A Bayesian regression tree approach to identify the effect of nanoparticles' properties on toxicity profiles

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose- and time-response surface smoothing. The resulting posterior distribution is sampled by Markov Chain Monte Carlo. This method allows for inference on a number of quantities of potential interest to substantive nanotoxicology, such as the importance of physico-chemical properties and their marginal effect on toxicity. We illustrate the application of our method to the analysis of a library of 24 nano metal oxides.Comment: Published at http://dx.doi.org/10.1214/14-AOAS797 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but also by other factors, such as duration bias and noisy watching. Duration bias refers to the tendency for users to spend more time on videos with longer durations, regardless of their actual interest level. Noisy watching, on the other hand, describes users taking time to determine whether they like a video or not, which can result in users spending time watching videos they do not like. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for indicating user interest. Furthermore, current methods primarily address duration bias and ignore the impact of noisy watching, which may limit their effectiveness in uncovering user interest from watch time. In this study, we first analyze the generation mechanism of users' watch time from a unified causal viewpoint. Specifically, we considered the watch time as a mixture of the user's actual interest level, the duration-biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D$^2$Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets and online A/B testing indicate the effectiveness of the proposed method.Comment: Accepted by Recsys'2

Thermal conduction simulation based on reconstruction digital rocks with respect to fractures

Effective thermal conductivity (ETC), as a necessary parameter in the thermal properties of rock, is affected by the pore structure and the thermal conduction conditions. To evaluate the effect of fractures and saturated fluids on sandstone’s thermal conductivity, we simulated thermal conduction along three orthogonal (X, Y, and Z) directions under air- and water-saturated conditions on reconstructed digital rocks with different fractures. The results show that the temperature distribution is separated by the fracture. The significant difference between the thermal conductivities of solid and fluid is the primary factor influencing the temperature distribution, and the thermal conduction mainly depends on the solid phase. A nonlinear reduction of ETC is observed with increasing fracture length and angle. Only when the values of the fracture length and angle are large, a negative effect of fracture aperture on the ETC is apparent. Based on the partial least squares (PLS) regression method, the fluid thermal conductivity shows the greatest positive influence on the ETC value. The fracture length and angle are two other factors significantly influencing the ETC, while the impact of fracture aperture may be ignored. We obtained a predictive equation of ETC which considers the related parameters of digital rocks, including the fracture length, fracture aperture, angle between the fracture and the heat flux direction, porosity, and the thermal conductivity of saturated fluid

A Bayesian regression tree approach to identify the effect of nanoparticles properties on toxicity profiles

We introduce a Bayesian multiple regression tree model to characterize relationships between physico-chemical properties of nanoparticles and their in-vitro toxicity over multiple doses and times of exposure. Unlike conventional models that rely on data summaries, our model solves the low sample size issue and avoids arbitrary loss of information by combining all measurements from a general exposure experiment across doses, times of exposure, and replicates. The proposed technique integrates Bayesian trees for modeling threshold effects and interactions, and penalized B-splines for dose and time-response surfaces smoothing. The resulting posterior distribution is sampled via a Markov Chain Monte Carlo algorithm. This method allows for inference on a number of quantities of potential interest to substantive nanotoxicology, such as the importance of physico-chemical properties and their marginal effect on toxicity. We illustrate the application of our method to the analysis of a library of 24 nano metal oxides

Relating Nanoparticle Properties to Biological Outcomes in Exposure Escalation Experiments

A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a battery of nanomaterials. We discuss a modeling strategy, that relates the outcome of an exposure escalation experiment to nanoparticle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physico-chemical descriptors. The proposed inferential framework results in summaries that are easily interpretable as simple probability statements. We present the application of the proposed method to a data set on 24 metal oxides nanoparticles, characterized in relation to their electrical, crystal and dissolution properties

Interplay Between Liver Type 1 Innate Lymphoid Cells and NK Cells Drives the Development of Alcoholic Steatohepatitis

BACKGROUND & AIMS: Liver contains high frequency of group 1 innate lymphoid cells (ILC), which are composed of comparable number of type 1 ILC (ILC1) and natural killer (NK) cells in steady state. Little is known about whether and how the interaction between ILC1 and NK cells affects the development of alcoholic liver disease. METHODS: A mouse model of chronic alcohol abuse plus single-binge (Gao-Binge model) was established. The levels of alanine aminotransferase/aspartate aminotransferase, hepatic lipid, and inflammatory cytokines or neutrophils were measured to evaluate the degree of liver injury, steatosis, and inflammation. Flow cytometric analysis, cell depletion, or adoptive transfer were used to interrogate the interaction between ILC1 and NK cells. RESULTS: Upon chronic alcohol consumption, NK cells, but not ILC1, underwent apoptosis, resulting in ILC1 dominance among group 1 ILC. Interleukin (IL) 17A expression was up-regulated, and increased IL17A was mainly derived from liver ILC1 after chronic alcohol feeding. Either depletion of ILC1 or neutralization of IL17A could significantly attenuate liver steatosis, inflammation, and injury in alcohol-fed mice. In contrast, normalization of the ILC1/NK cells ratio through NK cells transfer or expanding NK cells had a significant hepatoprotection against alcohol-induced steatohepatitis. Furthermore, NK cell-derived interferon gamma exerted a protective function via inhibiting IL17A production by liver ILC1 during alcoholic steatohepatitis. CONCLUSIONS: This is the first study showing that the interplay between liver ILC1 and NK cells occurs and drives the development of alcoholic steatohepatitis. Our findings support further exploration of liver ILC1 or NK cells as a therapeutic target for the treatment of alcohol-associated liver disease

Observation of E8 Particles in an Ising Chain Antiferromagnet

Near the transverse-field induced quantum critical point of the Ising chain, an exotic dynamic spectrum consisting of exactly eight particles was predicted, which is uniquely described by an emergent quantum integrable field theory with the symmetry of the $E_8$ Lie algebra, but rarely explored experimentally. Here we use high-resolution terahertz spectroscopy to resolve quantum spin dynamics of the quasi-one-dimensional Ising antiferromagnet BaCo$_2$V$_2$O$_8$ in an applied transverse field. By comparing to an analytical calculation of the dynamical spin correlations, we identify $E_8$ particles as well as their two-particle excitations.Comment: 6 pages, 3 figures, plus supplementary material