Social network distribution of syphilis self-testing among men who have sex with men in China: study protocol for a cluster randomized control trial.

BACKGROUND: Syphilis is a common sexually transmitted infection (STI) among men who have sex with men (MSM). Increasing syphilis testing is important to syphilis control. However, in low- and middle-income countries like China, syphilis testing rates remain low among MSM. We describe a randomized controlled trial protocol to examine the effectiveness of social network distribution approaches of syphilis self-testing among MSM in China. METHODS: We will recruit index and alter MSM. Indexes will be eligible if they: are born biologically male; aged 18 years or above; ever had sex with another man; are willing to distribute syphilis testing packages or referral links to their alters; and willing to provide personal contact information for future follow-up. Three hundred MSM will be recruited and randomly assigned in a 1:1:1 ratio into three arms: standard of care (control arm); standard syphilis self-testing (SST) delivery arm; and referral link SST delivery arm. Indexes will distribute SST packages or referral links to encourage alters to receive syphilis testing. All indexes will complete a baseline survey and a 3-month follow-up survey. Syphilis self-test results will be determined by photo verification via a digital platform. The primary outcome is the mean number of alters who returned verified syphilis testing results per index in each arm. DISCUSSION: The trial findings will provide practical implications in strengthening syphilis self-testing distribution and increasing syphilis testing uptake among MSM in China. This study also empowers MSM community in expanding syphilis testing by using their own social network. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2000036988 . Registered 26 August 2020 - Retrospectively registered

Characterization of Silver Nanoparticles Internalized by Arabidopsis Plants Using Single Particle ICP-MS Analysis

Plants act as a crucial interface between humans and their environment. The wide use of nanoparticles (NPs) has raised great concerns about their potential impacts on crop health and food safety, leading to an emerging research theme about the interaction between plants and NPs. However, up to this day even the basic issues concerning the eventual fate and characteristics of NPs after internalization are not clearly delineated due to the lack of a well-established technique for the quantitative analysis of NPs in plant tissues. We endeavored to combine a quantitative approach for NP analysis in plant tissues with TEM to localize the NPs. After using an enzymatic digestion to release the NPs from plant matrices, single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) is employed to determine the size distribution of silver nanoparticles (Ag NPs) in tissues of the model plant Arabidopsis thaliana after exposure to 10 nm Ag NPs. Our results show that Macerozyme R-10 treatment can release Ag NPs from Arabidopsis plants without changing the size of Ag NPs. The characteristics of Ag NPs obtained by SP-ICP-MS in both roots and shoots are in agreement with our transmission electron micrographs, demonstrating that the combination of an enzymatic digestion procedure with SP-ICP-MS is a powerful technique for quantitative determination of NPs in plant tissues. Our data reveal that Ag NPs tend to accumulate predominantly in the apoplast of root tissues whereby a minor portion is transported to shoot tissues. Furthermore, the fact that the measured size distribution of Ag NPs in plant tissue is centered at around 20.70 nm, which is larger than the initial 12.84 nm NP diameter, strongly implies that many internalized Ag NPs do not exist as intact individual particles anymore but are aggregated and/or biotransformed in the plant instead.MOE (Min. of Education, S’pore)Published versio

Stratified Object-Oriented Image Classification Based on Remote Sensing Image Scene Division

The traditional remote sensing image segmentation method uses the same set of parameters for the entire image. However, due to objects’ scale-dependent nature, the optimal segmentation parameters for an overall image may not be suitable for all objects. According to the idea of spatial dependence, the same kind of objects, which have the similar spatial scale, often gather in the same scene and form a scene. Based on this scenario, this paper proposes a stratified object-oriented image analysis method based on remote sensing image scene division. This method firstly uses middle semantic which can reflect an image’s visual complexity to classify the remote sensing image into different scenes, and then within each scene, an improved grid search algorithm is employed to optimize the segmentation result of each scene, so that the optimal scale can be utmostly adopted for each scene. Because the complexity of data is effectively reduced by stratified processing, local scale optimization ensures the overall classification accuracy of the whole image, which is practically meaningful for remote sensing geo-application

