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.

Polysaccharides from apple pomace exhibit anti-fatigue activity through increasing glycogen content.

The polysaccharides were isolated from apple pomace by hot-water extraction, and their anti-fatigue activity was evaluated in C2C12 muscle myoblasts and male Kunming mice. The purified polysaccharides from apple pomace (PAP) have a molecular weight of 1.74 × 105 Da and were composed of mannose, rhamnose, glucose, galactose and arabinose. In C2C12 myoblasts, PAP showed no cytotoxicity in the concentrations of 0-300 µg/ml. PAP treatment increased the glycogen content, while the ATP content was not affected in C2C12 myoblasts. Further investigation found that the activity and gene expression of glycogen synthase, rather than glycogen phosphorylase, were upregulated by PAP treatment. The studies in vivo showed that PAP treatment did not affect the food intake and weight again in mice. Importantly, PAP prolonged the exhaustive swimming time, increased hepatic and skeletal muscle glycogen levels, and effectively inhibited the accumulation of blood lactic and blood urea nitrogen in mice. Taken together, the results suggested that PAP exhibit anti-fatigue activity in vitro and in vivo through increasing glycogen content.

Key-point estimation of knee X-ray images using a parallel fusion decoding network.

BACKGROUND: High tibial osteotomy (HTO) is a knee preservation procedure used to treat osteoarthritis of the knee. Identifying the hinge point, surgical point, and Fujisawa point in the patient's knee X-ray before surgery is a critical task. The aim of this study was to propose an artificial intelligence-based method to effectively help surgeons select the location of these landmark points, which provides important reference for subsequent surgery. METHODS: We proposed PFDNet (parallel fusion decoding network), a novel convolutional neural network for key-point estimation of knee X-rays. PFDNet employs Res2Net for feature extraction in the network encoding phase and two partial decoders connected in parallel in the network decoding phase to finely aggregate the multiscale feature information produced by Res2Net. A total of 1842 knee X-ray images were trained, validated and predicted by PFDNet to determine whether the network could accurately detect key-points in the HTO surgical plan. RESULTS: At the hinge point, surgical point, and Fujisawa point, the average error and standard deviation from the calibration value in the PFDNet test results were 2.06 ± 1.165 mm, 2.713 ± 1.457 mm, and 2.015 ± 1.304 mm, respectively. This method exhibits superior performance compared with four convolutional neural network models that are also based on encoding and decoding frameworks: U-Net, ResUnet, SegNet, and FCN. CONCLUSION: The hinge point, surgical point, and Fujisawa point can be clearly selected by PFDNet from knee X-ray images and is locked to the millimeter level. The results show that the proposed artificial intelligence-based strategy can be instrumental in preoperative HTO planning.

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.

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.

Deficiency of Microglial Autophagy Increases the Density of Oligodendrocytes and Susceptibility to Severe Forms of Seizures.

Excessive activation of mTOR in microglia impairs CNS homeostasis and causes severe epilepsy. Autophagy constitutes an important part of mTOR signaling. The contribution of microglial autophagy to CNS homeostasis and epilepsy remains to be determined. Here, we report that ATG7KO mice deficient for autophagy in microglia display a marked increase of myelination markers, a higher density of mature oligodendrocytes (ODCs), and altered lengths of the nodes of Ranvier. Moreover, we found that deficiency of microglial autophagy (ATG7KO) leads to increased seizure susceptibility in three seizure models (pilocarpine, kainic acid, and amygdala kindling). We demonstrated that ATG7KO mice develop severe generalized seizures and display nearly 100% mortality to convulsions induced by pilocarpine and kainic acid. In the amygdala kindling model, we observed significant facilitation of contralateral propagation of seizures, a process underlying the development of generalized seizures. Taken together, our results reveal impaired microglial autophagy as a novel mechanism underlying altered homeostasis of ODCs and increased susceptibility to severe and fatal generalized seizures.

