Extrapolated cross-validation for randomized ensembles.

Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This paper introduces a cross-validation method, ECV (Extrapolated Cross-Validation), for tuning the ensemble and subsample sizes in randomized ensembles.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness.

Simultaneous inference for generalized linear models with unmeasured confounders.

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects.

Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT.

Recent advances in single-cell technologies enable joint profiling of multiple omics. These profiles can reveal the complex interplay of different regulatory layers in single cells; still, new challenges arise when integrating datasets with some features shared across experiments and others exclusive to a single source; combining information across these sources is called mosaic integration. The difficulties lie in imputing missing molecular layers to build a self-consistent atlas, finding a common latent space, and transferring learning to new data sources robustly.

Fermi-Level Depinning in Metal/Ge Junctions by Inserting a Carbon Nanotube Layer.

Germanium (Ge)-based devices are recognized as one of the most promising next-generation technologies for extending Moore's law. However, one of the critical issues is Fermi-level pinning (FLP) at the metal/n-Ge interface, and the resulting large contact resistance seriously degrades their performance. The insertion of a thin layer is one main technique for FLP modulation; however, the contact resistance is still limited by the remaining barrier height and the resistance induced by the insertion layer.

Improving protected area effectiveness through consideration of different human-pressure baselines.

Previous assessments of the effectiveness of protected areas (PAs) focused primarily on changes in human pressure over time and did not consider the different human-pressure baselines of PAs, thereby potentially over- or underestimating PA effectiveness. We developed a framework that considers both human-pressure baseline and change in human pressure over time and assessed the effectiveness of 338 PAs in China from 2010 to 2020. The initial state of human pressure on PAs was taken as the baseline, and changes in human pressure index (HPI) were further analyzed under different baselines.

Decoupling convolution network for characterizing the metastatic lymph nodes of breast cancer patients.

Purpose: The dual-energy computed tomography (DECT) technique is an emerging imaging tool that can better characterize material features and has the potential to be a noninvasive means of predicting lymph node metastasis. The purpose of this study was to establish a DECT-specified quantitative approach based on a neural network to characterize the sentinel lymph node (SLN).

Methods: With IRB approval, we retrospectively collected a total of 229 patients (100/229 metastasis) with biopsy proven breast cancer in this study.

Flexible 64 × 64 Pixel AMOLED Displays Driven by Uniform Carbon Nanotube Thin-Film Transistors.

Carbon nanotube (CNT) thin-film transistors are expected to be promising for use in flexible electronics including flexible and transparent integrated circuits and in wearable chemical and physical sensors and for driving the circuits of flexible display panels. However, current devices based on CNT channels suffer from poor performance uniformity and low manufacturing yield; therefore, they are still far from being practical. This is usually caused by nonuniform deposition of the semiconducting CNTs and the rough surface of flexible substrates.

Ultrahigh-performance transparent conductive films of carbon-welded isolated single-wall carbon nanotubes.

Single-wall carbon nanotubes (SWCNTs) are ideal for fabricating transparent conductive films because of their small diameter, good optical and electrical properties, and excellent flexibility. However, a high intertube Schottky junction resistance, together with the existence of aggregated bundles of SWCNTs, leads to a degraded optoelectronic performance of the films. We report a network of isolated SWCNTs prepared by an injection floating catalyst chemical vapor deposition method, in which crossed SWCNTs are welded together by graphitic carbon.