FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography.

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography.

Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction. Specifically, slices of the original anatomy (provided by computed tomography) and boundary optical measurement of imaged objects serve as inputs of a recurrent convolutional neural network encoded parallel to extract multimodal features, and 2D information from a few axial planes within the samples is explicitly incorporated, which enables 3DOL to recognize different imaged objects. Subsequently, the optical field is recovered under the constraint of the object geometry, and then the luminous source is segmented by a learnable Laplace operator from the recovered optical field, which obtains stable and high-quality reconstruction results with extremely few parameters. This strategy enable 3DOL to better understand the relationship between the boundary optical measurement, optical field, and luminous source to improve 3DOL's ability to work in a wide range of spectra. The results of numerical simulations, physical phantoms, and in vivo experiments demonstrate that 3DOL is a compatible deep-learning approach to tomographic imaging diverse objects. Moreover, the fully trained 3DOL under specific wavelengths can be generalized to other spectra in the 620-900 nm NIR-I window.

VoxDMRN: a voxelwise deep max-pooling residual network for bioluminescence tomography reconstruction.

Bioluminescence tomography (BLT) has extensive applications in preclinical studies for cancer research and drug development. However, the spatial resolution of BLT is inadequate because the numerical methods are limited for solving the physical models of photon propagation and the restriction of using tetrahedral meshes for reconstruction. We conducted a series of theoretical derivations and divided the BLT reconstruction process into two steps: feature extraction and nonlinear mapping. Inspired by deep learning, a voxelwise deep max-pooling residual network (VoxDMRN) is proposed to establish the nonlinear relationship between the internal bioluminescent source and surface boundary density to improve the spatial resolution in BLT reconstruction. The numerical simulation and in vivo experiments both demonstrated that VoxDMRN greatly improves the reconstruction performance regarding location accuracy, shape recovery capability, dual-source resolution, robustness, and in vivo practicability.

BIG Modulates Stem Cell Niche and Meristem Development via SCR/SHR Pathway in Arabidopsis Roots.

BIG, a regulator of polar auxin transport, is necessary to regulate the growth and development of Arabidopsis. Although mutations in the BIG gene cause severe root developmental defects, the exact mechanism remains unclear. Here, we report that disruption of the BIG gene resulted in decreased quiescent center (QC) activity and columella cell numbers, which was accompanied by the downregulation of WUSCHEL-RELATED HOMEOBOX5 (WOX5) gene expression. BIG affected auxin distribution by regulating the expression of PIN-FORMED proteins (PINs), but the root morphological defects of big mutants could not be rescued solely by increasing auxin transport. Although the loss of BIG gene function resulted in decreased expression of the PLT1 and PLT2 genes, genetic interaction assays indicate that this is not the main reason for the root morphological defects of big mutants. Furthermore, genetic interaction assays suggest that BIG affects the stem cell niche (SCN) activity through the SCRSCARECROW (SCR)/SHORT ROOT (SHR) pathway and BIG disruption reduces the expression of SCR and SHR genes. In conclusion, our findings reveal that the BIG gene maintains root meristem activity and SCN integrity mainly through the SCR/SHR pathway.

BIG regulates stomatal immunity and jasmonate production in Arabidopsis.

Plants have evolved an array of responses that provide them with protection from attack by microorganisms and other predators. Many of these mechanisms depend upon interactions between the plant hormones jasmonate (JA) and ethylene (ET). However, the molecular basis of these interactions is insufficiently understood. Gene expression and physiological assays with mutants were performed to investigate the role of Arabidopsis BIG gene in stress responses. BIG transcription is downregulated by methyl JA (MeJA), necrotrophic infection or mechanical injury. BIG deficiency promotes JA-dependent gene induction, increases JA production but restricts the accumulation of both ET and salicylic acid. JA-induced anthocyanin accumulation and chlorophyll degradation are enhanced and stomatal immunity is impaired by BIG disruption. Bacteria- and lipopolysaccaride (LPS)-induced stomatal closure is reduced in BIG gene mutants, which are hyper-susceptible to microbial pathogens with different lifestyles, but these mutants are less attractive to phytophagous insects. Our results indicate that BIG negatively and positively regulate the MYC2 and ERF1 arms of the JA signalling pathway. BIG warrants recognition as a new and distinct regulator that regulates JA responses, the synergistic interactions of JA and ET, and other hormonal interactions that reconcile the growth and defense dilemma in Arabidopsis.

