Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population.

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

Fibre-infused gel scaffolds guide cardiomyocyte alignment in 3D-printed ventricles.

Hydrogels are attractive materials for tissue engineering, but efforts to date have shown limited ability to produce the microstructural features necessary to promote cellular self-organization into hierarchical three-dimensional (3D) organ models. Here we develop a hydrogel ink containing prefabricated gelatin fibres to print 3D organ-level scaffolds that recapitulate the intra- and intercellular organization of the heart. The addition of prefabricated gelatin fibres to hydrogels enables the tailoring of the ink rheology, allowing for a controlled sol-gel transition to achieve precise printing of free-standing 3D structures without additional supporting materials. Shear-induced alignment of fibres during ink extrusion provides microscale geometric cues that promote the self-organization of cultured human cardiomyocytes into anisotropic muscular tissues in vitro. The resulting 3D-printed ventricle in vitro model exhibited biomimetic anisotropic electrophysiological and contractile properties.

Batch-based alternating direction methods of multipliers for Fourier ptychography.

Fourier ptychography (FP) has been developed as a general imaging tool for various applications. However, the redundancy data has to be enforced to get a stable recovery, leading to a large dataset and a high computational cost. Based on the additive property of the optical pupils in FP recovery, we report batch-based alternating direction methods of multipliers (ADMM) for FP reconstruction. The reported scheme is performed by implementing partial updates in sub-problems of the standard ADMM. We validate the reconstruction performance using both simulated and experimental measurements. Compared with the embedded pupil function recovery (EPRY) algorithm, the proposed algorithms can converge faster and produce higher-quality images.

Recreating the heart's helical structure-function relationship with focused rotary jet spinning.

Helical alignments within the heart's musculature have been speculated to be important in achieving physiological pumping efficiencies. Testing this possibility is difficult, however, because it is challenging to reproduce the fine spatial features and complex structures of the heart's musculature using current techniques. Here we report focused rotary jet spinning (FRJS), an additive manufacturing approach that enables rapid fabrication of micro/nanofiber scaffolds with programmable alignments in three-dimensional geometries. Seeding these scaffolds with cardiomyocytes enabled the biofabrication of tissue-engineered ventricles, with helically aligned models displaying more uniform deformations, greater apical shortening, and increased ejection fractions compared with circumferential alignments. The ability of FRJS to control fiber arrangements in three dimensions offers a streamlined approach to fabricating tissues and organs, with this work demonstrating how helical architectures contribute to cardiac performance.

High-throughput coating with biodegradable antimicrobial pullulan fibres extends shelf life and reduces weight loss in an avocado model.

Food waste and food safety motivate the need for improved food packaging solutions. However, current films/coatings addressing these issues are often limited by inefficient release dynamics that require large quantities of active ingredients. Here we developed antimicrobial pullulan fibre (APF)-based packaging that is biodegradable and capable of wrapping food substrates, increasing their longevity and enhancing their safety. APFs were spun using a high-throughput system, termed focused rotary jet spinning, with water as the only solvent, allowing the incorporation of naturally derived antimicrobial agents. Using avocados as a representative example, we demonstrate that APF-coated samples had their shelf life extended by inhibited proliferation of natural microflora, and lost less weight than uncoated control samples. This work offers a promising technique to produce scalable, low-cost and environmentally friendly biodegradable antimicrobial packaging systems.

Fattening chips: hypertrophy, feeding, and fasting of human white adipocytes in vitro.

