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Aiming to automatically monitor and improve stereoscopic image and video processing systems, stereoscopic image quality assessment approaches are becoming more and more important as 3D technology gains popularity. We propose a full-reference stereoscopic image quality assessment method that incorporate monocular and binocular features based on binocular competition and binocular integration. To start, we create a three-channel RGB fused view by fusing Gabor filter bank responses and disparity maps. Then, using the monocular view and the RGB fusion view, respectively, we extract monocular and binocular features. To alter the local features in the binocular features, we simultaneously estimate the saliency of the RGB fusion image. Finally, the monocular and binocular quality scores are calculated based on the monocular and binocular features, and the quality scores of the stereo image prediction are obtained by fusion. Performance testing in the LIVE 3D IQA database Phase I and Phase II. The results of the proposed method are compared with newer methods. The experimental results show good consistency and robustness.

Green and low-carbon development is an important part of global sustainable development. Green power trading provides strong support and assurance for promoting green and low-carbon development. Due to the long cycle of green power data chains and their susceptibility to malicious tampering, the integrity and traceability of data are difficult to guarantee. Therefore, this paper first proposes a security provenance model with enhanced relations based on the core structure of PROV and blockchain technology, which can securely capture provenance records, use the transfer time and number of transactions between various links in the traceability network as reasoning clues, realize the correlation tracing of the green electricity transfer process. Under the model, a traceability mechanism of green electricity is designed based on smart contracts. Trustworthy green electricity data collection is achieved through data filling and data verification techniques. Traceability query technique is adopted to achieve trustworthy traceability of green electricity. And the effectiveness of the proposed solution is demonstrated through simulation experiments.

To analyze the intensity of atmospheric turbulence in a satellite–ground laser communication link, it is important to consider the effect of increased atmospheric turbulence caused by wind speed. Atmospheric turbulence causes a change in the refractive index, which negatively impacts the quality and focusing ability of the laser beam by altering its phase front. To simulate the changes in amplitude and phase characteristics of laser beam propagation in atmospheric turbulence caused by wind speed, a transverse translation phase screen is used. To better understand and address the influence of atmospheric wind speed on the phase of optical signals in satellite–ground laser communication links, this paper proposes a Monte Carlo simulation method. This method utilizes the spatial and temporal variations in the refractive index in the atmosphere and integrates the principles of optical signal propagation in the atmosphere to simulate changes in the phase of optical signals under different wind speed conditions. By analyzing the variations in the received optical signal’s power, the Monte Carlo method is employed to simulate phase screens and logarithmic amplitude screens. Additionally, it models the probability density of the statistical behavior of received optical signal’s fluctuations, as well as the time autocorrelation coefficient of optical signals. This paper, under the coupling condition in satellite–ground laser communication links, conducted a Monte Carlo simulation experiment to analyze the characteristics of the optical signal’s fluctuations in the link and discovered that atmospheric wind speed affects the shape of the power spectral density model of the received optical signal. Increasing wind speed leads to a decrease in the time autocorrelation coefficient of the received optical signal and affects the coupling efficiency. The paper then used a cubic spline interpolation fitting method to verify the models of the power spectral density and the autocorrelation time coefficient of the optical signal. This provides a theoretical foundation and practical guidance for the optimization of satellite–ground laser communication systems.

Saliency-driven mesh simplification methods have shown promising results in maintaining visual detail, but effective simplification requires accurate 3D saliency maps. The conventional mesh saliency detection method may not capture salient regions in 3D models with texture. To address this issue, we propose a novel saliency detection method that fuses saliency maps from multi-view projections of textured models. Specifically, we introduce a texel descriptor that combines local convexity and chromatic aberration to capture texel saliency at multiple scales. Furthermore, we created a novel dataset that reflects human eye fixation patterns on textured models, which serves as an objective evaluation metric. Our experimental results demonstrate that our saliency-driven method outperforms existing approaches on several evaluation metrics. Our method source code can be accessed at https://github.com/bkballoon/mvsm-fusion and the dataset can be accessed at 10.5281/zenodo.8131602.

