Single-cell mosaic integration has revolutionized our understanding of cellular heterogeneity, offering unprecedented resolution into cellular states and contexts. While large language models (LLMs) have achieved success in the analysis of single-cell omics data, their application specifically focused on the integration of mosaic data remains limited. Current computational approaches often fall short in addressing these challenges. They frequently depend on assumptions of data completeness and uniform quality, failing to manage the variability and noise effectively introduced by missing data. Moreover, these models might not efficiently process large, diverse datasets or account for biological heterogeneity and long-ranging dependencies across various data types. To address these shortcomings, we introduce the Single-cell Mosaic Omics Nonlinear Integration and Clustering Analysis (scMonica) framework, which employs a LSTM-transformer hybrid architecture. This innovative model combines the strengths of Long Short-Term Memory (LSTM) networks, which excel at capturing long-range dependencies within sequential gene expression patterns, with transformers, renowned for their attention mechanisms that handle the complex, non-linear interactions characteristic of multi-layered datasets. By leveraging these complementary strengths, our approach enhances the integration process significantly, allowing for nuanced management of the intrinsic heterogeneity and sparsity of mosaic datasets. Comprehensive evaluations demonstrate the robustness and effectiveness of our approach, offering unparalleled versatility and accuracy in multi-omics data analysis. These advancements underscore scMonica’s potential to drive significant insights in single-cell developmental biology, oncology, and beyond. We discuss the underlying technologies, analyze their applications, and contemplate future directions that promise to extend the boundaries of both research and clinical domains.

Neural prosthetic technologies have helped many patients by restoring vision, hearing, or movement and relieving chronic pain or neurological disorders. While most neural prosthetic systems to date have used invasive or implantable devices for patients with inoperative or malfunctioning external body parts or internal organs, a much larger population of “healthy” people who suffer episodic or progressive cognitive impairments in daily life can benefit from noninvasive neural prostheses. For example, reduced alertness, lack of attention, or poor decision-making during monotonous, routine tasks can have catastrophic consequences. This study proposes a noninvasive mobile prosthetic platform for continuously monitoring high-temporal resolution brain dynamics without requiring application of conductive gels on the scalp. The proposed system features dry microelectromechanical system electroencephalography sensors, low-power signal acquisition, amplification and digitization, wireless telemetry, online artifact cancellation, and signal processing. Its implications for neural prostheses are examined in two sample studies: 1) cognitive-state monitoring of participants performing realistic driving tasks in the virtual-reality-based dynamic driving simulator and 2) the neural correlates of motion sickness in driving. The experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments.

We present a novel experimental paradigm and data analysis methodology for studying brain dynamics during sustained-attention tasks. 256-channel EEG data were recorded while subjects participated in hour-long simulated driving sessions. Every few seconds, the vehicle drifted away from the center of the left lane, and subjects were instructed to steer back to the lane center. The error of each drifting event was measured by the maximum absolute distance from the vehicle's position at deviation onset. EEG data were analyzed using independent component analysis and time-frequency analysis. An independent component with equivalent dipole sources located bilaterally in lateral occipital cortex exhibited multi-scale brain dynamics. Tonic (~20s) alpha-band power increased in high-error compared to low-error epochs, while phasic (~1s) alpha power was suppressed briefly after deviation onset, then increased strongly just before response offset. Other components also exhibited distinct tonic and/or phasic activity patterns relating to deviation onsets or response onsets.

Facial model coding is an integral part in MPEG-4 related applications. The generation of the facial model usually requires stereoscopic view of the face in the pre-processing stage. Although facial model can be successfully estimated from two stereo facial images, the occlusion effect and imprecise location of the feature point prohibit obtaining an accurate facial model. In this paper, several facial model estimation (FME) algorithms are proposed in order to find the precise facial model from a stereo or mono image sequence. Since a sequence of images is used to find the facial model, the problem of occlusion effects is less serious. An accurate facial model (within 7.21% error) can still be obtained by our schemes, even without the prior information on the three-dimensional position of the head with respect to the camera and the rotation axis/angle of the head's movement. This is the largest error of all FME algorithms presented in this paper when the subject does not wear eyeglasses. In addition, our schemes do not require precise camera parameters and avoid tedious camera calibration, thereby, simplifying the facial model extraction.

A 3-D facial model can be accurately obtained from: (1) stereo facial images or (2) front- and side-view facial images. The stereo images can be obtained by two different cameras at the same time. On the other hand, capturing front- and side-view facial images requires precise location of the camera. Either may be unavailable in many practical applications. We modify several existing techniques to automatically locate the feature point position from the front-view facial image. Then several schemes (such as minimum mean square error, minimum mean absolute error, and maximum a posteriori) are used to estimate the "depth" information of the 3-D facial model parameters from the single front-view facial image according to the anthropometric and a priori information. With this scheme, a 3-D facial model can be estimated from single front-view facial image. According to the simulation results, the estimated facial model matches the exact 3-D facial model, and the synthesized and original side-view facial images appear similar.

We propose an approach to efficiently compress and transmit multiresolution 3D facial models for multimedia and very low bit rate applications. A personal facial model is obtained by a 3D laser digitizer, and further re-quantized at several resolutions according to different scope of applications, such as animation, video game, and video conference. By deforming 2D templates to match and re-quantize a 3D digitized facial model, we obtain its compressed model. In the present study, we create hierarchical 2D facial wireframe templates at five resolutions. In the deformation procedure, 2D templates are adapted according to facial feature points and the proposed piecewise chainlet affine transformation (PCAT) method. The 3D digitized model after requantization are reduced significantly without perceptual loss. Moreover, the proposed multiresolution facial models possessed a hierarchical data structure and are apt to be progressively transmitted and displayed across the Internet.