Migration Experiences and Reported Sexual Behavior Among Young, Unmarried Female Migrants in Changzhou, China.

BackgroundChina has a large migrant population, including many young unmarried women. Little is known about their sexual behavior, contraceptive use, and risk of unintended pregnancy.Methods475 unmarried female migrants aged 15-24, working in 1 of 6 factories in 2 districts of Changzhou city, completed an anonymous self-administered questionnaire in May 2012 on demographic characteristics, work and living situation, and health. We examined demographic and migration experience predictors of sexual and contraceptive behavior using bivariate and multivariate regressions.Results30.1% of the respondents were sexually experienced, with the average age at first sex of 19 years (standard deviation=3). 37.8% reported using contraception at first sex, 58.0% reported using consistent contraception during the past year, and 28.0% reported having at least 1 unintended pregnancy with all unintended pregnancies resulting in abortion. Those who had had at least 1 abortion reported having on average 1.6 abortions [SD=1] in total. Migrating with a boyfriend and changing jobs fewer times were associated with being sexually experienced. Younger age, less education, and changing jobs more times were associated with inconsistent contraceptive use.ConclusionThese findings demonstrate there is an unmet need for reproductive health education and services where these women work as well as in their hometown communities. This education must begin early to reach young women before they migrate

Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge

Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can fool another model with a non-trivial probability. This gave birth to the transfer-based attack where the adversarial examples generated by a surrogate model are used to conduct black-box attacks. There are some work on generating the adversarial examples from a given surrogate model with better transferability. However, training a special surrogate model to generate adversarial examples with better transferability is relatively under-explored. This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model. This trained surrogate model is named dark surrogate model (DSM). The proposed method for training a DSM consists of two key components: a teacher model extracting dark knowledge, and the mixing augmentation skill enhancing dark knowledge of training data. We conducted extensive experiments to show that the proposed method can substantially improve the adversarial transferability of surrogate models across different architectures of surrogate models and optimizers for generating adversarial examples, and it can be applied to other scenarios of transfer-based attack that contain dark knowledge, like face verification. Our code is publicly available at \url{https://github.com/ydc123/Dark_Surrogate_Model}.Comment: Accepted at 2023 International Conference on Tools with Artificial Intelligence (ICTAI

Finite element modelling of cell mechanics and cell-material interactions

Ph.D thesisUnderstanding cell mechanics subjected to external stimuli is important to design microniche to direct cell migration, differentiation and proliferation. However, previous models have not elucidated the mechanisms during the mechanotransduction process. Therefore, the main objective of this thesis is to develop different types of cell models including structure-based and continuum-based models to study the cell response during interactions with external stimuli. The structure-based cell model consisting of discrete cellular components was adopted to study the cellular responses during atomic force microscope (AFM) indentation tests, which revealed the significant contribution of stress fibres (SFs) to apparent modulus. A continuum-based model has been developed to examine the effect of substrate thickness, lateral boundary and neighbouring cell on cell responses. In this model, the active behaviour of the cell was described by a SF formation model. Focal adhesion (FA) model driven by the SF contractility was implemented to account for the interactions with substrate. It has revealed that the thin layer of substrate enhanced the SF and FA formation. The SF concentration and integrin density decrease exponentially with increasing substrate thickness. Higher substrate stiffness attenuates the cell responses to thickness variation. Larger cell sizes promote the formation of SFs and enable deeper thickness sensing. Fixed lateral boundary of the substrate influences the SF and FA formation as well as the SF orientation. Soft substrate enables cells to sense the lateral displacement field created by another cell while stiff substrate hinders the cell-cell communication. Cell orients its SFs towards the neighbouring cell and could be influenced to polarize in this direction. These predictions are consistent with experimental findings. Furthermore, the physics underpinned by the modelling has improved our understanding of the substrate boundary sensing and mechanics regulated cell-cell communications. This modelling framework could be potentially adopted for rational design of biomaterials in tissue engineering

Bacillus pangenome and the answers hidden within

Objectives: We've been taught since we're young that bacteria are everywhere but are they really everywhere? To address this question, we created Bacillus pangenomes. Analysis of the pangenomes allowed us to answer questions such as whether biogeography affected the pangenome and its structure. Material & Methods: In this study, we relied heavily on high performance computing to generate the necessary data. Genomes were retrieved from NCIB and pangenomes were created with the micropan package for R, a software for statistical computing on Oklahoma State University's "Pete" compute cluster. Micropan and FigTree were used to create the blast distance and 16s rRNA phylogenetic trees, respectively. The calculated genomic differenced allowed us to compare how the 16s rRNA tree differed from the full genome tree. Principal Component Analysis (PCA) plots were also constructed to show the relationship between species in different environments and regions. Results: Our data indicated the pangenome size to differ based on environment and region. Heaps analysis showed the pangenomes to be open with an alpha value much lower than one independent from the number of genomes included in the pangenome. Conclusion: There is still much work that needed to be done but our preliminary results suggest that species within a genus tend to cluster together regardless of external factors and that the Bacillus has an open pangenome

Multimetric structural covariance in first-episode major depressive disorder: a graph theoretical analysis

Background: Abnormalities of cortical morphology have been consistently reported in major depressive disorder (MDD), with widespread focal alterations in cortical thickness, surface area and gyrification. However, it is unclear whether these distributed focal changes disrupt the system-level architecture (topology) of brain morphology in MDD. If present, such a topological disruption might explain the mechanisms that underlie altered cortical morphology in MDD. Methods: Seventy-six patients with first-episode MDD (33 male, 43 female) and 66 healthy controls (32 male, 34 female) underwent structural MRI scans. We calculated cortical indices, including cortical thickness, surface area and local gyrification index, using FreeSurfer. We constructed morphological covariance networks using the 3 cortical indices separately, and we analyzed the topological properties of these group-level morphological covariance networks using graph theoretical approaches. Results: Topological differences between patients with first-episode MDD and healthy controls were restricted to the thickness-based network. We found a significant decrease in global efficiency but an increase in local efficiency of the left superior frontal gyrus and the right paracentral lobule in patients with first-episode MDD. When we simulated targeted lesions affecting the most highly connected nodes, the thickness-based networks in patients with first-episode MDD disintegrated more rapidly than those in healthy controls. Limitations: Our sample of patients with first-episode MDD has limited generalizability to patients with chronic and recurrent MDD. Conclusion: A systems-level disruption in cortical thickness (but not surface area or gyrification) occurs in patients with first-episode MDD

RESA: Recurrent Feature-Shift Aggregator for Lane Detection

Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals inherent in lane annotations, lane detection task is still challenging. Thus, it is difficult for the ordinary convolutional neural network (CNN) to train in general scenes to catch subtle lane feature from the raw image. In this paper, we present a novel module named REcurrent Feature-Shift Aggregator (RESA) to enrich lane feature after preliminary feature extraction with an ordinary CNN. RESA takes advantage of strong shape priors of lanes and captures spatial relationships of pixels across rows and columns. It shifts sliced feature map recurrently in vertical and horizontal directions and enables each pixel to gather global information. RESA can conjecture lanes accurately in challenging scenarios with weak appearance clues by aggregating sliced feature map. Moreover, we propose a Bilateral Up-Sampling Decoder that combines coarse-grained and fine-detailed features in the up-sampling stage. It can recover the low-resolution feature map into pixel-wise prediction meticulously. Our method achieves state-of-the-art results on two popular lane detection benchmarks (CULane and Tusimple). Code has been made available at: https://github.com/ZJULearning/resa