Two-step reconstruction framework of fluorescence molecular tomography based on energy statistical probability.

Fluorescence molecular tomography (FMT), as a promising technique for early tumor detection, can non-invasively visualize the distribution of fluorescent marker probe three-dimensionally. However, FMT reconstruction is a severely ill-posed problem, which remains an obstacle to wider application of FMT. In this paper, a two-step reconstruction framework was proposed for FMT based on the energy statistical probability. First, the tissue structural information obtained from computed tomography (CT) is employed to associate the tissue optical parameters for rough solution in the global region. Then, according to the global-region reconstruction results, the probability that the target belongs to each region can be calculated. The region with the highest probability is delineated as region of interest to realize accurate and fast source reconstruction. Numerical simulations and in vivo experiments were carried out to evaluate the effectiveness of the proposed framework. The encouraging results demonstrate the significant effectiveness and potential of our method for practical FMT applications.

Group sparse-based Taylor expansion method for liver pharmacokinetic parameters imaging of dynamic fluorescence molecular tomography.

Objective.Pharmacokinetic parametric images obtained through dynamic fluorescence molecular tomography (DFMT) has ability of capturing dynamic changes in fluorescence concentration, thereby providing three-dimensional metabolic information for applications in biological research and drug development. However, data processing of DFMT is time-consuming, involves a vast amount of data, and the problem itself is ill-posed, which significantly limits the application of pharmacokinetic parametric images reconstruction. In this study, group sparse-based Taylor expansion method is proposed to address these problems.Approach.Firstly, Taylor expansion framework is introduced to reduce time and computational cost. Secondly, group sparsity based on structural prior is introduced to improve reconstruction accuracy. Thirdly, alternating iterative solution based on accelerated gradient descent algorithm is introduced to solve the problem.Main results.Numerical simulation andin vivoexperimental results demonstrate that, in comparison to existing methods, the proposed approach significantly enhances reconstruction speed without a degradation of quality, particularly when confronted with background fluorescence interference from other organs.Significance.Our research greatly reduces time and computational cost, providing strong support for real-time monitoring of liver metabolism.

Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects.

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT. Firstly, a parallel encoder is used to feature the original BLI & CT slices and integrate their features to distinguish the different tissue structure of imaging objects; Secondly, GRU is used to fit the spatial information of different slices and convert it into 3D features; Finally, the 3D features are decoded to the spacial and energy information of source by a symmetrical decoding structure. Our research suggested that this method can effectively compute the radiation intensity and the spatial distribution of the source for different imaging object.

Preparation facilitates response inhibition based on a stop-signal task.

This study demonstrated that the higher stop-signal probability condition showed a longer go reaction time and shorter stop-signal reaction time (SSRT) compared with the lower stop-signal probability condition. In addition, preparation cost was correlated with SSRT. These results suggest that preparation facilitates response inhibition.

Liver injury monitoring using dynamic fluorescence molecular tomography based on a time-energy difference strategy.

Dynamic fluorescence molecular tomography (DFMT) is a promising molecular imaging technique that offers the potential to monitor fast kinetic behaviors within small animals in three dimensions. Early monitoring of liver disease requires the ability to distinguish and analyze normal and injured liver tissues. However, the inherent ill-posed nature of the problem and energy signal interference between the normal and injured liver regions limit the practical application of liver injury monitoring. In this study, we propose a novel strategy based on time and energy, leveraging the temporal correlation in fluorescence molecular imaging (FMI) sequences and the metabolic differences between normal and injured liver tissue. Additionally, considering fluorescence signal distribution disparity between the injured and normal regions, we designed a universal Golden Ratio Primal-Dual Algorithm (GRPDA) to reconstruct both the normal and injured liver regions. Numerical simulation and in vivo experiment results demonstrate that the proposed strategy can effectively avoid signal interference between liver and liver injury energy and lead to significant improvements in morphology recovery and positioning accuracy compared to existing approaches. Our research presents a new perspective on distinguishing normal and injured liver tissues for early liver injury monitoring.