ABSTRACT Sugarcane is highly sensitive to changes in moisture, and increased drought severely restricts its growth and productivity. Recent studies have shown that plant growth-promoting microorganisms are essential to reduce the adverse effects of environmental stresses, especially drought. However, our knowledge about the dynamics of rhizosphere microbial community structure in sugarcane under varying degrees of drought stress is limited. We analyzed the effects of different degrees of drought stress on the rhizosphere microbial communities of Zhongzhe 1(ZZ1) and Zhongzhe 6(ZZ6) with differences in drought resistance, by combining soil enzyme activity, nutrient content, and physiological and morphological characteristics of sugarcane roots. The results showed that rhizosphere bacterial community began to change at a field capacity of 50%, enriching the sugarcane rhizosphere with drought-resistant bacteria. The core strains of ZZ1 and ZZ6 rhizosphere enrichment were mainly Streptomycetales, Sphingomonadales, and Rhizobiales. However, compared to ZZ1, the changes in rhizosphere bacterial abundance in ZZ6 were primarily associated with the abundance of Streptomycetales as drought levels increased. Rhizobiales and Streptomycetales, enriched in the rhizosphere of ZZ6 under drought, were positively correlated with root tip number and total root length (TRL), increasing the distribution area of roots and, thus, improving water and nutrient uptake by the roots thereby enhancing the resistance of sugarcane to drought stress. This research enhances our understanding of the composition of the rhizosphere microbial community in sugarcane under different levels of drought stress and its interaction with the roots, thereby providing valuable insights for enhancing drought resistance in sugarcane. Importance Drought stress is expected to further increase in intensity, frequency, and duration, causing substantial losses in sugarcane yields. Here, we exposed sugarcane to varying degrees of drought treatment during growth and quantified the eventual composition of the resulting sugarcane rhizosphere bacterial community groups. We found that sugarcane rhizosphere under mild drought began to recruit specific bacterial communities to resist drought stress and used the interactions of root tip number, total root length, and drought-resistant strains to improve sugarcane survival under drought. This research provides a theoretical basis for the rhizosphere microbiome to help sugarcane improve its resistance under different levels of drought stress.

Abstract The quality of images generated by computer graphics rendering algorithms is mainly affected by visible distortion at some pixel locations. Image quality assessment (IQA) metrics are commonly utilized to assess the quality of rendered images, but their results are a global difference value, which does not provide pixel‐wise differences to optimize the renderings. In contrast, visibility difference models including visual perception models and deep learning models can calculate pixel‐wise visibility difference between distorted images and reference images. However, they either are only applied to a single type of visible distortion or are seriously dependent on datasets. To this end, the authors propose a novel model, dubbed Human Visual Perception and Deep Learning Image Difference Metric (HPDL‐IDM), which combines the Human Visual System (HVS) model and deep learning. HPDL‐IDM primarily consists of two modules: (i) the visual perception feature calculation module, which calculates difference maps between various kinds of features extracted from the reference image and the distorted image according to the visual characteristics of human eyes and concatenates them, and (ii) the deep learning module, which utilizes a neural network of encoder–decoder structure to train on the LocvisVC and VisTexRes datasets whose input and output are these concatenated feature difference maps and the final image distortion visibility difference map respectively. Additionally, the authors pool the final difference map into a global difference value between 0 and 1 to apply their model to many image processing tasks related to Image quality metrics (IQMs). Experimental results show that HPDL‐IDM's generalization capacity and accuracy are improved by a large margin compared to other models.

