Sampling clustering based on multi-view attribute structural relations

In light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes. Notably, the practical significance of multi-view graph clustering is heightened due to the presence of diverse relationships within real-world graph data. Nonetheless, prevailing graph clustering techniques, predominantly grounded in deep learning neural networks, face challenges in effectively handling multi-view graph data. These challenges include the incapability to concurrently explore the relationships between multiple view structures and node attributes, as well as difficulties in processing multi-view graph data with varying features. To tackle these issues, this research proposes a straightforward yet effective multi-view graph clustering approach known as SLMGC. This approach uses graph filtering to filter noise, reduces computational complexity by extracting samples based on node importance, enhances clustering representations through graph contrastive regularization, and achieves the final clustering outcomes using a self-training clustering algorithm. Notably, unlike neural network algorithms, this approach avoids the need for intricate parameter settings. Comprehensive experiments validate the supremacy of the SLMGC approach in multi-view graph clustering endeavors when contrasted with prevailing deep neural network techniques.

Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data

A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal distortion. However, most current methods for estimating the dimension threshold rely on experience, such as observing the signal waveforms and spectrum, which may render the results too subjective and lacking in quantitative accuracy. Therefore, this study proposes a method to automatically estimate a suitable threshold. Time-frequency transformations are performed on the evoked state data to obtain the neural signal of interest and the noise signal in a specific time-frequency band, which are then used to construct the objective function describing the degree of noise suppression and signal distortion. The optimal value of the threshold in the selected range is obtained using the weighted-sum method. Our method was tested on two classical subspace projection algorithms using simulation and two sensory stimulation experiments. The thresholds estimated by the proposed method enabled the algorithms to achieve the best waveform recovery and source location error. Therefore, the threshold selected in this method enables subspace projection algorithms to achieve the best balance between noise removal and neural signal preservation in subsequent MEG analyses.

A Multivariate analysis on evoked components of Chinese semantic congruity: an OP-MEG study with EEG

The application of wearable magnetoencephalography using optically-pumped magnetometers has drawn extensive attention in the field of neuroscience. Electroencephalogram system can cover the whole head and reflect the overall activity of a large number of neurons. The efficacy of optically-pumped magnetometer in detecting event-related components can be validated through electroencephalogram results. Multivariate pattern analysis is capable of tracking the evolution of neurocognitive processes over time. In this paper, we adopted a classical Chinese semantic congruity paradigm and separately collected electroencephalogram and optically-pumped magnetometer signals. Then, we verified the consistency of optically-pumped magnetometer and electroencephalogram in detecting N400 using mutual information index. Multivariate pattern analysis revealed the difference in decoding performance of these two modalities, which can be further validated by dynamic/stable coding analysis on the temporal generalization matrix. The results from searchlight analysis provided a neural basis for this dissimilarity at the magnetoencephalography source level and the electroencephalogram sensor level. This study opens a new avenue for investigating the brain's coding patterns using wearable magnetoencephalography and reveals the differences in sensitivity between the two modalities in reflecting neuron representation patterns.

Global research trends of uranium-containing wastewater treatment based on bibliometric review

The generation of uranium-containing wastewater (UCW) during different stages of uranium mining, processing, and utilization presents a significant ecological and biospheric threat. Consequently, it is crucial for both sustainable development and the protection of human health to adopt appropriate methods for the treatment of UCW as well as the separation and enrichment of uranium. This study conducted a comprehensive search of the Web of Science Core Collection (WOSCC) database for publications related to UCW treatment between 1990 and 2022 to gain insight into current trends in the field. Subsequently, the annual publications, WOSCC categories, geographical distribution, major collaborations, prolific authors, influential journals, and highly cited publications were the subjects of a biliometric analysis that was subsequently carried out. The study findings indicate a significant rise in the overall number of publications in the research field between 1990 and 2022. China, India, and the USA emerged as the primary contributors in terms of publication count. The Chinese Academy of Sciences, the East China University of Technology, and the University of South China were identified as the key research institutions in this field. Furthermore, a majority of the publications in this field were distributed through prestigious journals with high impact factors, such as the Journal of Radioanalytical and Nuclear Chemistry. The top 3 journals were Radioanalytical and Nuclear Chemistry, Chemical Engineering Journal, and Journal of Hazardous Materials. The keyword co-occurrence and burst analysis revealed that the current research on UCW treatment mainly focuses on adsorption-based treatment methods, environmentally functional materials, uranium recovery, etc. Furthermore, the study of the adsorption efficiency of different adsorbent materials, as well as the strengthening and improvement of adsorbent material selectivity and capacity for the recovery of uranium, represents a research hotspot in the field of UCW treatment in the future. This study conducts a comprehensive overview of the current status and prospects of the UCW treatment, which can provide a valuable reference for gaining insights into the development trajectory of the UCW treatment.

Effect of dry eye on the reliability of keratometry for cataract surgery planning

PURPOSE

This study aimed to evaluate the effects of dry eye on the reproducibility of keratometry (K) measurements in patients presenting for cataract surgery.