Synthetic data generation with differential privacy via Bayesian networks

This paper describes PrivBayes, a differentially private method for generating synthetic datasets that was used in the 2018 Differential Privacy Synthetic Data Challenge organized by NIST.Ministry of Education (MOE)National Research Foundation (NRF)Published versionThis work was supported by the Ministry of Education Singapore (Number MOE2018-T2-2-091), and by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the funding agencies

A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification

Conventional geographic object-based image analysis (GEOBIA) land cover classification methods by using very high resolution images are hardly applicable due to their complex ground truth and manually selected features, while convolutional neural networks (CNNs) with many hidden layers provide the possibility of extracting deep features from very high resolution images. Compared with pixel-based CNNs, superpixel-based CNN classification, carrying on the idea of GEOBIA, is more efficient. However, superpixel-based CNNs are still problematic in terms of their processing units and accuracies. Firstly, the limitations of salt and pepper errors and low boundary adherence caused by superpixel segmentation still exist; secondly, this method uses the central point of the superpixel as the classification benchmark in identifying the category of the superpixel, which does not allow classification accuracy to be ensured. To solve such problems, this paper proposes a region-based majority voting CNN which combines the idea of GEOBIA and the deep learning technique. Firstly, training data was manually labeled and trained; secondly, images were segmented under multiresolution and the segmented regions were taken as basic processing units; then, point voters were generated within each segmented region and the perceptive fields of points voters were put into the multi-scale CNN to determine their categories. Eventually, the final category of each region was determined in the region majority voting system. The experiments and analyses indicate the following: 1. region-based majority voting CNNs can fully utilize their exclusive nature to extract abstract deep features from images; 2. compared with the pixel-based CNN and superpixel-based CNN, the region-based majority voting CNN is not only efficient but also capable of keeping better segmentation accuracy and boundary fit; 3. to a certain extent, region-based majority voting CNNs reduce the impact of the scale effect upon large objects; and 4. multi-scales containing small scales are more applicable for very high resolution image classification than the single scale

Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction

Family Material Hardship and Chinese Adolescents’ Problem Behaviors: A Moderated Mediation Analysis

<div><p>In the current study, we examined a moderated mediation model using the risk and resilience framework. Specifically, the impact of family material hardship on adolescent problem behaviors was examined in a Chinese sample; we used the family stress model framework to investigate parental depression and negative parenting as potential mediators of the relation between family material hardship and adolescents’ problem behaviors. In addition, based on resilience theory, we investigated adolescents’ resilience as a potential protective factor in the development of their internalizing and externalizing problems. Participants included 1,419 Chinese adolescents (mean age = 15.38 years, <i>SD</i> = 1.79) and their primary caregivers. After controlling for covariates (age, gender, location of family residence, and primary caregiver), we found that parental depression and negative parenting mediated the association between family material hardship and adolescents’ problem behaviors. Furthermore, the adolescent resilience moderated the relationship between negative parenting and internalizing problems in a protective-stabilizing pattern; in addition, a protective-reactive pattern also emerged when adolescent resilience was examined as a moderator of the relationship between negative parenting and externalizing problems. These findings contribute to a comprehensive understanding of the mechanisms of risk and resilience in youth development. Moreover, the findings have important implications for the prevention of adolescent problem behaviors.</p></div

Unstandardized path estimates for structural model with latent interaction effects.

<p>Age, gender, primary caregiver, and family location were included as covariates in this model, but are not shown in this figure. Dashed lines represent nonsignificant latent interaction paths at <i>p</i> < .05. Curved arrows among latent variables represent correlated errors. **<i>p</i> < .01, ***<i>p</i> < .001.</p

Resilience as a moderator between negative parenting and internalizing problems that illustrates the protective-stabilizing pattern.

<p>Resilience as a moderator between negative parenting and internalizing problems that illustrates the protective-stabilizing pattern.</p