A PCB image segmentation model based on rotational X-ray computed laminography imaging.

BACKGROUND: The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. OBJECTIVE: This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. METHODS: In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, with one integrating edge diffusion and l0 smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. RESULT: The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. CONCLUSIONS: Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model.

Effective focal spot measurement method for X-ray source based on the dynamic translation of a light barrier.

The accuracy of measuring the effective focal spot of the X-ray source directly affects the spatial resolution of computed tomography (CT) reconstructed images. This study proposes what we believe to be a novel approach to measure the effective focal spot based on the dynamic translation of light barrier using an accessible measuring device. This method discretizes the effective focal spot of the X-ray source into multiple subfocal spots with varying intensities and establishes a nonlinear model between the effective focal spot and measurement data. Measurement data are obtained by moving the light barrier to different positions using the electric displacement stage. The shape, size, and intensity distribution of the effective focal spot are determined by calculating the normalized weighting coefficients for each subfocal spot from measurement data. The measurement device is simple and easy to operate. Additionally, the obtained effective focal spot exhibits high accuracy, and a higher spatial resolution can be realized by reconstructing the CT images using the measured focal spot information. Numerical and real experiments validate the proposed method.

Dual spectral limited-angle CT imaging regularized by edge-preserving diffusion and smoothing.

Limited-angle CT scan is an effective way for nondestructive inspection of planar objects, and various methods have been proposed accordingly. When the scanned object contains high-absorption material, such as metal, existing methods may fail due to the beam hardening of X-rays. In order to overcome this problem, we adopt a dual spectral limited-angle CT scan and propose a corresponding image reconstruction algorithm, which takes the polychromatic property of the X-ray into consideration, makes basis material images free of beam hardening artifacts and metal artifacts, and then helps depress the limited-angle artifacts. Experimental results on both simulated PCB data and real data demonstrate the effectiveness of the proposed algorithm.

Orientation Control for Nickel-Based Single Crystal Superalloys: Grain Selection Method Assisted by Directional Columnar Grains.

The service performance of single crystal blades depends on the crystal orientation. A grain selection method assisted by directional columnar grains is studied to control the crystal orientation of Ni-based single crystal superalloys. The samples were produced by the Bridgman technique at withdrawal rates of 100 µm/s. During directional solidification, the directional columnar grains are partially melted, and a number of stray grains are formed in the transition zone just above the melt-back interface. The grain selected by this method was one that grew epitaxially along the un-melted directional columnar grains. Finally, the mechanism of selection grain and application prospect of this grain selection method assisted by directional columnar grains is discussed.

Nervous system manifestations related to COVID-19 and their possible mechanisms.

In December 2019, the novel coronavirus disease (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection broke. With the gradual deepening understanding of SARS-CoV-2 and COVID-19, researchers and clinicians noticed that this disease is closely related to the nervous system and has complex effects on the central nervous system (CNS) and peripheral nervous system (PNS). In this review, we summarize the effects and mechanisms of SARS-CoV-2 on the nervous system, including the pathways of invasion, direct and indirect effects, and associated neuropsychiatric diseases, to deepen our knowledge and understanding of the relationship between COVID-19 and the nervous system.

Construction of an Assisted Model Based on Natural Language Processing for Automatic Early Diagnosis of Autoimmune Encephalitis.