Adipose is a distributed organ that performs vital endocrine and energy homeostatic functions. Hypertrophy of white adipocytes is a primary mode of both adaptive and maladaptive weight gain in animals and predicts metabolic syndrome independent of obesity. Due to the failure of conventional culture to recapitulate adipocyte hypertrophy, technology for production of adult-size adipocytes would enable applications such as in vitro testing of weight loss therapeutics. To model adaptive adipocyte hypertrophy in vitro, we designed and built fat-on-a-chip using fiber networks inspired by extracellular matrix in adipose tissue. Fiber networks extended the lifespan of differentiated adipocytes, enabling growth to adult sizes. By micropatterning preadipocytes in a native cytoarchitecture and by adjusting cell-to-cell spacing, rates of hypertrophy were controlled independent of culture time or differentiation efficiency. In vitro hypertrophy followed a nonlinear, nonexponential growth model similar to human development and elicited transcriptomic changes that increased overall similarity with primary tissue. Cells on the chip responded to simulated meals and starvation, which potentiated some adipocyte endocrine and metabolic functions. To test the utility of the platform for therapeutic development, transcriptional network analysis was performed, and retinoic acid receptors were identified as candidate drug targets. Regulation by retinoid signaling was suggested further by pharmacological modulation, where activation accelerated and inhibition slowed hypertrophy. Altogether, this work presents technology for mature adipocyte engineering, addresses the regulation of cell growth, and informs broader applications for synthetic adipose in pharmaceutical development, regenerative medicine, and cellular agriculture.

Iterative X-ray spectroscopic ptychography.

Spectroscopic ptychography is a powerful technique to determine the chemical composition of a sample with high spatial resolution. In spectro-ptychography, a sample is rastered through a focused X-ray beam with varying photon energy so that a series of phaseless diffraction data are recorded. Each chemical component in the material under investigation has a characteristic absorption and phase contrast as a function of photon energy. Using a dictionary formed by the set of contrast functions of each energy for each chemical component, it is possible to obtain the chemical composition of the material from high-resolution multi-spectral images. This paper presents SPA (spectroscopic ptychography with alternating direction method of multipliers), a novel algorithm to iteratively solve the spectroscopic blind ptychography problem. First, a nonlinear spectro-ptychography model based on Poisson maximum likelihood is designed, and then the proposed method is constructed on the basis of fast iterative splitting operators. SPA can be used to retrieve spectral contrast when considering either a known or an incomplete (partially known) dictionary of reference spectra. By coupling the redundancy across different spectral measurements, the proposed algorithm can achieve higher reconstruction quality when compared with standard state-of-the-art two-step methods. It is demonstrated how SPA can recover accurate chemical maps from Poisson-noised measurements, and its enhanced robustness when reconstructing reduced-redundancy ptychography data using large scanning step sizes is shown.

Advanced denoising for X-ray ptychography.

The success of ptychographic imaging experiments strongly depends on achieving high signal-to-noise ratio. This is particularly important in nanoscale imaging experiments when diffraction signals are very weak and the experiments are accompanied by significant parasitic scattering (background), outliers or correlated noise sources. It is also critical when rare events, such as cosmic rays, or bad frames caused by electronic glitches or shutter timing malfunction take place. In this paper, we propose a novel iterative algorithm with rigorous analysis that exploits the direct forward model for parasitic noise and sample smoothness to achieve a thorough characterization and removal of structured and random noise. We present a formal description of the proposed algorithm and prove its convergence under mild conditions. Numerical experiments from simulations and real data (both soft and hard X-ray beamlines) demonstrate that the proposed algorithms produce better results when compared to state-of-the-art methods.

Denoising Poisson phaseless measurements via orthogonal dictionary learning.

Phaseless diffraction measurements recorded by CCD detectors are often affected by Poisson noise. In this paper, we propose a dictionary learning model by employing patches based sparsity in order to denoise such Poisson phaseless measurements. The model consists of three terms: (i) A representation term by an orthogonal dictionary, (ii) an L0 pseudo norm of the coefficient matrix, and (iii) a Kullback-Leibler divergence term to fit phaseless Poisson data. Fast alternating minimization method (AMM) and proximal alternating linearized minimization (PALM) are adopted to solve the proposed model, and especially the theoretical guarantee of the convergence of PALM is provided. The subproblems for these two algorithms both have fast solvers, and indeed, the solutions for the sparse coding and dictionary updating both have closed forms due to the orthogonality of learned dictionaries. Numerical experiments for phase retrieval using coded diffraction and ptychographic patterns are conducted to show the efficiency and robustness of proposed methods, which, by preserving texture features, produce visually and quantitatively improved restored images compared with other phase retrieval algorithms without regularization and local sparsity promoting algorithms.

Partially coherent ptychography by gradient decomposition of the probe.