METHODS

A non-randomized controlled clinical study was performed. Eighty-three eyes of eighty-three patients with cataracts who were enrolled in our hospital from March 2020 to July 2020 were studied. The mean non-invasive tear film break-up time (NIBUT), corneal fluorescein staining score, and ocular surface disease (OSD) SPEED II questionnaire were measured and recorded prior to surgery, and the patients were assigned to a "dry eye" group (n=35) or a "non-dry eye" group (n=48). The K of the patients was measured twice by a Tomey OA-2000 (an average of three times each). The difference of the mean K (ΔKm) and astigmatism vector (ΔKvector) between the two measurements was calculated. The ΔKm and ΔKvector between the two groups were compared. The relationship between the measurement parameters of dry eyes and the accuracy of the preoperative K values was analyzed.

RESULTS

ΔKm was 0.09 D [0.03; 0.19] in the non-dry eye control group and 0.28 D [0.18; 0.50] in the dry eye group, with a statistical difference between the two groups (P=0.005). The ΔKvector of the non-dry eye control group was 0.22 D [0.14; 0.42], and that of the dry eye group was 0.50 D [0.28; 1.06]. There was a significant difference between the two groups (P=0.010). Between the two groups, the percentage of the ΔKm and ΔKvector values greater than 0.5 D were statistically different (P<0.05). There was no significant difference in ΔKm between the groups with NIBUT>5s and NIBUT≤5s (P=0.537). There was no significant difference in ΔKm between groups≥2 and<2 on the OSD SPEED II questionnaire scores (P=0.442).

CONCLUSION

Dry eye can affect the reliability of keratometry measurements before cataract surgery, thereby affecting the accuracy of intraocular lens power calculations. In cataract surgery planning, it is necessary to detect subjective and objective indicators of dry eye in patients and carry out effective intervention so as to avoid refractive errors caused by inaccurate keratometry measurements.

Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features

Objective.This study aimed to develop an automatic and accurate method for severity assessment and localization of coronary artery disease (CAD) based on an optically pumped magnetometer magnetocardiography (MCG) system.Approach.We proposed spatiotemporal features based on the MCG one-dimensional signals, including amplitude, correlation, local binary pattern, and shape features. To estimate the severity of CAD, we classified the stenosis as absence or mild, moderate, or severe cases and extracted a subset of features suitable for assessment. To localize CAD, we classified CAD groups according to the location of the stenosis, including the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA), and separately extracted a subset of features suitable for determining the three CAD locations.Main results.For CAD severity assessment, a support vector machine (SVM) achieved the best result, with an accuracy of 75.1%, precision of 73.9%, sensitivity of 67.0%, specificity of 88.8%, F1-score of 69.8%, and area under the curve of 0.876. The highest accuracy and corresponding model for determining locations LAD, LCX, and RCA were 94.3% for the SVM, 84.4% for a discriminant analysis model, and 84.9% for the discriminant analysis model.Significance. The developed method enables the implementation of an automated system for severity assessment and localization of CAD. The amplitude and correlation features were key factors for severity assessment and localization. The proposed machine learning method can provide clinicians with an automatic and accurate diagnostic tool for interpreting MCG data related to CAD, possibly promoting clinical acceptance.

Research progress and hotspots on microbial remediation of heavy metal-contaminated soil: a systematic review and future perspectives

Microbial remediation technology has received much attention as a green, ecological, and inexpensive technology, and there is great potential for the application of microbial remediation technology for heavy metals (HMs) contaminated soil alone and in conjunction with other technologies in environmental remediation. To gain an in-depth understanding of the latest research progress, research hotspots, and development trends on microbial remediation of HMs-contaminated soil, and to objectively reflect the scientific contributions and impacts of relevant countries/regions, institutions, and individuals of this field, in this manuscript, ISI Web of Knowledge's Web of Science™ core collection database, data visualization, and analysis software Bibliometrix, VOSviewer, and HistCite Pro were used to collect and analyze the relevant literature from 2000 to 2022, and 1409 publications were subjected to scientometric analyses. It involved 327 journals, 5150 authors, 75 countries/regions, and 2740 keywords. The current progress and hotspots on microbial remediation of HMs-contaminated soil since the twenty-first century were analyzed in terms of the top 10 most productive countries (regions), high-yielding authors, source journals, important research institutions, and hotspots of research directions. Over the past 22 years, China, India, and the USA have been the countries with the most articles. The institution and author with the most publications are the Chinese Acad Sci and Zhu YG, respectively. Journal of Hazardous Materials is the most productive journal. The keywords showed 6 co-occurrence clusters. These findings revealed the research hotspots, knowledge gaps, and future exploration trends related to microbial remediation of HMs-contaminated soil.

Co-registration of OPM-MCG signals with CT using optical scanning

Magnetocardiography (MCG) can be used to noninvasively measure the electrophysiological activity of myocardial cells. The high spatial resolution of magnetic source localization can precisely determine the location of cardiomyopathy, which is of great significance for the diagnosis and treatment of cardiovascular disease. To perform magnetic source localization, MCG data must be co-registered with anatomical images. We propose a co-registration method that can be applied to OPM-MCG systems. In this method, the sensor array and the trunk of the subject are scanned using structured light-scanning technology, and the scan results are registered with the reconstructed structure using computed tomography (CT). This can increase the number of effective cloud points acquired and reduce the interference from respiratory motion. The scanning bed of the OPM-MCG system was modified to be consistent with the CT device, ensuring that the state of the body remains consistent between the cardiac magnetometry measurements and CT scans.

