The functional specificity of ERECTA-family receptors in Arabidopsis stomatal development is ensured by molecular chaperones in the endoplasmic reticulum.

Stomata are epidermal pores that control gas exchange between plants and the atmosphere. In Arabidopsis, the ERECTA family (ERECTAf) receptors, including ERECTA, ERECTA-LIKE 1 (ERL1) and ERL2, redundantly play pivotal roles in enforcing the 'one-cell-spacing' rule. Accumulating evidence has demonstrated that the functional specificities of receptors are likely associated with their differential subcellular dynamics. The endoplasmic reticulum (ER)-resident chaperone complex SDF2-ERdj3B-BiP functions in many aspects of plant development. We employed pharmacological treatments combined with cell biological and biochemical approaches to demonstrate that the abundance of ERECTA was reduced in the erdj3b-1 mutant, but the localization and dynamics of ERECTA were not noticeably affected. By contrast, the erdj3b mutation caused the retention of ERL1/ERL2 in the ER. Furthermore, we found that the function of SDF2-ERdj3B-BiP is implicated with the distinct roles of ERECTAf receptors. Our findings establish that the ERECTAf receptor-mediated signaling in stomatal development is ensured by the activities of the ER quality control system, which preferentially maintains the protein abundance of ERECTA and proper subcellular dynamics of ERL1/ERL2, prior to the receptors reaching their destination - the plasma membrane - to execute their functions.

Arabidopsis ERdj3B coordinates with ERECTA-family receptor kinases to regulate ovule development and the heat stress response.

The endoplasmic reticulum-localized DnaJ family 3B (ERdj3B), is a component of the stromal cell-derived factor 2 (SDF2)-ERdj3B-binding immunoglobulin protein (BiP) chaperone complex, which functions in protein folding, translocation, and quality control. We found that ERdj3B mutations affected integument development in the Ler ecotype but not in the Col-0 ecotype of Arabidopsis (Arabidopsis thaliana). Map-based cloning identified the ERECTA (ER) gene as a natural modifier of ERdj3B. The double mutation of ERdj3B and ER caused a major defect in the inner integument under heat stress. Additional mutation of the ER paralog ERECTA-LIKE 1 (ERL1) or ERL2 to the erdj3b er double mutant exacerbated the defective integument phenotype. The double mutation of ER and SDF2, the other component of the SDF2-ERdj3B-BiP complex, resulted in similar defects in the inner integument. Furthermore, both the protein abundance and plasma membrane partitioning of ER, ERL1, and ERL2 were markedly reduced in erdj3b plants, indicating that the SDF2-ERdj3B-BiP chaperone complex might control the translocation of ERECTA-family proteins from the endoplasmic reticulum to the plasma membrane. Our results suggest that the SDF2-ERdj3B-BiP complex functions in ovule development and the heat stress response in coordination with ERECTA-family receptor kinases.

Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder.

Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.

Physics-driven universal twin-image removal network for digital in-line holographic microscopy.

Digital in-line holographic microscopy (DIHM) enables efficient and cost-effective computational quantitative phase imaging with a large field of view, making it valuable for studying cell motility, migration, and bio-microfluidics. However, the quality of DIHM reconstructions is compromised by twin-image noise, posing a significant challenge. Conventional methods for mitigating this noise involve complex hardware setups or time-consuming algorithms with often limited effectiveness. In this work, we propose UTIRnet, a deep learning solution for fast, robust, and universally applicable twin-image suppression, trained exclusively on numerically generated datasets. The availability of open-source UTIRnet codes facilitates its implementation in various DIHM systems without the need for extensive experimental training data. Notably, our network ensures the consistency of reconstruction results with input holograms, imparting a physics-based foundation and enhancing reliability compared to conventional deep learning approaches. Experimental verification was conducted among others on live neural glial cell culture migration sensing, which is crucial for neurodegenerative disease research.

Distinct pre-COVID brain structural signatures in COVID-19-related post-traumatic stress symptoms and post-traumatic growth.

