Efficient data integration under prior probability shift.

Conventional supervised learning usually operates under the premise that data are collected from the same underlying population. However, challenges may arise when integrating new data from different populations, resulting in a phenomenon known as dataset shift. This paper focuses on prior probability shift, where the distribution of the outcome varies across datasets but the conditional distribution of features given the outcome remains the same. To tackle the challenges posed by such shift, we propose an estimation algorithm that can efficiently combine information from multiple sources. Unlike existing methods that are restricted to discrete outcomes, the proposed approach accommodates both discrete and continuous outcomes. It also handles high-dimensional covariate vectors through variable selection using an adaptive least absolute shrinkage and selection operator penalty, producing efficient estimates that possess the oracle property. Moreover, a novel semiparametric likelihood ratio test is proposed to check the validity of prior probability shift assumptions by embedding the null conditional density function into Neyman's smooth alternatives (Neyman, 1937) and testing study-specific parameters. We demonstrate the effectiveness of our proposed method through extensive simulations and a real data example. The proposed methods serve as a useful addition to the repertoire of tools for dealing dataset shifts.

Improved semiparametric estimation of the proportional rate model with recurrent event data.

Owing to its robustness properties, marginal interpretations, and ease of implementation, the pseudo-partial likelihood method proposed in the seminal papers of Pepe and Cai and Lin et al. has become the default approach for analyzing recurrent event data with Cox-type proportional rate models. However, the construction of the pseudo-partial score function ignores the dependency among recurrent events and thus can be inefficient. An attempt to investigate the asymptotic efficiency of weighted pseudo-partial likelihood estimation found that the optimal weight function involves the unknown variance-covariance process of the recurrent event process and may not have closed-form expression. Thus, instead of deriving the optimal weights, we propose to combine a system of pre-specified weighted pseudo-partial score equations via the generalized method of moments and empirical likelihood estimation. We show that a substantial efficiency gain can be easily achieved without imposing additional model assumptions. More importantly, the proposed estimation procedures can be implemented with existing software. Theoretical and numerical analyses show that the empirical likelihood estimator is more appealing than the generalized method of moments estimator when the sample size is sufficiently large. An analysis of readmission risk in colorectal cancer patients is presented to illustrate the proposed methodology.

Robust inference of conditional average treatment effects using dimension reduction.

Personalized treatment aims at tailoring treatments to individual characteristics. An important step is to understand how a treatment effect varies across individual characteristics, known as the conditional average treatment effect (CATE). In this study, we make robust inferences of the CATE from observational data, which becomes challenging with a multivariate confounder. To reduce the curse of dimensionality, while keeping the nonparametric advantages, we propose double dimension reductions that achieve different goal. First, we identify the central mean subspace of the CATE directly using dimension reduction in order to detect the most accurate and parsimonious structure of the CATE. Second, we use a nonparametric regression with a prior dimension reduction to impute counterfactual outcomes, which helps to improve the stability of the imputation. We establish the asymptotic properties of the proposed estimator, taking into account the two-step double dimension reduction, and propose an effective bootstrapping procedure without bootstrapping the estimated central mean subspace to make valid inferences. A simulation and applications show that the proposed estimator outperforms existing competitors.

Model selection among Dimension-Reduced generalized Cox models.

Conventional semiparametric hazards regression models rely on the specification of particular model formulations, such as proportional-hazards feature and single-index structures. Instead of checking these modeling assumptions one-by-one, we proposed a class of dimension-reduced generalized Cox models, and then a consistent model selection procedure among this class to select covariates with proportional-hazards feature and a proper model formulation for non-proportional-hazards covariates. In this class, the non-proportional-hazards covariates are treated in a nonparametric manner, and a partial sufficient dimension reduction is introduced to reduce the curse of dimensionality. A semiparametric efficient estimation is proposed to estimate these models. Based on the proposed estimation, we further constructed a cross-validation type criterion to consistently select the correct model among this class. Most importantly, this class of hazards regression models contains the fully nonparametric hazards regression model as the most saturated submodel, and hence no further model diagnosis is required. Overall speaking, this model selection approach is more effective than performing a sequence of conventional model checking. The proposed method is illustrated by simulation studies and a data example.

