Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

Due to cancer's complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patients makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods' abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanism of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balances reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance.Comment: 60 pages, 4 figures, 4 tables, 11 supplementary tables, 1 supplementary note, submitted to Nature Communication

The toolbox of porous anodic aluminum oxide–based nanocomposites: from preparation to application

Anodic aluminum oxide (AAO) templates have been intensively investigated during the past decades and have meanwhile been widely applied through both sacrificial and non-sacrificial pathways. In numerous non-sacrificial applications, the AAO membrane is maintained as part of the obtained composite materials; hence, the template structure and topography determine to a great extent the potential applications. Through-hole isotropic AAO features nanochannels that promote transfer of matter, while anisotropic AAO with barrier layer exhibits nanocavities suitable as independent and homogenous containers. By combining the two kinds of AAO membranes with diverse organic and inorganic materials through physical interactions or chemical bonds, AAO composites are designed and applied in versatile fields such as catalysis, drug release platform, separation membrane, optical appliances, sensors, cell culture, energy, and electronic devices. Therefore, within this review, a perspective on exhilarating prospect for complementary advancement on AAO composites both in preparation and application is provided

Asymmetric conditional correlations in stock returns

Modeling and estimation of correlation coefficients is a fundamental step in risk management, especially with the aftermath of the financial crisis in 2008, which challenged the traditional measuring of dependence in the financial market. Because of the serial dependence and small signal-to-noise ratio, patterns of the dependence in the data cannot be easily detected and modeled. This paper introduces a common factor analysis into the conditional correlation coefficients to extract the features of dependence. While statistical properties are thoroughly derived, extensive empirical analysis provides us with common patterns for the conditional correlation coefficients that give new insight into a number of important questions in financial data, especially the asymmetry of cross-correlations and the factors that drive the cross-correlations

Augmented Lagrangian preconditioners for the Oseen-Frank model of nematic and cholesteric liquid crystals

We propose a robust and efficient augmented Lagrangian-type preconditioner for solving linearizations of the Oseen-Frank model arising in cholesteric liquid crystals. By applying the augmented Lagrangian method, the Schur complement of the director block can be better approximated by the weighted mass matrix of the Lagrange multiplier, at the cost of making the augmented director block harder to solve. In order to solve the augmented director block, we develop a robust multigrid algorithm which includes an additive Schwarz relaxation that captures a pointwise version of the kernel of the semi-definite term. Furthermore, we prove that the augmented Lagrangian term improves the discrete enforcement of the unit-length constraint. Numerical experiments verify the efficiency of the algorithm and its robustness with respect to problem-related parameters (Frank constants and cholesteric pitch) and the mesh size

Factors Influencing Intention to Gamble Online

The objective of this research was to validate a model on online gambling intention. Given that there are many forms of online gambling, this research focused on sports betting. We adopted the Technology Acceptance Model (TAM) as our research model. Additionally, we included subjective norm as an antecedent to online gambling intention. We tested the model using data collected from a questionnaire survey. We collected 212 returns from students in a Chinese tertiary institution. The results provide support for the six hypotheses proposed in our research. We discussed the implications of the results for industry practitioners and gambling counselors