RFNet: Multivariate long sequence time-series forecasting based on recurrent representation and feature enhancement.

Multivariate time series exhibit complex patterns and structures involving interactions among multiple variables and long-term temporal dependencies, making multivariate long sequence time series forecasting (MLSTF) exceptionally challenging. Despite significant progress in Transformer-based methods in the MLSTF domain, many models still rely on stacked encoder-decoder architectures to capture complex time series patterns. This leads to increased computational complexity and overlooks spatial pattern information in multivariate time series, thereby limiting the model's performance.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories.

Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy.

Background: Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms.

Aim: Establish an objective measurement and evaluation of MDD.

Multi-level and joint attention networks on brain functional connectivity for cross-cognitive prediction.

Deep learning on resting-state functional MRI (rs-fMRI) has shown great success in predicting a single cognition or mental disease. Nevertheless, cognitive functions or mental diseases may share neural mechanisms that can benefit their prediction/classification. We propose a multi-level and joint attention (ML-Joint-Att) network to learn high-order representations of brain functional connectivities that are specific and shared across multiple tasks.

Light-sheet laser speckle imaging for cilia motility assessment.

Mucociliary clearance is an important innate defense mechanism predominantly mediated by ciliated cells in the upper respiratory tract. Ciliary motility on the respiratory epithelium surface and mucus pathogen trapping assist in maintaining healthy airways. Optical imaging methods have been used to obtain several indicators for assessing ciliary movement.

Identifying neuroimaging biomarkers of major depressive disorder from cortical hemodynamic responses using machine learning approaches.

Background: Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established.

Methods: Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD.

Optical breast atlas as a testbed for image reconstruction in optical mammography.

We present two optical breast atlases for optical mammography, aiming to advance the image reconstruction research by providing a common platform to test advanced image reconstruction algorithms. Each atlas consists of five individual breast models. The first atlas provides breast vasculature surface models, which are derived from human breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using image segmentation.

Multifunctional laser speckle imaging.

We have developed a multi-functional laser speckle imaging system, which can be operated in both the surface illumination laser speckle contrast imaging (SI-LSCI) mode and the line scan laser speckle contrast imaging (LS-LSCI) mode. The system has been applied to imaging the chicken embryos to visualize both the blood flow and morphological details of the vasculature. The experimental results demonstrated that LS-LSCI is capable of detecting and quantifying blood flow in blood vessels smaller and deeper than those detectable by conventional SI-LSCI.

4D modelling of fluid mechanics in the zebrafish embryonic heart.

Abnormal blood flow mechanics can result in pathological heart malformation, underlining the importance of understanding embryonic cardiac fluid mechanics. In the current study, we performed image-based computational fluid dynamics simulation of the zebrafish embryonic heart ventricles and characterized flow mechanics, organ dynamics, and energy dynamics in detail. 4D scans of 5 days post-fertilization embryonic hearts with GFP-labelled myocardium were acquired using line-scan focal modulation microscopy.

Quadrature demodulation in line-scan focal modulation microscopy for imaging three-dimensional zebrafish neural structure.

Visualizing biological processes in neuroscience requires in vivo functional imaging at single-neuron resolution, high image acquisition speed and strong optical sectioning ability. However, due to light scattering of in tissue, very often conventional wide-field fluorescence microscopes are unable to resolve cells in the presence of a strong out-of-focus background. Line-scan focal modulation microscopy enables high temporal resolution and good optical sectioning ability at the same time.

Three-dimensional cellular imaging in thick biological tissue with confocal detection of one-photon fluorescence in the near-infrared II window.

Fluorescence imaging in the second near-infrared optical window (NIR-II, 900-1700 nm) has become a technique of choice for noninvasive in vivo imaging in recent years. Greater penetration depths with high spatial resolution and low background can be achieved with this NIR-II window, owing to low autofluorescence within this optical range and reduced scattering of long wavelength photons. Here, we present a novel design of confocal laser scanning microscope tailored for imaging in the NIR-II window.

Laplace-domain diffuse optical measurement.

Time-domain diffuse optical measurement systems determine depth-resolved absorption changes by using the time of flight distribution of the detected photons. It is well known that certain feature data, such as the Laplace transform of the temporal point spread function, is sufficient for image reconstruction and diffuse optical sensing. Conventional time-domain systems require the acquisition of full temporal profiles of diffusive photons and then numerically compute the feature dataset, for example, Laplace transformed intensities for imaging applications.

Numerical modeling of two-photon focal modulation microscopy with a sinusoidal phase filter.

A spatiotemporal phase modulator (STPM) is theoretically investigated using the vectorial diffraction theory. The STPM is equivalent to a time-dependent phase-only pupil filter that alternates between a homogeneous filter and a stripe-shaped filter with a sinusoidal phase distribution. It is found that two-photon focal modulation microscopy (TPFMM) using this STPM can significantly suppress the background contribution from out-of-focus ballistic excitation and achieve almost the same resolution as two-photon microscopy.