Survival-related genes are diversified across cancers but generally enriched in cancer hallmark pathways.

BACKGROUND: Pan-cancer studies have disclosed many commonalities and differences in mutations, copy number variations, and gene expression alterations among cancers. Some of these features are significantly associated with clinical outcomes, and many prognosis-predictive biomarkers or biosignatures have been proposed for specific cancer types. Here, we systematically explored the biological functions and the distribution of survival-related genes (SRGs) across cancers. RESULTS: We carried out two different statistical survival models on the mRNA expression profiles in 33 cancer types from TCGA. We identified SRGs in each cancer type based on the Cox proportional hazards model and the log-rank test. We found a large difference in the number of SRGs among different cancer types, and most of the identified SRGs were specific to a particular cancer type. While these SRGs were unique to each cancer type, they were found mostly enriched in cancer hallmark pathways, e.g., cell proliferation, cell differentiation, DNA metabolism, and RNA metabolism. We also analyzed the association between cancer driver genes and SRGs and did not find significant over-representation amongst most cancers. CONCLUSIONS: In summary, our work identified all the SRGs for 33 cancer types from TCGA. In addition, the pan-cancer analysis revealed the similarities and the differences in the biological functions of SRGs across cancers. Given the potential of SRGs in clinical utility, our results can serve as a resource for basic research and biotech applications.

Allosteric coupling between transmembrane segment 4 and the selectivity filter of TALK1 potassium channels regulates their gating by extracellular pH.

Opening of two-pore domain K+ channels (K2Ps) is regulated by various external cues, such as pH, membrane tension, or temperature, which allosterically modulate the selectivity filter (SF) gate. However, how these cues cause conformational changes in the SF of some K2P channels remains unclear. Herein, we investigate the mechanisms by which extracellular pH affects gating in an alkaline-activated K2P channel, TALK1, using electrophysiology and molecular dynamics (MD) simulations. We show that R233, located at the N-terminal end of transmembrane segment 4, is the primary pHo sensor. This residue distally regulates the orientation of the carbonyl group at the S1 potassium-binding site through an interacting network composed of residues on transmembrane segment 4, the pore helix domain 1, and the SF. Moreover, in the presence of divalent cations, we found the acidic pH-activated R233E mutant recapitulates the network interactions of protonated R233. Intriguingly, our data further suggested stochastic coupling between R233 and the SF gate, which can be described by an allosteric gating model. We propose that this allosteric model could predict the hybrid pH sensitivity in heterodimeric channels with alkaline-activated and acidic-activated K2P subunits.

Kv1.1 channels regulate early postnatal neurogenesis in mouse hippocampus via the TrkB signaling pathway.

In the postnatal brain, neurogenesis occurs only within a few regions, such as the hippocampal sub-granular zone (SGZ). Postnatal neurogenesis is tightly regulated by factors that balance stem cell renewal with differentiation, and it gives rise to neurons that participate in learning and memory formation. The Kv1.1 channel, a voltage-gated potassium channel, was previously shown to suppress postnatal neurogenesis in the SGZ in a cell-autonomous manner. In this study, we have clarified the physiological and molecular mechanisms underlying Kv1.1-dependent postnatal neurogenesis. First, we discovered that the membrane potential of neural progenitor cells is highly dynamic during development. We further established a multinomial logistic regression model for cell-type classification based on the biophysical characteristics and corresponding cell markers. We found that the loss of Kv1.1 channel activity causes significant depolarization of type 2b neural progenitor cells. This depolarization is associated with increased tropomyosin receptor kinase B (TrkB) signaling and proliferation of neural progenitor cells; suppressing TrkB signaling reduces the extent of postnatal neurogenesis. Thus, our study defines the role of the Kv1.1 potassium channel in regulating the proliferation of postnatal neural progenitor cells in mouse hippocampus.

