Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels.

Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.

Center extraction method for reflected metallic surface fringes based on line structured light.

Using line structured light to measure metal surface topography, the extraction error of the stripe center is significant due to the influence of the optical characteristics of the metal surface and the scattering noise. This paper proposes a sub-pixel stripe center extraction method based on adaptive threshold segmentation and a gradient weighting strategy to address this issue. First, we analyze the characteristics of the stripe image of the measured metal's surface morphology. Relying on the morphological features of the image, the image is segmented to remove the effect of background noise and to obtain the region of interest in the image. Then, we use the gray-gravity method to get the rough center coordinates of the stripes. We extend the stripes in the width direction using the rough center coordinates as a reference to determine the center of the stripes for extraction after segmentation. Next, we adaptively determine the boundary threshold utilizing the region's grayscale. Finally, we use the gradient weighting strategy to extract the sub-pixel stripe center. The experimental results show that the proposed method effectively eliminates the interference of metal surface scattering on 3D reconstruction. The average height error of the measured standard block is 0.025 mm, and the repeatability of the measurement accuracy is 0.026 mm.

Cross2SynNet: cross-device-cross-modal synthesis of routine brain MRI sequences from CT with brain lesion.

OBJECTIVES: CT and MR are often needed to determine the location and extent of brain lesions collectively to improve diagnosis. However, patients with acute brain diseases cannot complete the MRI examination within a short time. The aim of the study is to devise a cross-device and cross-modal medical image synthesis (MIS) method Cross2SynNet for synthesizing routine brain MRI sequences of T1WI, T2WI, FLAIR, and DWI from CT with stroke and brain tumors. MATERIALS AND METHODS: For the retrospective study, the participants covered four different diseases of cerebral ischemic stroke (CIS-cohort), cerebral hemorrhage (CH-cohort), meningioma (M-cohort), glioma (G-cohort). The MIS model Cross2SynNet was established on the basic architecture of conditional generative adversarial network (CGAN), of which, the fully convolutional Transformer (FCT) module was adopted into generator to capture the short- and long-range dependencies between healthy and pathological tissues, and the edge loss function was to minimize the difference in gradient magnitude between synthetic image and ground truth. Three metrics of mean square error (MSE), peak signal-to-noise ratio (PSNR), and structure similarity index measure (SSIM) were used for evaluation. RESULTS: A total of 230 participants (mean patient age, 59.77 years ± 13.63 [standard deviation]; 163 men [71%] and 67 women [29%]) were included, including CIS-cohort (95 participants between Dec 2019 and Feb 2022), CH-cohort (69 participants between Jan 2020 and Dec 2021), M-cohort (40 participants between Sep 2018 and Dec 2021), and G-cohort (26 participants between Sep 2019 and Dec 2021). The Cross2SynNet achieved averaged values of MSE = 0.008, PSNR = 21.728, and SSIM = 0.758 when synthesizing MRIs from CT, outperforming the CycleGAN, pix2pix, RegGAN, Pix2PixHD, and ResViT. The Cross2SynNet could synthesize the brain lesion on pseudo DWI even if the CT image did not exhibit clear signal in the acute ischemic stroke patients. CONCLUSIONS: Cross2SynNet could achieve routine brain MRI synthesis of T1WI, T2WI, FLAIR, and DWI from CT with promising performance given the brain lesion of stroke and brain tumor.

s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.

OBJECTIVE: To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. METHODS: A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. RESULTS: The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. CONCLUSION: The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

Cell surface receptor kinase FERONIA linked to nutrient sensor TORC signaling controls root hair growth at low temperature linked to low nitrate in Arabidopsis thaliana.