Effects of pupil filter patterns in line-scan focal modulation microscopy.

Line-scan focal modulation microscopy (LSFMM) is an emerging imaging technique that affords high imaging speed and good optical sectioning at the same time. We present a systematic investigation into optimal design of the pupil filter for LSFMM in an attempt to achieve the best performance in terms of spatial resolutions, optical sectioning, and modulation depth. Scalar diffraction theory was used to compute light propagation and distribution in the system and theoretical predictions on system performance, which were then compared with experimental results.

Ultrahigh-speed line-scan SD-OCT for four-dimensional in vivo imaging of small animal models.

We report an ultrahigh-speed and high-resolution line-scan spectral-domain optical coherence tomography (SD-OCT) system that integrates a number of mechanisms for improving image quality. The illumination uniformity is significantly improved by the use of a Powell lens; Phase stepping and differential reconstruction are combined to suppress autocorrelation artifacts; Nonlocal means (NLM) is employed to enhance the signal to noise ratio while minimizing motion artifacts. The system is capable of acquiring cross-sectional images at more than 3,500 B-scans per second with sensitivities between 70dB and 90dB.

Augmented line-scan focal modulation microscopy for multi-dimensional imaging of zebrafish heart .

Multi-dimensional fluorescence imaging of live animal models demands strong optical sectioning, high spatial resolution, fast image acquisition, and minimal photobleaching. While conventional laser scanning microscopes are capable of deep penetration and sub-cellular resolution, they are generally too slow and causing excessive photobleaching for volumetric or time-lapse imaging. We demonstrate the performance of an augmented line-scan focal modulation microscope (aLSFMM), a high-speed imaging platform that affords above video-rate imaging speed by the use of line scanning.

Line-scan focal modulation microscopy.

We report the development of a line-scan focal modulation microscope (LSFMM) that is capable of high-speed image acquisition ( > 40 ?? fps ) with uncompromised optical sectioning capability. The improved background rejection and axial resolution of this imaging modality, enabled by focal modulation, are quantified with three-dimensional imaging data obtained from fluorescent beads. The signal-to-background ratio for the LSFMM system is one- to two-orders of magnitude higher than that for line-scanning confocal systems when imaging deep (up to 100 ?m) into a turbid medium of optical properties similar to biological tissues.

Spread spectrum time-resolved diffuse optical measurement system for enhanced sensitivity in detecting human brain activity.

Diffuse optical spectroscopy (DOS) and imaging methods have been widely applied to noninvasive detection of brain activity. We have designed and implemented a low cost, portable, real-time one-channel time-resolved DOS system for neuroscience studies. Phantom experiments were carried out to test the performance of the system.

A method to study the hemodynamics of chicken embryo's aortic arches using optical coherence tomography.

Congenital cardiovascular defects are the leading cause of birth defect related death. It has been hypothesized that fluid mechanical forces of embryonic blood flow affect cardiovascular development and play a role in congenital malformations. Studies in small animal embryos can improve our understanding of congenital malformations and can lead to better treatment.

In vivo simultaneous multispectral fluorescence imaging with spectral multiplexed volume holographic imaging system.

A simultaneous multispectral fluorescence imaging system incorporating multiplexed volume holographic grating (VHG) is developed to acquire multispectral images of an object in one shot. With the multiplexed VHG, the imaging system can provide the distribution and spectral characteristics of multiple fluorophores in the scene. The implementation and performance of the simultaneous multispectral imaging system are presented.

Hybrid wide-field and scanning microscopy for high-speed 3D imaging.

Wide-field optical microscopy is efficient and robust in biological imaging, but it lacks depth sectioning. In contrast, scanning microscopic techniques, such as confocal microscopy and multiphoton microscopy, have been successfully used for three-dimensional (3D) imaging with optical sectioning capability. However, these microscopic techniques are not very suitable for dynamic real-time imaging because they usually take a long time for temporal and spatial scanning.

In vivo label-free quantification of liver microcirculation using dual-modality microscopy.

Microcirculation lesion is a common symptom of chronic liver diseases in the form of vasculature deformation and circulation alteration. In acute to chronic liver diseases such as biliary atresia, microcirculation lesion can have an early onset. Detection of microcirculation lesion is meaningful for studying the progression of liver disease.

Real-time 3D particle manipulation visualized using volume holographic gratings.

Holographic optical tweezers (HOTs) extend optical trapping into three dimensions. Volume imaging then becomes a concern as trapped objects are easily moved out of focus of the imaging objective lens. Here we demonstrate a three-dimensional real-time interactive optical trapping, manipulating, and imaging system based on HOTs incorporated with volume holographic microscope.

Imaging single chiral nanoparticles in turbid media using circular-polarization optical coherence microscopy.

Optical coherence tomography (OCT) is a widely used structural imaging method. However, it has limited use in molecular imaging due to the lack of an effective contrast mechanism. Gold nanoparticles have been widely used as molecular probes for optical microcopy based on Surface Plasmon Resonance (SPR).