Sufficient dimension reduction with simultaneous estimation of effective dimensions for time-to-event data.

When there is not enough scientific knowledge to assume a particular regression model, sufficient dimension reduction is a flexible yet parsimonious nonparametric framework to study how covariates are associated with an outcome. We propose a novel estimator of low-dimensional composite scores, which can summarize the contribution of covariates on a right-censored survival outcome. The proposed estimator determines the degree of dimension reduction adaptively from data; it estimates the structural dimension, the central subspace and a rate-optimal smoothing bandwidth parameter simultaneously from a single criterion. The methodology is formulated in a counting process framework. Further, the estimation is free of the inverse probability weighting employed in existing methods, which often leads to instability in small samples. We derive the large sample properties for the estimated central subspace with data-adaptive structural dimension and bandwidth. The estimation can be easily implemented by a forward selection algorithm, and this implementation is justified by asymptotic convexity of the criterion in working dimensions. Numerical simulations and two real examples are given to illustrate the proposed method.

Joint sufficient dimension reduction for estimating continuous treatment effect functions.

The estimation of continuous treatment effect functions using observational data often requires parametric specification of the effect curves, the conditional distributions of outcomes and treatment assignments given multi-dimensional covariates. While nonparametric extensions are possible, they typically suffer from the curse of dimensionality. Dimension reduction is often inevitable and we propose a sufficient dimension reduction framework to balance parsimony and flexibility. The joint central subspace can be estimated at a n 1/2-rate without fixing its dimension in advance, and the treatment effect function is estimated by averaging local estimates of a reduced dimension. Asymptotic properties are studied. Unlike binary treatments, continuous treatments require multiple smoothing parameters of different asymptotic orders to borrow different facets of information, and their joint estimation is proposed by a non-standard version of the infinitesimal jackknife.

Joint sufficient dimension reduction and estimation of conditional and average treatment effects.

The estimation of treatment effects based on observational data usually involves multiple confounders, and dimension reduction is often desirable and sometimes inevitable. We first clarify the definition of a central subspace that is relevant for the efficient estimation of average treatment effects. A criterion is then proposed to simultaneously estimate the structural dimension, the basis matrix of the joint central subspace, and the optimal bandwidth for estimating the conditional treatment effects. The method can easily be implemented by forward selection. Semiparametric efficient estimation of average treatment effects can be achieved by averaging the conditional treatment effects with a different data-adaptive bandwidth to ensure optimal undersmoothing. Asymptotic properties of the estimated joint central subspace and the corresponding estimator of average treatment effects are studied. The proposed methods are applied to a nutritional study, where the covariate dimension is reduced from 11 to an effective dimension of one.

Bias and variance reduction in nonparametric estimation of time-dependent accuracy measures.

A new nonparametric approach is developed to estimate the time-dependent accuracy measure curves, which are defined on the cumulative cases and dynamic controls, for censored survival data. Based on an estimable survival process, the main intention of this study is to reduce the finite-sample biases of nearest neighbor estimators. The asymptotic variances of some retrospective accuracy measure estimators are further reduced by applying a smoothing technique to the underlying process of a marker. Meanwhile, practically feasible and theoretically valid procedures are proposed for bandwidth selection in the presented estimators. In addition, the proposed methodology can be reasonably extended to accommodate stratified survival data and survival data with multiple markers. As shown in the simulations, our new estimators outperform the nearest neighbor and inverse censoring weighted estimators. Data from the AIDS Clinical Trials Group study 175 and an angiographic coronary artery disease study are also used to illustrate the proposed methodology. Copyright © 2016 John Wiley & Sons, Ltd.

An energy analysis of torrefaction for upgrading microalga residue as a solid fuel.