Anchor cables in water-rich roadways frequently experience severe corrosion and resultant fractures due to the combined effects of mine water and high stress. However, the precise factors influencing corrosion on these cables remain ambiguous, presenting challenges to roadway support safety. This study investigated the impact of major ions present in weakly alkaline mine water on anchor cable corrosion using salt spray corrosion experiments. The results indicated that major anions such as Cl- notably influence anchor cable corrosion, which primarily manifested as pitting corrosion. The nominal yield strength decreased by 95.68–156.01 MPa, and the nominal tensile strength decreased by 133.44–202.77 MPa because of a decrease in the cross-sectional area. During the corrosion process, major anions such as Cl- continuously degraded the passivation film on the surface of the anchor cable, exposing its internal metal structure and exacerbating corrosion until eventual fracture failure occurred. Therefore, this paper proposes a corrosion protection technique for mine anchor cables to realize superior protective effects compared to conventional anchor cables used concurrently in field applications.

Abstract. Background:. The fetal growth charts in widest use in China were published by Hadlock >35 years ago and were established on data from several hundred of American pregnant women. After that, >100 fetal growth charts were published around the world. We attempted to assess the impact of applying the long-standing Hadlock charts and other charts in a Chinese population and to compare their ability to predict newborn small for gestational age (SGA). Methods:. For this retrospective observational study, we reviewed all pregnant women (n = 106,455) who booked prenatal care with ultrasound measurements for fetal biometry at the Shenzhen Maternity and Child Healthcare Hospital between 2012 and 2019. A fractional polynomial regression model was applied to generate Shenzhen fetal growth chart ranges for head circumference (HC), biparietal diameter (BPD), abdominal circumference (AC), and femur length (FL). The differences between Shenzhen charts and published charts were quantified by calculating the Z-score. The impact of applying these published charts was quantified by calculating the proportions of fetuses with biometric measurements below the 3rd centile of these charts. The sensitivity and area under the receiver operating characteristic curves of published charts to predict neonatal SGA (birthweight

For orbital angular momentum (OAM) recognition in atmosphere turbulence, how to design a self-adapted model is a challenging problem. To address this issue, an efficient deep learning framework that uses a derived extreme learning machine (ELM) has been put forward. Different from typical neural network methods, the provided analytical machine learning model can match the different OAM modes automatically. In the model selection phase, a multilayer ELM is adopted to quantify the laser spot characteristics. In the parameter optimization phase, a fast iterative shrinkage-thresholding algorithm makes the model present the analytic expression. After the feature extraction of the received intensity distributions, the proposed method develops a relationship between laser spot and OAM mode, thus building the steady neural network architecture for the new received vortex beam. The whole recognition process avoids the trial and error caused by user intervention, which makes the model suitable for a time-varying atmospheric environment. Numerical simulations are conducted on different experimental datasets. The results demonstrate that the proposed method has a better capacity for OAM recognition.

Abstract Background As reported, 27-93 % of pregnant women take at least one drug during pregnancy. However, drug exposure during pregnancy still lacks sufficient foetal safety evidence of human origin. It is urgent to fill the knowledge gap about medication safety during pregnancy for optimization of maternal disease treatment and pregnancy drug consultation. Methods and analysis The China Teratology Birth Cohort (CTBC) was established in 2019 and is a hospital-based open-ended prospective cohort study with the aim of assessing drug safety during pregnancy. Pregnant women who set up the pregnancy health records in the first trimester or who seek drug consultation regardless of gestational age in the member hospitals are recruited. Enrolled pregnant women need to be investigated four times, namely, 6–14 and 24–28 weeks of gestational age, before discharge after hospital delivery, and 28–42 days after birth. Maternal medication exposure during pregnancy is the focus of the CTBC. For drugs, information on the type, name, and route of medication; start and end time of medication; single dose; frequency of medication; dosage form; manufacturer; and reason for medication is collected. The adverse pregnancy outcomes collected in the study include birth defects, stillbirth, spontaneous abortion, preterm birth, post-term birth, low birth weight, macrosomia, small for gestational age, large for gestational age and low Apgar score. CTBC uses an electronic questionnaire for data collection and a cloud system for data management. Biological samples are collected if informed consents are obtained. Multi-level logistic regression, mixed-effect negative binomial distribution regression and spline function regression are used to explore the effect of drugs on the occurrence of birth defects. Discussion The findings of the study will assist in further understanding the risk of birth defects and other adverse pregnancy outcomes associated with maternal drug exposure and developing the optimal treatment plans and drug counselling for pregnant women. Trial registration This study was approved by the Research Ethics Committee of the West China Second Hospital of Sichuan University and registered at the Chinese Clinical Trial Registry ( http://www.chictr.org.cn/index.aspx , registration number ChiCTR1900022569 ).