INTRODUCTION: Early diagnosis and etiological treatment can effectively improve the prognosis of patients with autoimmune encephalitis (AE). However, anti-neuronal antibody tests which provide the definitive diagnosis require time and are not always abnormal. By using natural language processing (NLP) technology, our study proposes an assisted diagnostic method for early clinical diagnosis of AE and compares its sensitivity with that of previously established criteria. METHODS: Our model is based on the text classification model trained by the history of present illness (HPI) in electronic medical records (EMRs) that present a definite pathological diagnosis of AE or infectious encephalitis (IE). The definitive diagnosis of IE was based on the results of traditional etiological examinations. The definitive diagnosis of AE was based on the results of neuronal antibodies, and the diagnostic criteria of definite autoimmune limbic encephalitis proposed by Graus et al. used as the reference standard for antibody-negative AE. First, we automatically recognized and extracted symptoms for all HPI texts in EMRs by training a dataset of 552 cases. Second, four text classification models trained by a dataset of 199 cases were established for differential diagnosis of AE and IE based on a post-structuring text dataset of every HPI, which was completed using symptoms in English language after the process of normalization of synonyms. The optimal model was identified by evaluating and comparing the performance of the four models. Finally, combined with three typical symptoms and the results of standard paraclinical tests such as cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), or electroencephalogram (EEG) proposed from Graus criteria, an assisted early diagnostic model for AE was established on the basis of the text classification model with the best performance. RESULTS: The comparison results for the four models applied to the independent testing dataset showed the naïve Bayesian classifier with bag of words achieved the best performance, with an area under the receiver operating characteristic curve of 0.85, accuracy of 84.5% (95% confidence interval [CI] 74.0-92.0%), sensitivity of 86.7% (95% CI 69.3-96.2%), and specificity of 82.9% (95% CI 67.9-92.8%), respectively. Compared with the diagnostic criteria proposed previously, the early diagnostic sensitivity for possible AE using the assisted diagnostic model based on the independent testing dataset was improved from 73.3% (95% CI 54.1-87.7%) to 86.7% (95% CI 69.3-96.2%). CONCLUSIONS: The assisted diagnostic model could effectively increase the early diagnostic sensitivity for AE compared to previous diagnostic criteria, assist physicians in establishing the diagnosis of AE automatically after inputting the HPI and the results of standard paraclinical tests according to their narrative habits for describing symptoms, avoiding misdiagnosis and allowing for prompt initiation of specific treatment.

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid.

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope. Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage. Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly. Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists. Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer's primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

Heterologous Expression of the Lactobacillus sakei Multiple Copper Oxidase to Degrade Histamine and Tyramine at Different Environmental Conditions.

Biogenic amines (BAs) are produced by microbial decarboxylation in various foods. Histamine and tyramine are recognized as the most toxic of all BAs. Applying degrading amine enzymes such as multicopper oxidase (MCO) is considered an effective method to reduce BAs in food systems. This study analyzed the characterization of heterologously expressed MCO from L. sakei LS. Towards the typical substrate 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), the optimal temperature and pH for recombinant MCO (rMCO) were 25 °C and 3.0, respectively, with the specific enzyme activity of 1.27 U/mg. Then, the effect of different environmental factors on the degrading activity of MCO towards two kinds of BAs was investigated. The degradation activity of rMCO is independent of exogenous copper and mediators. Additionally, the oxidation ability of rMCO was improved for histamine and tyramine with an increased NaCl concentration. Several food matrices could influence the amine-oxidizing activity of rMCO. Although the histamine-degrading activities of rMCO were affected, this enzyme reached a degradation rate of 28.1% in the presence of surimi. Grape juice improved the tyramine degradation activity of rMCO by up to 31.18%. These characteristics of rMCO indicate that this enzyme would be a good candidate for degrading toxic biogenic amines in food systems.

An iterative reconstruction method based on monochromatic images for dual energy CT.

PURPOSE: Dual-energy computed tomography (DECT) scans objects using two different X-ray spectra to acquire more information, which is also called dual spectral CT (DSCT) in some articles. Compared to traditional CT, DECT exhibits superior material distinguishability. Therefore, DECT can be widely used in the medical and industrial domains. However, owing to the nonlinearity and ill condition of DECT, studies are underway on DECT reconstruction to obtain high quality images and achieve fast convergence speed. Therefore, in this study, we propose an iterative reconstruction method based on monochromatic images (IRM-MI) to rapidly obtain high-quality images in DECT reconstruction. METHODS: An IRM-MI is proposed for DECT. The proposed method converts DECT reconstruction problem from the basis material images decomposition to monochromatic images decomposition to significantly improve the convergence speed of DECT reconstruction by changing the coefficient matrix of the original equations to increase the angle of the high- and low-energy projection curves or reduce the condition number of the coefficient matrix. The monochromatic images were then decomposed into basis material images. Furthermore, we conducted numerical experiments to evaluate the performance of the proposed method. RESULTS: The decomposition results of the simulated data and real data experiments confirmed the effectiveness of the proposed method. Compared to the extended algebraic reconstruction technique (E-ART) method, the proposed method exhibited a significant increase in the convergence speed by increasing the angle of polychromatic projection curves or decreasing the condition number of the coefficient matrix, when choosing the appropriate monochromatic images. Therefore, the proposed method is also advantageous in acquiring high quality and rapidly converged images. CONCLUSIONS: We developed an iterative reconstruction method based on monochromatic images for the material decomposition for DECT. The numerical experiments using the proposed method validated its capability of decomposing the basis material images. Furthermore, the proposed method achieved faster convergence speed compared to the E-ART method.