Optical Co-Registration Method of Triaxial OPM-MEG and MRI

The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X -and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.

Realistic three-layer head phantom for optically pumped magnetometer-based magnetoencephalography

The advent of optically pumped magnetometer-based magnetoencephalography (OPM-MEG) has introduced new tools for neuroscience and clinical research. As it is still under development, the achievable performance of OPM-MEG remains to be tested, particularly in terms of source localization accuracy, which can be influenced by various factors, including software and hardware aspects. A feasible approach to comprehensively test the performance of the OPM-MEG system is to utilize a phantom that simulates the actual electrophysiological properties of the head while ensuring the precise locations of dipole sources. However, conventional water or dry phantoms can only simulate a single-sphere head model. In this work, a more realistic three-layer phantom was designed and fabricated. The proposed phantom included the scalp, skull, and cortex tissues of the head, as well as the simulated dipole sources. The scalp and cortex tissues were simulated using an electrolyte solution, while the dipole source was constructed from a coaxial cable. All main structures in the phantom were produced using 3D printing techniques, making the phantom easy to manufacture. The fabricated phantom was tested on a 36-channel OPM-MEG system, and the results showed that the dipole source inside the phantom could generate a magnetic field distribution on the scalp that was close to its theoretical values. The average source localization accuracy of 5.51 mm verified the effectiveness of the designed phantom and the performance of our OPM-MEG system. This work provides an effective test platform for OPM-MEG.

Magnetocardiography using optically pumped magnetometers array to detect acute myocardial infarction and premature ventricular contractions in dogs

OBJECTIVE

Optically pumped magnetometers (OPMs) are recently developed magnetocardiography (MCG) sensors that can detect cardiac diseases. This is of great clinical significance for detecting acute myocardial infarction (AMI) and premature ventricular contractions (PVC). This study investigates the use of an array of OPMs to detect heart disease in animals.

APPROACH

An array of OPMs was used to detect the MCG of AMI and PVC in dogs. We used four dogs in this study, and models of AMI with different degrees of severity were established by ligating the middle and proximal segments of the left anterior descending coronary artery. The dogs had PVC at the time of AMI. Continuous MCG time series with corresponding electrocardiograms (ECGs) and average MCG for each dog in different states are presented. The MCG features were extracted from the MCG butterfly diagram, magnetic field map, and pseudo current density map. The MCG features were used to quantify and compare with the gold-standard ECG measures.

MAIN RESULTS

The results show that MCG features can accurately distinguish different states of dogs. That is, an array of OPMs can effectively detect AMI and PVC in dogs.

SIGNIFICANCE

We conclude that the array of OPMs can detect heart diseases in animals. Moreover, OPMs can complement or even replace superconducting quantum interference devices for MCG measurement in animals and diagnosis of human heart diseases in the future.

Lactobacillus coryniformis subsp. torquens inhibits bone loss in obese mice via modification of the gut microbiota

High-fat diet (HFD)-induced obesity results in bone loss associated with an imbalanced gut microbiota and altered immune status. Probiotics are live microorganisms that are beneficial to the host and are important in maintaining bone health and gut homeostasis. In this study, the probiotic Lactobacillus coryniformis subsp. torquens (T3L) was isolated from traditional yak milk cheese produced in Lhasa and showed distinct acid and bile salt resistance as potential probiotics. Our data indicated that T3L not only reversed HFD-induced gut dysbiosis, as indicated by decreased Firmicutes-to-Bacteroidetes ratios but also reduced bone loss. The anti-obesity, microbiome-modulating, and bone-protective effects were transmissible via horizontal faeces transfer from T3L-treated mice to HFD-fed mice. The protective effects of T3L on bone mass were associated with regulatory T (Treg) cell-mediated inhibition of osteoclast differentiation. Our data indicate that T3L is a regulator of the gut microbiota and bone homeostasis in an animal model.

Optimization of Signal Space Separation for Optically Pumped Magnetometer in Magnetoencephalography

Magnetoencephalography (MEG) is a noninvasive functional neuroimaging modality but highly susceptible to environmental interference. Signal space separation (SSS) is a method for improving the SNR to separate the MEG signals from external interference. The origin and truncation values of SSS significantly affect the SSS performance. The origin value fluctuates with respect to the helmet array, and determining the truncation values using the traversal method is time-consuming; thus, this method is inappropriate for optically pumped magnetometer (OPM) systems with flexible array designs. Herein, an automatic optimization method for the SSS parameters is proposed. Virtual sources are set inside and outside the brain to simulate the signals of interest and interference, respectively, via forward model, with the sensor array as prior information. The objective function is determined as the error between the signals from simulated sources inside the brain and the SSS reconstructed signals; thus, the optimized parameters are solved inversely by minimizing the objective function. To validate the proposed method, a simulation analysis and MEG auditory-evoked experiments were conducted. For an OPM sensor array, this method can precisely determine the optimized origin and truncation values of the SSS simultaneously, and the auditory-evoked component, for example, N100, can be accurately located in the temporal cortex. The proposed optimization procedure outperforms the traditional method with regard to the computation time and accuracy, simplifying the SSS process in signal preprocessing and enhancing the performance of SSS denoising.

Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling

Deep learning has substantially improved the state-of-the-art in object detection and image classification. Deep learning usually requires large-scale labelled datasets to train the models; however, due to the restrictions in medical data sharing and accessibility and the expensive labelling cost, the application of deep learning in medical image classification has been dramatically hindered. In this study, we propose a novel method that leverages semi-supervised adversarial learning and pseudo-labelling to incorporate the unlabelled images in model learning. We validate the proposed method on two public databases, including ChestX-ray14 for lung disease classification and BreakHis for breast cancer histopathological image diagnosis. The results show that our method achieved highly effective performance with an accuracy of 93.15% while using only 30% of the labelled samples, which is comparable to the state-of-the-art accuracy for chest X-ray classification; it also outperformed the current methods in multi-class breast cancer histopathological image classification with a high accuracy of 96.87%.

Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network

To generate and evaluate post-therapeutic optical coherence tomography (OCT) images based on pre-therapeutic images with generative adversarial network (GAN) to predict the short-term response of patients with retinal vein occlusion (RVO) to anti-vascular endothelial growth factor (anti-VEGF) therapy. Real-world imaging data were retrospectively collected from 1 May 2017, to 1 June 2021. A total of 515 pairs of pre-and post-therapeutic OCT images of patients with RVO were included in the training set, while 68 pre-and post-therapeutic OCT images were included in the validation set. A pix2pixHD method was adopted to predict post-therapeutic OCT images in RVO patients after anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated by screening and evaluation experiments. We quantitatively and qualitatively assessed the prognostic accuracy of the synthetic post-therapeutic OCT images. The post-therapeutic OCT images generated by the pix2pixHD algorithm were comparable to the actual images in edema resorption response. Retinal specialists found most synthetic images (62/68) difficult to differentiate from the real ones. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic and real OCT images was 26.33 ± 15.81 μm. There was no statistical difference in CMT between the synthetic and the real images. In this retrospective study, the application of the pix2pixHD algorithm objectively predicted the short-term response of each patient to anti-VEGF therapy based on OCT images with high accuracy, suggestive of its clinical value, especially for screening patients with relatively poor prognosis and potentially guiding clinical treatment. Importantly, our artificial intelligence-based prediction approach’s non-invasiveness, repeatability, and cost-effectiveness can improve compliance and follow-up management of this patient population.

OMMR: Co-registration toolbox of OPM-MEG and MRI

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPM-MEG) has shown better flexibility in sensor configuration compared with the conventional superconducting quantum interference devices-based MEG system while being better suited for all-age groups. However, this flexibility presents challenges for the co-registration of MEG and magnetic resonance imaging (MRI), hindering adoption. This study presents a toolbox called OMMR, developed in Matlab, that facilitates the co-registration step for researchers and clinicians. OMMR integrates the co-registration methods of using the electromagnetic digitization system and two types of optical scanners (the structural-light and laser scanner). As the first open-source co-registration toolbox specifically for OPM-MEG, the toolbox aims to standardize the co-registration process and set the ground for future applications of OPM-MEG.

Scanning multi-channel spin-exchange relaxation-free atomic magnetometer with high spatial and time resolution: publisher's note

This publisher's note contains a correction to Opt. Lett.47, 3908 (2022)10.1364/OL.465832.

Spatial Accuracy Evaluation of Magnetic Source Imaging Methods on OPM-based MEG

The emergence of the optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has led to new developments in MEG technology. The source imaging results of different magnetic source imaging (MSI) methods show considerable differences, which makes it difficult for researchers to choose an appropriate method. This study assessed time-domain MSI methods implemented in the Brainstorm, FieldTrip, and SPM12 toolboxes using simulations. We proposed using a metric, variational free energy under the Bayesian framework, as an indicator to evaluate source imaging results because it does not require the ground truth of sources but uses the fitness of the measurement data. Our simulations demonstrated the effectiveness of the variational free energy in indicating the quality of the source reconstruction results. We then applied each MSI method to the real OPM-MEG experimental data. We aimed to highlight the characteristics of each method and provide references for researchers choosing an appropriate MSI method.

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MSI methods implemented in Brainstorm, FieldTrip, and SPM12 are evaluated

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Variational free energy is proposed to compare different source imaging results

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Metrics, DLE, SD, and resolution matrix, are included to assess different methods

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MSI methods were applied to imaging sources in a median nerve OPM-MEG experiment

Scanning a multi-channel spin-exchange relaxation-free atomic magnetometer with high spatial and time resolution

The emerging multi-channel spin-exchange relaxation-free (SERF) atomic magnetometer is a promising candidate for non-intrusive biomagnetism imaging. In this study, we propose a scanning 9-channel SERF magnetometer based on an acousto-optic modulator (AOM). Using the diffraction light of the AOM as the probe laser (with a low laser power of 1.7 mW), 9 channels were rapidly scanned by altering the diffraction angle. The scanning imaging scheme provides a new, to the best of our knowledge, approach for multi-channel magnetic field measurement and realizes a single-channel sensitivity of about 3 fT/Hz^1/2, a spatial resolution of 0.6 mm, and a time resolution of about 2.7 ms, which is well suited for real-time extremely weak magnetic field imaging.