Microglial mTOR is Neuronal Protective and Antiepileptogenic in the Pilocarpine Model of Temporal Lobe Epilepsy.

Excessive activation of mammalian target of rapamycin (mTOR) signaling is epileptogenic in genetic epilepsy. However, the exact role of microglial mTOR in acquired epilepsy remains to be clarified. In the present study, we found that mTOR is strongly activated in microglia following excitatory injury elicited by status epilepticus. To determine the role of microglial mTOR signaling in excitatory injury and epileptogenesis, we generated mice with restrictive deletion of mTOR in microglia. Both male and female mice were used in the present study. We found that mTOR-deficient microglia lost their typical proliferative and inflammatory responses to excitatory injury, whereas the proliferation of astrocytes was preserved. In addition, mTOR-deficient microglia did not effectively engulf injured/dying neurons. More importantly, microglial mTOR-deficient mice displayed increased neuronal loss and developed more severe spontaneous seizures. These findings suggest that microglial mTOR plays a protective role in mitigating neuronal loss and attenuating epileptogenesis in the excitatory injury model of epilepsy.SIGNIFICANCE STATEMENT The mammalian target of rapamycin (mTOR) pathway is strongly implicated in epilepsy. However, the effect of mTOR inhibitors in preclinical models of acquired epilepsy is inconsistent. The broad presence of mTOR signaling in various brain cells could prevent mTOR inhibitors from achieving a net therapeutic effect. This conundrum has spurred further investigation of the cell type-specific effects of mTOR signaling in the CNS. We found that activation of microglial mTOR is antiepileptogenic. Thus, microglial mTOR activation represents a novel antiepileptogenic route that appears to parallel the proepileptogenic route of neuronal mTOR activation. This may explain why the net effect of mTOR inhibitors is paradoxical in the acquired models of epilepsy. Our findings could better guide the use of mTOR inhibitors in preventing acquired epilepsy.

IFN-γ and IL-17A regulate intestinal crypt production of CXCL10 in the healthy and inflamed colon.

During intestinal inflammation, immature cells within the intestinal crypt are called upon to replenish lost epithelial cell populations, promote tissue regeneration, and restore barrier integrity. Inflammatory mediators including TH1/TH17-associated cytokines influence tissue health and regenerative processes, yet how these cytokines directly influence the colon crypt epithelium and whether the crypt remains responsive to these cytokines during active damage and repair, remain unclear. Here, using laser-capture microdissection and primary colon organoid culture, we show that the cytokine milieu regulates the ability of the colonic crypt epithelium to participate in proinflammatory signaling. IFN-γ induces the TH1-recruiting, proinflammatory chemokine CXCL10/IP10 in primary murine intestinal crypt epithelium. CXCL10 was also induced in colonic organoids derived from mice with active, experimentally induced colitis, suggesting that the crypt can actively secrete CXCL10 in select cytokine environments during colitis. Colon expression of cxcl10 further increased during infectious and noninfectious colitis in Il17a-/- mice, demonstrating that IL-17A exerts a negative effect on CXCL10 in vivo. Furthermore, IL-17A directly antagonized CXCL10 production in ex vivo organoid cultures derived from healthy murine colons. Interestingly, direct antagonism of CXCL10 was not observed in organoids derived from colitic mouse colons bearing active lesions. These data, highlighting the complex interplay between the cytokine milieu and crypt epithelia, demonstrate proinflammatory chemokines can be induced within the colonic crypt and suggest the crypt remains responsive to cytokine modulation during inflammation.NEW & NOTEWORTHY Upon damage, the intestinal epithelium regenerates to restore barrier function. Here we observe that the local colonic cytokine milieu controls the production of procolitic chemokines within the crypt base and colon crypts remain responsive to cytokines during inflammation. IFN-γ promotes, while IL-17 antagonizes, CXCL10 production in healthy colonic crypts, while responses to cytokines differ in inflamed colon epithelium. These data reveal novel insight into colon crypt responses and inflammation-relevant alterations in signaling.