The real-time rendering of large-scale terrain scenes has been a challenge and is a popular research topic in the fields of 3D games and virtual reality. To fully utilize GPU features, this paper proposes a real-time rendering algorithm for large-scale regular grid terrain based on GPU Tessellation. The CPU processes a coarse-grained quadtree to generate terrain patches and then passes the patches to the GPU after view culling. Then, the GPU refines the terrain by using GPU Tessellation and displacement mapping. The most important advantage of our method is that during the tessellation stage, a new rule that the distance, screen space projection error and variance of the height, as the standard of terrain roughness, is designed. During the tessellation stage, the distance, screen space projection error and variance of the height, as the standard of terrain roughness, are considered. The experimental results show that our algorithm can greatly reduce the CPU processing time and main memory space requirement, and fully utilize the new features of the GPU; In conclusion, the proposed approach can render large-scale terrain with high-precision in real-time.

Denoising-based techniques have recently been shown to be effective for accelerating path tracing rendering methods. However, there remains a problem which is input images need the minimum necessary samples number in order to ensure the quality of the output. In this paper, we propose a new accelerated path tracing approach with generative adversarial networks(GAN) and matrix completion. Unlike the methods based on denoising with neural network, we randomly render part of pixels of input image, which are much less than other methods. Next, we utilize the trained GAN to pre-complete the initializing missing pixels. Because of the accuracy and fast-convergence of GAN, our pre-completion results are more accurate than other methods. Then, according to the results of pre-completion, we present the pre-completed images as a low-rank matrix and make use of the matrix completion to recovers missing values accurately even in high details. To improve the efficiency of solving matrix completion, we modified the original weighted nuclear norm minimization with a parameter adjustment(PAWNNM) strategy. The result shows better visual quality, texture details and convergence efficiency than the state-of-the-art acceleration methods, especially the methods based on denoising with neural network.

Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality of the acquired image may not be ideal for follow-up research to make suitable judgment. In order to obtain clearer images, we propose a dual-path adversarial generation network model algorithm that particularly improves the accuracy of the satellite remote sensing image super-resolution. This network involves a dual-path convolution operation in a generator structure, a feature mapping attention mechanism that first extracts important feature information from a low-resolution image, and an enhanced deep convolutional network to extract the deep feature information of the image. The deep feature information and the important feature information are then fused in the reconstruction layer. Furthermore, we also improve the algorithm structure of the loss function and discriminator to achieve a relatively optimal balance between the output image and the discriminator, so as to restore the super-resolution image closer to human perception. Our algorithm was validated on the public UCAS-AOD datasets, and the obtained results showed significantly improved performance compared to other methods, thus exhibiting a real advantage in supporting various image-related field applications such as navigation monitoring.