Retinal Microvascular Signs in Pre- and Early-Stage Diabetic Retinopathy Detected Using Wide-Field Swept-Source Optical Coherence Tomographic Angiography

Purpose Using a wide-field, high-resolution swept-source optical coherence tomographic angiography (OCTA), this study investigated microvascular abnormalities in patients with pre- and early-stage diabetic retinopathy. Methods 38 eyes of 20 people with diabetes mellitus (DM) type 2 without diabetic retinopathy (DR) and 39 eyes of 21 people with DR were enrolled in this observational and cross-sectional cohort study, and a refractive error-matched group consisting of 42 eyes of 21 non-diabetic subjects of similar age were set as the control. Each participant underwent a wide-field swept-source OCTA. On OCTA scans (1.2 cm × 1.2 cm), the mean central macular thickness (CMT), the vessel density of the inner retina, superficial capillary plexus (SCP), and deep capillary plexus (DCP) were independently measured in the whole area (1.2 cm diameter) via concentric rings with varying radii (0–0.3, 0.3–0.6, 0.6–0.9, and 0.9–1.2 cm). Results Patients whose eyes had pre-and early-stage DR showed significantly decreased vessel density in the inner retina, SCP, DCP and CMT (early-stage DR) compared with the control. In addition, compared with the average values upon wide-field OCTA, the decreases were even more pronounced for concentric rings with a radius of 0.9–1.2 cm in terms of the inner retina, SCP, DCP and CMT. Conclusions Widefield OCTA allows for a more thorough assessment of retinal changes in patients with pre- and early-stage DR.; retinal microvascular abnormalities were observed in both groups. In addition, the decreases in retinal vessel density were more significant in the peripheral concentric ring with a radius of 0.9–1.2 cm. The application of novel and wide-field OCTA could potentially help to detect earlier diabetic microvascular abnormalities.

DMS-SK/BLSTM-CTC Hybrid Network for Gesture/Speech Fusion and Its Application in Lunar Robot–Astronauts Interaction

In the future manned lunar exploration mission, astronauts would work with the lunar robots, which has a high requirement for human–robot interaction (HRI). As the accuracy of gesture recognition interaction does not fulfill the requirement for human–robot joint exploration missions, we propose the DMS-SK/BLSTM-CTC hybrid network to improve the performance of HRI. For gesture recognition, considering VGG-SK has low accuracy and complex architecture, we delete the fourth convolution module, optimize the last global pooling layer, introduce dilated convolution block and multiscale convolution block in VGG-SK, and get the DMS-SK-based gesture recognition sub-network. Compared with the traditional recognition methods, the accuracy and performance of DMS-SK improve. For speech recognition, considering that Bidirectional long–short-term memory unit (BLSTM) has the advantages of processing temporal information, and the Connectionist Temporal Classification (CTC) algorithm can simplify speech data preprocessing, we use BLSTM based on CTC as the speech recognition sub-network. Finally, we combine DMS-SK with BLSTM-CTC, and propose the DMS-SK/BLSTM-CTC hybrid network as the gesture/speech hybrid network. In addition, we use 10 gestures in the American Sign Language (ASL) dataset and 10 speech commands to construct the gesture/speech hybrid dataset. Experimental results show that compared with the pure gesture or pure speech networks, the recognition accuracy of the gesture-speech hybrid network improves by 2% and 12%, respectively, its accuracy reaches 97.38%, which fulfills the requirement of astronauts for HRI.

Multiple Source Detection based on Spatial Clustering and Its Applications on Wearable OPM-MEG

OBJECTIVE

Magnetoencephalography (MEG) is a non-invasive technique that measures the magnetic fields of brain activity. In particular, a new type of optically pumped magnetometer (OPM)-based wearable MEG system has been developed in recent years. Source localization in MEG can provide signals and locations of brain activity. However, conventional source localization methods face the difficulty of accurately estimating multiple sources. The present study presented a new parametric method to estimate the number of sources and localize multiple sources. In addition, we applied the proposed method to a constructed wearable OPM-MEG system.

METHODS

We used spatial clustering of the dipole spatial distribution to detect sources. The spatial distribution of dipoles was obtained by segmenting the MEG data temporally into slices and then estimating the parameters of the dipoles on each data slice using the particle swarm optimization algorithm. Spatial clustering was performed using the spatial-temporal density-based spatial clustering of applications with a noise algorithm. The performance of our approach for detecting multiple sources was compared with that of four typical benchmark algorithms using the OPM-MEG sensor configuration.

RESULTS

The simulation results showed that the proposed method had the best performance for detecting multiple sources. Moreover, the effectiveness of the method was verified by a multimodel sensory stimuli experiment on a real constructed 31-channel OPM-MEG.

CONCLUSION

Our study provides an effective method for the detection of multiple sources.

SIGNIFICANCE

With the improvement of the source localization methods, MEG may have a wider range of applications in neuroscience and clinical research.

Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach

In recent years, optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has shown potential for analyzing brain activity. It has a flexible sensor configuration and comparable sensitivity to conventional SQUID-MEG. We constructed a 32-channel OPM-MEG system and used it to measure cortical responses to median and ulnar nerve stimulations. Traditional magnetic source imaging methods tend to blur the spatial extent of sources. Accurate estimation of the spatial size of the source is important for studying the organization of brain somatotopy and for pre-surgical functional mapping. We proposed a new method called variational free energy-based spatial smoothing estimation (FESSE) to enhance the accuracy of mapping somatosensory cortex responses. A series of computer simulations based on the OPM-MEG showed better performance than the three types of competing methods under different levels of signal-to-noise ratios, source patch sizes, and co-registration errors. FESSE was then applied to the source imaging of the OPM-MEG experimental data.

Assessment of choroidal structural changes in patients with pre- and early-stage clinical diabetic retinopathy using wide-field SS-OCTA

PURPOSE

To investigate the micro-vascular changes in choroidal structures in patients with pre- and early-stage clinical diabetic retinopathy (DR) using wide-field Swept-Source Optical Coherence Tomography Angiography (SS-OCTA).

METHOD

This observational cross-sectional study included 131 eyes of 68 subjects that were divided into healthy controls (group 1, n = 46), pre-DR (group 2, n = 43), early-stage DR (group 3, n = 42) cohorts. All participants that underwent SS-OCTA examination were inpatients in the department of Ophthalmology and the department of Endocrinology, Qilu Hospital, Shandong University, and Department of Ophthalmology, Aier Eye Hospital, Jinan, from July 11, 2021 to March 17, 2022. The choroidal vascularity index (CVI), choroidal thickness (ChT) and central macular thickness (CMT) in the whole area (diameter of 12 mm) and concentric rings with different ranges (0-3, 3-6, 6-9, and 9-12 mm) were recorded and analyzed from the OCTA image.

RESULT

Compared with healthy eyes, decreases in CVI and ChT were found in the eyes of patients with pre-or early-stage DR. The changes were more significant in the peripheral choroid, with the most prominent abnormalities in the 9-12mm area (P < 0.001). However, there was no obvious difference in the average CMT value. Furthermore, CVI and ChT were significantly correlated with the duration of diabetes in the range of 6-9 and 9-12 mm (Ps < 0.05; Correlation coefficient = -0.549, -0.395, respectively), with the strongest correlation (Ps < 0.01; Correlation coefficient = -0.597, -0.413, respectively) observed at 9-12 mm.

CONCLUSION

The CVI and ChT values of diabetic patients are significantly lower than in healthy controls, especially in patients with early-stage DR. In addition, the peripheral choroidal capillaries are more susceptible to early DM-induced injury than in the central area.

The influence of modulated magnetic field on light absorption in SERF atomic magnetometer

A single-beam spin-exchange relaxation-free atomic magnetometer is ultra-sensitive in the zero field, which has great potential for the detection of a magnetoencephalogram. The addition of a modulated magnetic field is an important approach to achieve high sensitivity for devices of this kind. In this study, we discovered that the amplitude and frequency of the modulated magnetic field (modulation index 0-3) both influence the light absorption. We defined this effect into a function by combining theoretical analysis and the results of experiments. It is discovered that the transmission intensity decreases with an increase in the modulation index. This effect is weakened under the application of a high modulation index. In addition, the transmission intensity and bias magnetic field no longer follow a strict Lorentz curve, while a high degree of fit can be achieved by applying the numerical solution of the Bloch function. A compact magnetometer with a volume of 10 cm³ and a sensitivity of 20 fT/Hz is developed based on the single beam scheme for the proof of concept. Our study is crucial in two aspects: (1) Obtaining high sensitivity through a short measurement period and (2) alignment of the scale factor of the individual magnetometer in a detection array, which further pave the way for improvement in a magnetometer's performance under a variety of optics platforms.

Co-registration Comparison of On-Scalp Magnetoencephalography and Magnetic Resonance Imaging

Magnetoencephalography (MEG) can non-invasively measure the electromagnetic activity of the brain. A new type of MEG, on-scalp MEG, has attracted the attention of researchers recently. Compared to the conventional SQUID-MEG, on-scalp MEG constructed with optically pumped magnetometers is wearable and has a high signal-to-noise ratio. While the co-registration between MEG and magnetic resonance imaging (MRI) significantly influences the source localization accuracy, co-registration error requires assessment, and quantification. Recent studies have evaluated the co-registration error of on-scalp MEG mainly based on the surface fit error or the repeatability error of different measurements, which do not reflect the true co-registration error. In this study, a three-dimensional-printed reference phantom was constructed to provide the ground truth of MEG sensor locations and orientations relative to MRI. The co-registration performances of commonly used three devices—electromagnetic digitization system, structured-light scanner, and laser scanner—were compared and quantified by the indices of final co-registration errors in the reference phantom and human experiments. Furthermore, the influence of the co-registration error on the performance of source localization was analyzed via simulations. The laser scanner had the best co-registration accuracy (rotation error of 0.23° and translation error of 0.76 mm based on the phantom experiment), whereas the structured-light scanner had the best cost performance. The results of this study provide recommendations and precautions for researchers regarding selecting and using an appropriate device for the co-registration of on-scalp MEG and MRI.