Root hairs (RH) are excellent model systems for studying cell size and polarity since they elongate several hundred-fold their original size. Their tip growth is determined both by intrinsic and environmental signals. Although nutrient availability and temperature are key factors for a sustained plant growth, the molecular mechanisms underlying their sensing and downstream signaling pathways remain unclear. We use genetics to address the roles of the cell surface receptor kinase FERONIA (FER) and the nutrient sensing TOR Complex 1 (TORC) in RH growth. We identified that low temperature (10°C) triggers a strong RH elongation response in Arabidopsis thaliana involving FER and TORC. We found that FER is required to perceive limited nutrient availability caused by low temperature. FERONIA interacts with and activates TORC-downstream components to trigger RH growth. In addition, the small GTPase Rho of plants 2 (ROP2) is also involved in this RH growth response linking FER and TOR. We also found that limited nitrogen nutrient availability can mimic the RH growth response at 10°C in a NRT1.1-dependent manner. These results uncover a molecular mechanism by which a central hub composed by FER-ROP2-TORC is involved in the control of RH elongation under low temperature and nitrogen deficiency.

Rotation axis calibration of a 3D scanning system based on dual-turntable angle cancellation.

Rotation axis calibration is crucial for high-precision automatic point cloud stitching in turntable-based 3D scanning systems. To achieve a 360° sampling with a 2D calibrator in rotation axis calibration, this paper proposes a dual-turntable angle cancellation (DTAC) method. DTAC introduces an auxiliary turntable to keep a proper relative angle between the 3D sensor and the calibrator during the calibration process. The auxiliary turntable rotates at the same and opposite angle as the main turntable and cancels the increment of the relative angle. By projecting the feature points on the planar calibrator from real-world space to virtual calibration space, the projected points all share the same rotation axis of the main turntable. Further, a layered circle center extraction (LCCE) algorithm is applied to deal with outlier data points. The algorithm uses a two-step robust estimation strategy combining RANSAC circle fitting with a median noise filter for circle center selection. The standard ball reconstruction experiment shows that the 3D system calibrated by the method achieves a mean absolute error of 0.022 mm and root mean square error of 0.025 mm within the measurement distance of 60-70 cm. Point cloud stitching experiments of different types of objects show that our method outperforms other state-of-the-art methods in stitching accuracy. The DTAC method and LCCE algorithm can improve turntable-based 3D scanning systems.

Triple-output phase unwrapping network with a physical prior in fringe projection profilometry.

Deep learning has been attracting more and more attention in the phase unwrapping of fringe projection profilometry (FPP) in recent years. In order to improve the accuracy of the deep-learning-based unwrapped phase methods from a single fringe pattern, this paper proposes a single-input triple-output neural network structure with a physical prior. In the proposed network, a single-input triple-output network structure is developed to convert the input fringe pattern into three intermediate outputs: the wrapped phase, the fringe order, the coarse unwrapped phase, and the final output high-precision unwrapped phase from the three outputs. Moreover, a new, to the best of our knowledge, loss function is designed to improve the performance of the model using a physical prior about these three outputs in FPP. Numerous experiments demonstrated that the proposed network is able to improve the accuracy of the unwrapped phase, which can also be extended to other deep learning phase unwrapping models.

Dynamic Yarn-Tension Detection Using Machine Vision Combined with a Tension Observer.

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton's principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.

A molecular link between autophagy and circadian rhythm in plants.

Extremely high or low autophagy levels disrupt plant survival under nutrient starvation. Recently, autophagy has been reported to display rhythms in animals. However, the mechanism of circadian regulation of autophagy is still unclear. Here, we observed that autophagy has a robust rhythm and that various autophagy-related genes (ATGs) are rhythmically expressed in Arabidopsis. Chromatin immunoprecipitation (ChIP) and dual-luciferase (LUC) analyses showed that the core oscillator gene TIMING OF CAB EXPRESSION 1 (TOC1) directly binds to the promoters of ATG (ATG1a, ATG2, and ATG8d) and negatively regulates autophagy activities under nutritional stress. Furthermore, autophagy defects might affect endogenous rhythms by reducing the rhythm amplitude of TOC1 and shortening the rhythm period of CIRCADIAN CLOCK-ASSOCIATED 1 (CCA1). Autophagy is essential for the circadian clock pattern in seedling development and plant sensitivity to nutritional deficiencies. Taken together, our studies reveal a plant strategy in which the TOC1-ATG axis involved in autophagy-rhythm crosstalk to fine-tune the intensity of autophagy.

