index (ξ) was designed to evaluate the pepper noise in 1830 band-difference results. Then a level segmentation method was used to delineate the oil slicks under various sunglint from a minimum pepper noise image. Our study provides a preliminary reference for airborne optical remote sensing of oil slicks under various levels of sunglint.

Overall bacterial community composition and abundance of nitrifiers and denitrifiers in a typical macrotidal estuary.

Coupled nitrogen cycling processes can alleviate the negative effects of eutrophication caused by excessive nitrogen load in estuarine ecosystems. The abundance and diversity of nitrifiers and denitrifiers across different environmental gradients were examined in the sediment of Hangzhou Bay. Quantitative PCR and Pearson's correlation analyses suggested that the bacterial ammonia-oxidizers (AOB) were the dominant phylotypes capable of ammonia oxidation, while the nirS-encoding denitrifiers predominated in the denitrification process. Simultaneously, nitrite and pH were found to be the two major factors influencing amoA and nir gene abundances, and the distribution of bacterial communities. Moreover, the ratio of nirS/AOB amoA gene abundance showed negative correlation with nitrite concentration. Fluorescence in situ hybridization further demonstrated that AOB and acetate-denitrifying cells were closely connected and formed obvious aggregates in the sediment. Together, all these results provided us a preliminary insight for coupled nitrification-denitrification processes in the sediment of Hangzhou Bay.