Search for: All records

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. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE’s efficacy in integrative analyses of multiomic datasets with continuous cell population structures. 

Abstract Generalized cross-validation (GCV) is a widely used method for estimating the squared out-of-sample prediction risk that employs scalar degrees of freedom adjustment (in a multiplicative sense) to the squared training error. In this paper, we examine the consistency of GCV for estimating the prediction risk of arbitrary ensembles of penalized least-squares estimators. We show that GCV is inconsistent for any finite ensemble of size greater than one. Towards repairing this shortcoming, we identify a correction that involves an additional scalar correction (in an additive sense) based on degrees of freedom adjusted training errors from each ensemble component. The proposed estimator (termed CGCV) maintains the computational advantages of GCV and requires neither sample splitting, model refitting, or out-of-bag risk estimation. The estimator stems from a finer inspection of the ensemble risk decomposition and two intermediate risk estimators for the components in this decomposition. We provide a non-asymptotic analysis of the CGCV and the two intermediate risk estimators for ensembles of convex penalized estimators under Gaussian features and a linear response model. Furthermore, in the special case of ridge regression, we extend the analysis to general feature and response distributions using random matrix theory, which establishes model-free uniform consistency of CGCV. 

Out of the several hundred copies ofrRNAgenes arranged in the nucleolar organizing regions (NOR) of the five human acrocentric chromosomes, ~50% remain transcriptionally inactive. NOR-associated sequences and epigenetic modifications contribute to the differential expression of rRNAs. However, the mechanism(s) controlling the dosage of active versus inactiverRNAgenes within each NOR in mammals is yet to be determined. We have discovered a family of ncRNAs, SNULs (Single NUcleolus Localized RNA), which form constrained sub-nucleolar territories on individual NORs and influence rRNA expression. Individual members of the SNULs monoallelically associate with specific NOR-containing chromosomes. SNULs share sequence similarity to pre-rRNA and localize in the sub-nucleolar compartment with pre-rRNA. Finally, SNULs control rRNA expression by influencing pre-rRNA sorting to the DFC compartment and pre-rRNA processing. Our study discovered a novel class of ncRNAs influencing rRNA expression by forming constrained nucleolar territories on individual NORs. 

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems. 

In the current work, we combined different physical and chemical modifications of carbon nanofibers through the creation of micro-, meso-, and macro-pores as well as the incorporation of nitrogen groups in cyclic polyacrylonitrile (CPAN) using gas-assisted electrospinning and air-controlled electrospray processes. We incorporated them into electrode and interlayer in Li–Sulfur batteries. First, we controlled pore size and distributions in mesoporous carbon fibers (mpCNF) via adding polymethyl methacrylate as a sacrificial polymer to the polyacrylonitrile carbon precursor, followed by varying activation conditions. Secondly, nitrogen groups were introduced via cyclization of PAN on mesoporous carbon nanofibers (mpCPAN). We compared the synergistic effects of all these features in cathode substrate and interlayer on the performance Li–Sulfur batteries and used various characterization tools to understand them. Our results revealed that coating CPAN on both mesoporous carbon cathode and interlayer greatly enhanced the rate capability and capacity retention, leading to the capacity of 1000 mAh/g at 2 C and 1200 mAh/g at 0.5 C with the capability retention of 88% after 100 cycles. The presence of nitrogen groups and mesopores in both cathodes and interlayers resulted in more effective polysulfide confinement and also show more promise for higher loading systems. 

Abstract Freestanding single‐crystalline nanomembranes have gained increasing attention as promising platforms for both fundamental research and advanced electronic applications. However, internal stress gradients arising from epitaxial strain within the oxide membranes often result in high crack density during fabrication, leading to unsatisfactory yield and limited reliability. Here, an elastically graded polymer (EGP) support that enables wafer‐scale crack‐free transfer of single‐crystalline oxide membranes are developed. The engineered elastic gradient within the EGP accommodates the internal strain of the oxide membrane, effectively minimizing crack formation during lift‐off. Notably, this ability to spatially control the interfacial stiffness between the polymer and the oxide film enables crack suppression under both tensile and compressive strain. This approach provides a robust and scalable route to producing high‐quality freestanding oxide membranes, paving the way not only for their integration into novel device architecture but also opening new avenues for scientific exploration of functional systems. 

The electrospray process produces micro/nanodroplets for various applications such as thin and uniform coatings, drug carriers and mass spectrometry. In this paper, we study the spray processes of viscoelastic jets using simulations and experiments. In discretized modeling, the jet is perturbed with axisymmetric instability and the growth of this instability causes the jet to break into droplets. For the experiments, a solution of polyvinyl alcohol in water is sprayed and is visualized using a high-speed camera. The droplet size distribution is studied from simulations with experiments for three spray cases: electrospray, air spray, and air-controlled electrospray. Our simulations and experiments reveal that the electric field is effective in reducing the droplet size, while air flow offers more jet break-ups and thus a larger number of droplets. As a result, air-controlled electrospray where these two driving forces are synergistically combined leads to a larger number of smaller droplets than electrospray or air spray. Finally, we applied three spray processes to obtain a deposition of sulfur/mesoporous carbon/graphene/polymer binder composites as a lithium sulfur battery cathode and demonstrated that air-controlled electrospray leads to a higher capacity and rate capability than other processes, exhibiting 800 mA h g −1 at 0.5C and 600 mA h g −1 at 2C.