[Autologous stem cell transplantation improves outcomes of patients with multiple myeloma receiving proteasome inhibitors and lenalidomide treatment]

OBJECTIVE

To evaluate the effect of autologous stem cell transplantation (ASCT) on treatment response and survival outcomes in patients with newly diagnosed multiple myeloma (MM) receiving treatments with proteasome inhibitors and lenalidomide.

METHODS

We retrospectively collected the clinical data of newly diagnosed MM patients, who were eligible for ASCT and received proteasome inhibitors or lenalidomide-based treatment in our hospital from January, 2015 to December, 2019. The patients were divided into transplantation group and non-transplantation group, and in transplantation group, the patients received 4 to 6 courses of induction therapy with proteasome inhibitors or lenalidomide before ASCT, while those in the non-transplantation group received more than 8 courses of induction and consolidation therapy with proteasome inhibitors or lenalidomide-based regimens. The therapeutic efficacy and survival outcomes of the patinets were compared between the two groups.

RESULTS

A total of 105 patients were enrolled in the study, including 48 (45.7%) in transplantation group and 57 (54.3%) in non-transplantation group. The two groups were matched for gender, age and treatment response after 4 courses of induction therapy (P > 0.05). The rate of optimal response before relapse differed significantly between the two groups (P=0.000), and the patients receiving ASCT had significantly higher rates of complete response (85.4% vs 54.4%, P= 0.001) and very good partial response or better (95.8% vs 73.7%, P=0.002) than those without ASCT. At the end of follow-up, the median progression-free survival in the transplantation group was not reached, as compared with 29 months in the nontransplantation group (P=0.013). The median overall survival (OS) in the two groups was not reached, but the OS was better in the transplant group than in the non-transplant group (P=0.022).

CONCLUSION

ASCT can further improve the depth of remission and survival outcomes in patients with newly diagnosed MM receiving treatments with proteasome inhibitors and lenalidomide.

A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array

In this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

Double-loop control and intelligent parameter tuning for the temperature control system of a DBR semiconductor laser

Aiming at lower startup power consumption, stronger thermal load adaptability, easier parameters adjustment, and higher parameter tuning efficiency for the temperature control system of a distributed Bragg reflector (DBR) semiconductor laser, this paper employs the double-loop control and intelligent parameter tuning methods. First, the thermal equivalent circuit model is established for the laser temperature control system, which has stronger thermal load adaptability than the traditional transfer function model. In order to improve the modeling speed and accuracy, a mean impact value (MIV) quantum particle swarm optimization (QPSO) intelligent algorithm is proposed to tune the model parameters. A double-loop temperature control system is set up on this basis. Then, the MIV-QPSO intelligent algorithm is used to tune the control parameters, which shortens the settling time, increases the tuning efficiency, and improves the temperature control effect. The feasibility and effectiveness of the proposed methods are verified through the MATLAB/Simulink simulation of the laser temperature control process.

Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

BACKGROUND

The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients.

OBJECTIVE

This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans.

METHODS

In this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons.

RESULTS

DNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P<.001 in specificity and accuracy).

CONCLUSIONS

Deep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

Identification of translationally controlled tumor protein in promotion of DNA homologous recombination repair in cancer cells by affinity proteomics

Translationally controlled tumor protein(TCTP) has been implicated in the regulation of apoptosis, DNA repair and drug resistance. However, the underlying molecular mechanisms are poorly defined. To better understand the molecular mechanisms underlying TCTP involved in cellular processes, we performed an affinity purification-based proteomic profiling to identify proteins interacting with TCTP in human cervical cancer HeLa cells. We found that a group of proteins involved in DNA repair are enriched in the potential TCTP interactome. Silencing TCTP by short hairpin RNA in breast carcinoma MCF-7 cells leads to the declined repair efficiency for DNA double-strand breaks on the GFP-Pem1 reporter gene by homologous recombination, the persistent activation and the prolonged retention of γH2AX and Rad51 foci following ionizing radiation. Reciprocal immunoprecipitations indicated that TCTP forms complexes with Rad51 in vivo, and the stability maintenance of Rad51 requires TCTP in MCF-7 cells under normal cell culture conditions. Moreover, inactivation of TCTP by sertraline treatment enhances UVC irradiation-induced apoptosis in MCF-7 cells, and causes sensitization to DNA-damaging drug etoposide and DNA repair inhibitor olaparib. Thus, we have identified an important role of TCTP in promoting DNA double-stand break repair via facilitating DNA homologous recombination processes and highlighted the great potential of TCTP as a drug target to enhance conventional chemotherapy for cancer patients with high levels of TCTP expression.