Post-traumatic stress symptoms and post-traumatic growth are common co-occurring psychological responses following exposure to traumatic events (such as COVID-19 pandemic), their mutual relationship remains unclear. To explore this relationship, structural magnetic resonance imaging data were acquired from 115 general college students before the COVID-19 pandemic, and follow-up post-traumatic stress symptoms and post-traumatic growth measurements were collected during the pandemic. Voxel-based morphometry was conducted and individual structural covariance networks based on gray matter volume were further analyzed using graph theory and partial least squares correlation. Behavioral correlation found no significant relationship between post-traumatic stress symptoms and post-traumatic growth. Voxel-based morphometry analyses showed that post-traumatic stress symptoms were positively correlated with gray matter volume in medial prefrontal cortex/dorsal anterior cingulate cortex, and post-traumatic growth was negatively correlated with gray matter volume in left dorsolateral prefrontal cortex. Structural covariance network analyses found that post-traumatic stress symptoms were negatively correlated with the local efficiency and clustering coefficient of the network. Moreover, partial least squares correlation showed that post-traumatic stress symptoms were correlated with pronounced nodal properties patterns in default mode, sensory and motor regions, and a marginal correlation of post-traumatic growth with a nodal property pattern in emotion regulation-related regions. This study advances our understanding of the neurobiological substrates of post-traumatic stress symptoms and post-traumatic growth, and suggests that they may have different neuroanatomical features.

Combined PET/CT and Autoantibody Detection in Lung Nodules Diagnosis.

Objective: To assess the usefulness of combining positron emission tomography/computed tomography (PET/CT) with lung cancer autoantibody detection in identifying and managing lung nodules. Methods: The researchers identified 160 patients with pulmonary nodules admitted to their hospital between January 2018 and January 2021. These patients were designated as the experimental group. Additionally, 60 healthy individuals without pulmonary nodules were admitted to the hospital during the same period. The individuals constituted the control group. All study participants underwent digital PET/CT detection and had their lung cancer autoantibody levels determined through enzyme-linked immunosorbent assay. Further testing, such as puncture or surgical pathology, was performed for patients with lung nodules. The aim was to evaluate the significance of combining PET/CT with autoantibody detection in diagnosing and treating lung nodules. Results: The study found that testing multiple autoantibodies together increased sensitivity and accuracy compared to testing individual autoantibodies. Combining PET/CT screening with autoantibody detection improved the diagnostic rate for identifying lung nodules, including benign and suspected malignant ones. Several autoantibodies were significantly higher in the experimental group compared to the control group. Testing for multiple autoantibodies showed higher sensitivity and accuracy than testing for one. Pathological examination confirmed 129 benign nodules and 31 malignant nodules. The median SUVmax values were measured at 0.7 for benign nodules and 4.8 for malignant nodules. The diagnostic efficacy of PET/CT combined with autoantibodies was determined through comparison with pathology testing and was as follows: PET/CT combined with autoantibody detection > PET/CT > autoantibody detection. Conclusion: Combining PET/CT with the detection of autoantibodies enhances the positive diagnostic rate and accuracy of lung nodules in the case of lung cancer. The SUVmax also shows excellent potential as a supplement in diagnosing both benign and malignant lung nodules, providing valuable guidance in determining the pathological types.

A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources.

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms.

On-Chip Mirror Enhanced Multiphoton Upconversion Super-Resolution Microscopy.

Multiphoton upconversion super-resolution microscopy (MPUM) is a promising imaging modality, which can provide increased resolution and penetration depth by using nonlinear near-infrared emission light through the so-called transparent biological window. However, a high excitation power is needed to achieve emission saturation, which increases phototoxicity. Here, we present an approach to realize the nonlinear saturation emission under a low excitation power by a simply designed on-chip mirror. The interference of the local electromagnetic field can easily confine the point spread function to a specific area to increase the excitation efficiency, which enables emission saturation under a lower excitation power. With no additional complexity, the mirror assists to decrease the excitation power by 10-fold and facilities the achievement of a lateral resolution around 35 nm, 1/28th of the excitation wavelength, in imaging of a single nanoparticle on-chip. This method offers a simple solution for super-resolution enhancement by a predesigned on-chip device.