Coherent ptychographic imaging experiments often discard the majority of the flux from a light source to define the coherence of the illumination. Even when the coherent flux is sufficient, the stability required during an exposure is another important limiting factor. Partial coherence analysis can considerably reduce these limitations. A partially coherent illumination can often be written as the superposition of a single coherent illumination convolved with a separable translational kernel. This article proposes the gradient decomposition of the probe (GDP), a model that exploits translational kernel separability, coupling the variances of the kernel with the transverse coherence. An efficient first-order splitting algorithm (GDP-ADMM) for solving the proposed nonlinear optimization problem is described. Numerical experiments demonstrate the effectiveness of the proposed method with Gaussian and binary kernel functions in fly-scan measurements. Remarkably, GDP-ADMM using nanoprobes produces satisfactory results even when the ratio between the kernel width and the beam size is more than one, or when the distance between successive acquisitions is twice as large as the beam width.

Dual-Excitation Nanocellulose Plasmonic Membranes for Molecular and Cellular SERS Detection.

We demonstrate that cellulose nanofiber (CNF) biomaterials with high transparency and mechanical robustness can be combined with gold nanorods to form a multifunctional porous membrane for dual-mode surface-enhanced Raman scattering (SERS) detection of both small molecules and cells. The nanoporous nature of the nanofiber membranes allows for effective molecular filtration and preconcentration of the analytes, further boosting the SERS performance. Specifically, because of the low fluorescence and Raman background of the CNF matrix, extremely low loading density of gold nanorods can be used. The nanorod assemblies within the CNF network can be resonantly driven by a 532 nm laser (transverse plasmonic mode) and near resonantly driven at by a 785 nm laser (longitudinal mode), facilitating dual operational modes at two excitation wavelengths. The shorter wavelength excitation mode yields better Raman scattering efficiency and has been demonstrated to be capable of detecting rhodamine 6G (R6G) dyes down to picomolar concentrations. On the other hand, the longer wavelength excitation mode provides autofluorescence suppression for the better detection of microorganisms such as Escherichia coli, shortening the required integration time from hours to minutes. Upon drastically lowering the spectral background noise and utilizing nanofiltration, the plasmonic CNF membranes reported here show significantly improved SERS sensitivity and detection fidelity as compared to traditional metal, metal oxide, synthetic polymer, and paper SERS substrates.

Post-sulfonation of cellulose nanofibrils with a one-step reaction to improve dispersibility.

Cellulose nanofibrils (CNF) were sulfonated and the dispersion quality was compared to unfunctionalized and 2,2,6,6-tetramethylpiperdine-1-oxyl radical (TEMPO) post-oxidation treatment of existing CNF (mechanically fibrillated pulp). A post-sulfonation treatment on existing CNF in chlorosulfonic acid and dimethylformamide (DMF) resulted in sulfonated CNF that retained a fibril-like morphology. There was a small decrease in the cellulose crystallinity index for the sulfonated CNF, but this was much lower than the reported regioselective oxidative bisulfite pretreatment method used to make sulfonated CNF. The current approach was extremely quick, and 5min of reaction time was sufficient to result in significant improvements in dispersibility compared to unfunctionalized CNF. The sulfonated CNF and TEMPO oxidized CNF had better dispersibility compared to the unfunctionalized CNF when dispersed in DMF and water, and in many cases the sulfonated CNF had better dispersibility than the TEMPO CNF. It was found that when CNF was dispersed in DMF the TEMPO CNF formed carboxyl dimethylammonium groups, while the sulfonated CNF formed formate groups.

A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction.

Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L0 regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.

Individually Dispersed Wood-Based Cellulose Nanocrystals.

Good dispersion of cellulose nanocrystals (CNCs) in the polymer matrix is one of the key factors to obtaining good properties in the resulting nanocomposites. However, the preparation of individually dispersed CNCs in solvents or in polymer matrices has been a challenge. In this study, individually dispersed wood-based CNCs have been successfully prepared in solvents, including dimethylformamide (DMF), H2O, and a mixture of H2O/DMF, by sonication of moisture-containing CNCs. The CNCs dispersions were characterized by dynamic light scattering (DLS). It is found that CNCs containing above about 3.8 wt % moisture is critical for achieving individually dispersed CNC in solvents. Hydrodynamic radius (Rh) of CNCs is smaller in H2O/DMF co-solvent mixture than that in pure DMF or in pure H2O under same sonication treatment conditions. Experimental results have been corroborated using molecular simulation study.