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.

Mechanistic Insight into the Development of TNBS-Mediated Intestinal Fibrosis and Evaluating the Inhibitory Effects of Rapamycin.

Significant studies have been carried out to understand effective management of intestinal fibrosis. However, the lack of better knowledge of fibrosis has hindered the development of a preventative drug. Primarily, finding a suitable animal model is challenging in understanding the mechanism of Crohn's-associated intestinal fibrosis pathology. Here, we adopted an effective method where TNBS chemical exposure to mice rectums produces substantially deep ulceration and chronic inflammation, and the mice then chronically develop intestinal fibrosis. Also, we describe a technique where a rapamycin injection shows inhibitory effects on TNBS-mediated fibrosis in the mouse model. To assess the underlying mechanism of fibrosis, we methodically discuss a procedure for purifying Cx3Cr1+ cells from the lamina propria of TNBS-treated and control mice. This detailed protocol will be helpful to researchers who are investigating the mechanism of fibrosis and pave the path to find a better therapeutic invention for Crohn's-associated intestinal fibrosis.

Targeting Microglia Using Cx3cr1-Cre Lines: Revisiting the Specificity.

Microglia play a pivotal role in maintaining homeostasis of the CNS. There is growing interest in understanding how microglia influence normal brain function and disease progression. Several microglia-specific Cx3cr1-Cre lines have been developed and have become indispensable tools in many investigations of microglial function. However, some recent studies have reported that these lines may have significant leakage into neurons. Other studies have reported that Cx3cr1 is expressed in non-microglial cells, including neurons and astrocytes, in vitro or in vivo either during brain development or upon neurological insult. All these reports raise serious concerns about the trustworthiness of these Cre-lines and whether the conclusions drawn from previous studies are valid. Here, we found that a floxed fluorescent reporter mouse line which has been frequently used to verify Cre lines displayed spontaneous expression of the GFP reporter, independent of Cre recombinase, thus revealing a potential caveat in assessing cre lines. We further confirmed that two Cx3cr1-Cre mouse lines can drive fluorescent reporter expression largely restrictively in microglia. Finally, we clarified that these two mouse lines maintain microglia-specific expression even following excitatory injury. Together, our findings confirm that two previously created Cx3cr1-Cre lines remain as invaluable tools for studying microglia. Moreover, to ensure the quality of data generated and the soundness of conclusions drawn from such data, it should be compulsory to thoroughly examine reporter lines for spontaneous leakiness when labeling cells to study CNS function and diseases.

Induction of autophagy in Cx3cr1+ mononuclear cells limits IL-23/IL-22 axis-mediated intestinal fibrosis.

Intestinal fibrosis is an excessive proliferation of myofibroblasts and deposition of collagen, a condition frequently seen in Crohn's disease (CD). The mechanism underlying myofibroblast hyper-proliferation in CD needs to be better understood. In this report, we found that mTOR inhibitor rapamycin or mTOR deletion in CX3Cr1+ mononuclear phagocytes inhibits expression of interleukin (IL)-23, accompanied by reduced intestinal production of IL-22 and ameliorated fibrosis in the TNBS-induced fibrosis mouse model. This inhibition of IL-23 expression is associated with elevated autophagy activity. Ablating the autophagy gene Atg7 increases the expression of IL-23, leading to increased expression of IL-22 and increased fibrosis. Both induction of IL-22 and intestinal fibrosis occurred in RAG-/- mice and depletion of innate lymphoid cells (ILCs) attenuates the fibrotic reaction, suggesting that the pro-fibrotic process is independent of T and B cells. Moreover, IL-22 facilitates the transformation of fibroblasts into myofibroblasts. Finally, the fibrotic reaction was attenuated upon neutralization of either IL-23 or IL-22. Altogether, this study elucidated a signaling cascade underlying intestinal fibrosis in which altered mTOR/autophagy in CX3Cr1+ mononuclear phagocytes up-regulates the IL-23/IL-22 axis, leading to an excessive fibrotic response. Thus, our findings suggest that this cascade could be a therapeutic target for alleviation of CD fibrosis.