Therapeutic targets and potential agents for the treatment of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has become a global crisis. As of November 9, COVID-19 has already spread to more than 190 countries with 50,000,000 infections and 1,250,000 deaths. Effective therapeutics and drugs are in high demand. The structure of SARS-CoV-2 is highly conserved with those of SARS-CoV and Middle East respiratory syndrome-CoV. Enzymes, including RdRp, Mpro /3CLpro , and PLpro , which play important roles in viral transcription and replication, have been regarded as key targets for therapies against coronaviruses, including SARS-CoV-2. The identification of readily available drugs for repositioning in COVID-19 therapy is a relatively rapid approach for clinical treatment, and a series of approved or candidate drugs have been proven to be efficient against COVID-19 in preclinical or clinical studies. This review summarizes recent progress in the development of drugs against SARS-CoV-2 and the targets involved.

Catalytic Roles of Coenzyme Pyridoxal-5'-phosphate (PLP) in PLP-dependent Enzymes: Reaction Pathway for Methionine-γ-lyase-catalyzed L-methionine Depletion.

Pyridoxal-5'-phosphate (PLP), the active form of vitamin B6, is an important and versatile coenzyme involved in a variety of enzymatic reactions, accounting for about 4% of all classified activities. However, the detailed catalytic reaction pathways for PLP-dependent enzymes remain to be explored. Methionine-γ-lyase (MGL), a promising alternative anti-tumor agent to conventional chemotherapies whose catalytic mechanism is highly desired for guiding further development of re-engineered enzymes, was used as a representative PLP-dependent enzyme, and the catalytic mechanism for L-Met elimination by MGL was explored at the first-principles quantum mechanical/molecular mechanical (QM/MM) level with umbrella sampling. The QM/MM calculations revealed that the enzymatic reaction pathway consists of 4 stages for a total of 19 reaction steps with five intermediates captured in available crystal structures. Furthermore, the more comprehensive role of PLP was revealed. Besides the commonly known role of "electron sink", coenzyme PLP can also assist proton transfer and temporarily store the excess proton generated in some intermediate states by using its hydroxyl group and phosphate group. Thus, PLP is participated in most of the 19 steps. This study not only provided a theoretical basis for further development and re-engineering MGL as a potential anti-tumor agent, but also revealed the comprehensive role of PLP which could be used to explore the mechanisms of other PLP-dependent enzymes.

4D-AirNet: a temporally-resolved CBCT slice reconstruction method synergizing analytical and iterative method with deep learning.

Four-dimensional (4D) cone-beam CT (CBCT) reconstructs temporally-resolved phases of 3D volumes often with the same amount of projection data that are meant for reconstructing a single 3D volume. 4D CBCT is a sparse-data problem that is very challenging for high-quality 4D CBCT image reconstruction. Here we develop a new method, namely 4D-AirNet, that synergizes analytical and iterative method with deep learning for high-quality temporally-resolved CBCT slice reconstruction. 4D-AirNet is an unrolling method using the optimization framework of fused analytical and iterative reconstruction (AIR), which is based on proximal forward-backward splitting (PFBS). Three different strategies are developed for 4D-AirNet: random-phase (RP), prior-guided (PG), and all-phase (AP). RP-AirNet and PG-AirNet utilize phase-by-phase training and reconstruction, while PG-AirNet also uses a prior image reconstructed with all-phase projection data. Dense connectivity is built into 4D-AirNet networks for improved reconstruction quality. In contrast, AP-AirNet trains and reconstructs all phases simultaneously. In addition, the joint regularization method of DL and conventional spatiotemporal total variation (TV) is investigated. 4D-AirNet methods were evaluated in comparison with conventional iterative (TV) and deep learning (LEARN) methods, using simulated 2D-t CBCT scans from a lung dataset with various sparse-data levels. The reconstruction results suggest 4D-AirNet methods outperform TV and LEARN, and AP-AirNet provides the best reconstruction quality overall.

AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT.

PURPOSE: Sparse-data computed tomography (CT) frequently occurs, such as breast tomosynthesis, C-arm CT, on-board four-dimensional cone-beam CT (4D CBCT), and industrial CT. However, sparse-data image reconstruction remains challenging due to highly undersampled data. This work develops a data-driven image reconstruction method for sparse-data CT using deep neural networks (DNN). METHODS: The new method so-called AirNet is designed to incorporate the benefits from analytical reconstruction method (AR), iterative reconstruction method (IR), and DNN. It is built upon fused analytical and iterative reconstruction (AIR) that synergizes AR and IR via the optimization framework of modified proximal forward-backward splitting (PFBS). By unrolling PFBS into IR updates of CT data fidelity and DNN regularization with residual learning, AirNet utilizes AR such as FBP during the data fidelity, introduces dense connectivity into DNN regularization, and learns PFBS coefficients and DNN parameters that minimize the loss function during the training stage; and then AirNet with trained parameters can be used for end-to-end image reconstruction. RESULTS: A CT atlas of 100 prostate scans was used to validate the AirNet in comparison with state-of-art DNN-based postprocessing and image reconstruction methods. The validation loss in AirNet had the fastest decreasing rate, owing to inherited fast convergence from AIR. AirNet was robust to noise in projection data and content differences between the training set and the images to be reconstructed. The impact of image quality on radiotherapy treatment planning was evaluated for both photon and proton therapy, and AirNet achieved the best treatment plan quality, especially for proton therapy. For example, with limited-angle data, the maximal target dose for AirNet was 109.5% in comparison with the ground truth 109.1%, while it was significantly elevated to 115.1% and 128.1% for FBPConvNet and LEARN, respectively. CONCLUSIONS: A new image reconstruction AirNet is developed for sparse-data CT image reconstruction. AirNet achieved the best image reconstruction quality both visually and quantitatively among all methods under comparison for all sparse-data scenarios (sparse-view and limited-angle), and provided the best photon and proton treatment plan quality based on sparse-data CT.

Projection data smoothing through noise-level weighted total variation regularization for low-dose computed tomography.

Reducing radiation dose while maintaining the quality of the reconstructed images is a major challenge in the computed tomography (CT) community. In light of the non-stationary Gaussian noise distribution, we developed a model that incorporates a noise-level weighted total variation (NWTV) regularization term for denoising the projection data. Contrary to the well-known edge-weighted total variation method, which aims for better edge preserving, the proposed NWTV tries to adapt the regularization with the spatially varying noise levels. Experiments on simulated data as well as the real imaging data suggest that the proposed NWTV regularization could achieve quite competitive results. For sinograms with sharp edges, the NWTV could do a better job at balancing noise reduction and edge preserving, such that noise is removed in a more uniform manner. Another conclusion from our experiments is that the well-recognized stair-casing artifacts of TV regularization play little role in the reconstructed images when the NWTV method is applied to low-dose CT imaging data.

Edge information diffusion based reconstruction (EIDR) for cone beam computed laminography.

Computed laminography (CL) is a prospective nondestructive testing technique for flat object inspection in industrial applications. However, CL image reconstruction is a challenging task because incomplete projection data are acquired from the CL scan. When a conventional computed tomography (CT) reconstruction method is applied to cone beam CL data, the vertical edges (singularities in the z-direction) in the reconstructed image would be blurred. On the contrary, the horizontal edges (singularities within slices) can be quite accurately reconstructed. Based on this key observation, an edge information diffusion method is developed, which fixes the horizontal edges and propagates their values within the slices. An effective CL reconstruction method is then proposed for flat object inspection by combining the edge information diffusion procedure, which plays the role of regularization, with conventional CT image reconstruction algorithms. Experiments on both simulated data and real data are performed to verify the effectiveness of the proposed method. The results show that the proposed method can effectively suppress the inter-slice aliasing and blurring caused by incompleteness of the CL scan data, and that it outperforms other state-of-the-art methods.