Enhanced luminescence of CaF2 :Er3+ /Nd3+ nanoparticles coated by fluorinated ß-diketone.

In this study, several CaF2 nanoparticles doped with individual ions Nd3+ and Er3+ were synthesized with ligands pentafluorobenzoyl trifluoroacetone (PBTA) and 2-naphthoyltrifluoroacetone (NTA), which served as modifiers. Influence of the modifiers on the luminescence properties of the nanoparticles was investigated. The as-prepared nanoparticles had a similar size and dispersion with an irregular spherical shape, which reduced the influence of particle size on the luminescence of the nanoparticles. The luminescence spectra of these nanoparticles showed the characteristic luminescence of the central lanthanide ions under excitation of the maximum excitation wavelength. Compared with nanoparticles modified by the NTA ligand, nanoparticles modified by the PBTA ligand possessed stronger luminescence intensity for the doping Nd3+ or Er3+ ions.

TMK4 receptor kinase negatively modulates ABA signaling by phosphorylating ABI2 and enhancing its activity.

In plants, clade A type 2C protein phosphatases (PP2CAs) have emerged as major players in abscisic acid (ABA)-regulated stress responses by inhibiting protein kinase activity. However, how different internal and external environmental signals modulate the activity of PP2CAs are not well known. The transmembrane kinase (TMK) protein 4 (TMK4), one member of a previously identified receptor kinase subfamily on the plasma membrane that plays vital roles in plant cell growth, directly interacts with PP2CAs member (ABA-Insensitive 2, ABI2). tmk4 mutant is hypersensitive to ABA in both ABA-inhibited seed germination and primary root growth, indicating that TMK4 is a negative regulator in ABA signaling pathway. Further analyses indicate that TMK4 phosphorylates ABI2 at three conserved Ser residues, thus enhancing the activity of ABI2. The phosphorylation-mimic ABI2S139DS140DS266D can complement but non-phosphorylated form ABI2S139AS140AS266A cannot complement ABA hypersensitive phenotype of the loss-of-function mutant abi1-2abi2-2. This study provides a previously unidentified mechanism for positively regulating ABI2 by a plasma membrane protein kinase.

Application of deep learning in quantitative analysis of the infrared spectrum of logging gas.

Infrared spectrum analysis technology can perform fast and nondestructive detection of gas and has been widely used in many fields. This work studies the quantitative analysis technology of the infrared spectrum based on deep learning. The experimental results show that the quantitative analysis model of logging gas established here can reach 100% recognition accuracy for elemental gas; further, the accuracy rate of spectral of mixed gas recognition reached 98%, indicating that the infrared spectrum logging gas detection model based on deep learning can quickly and accurately perform quantitative analysis of logging gas.

Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks.

During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1.5 × 1.5 × 1.6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.

Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement.

We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN). Moreover, the training data is cropped into small overlapped patches to expand the training samples for the DNN. The performance of the proposed approach is verified by experimental projection fringe patterns with applications in dynamic fringe projection 3D measurement.

FERONIA phosphorylates E3 ubiquitin ligase ATL6 to modulate the stability of 14-3-3 proteins in response to the carbon/nitrogen ratio.

The ratio between carbon (C) and nitrogen (N) utilization must be precisely coordinated to enable plant growth. Although numerous physiological studies have examined carbon/nitrogen (C/N) ratios, the mechanisms of sensing the C/N balance and C/N signaling remain elusive. Here, we report that a mutation of FERONIA (FER), a receptor kinase that plays versatile roles in plant cell growth and stress responses, caused hypersensitivity to a high C/N ratio in Arabidopsis. In contrast, FER-overexpressing plants displayed more resistant phenotypes. FER can interact with and phosphorylate ATL6, an E3 ubiquitin ligase that has been shown to regulate plant C/N responses. FER-mediated ATL6 phosphorylation enhanced the interaction between ATL6 and its previously identified target 14-3-3 proteins, thus decreasing 14-3-3 protein levels, leading to an increased insensitivity to high C/N ratios. Further analyses showed that the rapid alkalinization factor peptide (RALF1), which is a ligand of FER, also influenced the stability of 14-3-3 proteins via a FER-ATL6-mediated pathway. These findings reveal a novel regulatory mechanism that links the RALF1/FER-ATL6 pathway to whole-plant C/N responses and growth.