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.

Covalent immobilization of alkaline proteinase on amino-functionalized magnetic nanoparticles and application in soy protein hydrolysis.

Magnetic nanoparticles (MNPs) were synthesized and surface modified with (3-Aminopropyl)triethoxysilane (APTES). The alkaline proteinase (AP) was covalently immobilized on the APTES-modified MNPs through glutaraldehyde linkage. The resulting AP-loaded MNPs have an average size of 84 nm in aqueous solution, and a magnetization of 40 emu/g, endowing the immobilized enzyme with excellent magnetic responsively and dispersity. The maximum amount of AP and catalytic activity immobilized 1.0 mg MNPs was 120 µg and 25.3 units, respectively. Immobilized AP showed maximum activity at pH 10.0 and 50°C. Compared with free enzyme, the immobilized AP exhibited better storage stability. Moreover, immobilized AP can be reused 10 times and still maintained about 50% of its initial activity. The degree of hydrolysis of soy protein hydrolysates for immobilized AP could reach 19.0%, which was closer to the value of free enzyme. The molecular weight (M.W.) analysis showed that the soy protein was hydrolyzed successfully into small peptides of two main fractions with an average M.W. of 742 and 2126 Da. This study indicated that the immobilized AP could be used to hydrolyze continuously soy protein for potential industry application. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2756, 2019.

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.

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.

Noninflammatory Changes of Microglia Are Sufficient to Cause Epilepsy.

Microglia are well known to play a critical role in maintaining brain homeostasis. However, their role in epileptogenesis has yet to be determined. Here, we demonstrate that elevated mTOR signaling in mouse microglia leads to phenotypic changes, including an amoeboid-like morphology, increased proliferation, and robust phagocytosis activity, but without a significant induction of pro-inflammatory cytokines. We further provide evidence that these noninflammatory changes in microglia disrupt homeostasis of the CNS, leading to reduced synapse density, marked microglial infiltration into hippocampal pyramidal layers, moderate neuronal degeneration, and massive proliferation of astrocytes. Moreover, the mice thus affected develop severe early-onset spontaneous recurrent seizures (SRSs). Therefore, we have revealed an epileptogenic mechanism that is independent of the microglial inflammatory response. Our data suggest that microglia could be an opportune target for epilepsy prevention.

Information-weighted constrained regularization for particle size distribution recovery in multiangle dynamic light scattering.

In particle size measurement with dynamic light scattering (DLS), it is difficult to get an accurate recovery of a bimodal particle size distribution (PSD) with a peak position ratio less than ~2:1, especially when large particles (>350nm) are present. This is due to the inherent noise in the autocorrelation function (ACF) data and the scarce utilization of PSD information during the inversion process. In this paper, the PSD information distribution in the ACF data is investigated. It was found that the initial decay section of the ACF contains more information, especially for a bimodal PSD. Based on this, an information-weighted constrained regularization (IWCR) method is proposed in this paper and applied in multiangle DLS analysis for bimodal PSD recovery. By using larger (or smaller) coefficients for weighting the ACF data, more (or less) weight can then be given to the initial part of the ACF. In this way, the IWCR method can enhance utilization of the PSD information in the ACF data, and effectively weaken the effect of noise at large delay time on PSD recovery. Using this method, bimodal PSDs (with nominal diameters of 400:608 nm, 448:608 nm, 500:600 nm) were recovered successfully from simulated data and it appears that the IWCR method can improve the recovery resolution for closely spaced bimodal particles. Results of the PSD recovery from experimental DLS data confirm the performance of this method.

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.