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.

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network.

Background: Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity. Purpose: To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN). Methods: Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores. Results: The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores. Conclusion: The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI. Supplementary Information: The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.

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.

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.

A hybrid spatiotemporal deep belief network and sparse representation-based framework reveals multilevel core functional components in decoding multitask fMRI signals.

Decoding human brain activity on various task-based functional brain imaging data is of great significance for uncovering the functioning mechanism of the human mind. Currently, most feature extraction model-based methods for brain state decoding are shallow machine learning models, which may struggle to capture complex and precise spatiotemporal patterns of brain activity from the highly noisy fMRI raw data. Moreover, although decoding models based on deep learning methods benefit from their multilayer structure that could extract spatiotemporal features at multiscale, the relatively large populations of fMRI datasets are indispensable, and the explainability of their results is elusive. To address the above problems, we proposed a computational framework based on hybrid spatiotemporal deep belief network and sparse representations to differentiate multitask fMRI (tfMRI) signals. Using a relatively small cohort of tfMRI data as a test bed, our framework can achieve an average classification accuracy of 97.86% and define the multilevel temporal and spatial patterns of multiple cognitive tasks. Intriguingly, our model can characterize the key components for differentiating the multitask fMRI signals. Overall, the proposed framework can identify the interpretable and discriminative fMRI composition patterns at multiple scales, offering an effective methodology for basic neuroscience and clinical research with relatively small cohorts.

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli.

One of human brain's remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain's dynamic changes during the processing of visual and auditory stimuli.

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.

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.

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.

Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation.

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm.

Ferritin confers protection against iron-mediated neurotoxicity and ferroptosis through iron chelating mechanisms in MPP+-induced MES23.5 dopaminergic cells.

Ferritin is the main iron storage protein and plays an important role in maintaining iron homeostasis. In a previous study, we reported that apoferritin exerted a neuroprotective effect against MPTP by regulation of brain iron metabolism and ferroptosis. However, the precise cellular mechanisms of extracellular ferritin underlying this protection are not fully elucidated. Ferritin was reported to be localized in different intracellular compartments, cytoplasm or released outside cells. Here we demonstrated that the intracellular iron increased after iron treatment in primary cultured astrocytes. These iron-loaded astrocytes released more ferritin in order to buffer extracellular iron. Using co-culture system of primary cultured astrocytes and MES23.5 dopaminergic cells, we showed that ferritin released by astrocytes could enter MES23.5 dopaminergic cells. And primary cultured astrocytes protected MES23.5 dopaminergic cells against 1-methyl-4-phenylpyridinium ion (MPP+)-induced neurotoxicity and ferroptosis. In addition, we found that exogenous Apoferritin or Ferritin pretreatment could significantly inhibit MPP+-induced cell damage by restoring the cell viability and mitochondrial transmembrane potential (ΔΨm). Furthermore, exogenous Apoferritin and Ferritin might also protect MES23.5 dopaminergic cells against MPP+ by decreasing reactive oxygen species (ROS) and inhibiting the increase of the labile iron pool (LIP). This suggests that astrocytes increased ferritin release to respond to iron overload, which might inhibit iron-mediated oxidative damage and ferroptosis of dopamine (DA) neurons in Parkinson's disease (PD).

3D velocity field reconstruction of gas-liquid two-phase flow based on space-time multi-scale binocular-PIV technology.

for particle image velocimetry (PIV) technique, the two-dimensional (2D) PIV by one camera can only obtain 2D velocity field, while three-dimensional (3D) PIV based on tomography by three or four cameras is always complex and expensive. In this work, a binocular-PIV technology based on two cameras was proposed to reconstruct the 3D velocity field of gas-liquid two-phase flow, which is a combination of the binocular stereo vision and cross-correlation based on fast Fourier transform (CC-FFT). The depth of particle was calculated by binocular stereo vision on space scale, and the plane displacement of particles was acquired by CC-FFT on time scale. Experimental results have proved the effectiveness of the proposed method in 3D reconstruction of velocity field for gas-liquid two-phase flow.

Design and evaluation of magnetic field strength detection devices for millimeter-sized planar square inductors based on a mutual coupling model.