Loss induced coherent combining in InP-Si3N4 hybrid platform.

Loss, as a time-reversed counterpart of gain, can also be used to control lasing in an optical system with coupled cavities. In this study, by manipulating mirror losses at different output ports of coupled Fabry-Perot cavities, an integrated coherently combined laser system is proposed and experimentally demonstrated in the InP-Si3N4 hybrid platform. Two InP-based reflective semiconductor amplifiers are coherently combined through an adiabatic 50:50 directional coupler in silicon nitride. The combining efficiency is ~92% at ~2× threshold. The novel system not only realizes the miniaturization of coherent laser beam combining but also provides a chip-scale platform to study the coherent coupling between coupled laser cavities.

Two-dimensional photonic crystal Bragg lasers with triangular lattice for monolithic coherent beam combining.

We demonstrate an electrically pumped, single-mode, large area, edge-emitting InGaAsP/InP two-dimensional photonic crystal (PC) Bragg laser with triangular lattice. The laser operates in the single transverse and longitudinal modes with a single lobe, near-diffraction-limited far field. We compare the performance of the triangular-lattice PC Bragg laser with the rectangular-lattice PC Bragg laser fabricated from the same wafer and find that their performances are comparable. Then, we combine two single triangular-lattice PC Bragg lasers that tilt to opposite directions by taking advantage of the symmetry of the single emitter cavity mode. The measurement results show that the combined PC Bragg lasers provide the near-diffraction-limited output beam, and the single wavelength operation is also maintained in the coherently combined broad-area PC Bragg lasers.

Detector-unit-dependent calibration for polychromatic projections of rock core CT.

Computed tomography (CT) plays an important role in digital rock analysis, which is a new prospective technique for oil and gas industry. But the artifacts in CT images will influence the accuracy of the digital rock model. In this study, we proposed and demonstrated a novel method to restore detector-unit-dependent functions for polychromatic projection calibration by scanning some simple shaped reference samples. As long as the attenuation coefficients of the reference samples are similar to the scanned object, the size or position is not needed to be exactly known. Both simulated and real data were used to verify the proposed method. The results showed that the new method reduced both beam hardening artifacts and ring artifacts effectively. Moreover, the method appeared to be quite robust.

Attenuator design method for dedicated whole-core CT.

In whole-core CT imaging, scanned data corresponding to the central portion of a cylindrical core often suffer from photon starvation, because increasing photon flux will cause overflow on some detector units under the restriction of detector dynamic range. Either photon starvation or data overflow will lead to increased noise or severe artifacts in the reconstructed CT image. In addition, cupping shaped beam hardening artifacts also appear in the whole-core CT image. In this paper, we present a method to design an attenuator for cone beam whole-core CT, which not only reduces the dynamic range requirement for high SNR data scanning, but also corrects beam hardening artifacts. Both simulation and real data are employed to verify our design method.

Automatic Synthesis of Panoramic Radiographs from Dental Cone Beam Computed Tomography Data.

In this paper, we propose an automatic method of synthesizing panoramic radiographs from dental cone beam computed tomography (CBCT) data for directly observing the whole dentition without the superimposition of other structures. This method consists of three major steps. First, the dental arch curve is generated from the maximum intensity projection (MIP) of 3D CBCT data. Then, based on this curve, the long axial curves of the upper and lower teeth are extracted to create a 3D panoramic curved surface describing the whole dentition. Finally, the panoramic radiograph is synthesized by developing this 3D surface. Both open-bite shaped and closed-bite shaped dental CBCT datasets were applied in this study, and the resulting images were analyzed to evaluate the effectiveness of this method. With the proposed method, a single-slice panoramic radiograph can clearly and completely show the whole dentition without the blur and superimposition of other dental structures. Moreover, thickened panoramic radiographs can also be synthesized with increased slice thickness to show more features, such as the mandibular nerve canal. One feature of the proposed method is that it is automatically performed without human intervention. Another feature of the proposed method is that it requires thinner panoramic radiographs to show the whole dentition than those produced by other existing methods, which contributes to the clarity of the anatomical structures, including the enamel, dentine and pulp. In addition, this method can rapidly process common dental CBCT data. The speed and image quality of this method make it an attractive option for observing the whole dentition in a clinical setting.