Predicting pregnancy has been a fundamental problem in women’s health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women’s health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models – a logistic regression model, and 3 LSTM models – to predict a woman’s probability of becoming pregnant using data from a women’s health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women’s health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential.

Ever since the social networks became the focus of a great number of researches, the privacy risks of published network data have also raised considerable concerns. To evaluate users' privacy risks, researchers have developed methods to deanonymize the networks and identify the same person in the different networks. However, the existing solutions either require high-quality seed mappings for cold start, or exhibit low accuracy without fully exploiting the structural information, and entail high computation expense. In this paper, we propose a fast and effective seedless network de-anonymization approach simply relying on structural information, named RoleMatch. RoleMatch equips with a new pairwise node similarity measure and an efficient node matching algorithm. Through testing RoleMatch with both real and synthesized social networks, which are anonymized by several popular anonymization algorithms, we demonstrate that the RoleMatch receives superior performance compared with existing de-anonymization algorithms.

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Electroporation (EP)-mediated DNA immunization can elicit effective immune responses in a variety of animals, and is widely used in research studies and clinical trials. However, high-pulse voltage, high DNA dose and multiple immunizations are still required to achieve considerable immune responses. To further improve the efficiency of EP-mediated DNA immunization, many parameters have been tried and optimized in recent years. In our early research, we found that the short noncoding DNA fragments (sf-DNA) can significantly enhance EP-mediated transgene expression of reporter genes. In this study, we tested the effect of sf-DNA on the immune potency of EP-mediated hepatitis B virus (HBV) DNA vaccination in a mouse model. The results show that the use of sf-DNA in EP-mediated HBV DNA vaccination leads to an enhanced expression of the HBV surface antigen, resulting in higher cellular and humoral responses. Furthermore, the immune responses in the sf-DNA-mediated 120 V cm(-1) EP immunization group were higher than that of the 200 V cm(-1) EP without sf-DNA groups. These data suggest that the sf-DNA can be used as an effective helper molecule to improve the immune response of EP-mediated HBV DNA vaccination, which may make the EP-mediated DNA vaccination more effective and suitable for animal and clinical application.

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