Focusing light with a metal film coated patchy particle.

Microsphere-assisted super-resolution imaging is a promising technique that can significantly enhance the resolution of conventional optical microscopes. The focus of a classical microsphere is called photonic nanojet, which is a symmetric high-intensity electromagnetic field. Recently, patchy microspheres have been reported to have superior imaging performance than pristine microspheres, and coating microspheres with metal films leads to the formation of photonic hooks, which can enhance the imaging contrast of microspheres. Understanding the influence of metal patches on the near-field focusing of patchy particles is important for the rational design of a nanostructured microlens. In this work, we theoretically and experimentally showed that the light waves can be focused and engineered using patchy particles. When coating dielectric particles with Ag films, light beams with a hook-like structure or S-shaped structure can be generated. Simulation results show that the waveguide ability of metal films and the geometric asymmetry of patchy particles cause the formation of S-shaped light beams. Compared with classical photonic hooks, S-shaped photonic hooks have a longer effective length and a smaller beam waist at far-field region. Experiments were also carried out to demonstrate the generation of classical and S-shaped photonic hooks from patchy microspheres.

Unifying temporal phase unwrapping framework using deep learning.

Temporal phase unwrapping (TPU) is significant for recovering an unambiguous phase of discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Generally, temporal phase unwrapping algorithms can be classified into three groups: the multi-frequency (hierarchical) approach, the multi-wavelength (heterodyne) approach, and the number-theoretic approach. For all of them, extra fringe patterns of different spatial frequencies are required for retrieving the absolute phase. Due to the influence of image noise, people have to use many auxiliary patterns for high-accuracy phase unwrapping. Consequently, image noise limits the efficiency and the measurement speed greatly. Further, these three groups of TPU algorithms have their own theories and are usually applied in different ways. In this work, for the first time to our knowledge, we show that a generalized framework using deep learning can be developed to perform the TPU task for different groups of TPU algorithms. Experimental results show that benefiting from the assistance of deep learning the proposed framework can mitigate the impact of noise effectively and enhance the phase unwrapping reliability significantly without increasing the number of auxiliary patterns for different TPU approaches. We believe that the proposed method demonstrates great potential for developing powerful and reliable phase retrieval techniques.

Learning-based single-shot long-range synthetic aperture Fourier ptychographic imaging with a camera array.

In this Letter, we report a new long-range synthetic aperture Fourier ptychographic imaging technique, termed learning-based single-shot synthetic aperture imaging (LSS-SAI). LSS-SAI uses a camera array to record low-resolution intensity images corresponding to different non-overlapping spectral regions in parallel, which are synthesized to reconstruct a super-resolved high-quality image based on a physical model-based dual-regression deep neural network. Compared with conventional macroscopic Fourier ptychographic imaging, LSS-SAI overcomes the stringent requirement on a large amount of raw data with a high spectral overlapping ratio for high-resolution, high signal-to-noise imaging of reflective objects with diffuse surfaces, making single-shot long-range synthetic aperture imaging possible. Experimental results on rough reflective samples show that our approach can improve the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 10.56 dB and 0.26, respectively. We also demonstrate the single-shot ptychography capability of the proposed approach by the synthetic aperture imaging of a dynamic scene at a camera-limited speed (30 fps). To the best of our knowledge, this is the first demonstration of macroscopic Fourier ptychography to single-shot synthetic aperture imaging of dynamic events.

Motion-resistant structured illumination microscopy based on principal component analysis.

Structured illumination microscopy (SIM) has become one of the most significant super-resolution techniques in bioscience for observing live-cell dynamics, thanks to fast full-field imaging and low photodamage. However, artifact-free SIM super-resolution reconstruction requires precise knowledge about variable environment-sensitive illumination parameters. Conventional algorithms typically, under the premise of known and reliable constant phase shifts, compensate for residual parameters, which will be easily broken by motion factors such as environment and medium perturbations, and sample offsets. In this Letter, we propose a robust motion-resistant SIM algorithm based on principal component analysis (mrPCA-SIM), which can efficiently compensate for nonuniform pixel shifts and phase errors in each raw illumination image. Experiments demonstrate that mrPCA-SIM achieves more robust imaging quality in complex, unstable conditions compared with conventional methods, promising a more compatible and flexible imaging tool for live cells.