The torrefaction characteristics and energy utilization of microalga Chlamydomonas sp. JSC4 (C. sp. JSC4) residue under the combination of temperature and duration are studied by examining contour maps. The torrefaction temperature on the contour line of solid yield has a trend to linearly decrease with increasing duration. An index of relative energy efficiency (REE) is introduced to identify the performance of energy utilization for upgrading biomass. For a fixed energy yield, the optimal operation can be found to maximize the heating value of the biomass and minimize the solid yield. The energy utilization under the combination of a high temperature and a short duration is more efficient than that of a low temperature and a long duration. The maximum REE along the contour line of energy yield is always exhibited at the highest temperature (300°C) where the energy efficiency can be enlarged by a factor of at least 2.36.

Thermochemical conversion of microalgal biomass into biofuels: a review.

Following first-generation and second-generation biofuels produced from food and non-food crops, respectively, algal biomass has become an important feedstock for the production of third-generation biofuels. Microalgal biomass is characterized by rapid growth and high carbon fixing efficiency when they grow. On account of potential of mass production and greenhouse gas uptake, microalgae are promising feedstocks for biofuels development. Thermochemical conversion is an effective process for biofuel production from biomass. The technology mainly includes torrefaction, liquefaction, pyrolysis, and gasification. Through these conversion technologies, solid, liquid, and gaseous biofuels are produced from microalgae for heat and power generation. The liquid bio-oils can further be upgraded for chemicals, while the synthesis gas can be synthesized into liquid fuels. This paper aims to provide a state-of-the-art review of the thermochemical conversion technologies of microalgal biomass into fuels. Detailed conversion processes and their outcome are also addressed.

Thermal decomposition dynamics and severity of microalgae residues in torrefaction.

To figure out the torrefaction characteristics and weight loss dynamics of microalgae residues, the thermogravimetric analyses of two microalgae (Chlamydomonas sp. JSC4 and Chlorella sorokiniana CY1) residues are carried out. A parameter of torrefaction severity index (TSI) in the range of 0-1, in terms of weight loss ratio between a certain operation and a reference operation, is defined to indicate the degree of biomass thermal degradation due to torrefaction. The TSI profiles of the two residues are similar to each other; therefore, the parameter may be used to describe the torrefaction extents of various biomass materials. The curvature of TSI profile along light torrefaction is slight, elucidating its slight impact on biomass thermal degradation. The sharp curvature along severe torrefaction in the initial pretreatment period reveals that biomass upgraded with high temperature and short duration is more effective than using low temperature with long duration.

Molecular dynamics investigations of liquid-vapor interaction and adsorption of formaldehyde, oxocarbons, and water in graphitic slit pores.

Formaldehyde exposure has been associated with several human cancers, including leukemia and nasopharyngeal carcinoma, motivating the present investigation on the microscopic adsorption behaviors of formaldehyde in multi-component-mixture-filled micropores. Molecular dynamics (MD) simulation was used to investigate the liquid-vapor interaction and adsorption of formaldehyde, oxocarbons, and water in graphitic slit pores. The effects of the slit width, system temperature, concentration, and the constituent ratio of the mixture on the diffusion and adsorption properties are studied. As a result of interactions between the components, the z-directional self-diffusivity (D(z)) in the mixture substantially decreased by about one order of magnitude as compared with that of pure (single-constituent) adsorbates. When the concentration exceeds a certain threshold, the D(z) values dramatically decrease due to over-saturation inducing barriers to diffusion. The binding energy between the adsorbate and graphite at the first adsorption monolayer is calculated to be 3.99, 2.01, 3.49, and 2.67 kcal mol(-1) for CO2, CO, CH2O, and H2O, respectively. These values agree well with those calculated using the density functional theory coupled cluster method and experimental results. A low solubility of CO2 in water and water preferring to react with CH2O, forming hydrated methanediol clusters, are observed. Because the cohesion in a hydrated methanediol cluster is much higher than the adhesion between clusters and the graphitic surface, the hydrated methanediol clusters were hydrophobic, exhibiting a large contact angle on graphite.