Semiparametric sensitivity analysis: unmeasured confounding in observational studies.

Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.