Parallel synthetic aperture transport-of-intensity diffraction tomography with annular illumination.

Transport-of-intensity diffraction tomography (TIDT) is a recently developed label-free computational microscopy technique that retrieves high-resolution three-dimensional (3D) refractive index (RI) distribution of biological specimens from 3D intensity-only measurements. However, the non-interferometric synthetic aperture in TIDT is generally achieved sequentially through the acquisition of a large number of through-focus intensity stacks captured at different illumination angles, resulting in a very cumbersome and redundant data acquisition process. To this end, we present a parallel implementation of a synthetic aperture in TIDT (PSA-TIDT) with annular illumination. We found that the matched annular illumination provides a mirror-symmetric 3D optical transfer function, indicating the analyticity in the upper half-plane of the complex phase function, which allows for recovery of the 3D RI from a single intensity stack. We experimentally validated PSA-TIDT by conducting high-resolution tomographic imaging of various unlabeled biological samples, including human breast cancer cell lines (MCF-7), human hepatocyte carcinoma cell lines (HepG2), Henrietta Lacks (HeLa) cells, and red blood cells (RBCs).

Exploring epidemic voluntary vaccinating behavior based on information-driven decisions and benefit-cost analysis.

A complex dynamic interplay exists between epidemic transmission and vaccination, which is significantly influenced by human behavioral responses. We construct a research framework combining both the function modeling of the cumulative global COVID-19 information and limited individuals' information processing capacity employing the Gompertz model for growing processes. Meanwhile, we built a function representing the decision to get vaccinated following benefit-cost analysis considered the choices made by people in each scenario have an influence from altruism, free-riding and immunity escaping capacity. Through the mean-field calculation analysis and using a fourth-order Runge-Kutta method with constant step size, we obtain plots from numerical simulations. We found that only when the total number of infectious individuals proves sufficient to reach and exceed a certain level will the individuals face a better trade-off in determining whether to get vaccinated against the diseases based on that information. Besides, authoritative media have a higher decisive influence and efforts should be focused on extending the duration of vaccine protection, which is beneficial to inhibit the outbreaks of epidemics. Our work elucidates that reducing the negative payoff brought about by the free-riding behavior for individuals or improving the positive payoff from the altruistic motivation helps to control the disease in cultures that value social benefits, vaccination willingness is generally stronger. We also note that at a high risk of infection, the decision of vaccination is highly correlated with global epidemic information concerning COVID-19 infection, while at times of lower risk, it depends on the game theoretic vaccine strategy. The findings demonstrate that improving health literacy, ensuring open and transparent information on vaccine safety and efficacy as a public health priority can be an effective strategy for mitigating inequalities in health education, as well as alleviating the phenomenon that immunity escaping abilities is more likely to panic by populations with high levels of education. In addition, prosocial nudges are great ways to bridge these immunity gaps that can contribute to implementing government public health control measures, creating a positive feedback loop.

COVID-19 vicarious traumatization links functional connectome to general distress.

As characterized by repeated exposure of others' trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals' vicarious traumatization scores (q2 = -0.18, MSE = 617, r [predicted, actual] = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.

Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement.

Single-shot fringe projection profilometry (FPP) is essential for retrieving the absolute depth information of the objects in high-speed dynamic scenes. High-precision 3D reconstruction using only one single pattern has become the ultimate goal in FPP. The frequency-multiplexing (FM) method is a promising strategy for realizing single-shot absolute 3D measurement by compounding multi-frequency fringe information for phase unwrapping. In order to solve the problem of serious spectrum aliasing caused by multiplexing schemes that cannot be removed by traditional spectrum analysis algorithms, we apply deep learning to frequency multiplexing composite fringe projection and propose a composite fringe projection deep learning profilometry (CDLP). By combining physical model and data-driven approaches, we demonstrate that the model generated by training an improved deep convolutional neural network can directly perform high-precision and unambiguous phase retrieval on a single-shot spatial frequency multiplexing composite fringe image. Experiments on both static and dynamic scenes demonstrate that our method can retrieve robust and unambiguous phases information while avoiding spectrum aliasing and reconstruct high-quality absolute 3D surfaces of objects only by projecting a single composite fringe image.