The “big six” countries (Bangladesh, India, Indonesia, Myanmar, Nepal, and Thailand) in the World Health Organization South-East Asia Region (WHO SEAR) are currently facing severe challenges in measles elimination and consequent childhood mortality reduction, with inadequacies and inequalities in the coverage of the measles-containing-vaccine first-dose (MCV1) being major obstacles. However, these issues of inequality in MCV1 coverage have not yet been systematically examined. We used data from the latest Demographic and Health Surveys and Multiple Indicator Cluster Surveys. To provide a comprehensive picture of existing MCV1 coverage gaps, data were disaggregated by geographic location, as well as by socioeconomic and nutritional dimensions. National MCV1 coverage ranged from 77% in Myanmar to 92% in Thailand. Only nine of the 104 sub-national districts had achieved the 95% MCV1 coverage goal as set by the WHO. Geographic inequalities were more pronounced in countries with lower coverage levels. Areas in clusters with poor MCV1 coverage performances as well as disadvantaged socioeconomic profiles require increased attention. Inequalities were evident in all countries, except Thailand, and were more pronounced in the sectors of wealth, education, antenatal care (ANC) status, and vitamin A supplementation (VAS) when compared against the areas of gender and urban/rural residence. Wealth-related inequality in Bangladesh, education-related inequality in Indonesia, ANC-related inequalities in Myanmar and Nepal, and VAS-related inequalities in Indonesia and Myanmar were all noteworthy. Equity-oriented changes in policies focusing on health promotion and integrated interventions among disadvantaged populations need to be implemented in order to increase MCV1 coverage and reduce childhood mortality.

A novel reconstruction algorithm is presented to address the noise artifacts of path tracing. SURE (Stein's unbiased risk estimator) is adopted to estimate the noise level per pixel that guides adaptive sampling process. Modified MLPs (multilayer perceptron) network is used to predict the optimal reconstruction parameters. In sampling stage, coarse samples are firstly generated. Then each noise level is estimated with SURE. Additional samples are distributed to the pixels with high noise level. Next, we extract a few features from the results of adaptive sampling used for the subsequent reconstruction stage. In reconstruction stage, modified MLPs network is adopted to model a complex relationship between extracted features and optimal reconstruction parameters. An anisotropic filter is used to reconstruct the final images with the parameters predicted by neural networks. Compared to the state-of-the-art methods, experiment results demonstrate that our algorithm performs better than other methods in numerical error and visual image quality.

BackgroundThe influence of the safety and security of environments on early childhood development (ECD) has been under-explored. Although housing might be linked to ECD by affecting a child's health and a parent's ability to provide adequate care, only a few studies have examined this factor. We hypothesized that housing environment is associated with ECD in sub-Saharan Africa (SSA).Methods and findingsFrom 92,433 children aged 36 to 59 months who participated in Multiple Indicator Cluster Survey (MICS) in 20 SSA countries, 88,271 were tested for cognitive and social-emotional development using the Early Childhood Development Index (ECDI) questionnaire and were thus included in this cross-sectional analysis. Children's mean age was 47.2 months, and 49.8% were girls. Children were considered developmentally on track in a certain domain if they failed no more than 1 ECDI item in that domain. In each country, we used conditional logistic regression models to estimate the association between improved housing (housing with finished building materials, improved drinking water, improved sanitation facilities, and sufficient living area) and children's cognitive and social-emotional development, accounting for contextual effects and socioeconomic factors. Estimates from each country were pooled using random-effects meta-analyses. Subgroup analyses were conducted by the child's gender, maternal education, and household wealth quintiles. On-track cognitive development was associated with improved housing (odds ratio [OR] = 1.15, 95% CI 1.06 to 1.24, p < 0.001), improved drinking water (OR = 1.07, 95% CI 1.00 to 1.14, p = 0.046), improved sanitation facilities (OR = 1.15, 95% CI 1.03 to 1.28, p = 0.014), and sufficient living area (OR = 1.06, 95% CI 1.01 to 1.10, p = 0.018). On-track social-emotional development was associated with improved housing only in girls (OR = 1.14, 95% CI 1.04 to 1.25, p = 0.006). The main limitations of this study included the cross-sectional nature of the datasets and the use of the ECDI, which lacks sensitivity to measure ECD outcomes.ConclusionsIn this study, we observed that improved housing was associated with on-track cognitive development and with on-track social-emotional development in girls. These findings suggest that housing improvement in SSA may be associated not only with benefits for children's physical health but also with broader aspects of healthy child development.

