Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks. First, they have to make a trade-off between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions. Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance. In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. The core of AFN is a logarithmic transformation layer to convert the power of each feature in a feature combination into the coefficient to be learned. The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the start-of-the-arts.Comment: Accepted by AAAI'2

Instability Mechanism of Osimertinib in Plasma and a Solving Strategy in the Pharmacokinetics Study

Osimertinib is a third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) and a star medication used to treat non-small-cell lung carcinomas (NSCLCs). It has caused broad public concern that osimertinib has relatively low stability in plasma. We explored why osimertinib and its primary metabolites AZ-5104 and AZ-7550 are unstable in rat plasma. Our results suggested that it is the main reason inducing their unstable phenomenon that the Michael addition reaction was putatively produced between the Michael acceptor of osimertinib and the cysteine in the plasma matrix. Consequently, we identified a method to stabilize osimertinib and its metabolite contents in plasma. The assay was observed to enhance the stability of osimertinib, AZ-5104, and AZ-7550 significantly. The validated method was subsequently applied to perform the pharmacokinetic study for osimertinib in rats with the newly established, elegant, and optimized ultra-performance liquid chromatography–tandem mass spectrometer (UPLC-MS/MS) strategy. The assay was assessed for accuracy, precision, matrix effects, recovery, and stability. This study can help understand the pharmacological effects of osimertinib and promote a solution for the similar problem of other Michael acceptor-contained third-generation EGFR-TKI

Co-delivery of siRNAs and anti-cancer drugs using layered double hydroxide nanoparticles

In this research we employed layered double hydroxide nanoparticles (LDHs) to simultaneously deliver an anticancer drug 5-fluorouracil (5-FU) and Allstars Cell Death siRNA (CD-siRNA) for effective cancer treatment. The strategy takes advantage of the LDH anion exchange capacity to intercalate 5-FU into its interlayer spacing and load siRNA on the surface of LDH nanoparticles. LDH nanoparticles have been previously demonstrated as an effective cellular delivery system for 5-FU and siRNA separately in various investigations. More excitedly, the combination of CD-siRNA and anticancer drug 5-FU with the same LDH particles significantly enhanced cytotoxicity to three cancer cell lines, e.g. MCF-7, U2OS and HCT-116, compared to the single treatment with either CD-siRNA or 5-FU. This enhancement is probably a result of coordinate mitochondrial damage process. Thus, the strategy to co-deliver siRNA and an anticancer drug by LDHs has great potential to overcome the drug resistance and enhance cancer treatment

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

C-MFD 2.0: Developing a Chinese Moral Foundation Dictionary

Our paper describes the development of a Chinese Moral Foundation Dictionary (C-MFD 2.0) to assist in the automated detection and analysis of moral intuition in the Chinese context. We took the approach of method- theory co-development and followed a human-led and machine-assisted procedure that emphasized opportunities for theoretical advancement, which enhances the validity of the computational tool. Starting from the existing Chinese translation of the established English moral foundation dictionaries, we enriched the dictionary with a list of unique Chinese moral-relevant words. Five experts went through four rounds of coding, followed by the validation of 202 crowd coders. The C-MFD 2.0 identifies not only the classic five moral foundations but also proposes several potential moral foundation candidates in the Chinese context. It demonstrates acceptable validity in detecting moral foundation values from Chinese texts. Our paper illustrates how a non-English moral foundation dictionary can be built for computational analysis and furthers moral foundation theory in a cross-language context

Data from identification of diagnostic biomarkers and metabolic pathway shifts of heat-stressed lactating dairy cows

Controlling heat stress (HS) is a global challenge for the dairy industry. In this work, an integrated metabolomics and lipidomics approach using 1H nuclear magnetic resonance (NMR) and ultra-fast LC–MS in combination with multivariate analyses was employed to investigate the discrimination of plasma metabolic profiles between HS-free and HS lactating dairy cows. Here we provide the information about the acquiring and processing of raw data obtained by 1H NMR and LC–MS techniques. The data of present study are related to the research article “Identification of diagnostic biomarkers and metabolic pathway shifts of heat-stressed lactating dairy cows” in the Journal of Proteomics (Tian et al., J. Proteomics, (2015), doi:10.1016/j.jprot.2015.04.014)