Micro-magnetic stimulation is a research hotspot in the field of neuromodulation. However, it is difficult to measure the weak magnetic field produced by a millimeter-sized inductor. In this study, a mutual inductance model considering different positions and sizes was established for a common planar square spiral coil micro-magnetic stimulator. A physical model was simulated using the Comsol finite element method to verify the accuracy of the mutual inductance model. A weak magnetic field detection system was constructed using the TI AD8130 and NE5532 chips, and the magnetic field strengths of excitation micro-coils sized 3.612 × 3.612 and 5.55 × 5.55 mm2 were measured. The results show that when the size ratio of the detection coil (DC) to the excitation coil (EC) is under a specific ratio (DC:EC = 1:1, 2:1, 1.53:1,2.36:1), the measurement range of the magnetic field strength is in the range 0-3.06 mT with an error of 0.05 mT, and the frequency is in the range 1-120 kHz. The measurement accuracy rate reaches 97.62%. The results of this study have potential application in the measurement of the weak magnetic field.

Hierarchical Individual Naturalistic Functional Brain Networks with Group Consistency uncovered by a Two-Stage NAS-Volumetric Sparse DBN Framework.

The functional magnetic resonance imaging under naturalistic paradigm (NfMRI) showed great advantages in identifying complex and interactive functional brain networks due to its dynamics and multimodal information. In recent years, various deep learning models, such as deep convolutional autoencoder (DCAE), deep belief network (DBN) and volumetric sparse deep belief network (vsDBN), can obtain hierarchical functional brain networks (FBN) and temporal features from fMRI data. Among them, the vsDBN model revealed a good capability in identifying hierarchical FBNs by modelling fMRI volume images. However, due to the high dimensionality of fMRI volumes and the diverse training parameters of deep learning methods, especially the network architecture that is the most critical parameter for uncovering the hierarchical organization of human brain function, researchers still face challenges in designing an appropriate deep learning framework with automatic network architecture optimization to model volumetric NfMRI. In addition, most of the existing deep learning models ignore the group-wise consistency and inter-subject variation properties embedded in NfMRI volumes. To solve these problems, we proposed a two-stage neural architecture search and vs DBN model (two-stage NAS-vsDBN model) to identify the hierarchical human brain spatio-temporal features possessing both group-consistency and individual-uniqueness under naturalistic condition. Moreover, our model defined reliable network structure for modelling volumetric NfMRI data via NAS framework, and the group-level and individual-level FBNs and associated temporal features exhibited great consistency. In general, our method well identified the hierarchical temporal and spatial features of the brain function and revealed the crucial properties of neural processes under natural viewing condition.Significance StatementIn this paper, we proposed and applied a novel analytical strategy - a two-stage NAS-vsDBN model to identify both group-level and individual-level spatio-temporal features at multi-scales from volumetric NfMRI data. The proposed PSO-based NAS framework can find optimal neural structure for both group-wise and individual-level vs-DBN models. Furthermore, with well-established correspondence between two stages of vsDBN models, our model can effectively detect group-level FBNs that reveal the consistency in neural processes across subjects and individual-level FBNs that maintain the subject specific variability, verifying the inherent property of brain function under naturalistic condition.

Multitask fMRI Data Classification via Group-Wise Hybrid Temporal and Spatial Sparse Representations.

Task-based functional magnetic resonance imaging (tfMRI) has been widely used to induce functional brain activities corresponding to various cognitive tasks. A relatively under-explored question is whether there exist fundamental differences in fMRI signal composition patterns that can effectively classify the task states of tfMRI data, furthermore, whether there exist key functional components in characterizing the diverse tfMRI signals. Recently, fMRI signal composition patterns of multiple tasks have been investigated via deep learning models, where relatively large populations of fMRI datasets are indispensable and the neurologic meaning of their results is elusive. Thus, the major challenges arise from the high dimensionality, low signal-to-noise ratio, interindividual variability, a small sample size of fMRI data, and the explainability of classification results. To address the above challenges, we proposed a computational framework based on group-wise hybrid temporal and spatial sparse representations (HTSSR) to identify and differentiate multitask fMRI signal composition patterns. Using relatively small cohorts of Human Connectome Project (HCP) tfMRI data as test-bed, the experimental results demonstrated that the multitask of fMRI data can be successfully classified with an average accuracy of 96.67%, where the key components in differentiating the multitask can be characterized, suggesting the effectiveness and explainability of the proposed method. Moreover, both task-related components and resting-state networks (RSNs) can be reliably detected. Therefore, our study proposed a novel framework that identifies the interpretable and discriminative fMRI composition patterns and can be potentially applied for controlling fMRI data quality and inferring biomarkers in brain disorders with small sample neuroimaging datasets.

The RALF1-FERONIA complex interacts with and activates TOR signaling in response to low nutrients.