A Multi-View Stereo Measurement System Based on a Laser Scanner for Fine Workpieces.

A new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces is presented using a laser line-scanning sensor. Due to registration errors, measurement errors, deformations, etc., a fast and accurate method is important in machine vision measurement. This paper builds a convenient and economic multi-view stereo (MVS) measurement system based on a linear stage and a rotary stage to reconstruct the measured object surface completely and accurately. In the proposed technique, the linear stage is used to generate the trigger signal and synchronize the laser sensor scanning; the rotary stage is used to rotate the object and obtain multi-view point cloud data, and then the multi-view point cloud data are registered and integrated into a 3D model. The measurement results show a measurement accuracy of 0.075 mm for a 360° reconstruction in 34 s, and some evaluation experiments were carried out to demonstrate the validity and practicability of the proposed technique.

Assessment of Fringe Pattern Decomposition with a Cross-Correlation Index for Phase Retrieval in Fringe Projection 3D Measurements.

Phase retrieval from single frame projection fringe patterns, a fundamental and challenging problem in fringe projection measurement, attracts wide attention and various new methods have emerged to address this challenge. Many phase retrieval methods are based on the decomposition of fringe patterns into a background part and a fringe part, and then the phase is obtained from the decomposed fringe part. However, the decomposition results are subject to the selection of model parameters, which is usually performed manually by trial and error due to the lack of decomposition assessment rules under a no ground truth data situation. In this paper, we propose a cross-correlation index to assess the decomposition and phase retrieval results without the need of ground truth data. The feasibility of the proposed metric is verified by simulated and real fringe patterns with the well-known Fourier transform method and recently proposed Shearlet transform method. This work contributes to the automatic phase retrieval and three-dimensional (3D) measurement with less human intervention, and can be potentially employed in other fields such as phase retrieval in digital holography.

Structured-Light Based 3D Reconstruction System for Cultural Relic Packaging.

The non-contact three-dimensional measurement and reconstruction techniques have played a significant role in the packaging and transportation of precious cultural relics. This paper develops a structured light based three-dimensional measurement system, with a low-cost for cultural relics packaging. The structured light based system performs rapid measurements and generates 3D point cloud data, which is then denoised, registered and merged to achieve accurate 3D reconstruction for cultural relics. The multi-frequency heterodyne method and the method in this paper are compared. It is shown that the relative accuracy of the proposed low-cost system can reach a level of 1/1000. The high efficiency of the system is demonstrated through experimental results.

Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks.

This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.

Lp-norm-residual constrained regularization model for estimation of particle size distribution in dynamic light scattering.

In particle size measurement using dynamic light scattering (DLS), noise makes the estimation of the particle size distribution (PSD) from the autocorrelation function data unreliable, and a regularization technique is usually required to estimate a reasonable PSD. In this paper, we propose an Lp-norm-residual constrained regularization model for the estimation of the PSD from DLS data based on the Lp norm of the fitting residual. Our model is a generalization of the existing, commonly used L2-norm-residual-based regularization methods such as CONTIN and constrained Tikhonov regularization. The estimation of PSDs by the proposed model, using different Lp norms of the fitting residual for p=1, 2, 10, and ∞, is studied and their performance is determined using simulated and experimental data. Results show that our proposed model with p=1 is less sensitive to noise and improves stability and accuracy in the estimation of PSDs for unimodal and bimodal systems. The model with p=1 is particularly applicable to the noisy or bimodal PSD cases.