The relationship between perceived social support with anxiety, depression, and insomnia among Chinese college students during the COVID-19 pandemic: The mediating role of self-control.

Object: We aimed to investigate the associations between perceived social support and anxiety, depression, and sleep disturbance via self-control among Chinese college students during the COVID-19 pandemic. Materials and methods: The Perceived Social Support Scale, Self-control Scale, Self-rating Anxiety Scale, Self-rating Depression Scale, and Insomnia Severity Index Scale were used to survey 1,997 college students during the COVID-19 pandemic, who submitted valid questionnaires (M age = 19.93, SD age = 1.47, Range = 18-24 years, 62% female). Results: The perceived social support and self-control were significantly positively correlated, and they were significantly and negatively associated with anxiety, depression, and insomnia. Further analysis found that self-control partially mediated the relationships between perceived social support with anxiety, depression, and insomnia. Conclusion: During the COVID-19 pandemic, Chinese college students' self-control played a partial mediating effect in the relationships between perceived social support and anxiety, depression, and insomnia. This study provides new insights and inspiration for improving college students' mental health in the context of the pandemic.

The relationship between schizotypal personality features and mind wandering among college students during COVID-19 pandemic: A moderator of depression.

Introduction: Although the impact of the COVID-19 pandemic on people's mental health has been well documented in many studies, the schizotypal personality features in the general population have not received sufficient attention. Methods: Study 1 is a longitudinal study tracking changes in schizotypal personality features among college students during the COVID-19 pandemic. A total of 153 Chinese college students were assessed using the Schizotypal Personality Questionnaire. Study 2 explored the relationship between schizotypal personality features, mind wandering, and depression. A total of 557 college students completed the Schizotypal Personality Questionnaire, the Beck Depression Inventory, and the Mind-Wandering Questionnaire during the COVID-19 pandemic. Results: Study 1 results showed that the scores from later stages in the pandemic were significantly higher than those from the initial stages on each dimension of schizotypal personality, which means that the schizotypal personality features became more obvious during the COVID-19 pandemic. Study 2 results showed that there was a positive correlation between schizotypal personality features, depression, and mind wandering. Discussion: Depression played a moderating role in the relationship between schizotypal personality features and mind wandering. The schizotypal personality features of college students increase during COVID-19; it has a positive relationship with mind wandering; depression moderates the relationship. We discussed these findings and provided some suggestions about future research.

End-To-End Bioluminescence Tomography Reconstruction Based On Convolution Neural Network Scheme.

Bioluminescence tomography (BLT) has received a lot of attention as an important technique in bio-optical imaging. Compared with traditional methods, neural network methods have the advantages of fast reconstruction speed and support for batch processing. In this paper, we propose a end-to-end BLT reconstruction based on convolution neural networks scheme. First, 3000 datasets with single source and dual sources projection were conducted by Monte Carlo method, respectively. And three convolution neural networks (VGGNet, ResNet, and DenseNet) were adopted to feature extraction. Then, the filtered features were used as input to the multi-layer perceptron (MLP) to predict the source location. The results of numerical simulation and simulation experiments show, compared with traditional methods, the advantages of our method are including high reconstruction accuracy, faster reconstruction, few parameters, simple reconstruction process and support for batch processing.

L1-L2 Minimization Via A Proximal Operator For Fluorescence Molecular Tomography.

Fluorescent Molecular Tomography (FMT) is a highly sensitive and noninvasive imaging method that provides three-dimensional distribution of biomarkers by noninvasive detection of fluorescent marker probes. However, due to the light scattering effect and ill-posedness of inverse problems, it is challenging to develop an efficient construction method that can provide the exact location and morphology of the fluorescence distribution. In this paper, we proposed L1-L2 norm regularization to improve FMT reconstruction. In our research, proximal operators of non-convex L1 -L2 norm and forward-backward splitting method was adopted to solve the inverse problem of FMT. Simulation results on heterogeneous mouse model demonstrated that the proposed FBS method is superior to IVTCG, DCA and IRW-L1/2 reconstruction methods in location accuracy and other aspects.