Target of rapamycin (TOR) kinase is an evolutionarily conserved major regulator of nutrient metabolism and organismal growth in eukaryotes. In plants, nutrients are remobilized and reallocated between shoots and roots under low-nutrient conditions, and nitrogen and nitrogen-related nutrients (e.g., amino acids) are key upstream signals leading to TOR activation in shoots under low-nutrient conditions. However, how these forms of nitrogen can be sensed to activate TOR in plants is still poorly understood. Here we report that the Arabidopsis receptor kinase FERONIA (FER) interacts with the TOR pathway to regulate nutrient (nitrogen and amino acid) signaling under low-nutrient conditions and exerts similar metabolic effects in response to nitrogen deficiency. We found that FER and its partner, RPM1-induced protein kinase (RIPK), interact with the TOR/RAPTOR complex to positively modulate TOR signaling activity. During this process, the receptor complex FER/RIPK phosphorylates the TOR complex component RAPTOR1B. The RALF1 peptide, a ligand of the FER/RIPK receptor complex, increases TOR activation in the young leaf by enhancing FER-TOR interactions, leading to promotion of true leaf growth in Arabidopsis under low-nutrient conditions. Furthermore, we showed that specific amino acids (e.g., Gln, Asp, and Gly) promote true leaf growth under nitrogen-deficient conditions via the FER-TOR axis. Collectively, our study reveals a mechanism by which the RALF1-FER pathway activates TOR in the plant adaptive response to low nutrients and suggests that plants prioritize nutritional stress response over RALF1-mediated inhibition of cell growth under low-nutrient conditions.

Blended fabric with integrated neural network based on attention mechanism qualitative identification method of near infrared spectroscopy.

Near Infrared spectroscopy (NIRS) qualitative analysis technology has shown excellent development potential in the field of blend fabrics. However, the qualitative detection method based on the convolutional neural network (CNN) is difficult to accurately extract the feature of the spectral data, which will lead to missing detection or false detection; when using deep learning to build a qualitative detection model, due to interference of the external environment and other factors, the spectral data collected may have outliers, this means that the knowledge generalization on anomalous testing data, which may have a different distribution of that of the training set, is not trivial, which will also lead to missing detection or false detection. To solve the above problems, this paper proposes a novel qualitative detection neural network by analyzing the near infrared spectral data of blend fabrics. Firstly, we remove the convolutional layer and pooling layer of the CNN, making full use of the feature to enhance the feature representation ability of the model. Secondly, adding the L1 norm of the feature coefficients as a penalty term to the loss function to force those features with high redundancy to become weaker. Thirdly, in order to improve the recognition accuracy of the anomalous spectral data and minimize the model uncertainty, an ensemble machine learning approach utilizing 5 neural networks in parallel is used. To show the superiority of our proposed method, the existing methods are used as competitive methods to compare with our method. Our homemade dataset contains 3482 samples of blend fabrics with 9 different compositions. The results show that the Micro-F1-score, Micro-Specificity, Weight-F1-score, and Weight-Specificity of this method respectively 99.71%, 99.96%, 99.73%, and 99.99%, the results further confirm the method has higher analysis accuracy and stability. In addition, the method proposed in this paper can greatly improve the recognition accuracy of the anomalous spectral data. It has important practical value in the qualitative detection of blend fabrics.

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.

Professional chess expertise modulates whole brain functional connectivity pattern homogeneity and couplings.

Previous studies have revealed changed functional connectivity patterns between brain areas in chess players using resting-state functional magnetic resonance imaging (rs-fMRI). However, how to exactly characterize the voxel-wise whole brain functional connectivity pattern changes in chess players remains unclear. It could provide more convincing evidence for establishing the relationship between long-term chess practice and brain function changes. In this study, we employed newly developed whole brain functional connectivity pattern homogeneity (FcHo) method to identify the voxel-wise changes of functional connectivity patterns in 28 chess master players and 27 healthy novices. Seed-based functional connectivity analysis was used to identify the alteration of corresponding functional couplings. FcHo analysis revealed significantly increased whole brain functional connectivity pattern similarity in anterior cingulate cortex (ACC), anterior middle temporal gyrus (aMTG), primary visual cortex (V1), and decreased FcHo in thalamus and precentral gyrus in chess players. Resting-state functional connectivity analyses identified chess players showing decreased functional connections between V1 and precentral gyrus. Besides, a linear support vector machine (SVM) based classification achieved an accuracy of 85.45%, a sensitivity of 85.71% and a specificity of 85.19% to differentiate chess players from novices by leave-one-out cross-validation. Finally, correlation analyses revealed that the mean FcHo values of thalamus were significantly negatively correlated with the training time. Our findings provide new evidences for the important roles of ACC, aMTG, V1, thalamus and precentral gyrus in chess players. The findings also indicate that long-term professional chess training may enhance the semantic and episodic processing, efficiency of visual-motor transformation, and cognitive ability.