An average bit error ratio (ABER) performance model for multiple phase shift keying (MPSK) based on a balanced detector with a fiber is presented in the free-space link for the first time. The Johnson SB probability distribution function (pdf), to the best of our knowledge, is first experimentally explored, which can be used to describe the fading characteristics of an optical signal coupled into a single-mode fiber (SMF) in an atmospheric turbulence channel. Subsequently, an ABER expression is established by combining the photon characteristics of the balanced detector with the fiber. The numerical results show that the system has the most superior ABER performance when the splitting ratio is 0.5 and the quantum efficiency of the two photodetectors is equal. Moreover, the communication performances can be optimized by adjusting parameters, such as increasing the system bandwidth, selecting the appropriate modulation order, and improving the received optical power. Finally, the MPSK-signal-to-noise-ratio (SNR) model is also studied to evaluate system communication performance. Through our asymptotic analysis, if the required ABER falls below the 7% forward error correction (FEC) limit of 3.8 × 10-3, the SNR should maintain at least 38 dB or more, while the normalized fluctuation variance deteriorates to 5.2441. This paper provides a parameter reference for designing the MPSK free-space optical (FSO) communication system, especially the fiber-coupling receiver.

Information extracted from trajectory data is very useful in many practical application scenarios. Before trajectories for data mining are published, they need to be processed to protect the privacy of the trajectories' bodies. In this paper, a method for such privacy protection is proposed. Our method guarantees that the generated trajectory points satisfy the k-anonymity by interchanging the positions of the trajectory points on the k-core subnet of the relation network. The method treats the trajectory points as the privacy protection object. It overcomes the curse of dimensionality resulting from the K-anonymity of trajectories, and reduces the distortion of the generated trajectories significantly. Moreover, our proposed strategy can preserve the original positions of the trajectory points. Experiments on both real-life and synthetic data sets are carried out with different methods for comparison. The results show that our method has greater efficiency and lower distortion of the processed trajectories.

Mutually complementary bases are crucial to secure quantum key distribution (QKD). By aiming at free-space QKD with use of spatially structured photons, the effect of turbulence on the mutual unbiasedness of two complementary bases relevant to photonic orbital-angular-momentum (OAM) modes is modeled theoretically, with novel insightful expressions and physical explanations yielded. For two complementary bases constructed from Laguerre-Gaussian (LG) modes with a fixed radial index of zero, it is shown that the superposed joint-two-LG-mode (JTLGM) correlation function plays a fundamental role in determining the turbulence-induced mutual-unbiasedness decay. The mutual information between the sent and detected photonic states is used as a metric to quantify the degree of turbulence-induced mutual-unbiasedness decay. It is found that the degree of turbulence-induced mutual-unbiasedness decay depends on the scaled atmospheric coherence width, anisotropy of turbulence and contents of the complementary bases, and it can become non-negligible in certain cases.

In this paper, we present a denoising network composed of a kernel prediction network and a deep generative adversarial network to construct an end-to-end overall network structure. The network structure consists of three parts: the Kernel Prediction Network (KPN), the Deep Generation Adversarial Network (DGAN), and the image reconstruction model. The kernel prediction network model takes the auxiliary feature information image as the input, passes through the source information encoder, the feature information encoder, and the kernel predictor, and finally generates a prediction kernel for each pixel. The generated adversarial network model is divided into two parts: the generator model and the multiscale discriminator model. The generator model takes the noisy Monte Carlo-rendered image as the input, passes through the symmetric encoder–decoder structure and the residual block structure, and finally outputs the rendered image with preliminary denoising. Then, the prediction kernel and the preliminarily denoised rendered image is sent to the image reconstruction model for reconstruction, and the prediction kernel is applied to the preliminarily denoised rendered image to obtain a preliminarily reconstructed result image. To further improve the quality of the result and to be more robust, the initially reconstructed rendered image undergoes four iterations of filtering for further denoising. Finally, after four iterations of the image reconstruction model, the final denoised image is presented as the output. This denoised image is applied to the loss function. We compared the results from our approach with state-of-the-art results by using the structural similarity index (SSIM) values and peak signal-to-noise ratio (PSNR) values, and we reported a better performance.