Phase-unwrapping-free 3D reconstruction in structured light field system based on varied auxiliary point.

Three-dimensional (3D) reconstruction is an essential task in structured light field (SLF) related techniques and applications. This paper presents a new method to reconstruct a 3D object point by using many auxiliary points adjacent to it. The relationship between two points in a SLF system is derived. Different from conventional "direct" methods that reconstruct 3D coordinates of the object point by using phase, slope, disparity etc., the proposed method is an "indirect" method as the 3D coordinates of auxiliary points are not needed. Based on the auxiliary point theory, the wrapped phase obtained by 4-step phase-shifting method is sufficient for 3D reconstruction, without the need for phase unwrapping. To the best of our knowledge, this is the first strategy that combines the intrinsic characteristics of structured light and light field for phase-unwrapping-free 3D reconstruction. This paper also analyzes the constraints between system architecture parameters and phase rectification, phase to depth ratio, and presents a relatively simple criterion to guide the system design. Experimental results show that, with an appropriate system architecture, the proposed method can realize accurate, unambiguous, and reliable 3D reconstruction without phase unwrapping.

Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis.

Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.

Single-exposure 3D label-free microscopy based on color-multiplexed intensity diffraction tomography.

We present a 3D label-free refractive index (RI) imaging technique based on single-exposure intensity diffraction tomography (sIDT) using a color-multiplexed illumination scheme. In our method, the chromatic light-emitting diodes (LEDs) corresponding R/G/B channels in an annular programmable ring provide oblique illumination geometry that precisely matches the objective's numerical aperture. A color intensity image encoding the scattering field of the specimen from different directions is captured, and monochromatic intensity images concerning three color channels are separated and then used to recover the 3D RI distribution of the object following the process of IDT. In addition, the axial chromatic dispersion of focal lengths at different wavelengths introduced by the chromatic aberration of the objective lens and the spatial position misalignment of the ring LED source in the imaging system's transfer functions modeling are both corrected to significantly reduce the artifacts in the slice-based deconvolution procedure for the reconstruction of 3D RI distribution. Experimental results on MCF-7, Spirulina algae, and living Caenorhabditis elegans samples demonstrate the reliable performance of the sIDT method in label-free, high-throughput, and real-time (∼24 fps) 3D volumetric biological imaging applications.

Single-shot lensfree on-chip quantitative phase microscopy with partially coherent LED illumination.

We propose a single-shot lens-free phase retrieval (SSLFPR) method in a lens-free on-chip microscopy (LFOCM) system based on a partially coherent light emitting diode (LED) illumination. The finite bandwidth (∼23.95 nm) of LED illumination is divided into a series of quasi-monochromatic components according to the LED spectrum measured by a spectrometer. When the "virtual wavelength scanning" phase retrieval method is combined with the dynamic phase support constraint, the resolution loss caused by the spatiotemporal partial coherence of the light source can be effectively compensated. At the same time, the nonlinearity characteristics of the support constraint help to further improve the imaging resolution, accelerate the convergence of the iteration process, and greatly eliminate the artifacts. Based on the proposed SSLFPR method, we demonstrate that the phase information of samples (including phase resolution target and polystyrene microspheres) illuminated by a LED can be accurately retrieved based on one single diffraction pattern. The SSLFPR method has a half-width resolution of 977 nm across a large field-of-view (FOV) of 19.53 mm2, which is 1.41 × the resolution of the conventional approach. We also imaged living Henrietta Lacks (HeLa) cells cultured in vitro, further demonstrating the real-time single-shot quantitative phase imaging (QPI) capability of SSLFPR for dynamic samples. Given its simple hardware, high throughput, and single-frame high-resolution QPI capability, SSLFPR is expected to be adopted in a wide range of biological and medical applications.