Volume Preserved Mass-Spring Model with Novel Constraints for Soft Tissue Deformation.

An interactive surgical simulation system needs to meet three main requirements, speed, accuracy, and stability. In this paper, we present a stable and accurate method for animating mass-spring systems in real time. An integration scheme derived from explicit integration is used to obtain interactive realistic animation for a multiobject environment. We explore a predictor-corrector approach by correcting the estimation of the explicit integration in a poststep process. We introduce novel constraints on positions into the mass-spring model (MSM) to model the nonlinearity and preserve volume for the realistic simulation of the incompressibility. We verify the proposed MSM by comparing its deformations with the reference deformations of the nonlinear finite-element method. Moreover, experiments on porcine organs are designed for the evaluation of the multiobject deformation. Using a pair of freshly harvested porcine liver and gallbladder, the real organ deformations are acquired by computed tomography and used as the reference ground truth. Compared to the porcine model, our model achieves a 1.502 mm mean absolute error measured at landmark locations for cases with small deformation (the largest deformation is 49.109 mm) and a 3.639 mm mean absolute error for cases with large deformation (the largest deformation is 83.137 mm). The changes of volume for the two deformations are limited to 0.030% and 0.057%, respectively. Finally, an implementation in a virtual reality environment for laparoscopic cholecystectomy demonstrates that our model is capable to simulate large deformation and preserve volume in real-time calculations.

The L0 Regularized Mumford-Shah Model for Bias Correction and Segmentation of Medical Images.

We propose a new variant of the Mumford-Shah model for simultaneous bias correction and segmentation of images with intensity inhomogeneity. First, based on the model of images with intensity inhomogeneity, we introduce an L0 gradient regularizer to model the true intensity and a smooth regularizer to model the bias field. In addition, we derive a new data fidelity using the local intensity properties to allow the bias field to be influenced by its neighborhood. Second, we use a two-stage segmentation method, where the fast alternating direction method is implemented in the first stage for the recovery of true intensity and bias field and a simple thresholding is used in the second stage for segmentation. Different from most of the existing methods for simultaneous bias correction and segmentation, we estimate the bias field and true intensity without fixing either the number of the regions or their values in advance. Our method has been validated on medical images of various modalities with intensity inhomogeneity. Compared with the state-of-art approaches and the well-known brain software tools, our model is fast, accurate, and robust with initializations.

Gel Spinning of Polyacrylonitrile/Cellulose Nanocrystal Composite Fibers.

Polyacrylonitrile (PAN)/cellulose nanocrytal (CNC) fibers containing 0, 1, 5, and 10 wt % CNCs have been successfully produced by gel spinning. The rheological properties of solutions were investigated and the results showed that the complex viscosity and storage modulus of solutions were significantly affected by the presence of CNCs in the solution. Structure, morphology, mechanical properties and dynamic mechanical properties of these fibers have been investigated. Tensile modulus and strength increased from 14.5 to 19.6 GPa and from 624 to 709 MPa, respectively, as CNC loading increased from 0 to 10 wt %. Wide-angle X-ray diffraction results showed better PAN chain alignment and higher PAN crystallinity with the incorporation of CNCs.

Synchronous simulation for deformation of liver and gallbladder with stretch and compression compensation.

One challenge in surgical simulation is to design stable deformable models to simulate the dynamics of organs synchronously. In this paper, we develop a novel mass-spring model on the tetrahedral meshes for soft organs such as the liver and gallbladder, which can stably deform with large time steps. We model the contact forces between the organs as a kind of forces generated by the tensions of repulsive springs connecting in between the organs. The simulation system couples a pair of constraints on the length of springs with an implicit integration method. Based on the novel constraints, our simulator can efficiently preserve the volumes and geometric properties of the liver and gallbladder during the simulation. The numerical examples demonstrate that the proposed simulation system can provide realistic and stable deformable results.