With the development of handheld video capturing devices, video stabilization becomes increasingly important. The gyroscope-based video stabilization methods perform promising ability, since they can return more reliable three-dimensional (3D) camera rotation estimation, especially when there are many moving objects in scenes or there are serious motion blur or illumination changes. However, the gyroscope-based methods depend on the camera intrinsic parameters to execute video stabilization. Therefore, a self-calibrated spherical video stabilization method was proposed. It builds a virtual sphere, of which the spherical radius is calibrated automatically, and then projects each frame of the video to the sphere. Through the inverse rotation of the spherical image according to the rotation jitter component, the dependence on the camera intrinsic parameters is relaxed. The experimental results showed that the proposed method does not need to calibrate the camera and it can suppress the camera jitter by binding the gyroscope on the camera. Moreover, compared with other state-of-the-art methods, the proposed method can improve the peak signal-to-noise ratio, the structural similarity metric, the cropping ratio, the distortion score, and the stability score.

Early intervention of gestational diabetes mellitus (GDM) is effective in reducing pregnancy disorders. Fetal growth, measured by routine ultrasound scan a few weeks earlier before GDM diagnosis, might be useful to identify women at high risk of GDM. In the study, generalized estimating equations were applied to examine the associations between ultrasonic indicators of abnormal fetal growth at 22–24 weeks and the risk of subsequent GDM diagnosis. Of 44,179 deliveries, 8324 (18.8%) were diagnosed with GDM between 24 and 28 weeks. At 22–24 weeks, fetal head circumference (HC) < 10th, fetal femur length (FL) < 10th, and estimated fetal weight (EFW) < 10th percentile were associated with 13% to 17% increased risks of maternal GDM diagnosis. Small fetal size appeared to be especially predictive of GDM among women who were parous. Fetal growth in the highest decile of abdominal circumference (AC), HC, FL and EFW was not associated with risk of subsequent GDM. The observed mean difference in fetal size across gestation by GDM was small; there was less than 1 mm difference for AC, HC, and FL, and less than 5 g for EFW before 24 weeks. Despite similar mean fetal growth among women who were and were not later diagnosed with GDM, mothers with fetuses in the lowest decile of HC, FL and EFW at 22–24 weeks tended to have higher risk of GDM.

Clay-based flocculation techniques have been developed to mitigate harmful algal blooms; however, the potential ecological impacts on the microbial community are poorly understood. In this study, chemical measurements were combined with 16S rRNA sequencing to characterize the microbial community response to different flocculation techniques, including controls, clay flocculation, clay flocculation with zeolite, and clay flocculation with O2 added zeolite capping. Sediment bacterial biomass measured by PLFA were not significantly altered by the various flocculation techniques used. However, 16S rRNA sequencing revealed differences in water microbial community structure between treatments with and without zeolite capping. The differences were related to significant reductions of total nitrogen (TN), total phosphorus (TP) and ammonia (NH4+) concentration and increase of nitrate (NO3-) concentration in zeolite and O2 loaded zeolite capping. The relative abundance of ammonia oxidizing bacteria increased four-fold in zeolite capping microcosms, suggesting zeolite promoted absorbed ammonia removal in the benthic zone. Zeolite-capping promoted bacteria nitrogen cycling activities at the water-sediment interface. Potential pathogens that are usually adapted to eutrophic water bodies were reduced after clay flocculation. This study demonstrated clay flocculation did not decrease bacterial populations overall and may reduce regulatory indicators and pathogenic contaminants in water. Zeolite capping may also help prevent nutrients from being released back into the water thus preventing additional algal blooms.IMPORTANCE Despite the effectiveness of clay flocculation for removing harmful algal blooms as highlighted in numerous studies, the potential ecological impacts on the microbial community have rarely been investigated. Characterization of clay flocculation treated algal bloom water and sediment microbial community provides new insights into the ecological impacts of this algal bloom controlling technology.