Correlation between TRAIL and caspase-8 expression and their relationship with cell proliferation and apoptosis in human osteosarcoma

Osteosarcoma is a common malignant bone tumor that mainly affects children and adolescents. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a member of the tumor necrosis factor superfamily. Caspase-8 appears in the upstream of apoptosis signaling pathway among caspases. We investigated TRAIL and caspase-8 levels in osteosarcoma patients to determine their correlation with cell proliferation and apoptosis. Osteosarcoma and osteochondroma patients receiving surgery in our hospital were selected. TRAIL and caspase-8 expression levels in tissue were determined by immunohistochemistry, and protein levels in cells were evaluated by western blotting. Human osteosarcoma cell viability was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The osteosarcoma and osteochondroma cell cycles and apoptosis were investigated by flow cytometry. Correlation analysis was applied to TRAIL and caspase-8 levels during cell apoptosis. Positive TRAIL and caspase-8 expression rates in osteosarcoma tissue were significantly lower than in the controls (P < 0.05). TRAIL (0.114 ± 0.002) and caspase-8 (0.352 ± 0.124) levels in experimental cells were obviously lower than in the controls (P < 0.05). Osteosarcoma cells in the experimental group demonstrated higher proliferation and lower apoptosis at 24, 48, and 72 h (P < 0.05). The experimental cell number increased in the G1 stage and decreased in the S stage (P < 0.05). TRAIL and caspase-8 proteins showed positive correlation with apoptosis in osteosarcoma (P < 0.05). Human osteosarcoma presented reduced TRAIL and caspase-8 levels with enhanced cell proliferation and reduced apoptosis. TRAIL and caspase-8 expression levels were positively correlated with apoptosis in osteosarcoma.

The physics for the formation of cell-in-cell structures

The formations of cell-in-cell structures have been found in several important biological processes. Recent studies have shed light on the biochemical signaling pathways as well as the quantitative understandings of the underlying physics. Multiple new features that regulate the cellular engulfment have been identified. However, the driving forces promoting the structural formation are still under debate. This review focuses on the recent progress and discusses the potential significance of the existing physical models.

Reversal of multidrug resistance of gastric cancer cells by down-regulation of ZNRD1 with ZNRD1 siRNA

The over-expression of a new zinc ribbon (ZNRD1) gene has been shown previously to promote a multidrug-resistant phenotype in gastric cancer cells through the up-regulation of permeability-glycoprotein (P-gp). In the present study, siRNA eukaryotic expression vectors of ZNRD1 are constructed and transfected into SGC7901/VCR cells to examine whether or not down-regulation of ZNRD1 increases cell sensitivity towards chemotherapeutic drugs. After transfection, ZNRD1 expression decreased dramatically in ZNRD1 siRNA transfectants compared with that in parental cells and empty vector control cells. Down-regulation of ZNRD1 significantly enhanced the sensitivity of SGC7901/VCR cells to vincristine, adriamycin and etoposide, but not to 5-fluorouracil and cisplatin. Cell capacity to efflux adriamycin decreased markedly in ZNRD1 siRNA transfectants, and correlation between ZNRD1 down-regulation and increased multidrug resistance 1 (MDR1) gene transcriptional activity was observed. These results suggest that the ZNRD1 siRNA constructs down-regulate the expression of ZNRD1 effectively and reverse the resistant phenotype of gastric cancer cells. Furthermore, ZNRD1 might influence transcription of the MDR1 gene and thus play an important role in multidrug resistance in gastric carcinoma.

Growth in neurofibromatosis 1 microdeletion patients

Microdeletions of the entire NF1 gene and surrounding genomic region occur in about 5% of patients with neurofibromatosis 1 (NF1). NF1 microdeletion patients usually have more cutaneous and plexiform neurofibromas and a higher risk of developing malignant peripheral nerve sheath tumors than other people with NF1. Somatic overgrowth has also been observed in NF1 microdeletion patients, an observation that is remarkable because most NF1 patients are smaller than average for age and sex. We studied longitudinal measurements of height, weight, and head circumference in 56 patients with NF1 microdeletions and 226 NF1 patients with other kinds of mutations. Although children with NF1 microdeletions were much taller than non-deletion NF1 patients at all ages after 2 years, the lengths of deletion and nondeletion NF1 patients were similar in early infancy. NF1 microdeletion patients tended to be heavier than other NF1 patients, but height or weight more than 3 standard deviations above the mean for age and sex was infrequent in children with NF1 microdeletions. Head circumference and age of puberty were similar in deletion and non-deletion NF1 patients. The pattern of growth differs substantially in deletion and non-deletion NF1 patients, but the pathogenic basis for this difference is unknown.

High cytoplasmic expression of SALL4 predicts a malignant phenotype and poor prognosis of breast invasive ductal carcinoma

Sal-like 4(SALL4) is significant for maintaining self-renewal and pluripotency in embryonic stem cells, cancer cells and perhaps even cancer stem cells. The expression of SALL4 has been recorded in various kinds of cancers and is deemed to have a clinical value for diagnosis. However, little information on SALL4 expression has been illustrated in breast cancer. In this study, the expression of SALL4 was scrutinized by immunohistochemical analysis in breast invasive ductal carcinoma in a large cohort of 160 patients. High cytoplasmic expression of SALL4 was detected in breast cancer tissues compared with normal adjacent tissues. High SALL4 expression was associated with advanced tumor invasion (p = 0.019), lymph node stage (p = 0.027), ER (p = 0.030), PR (p = 0.037), HER2 (p = 0.019) and TNBC (p = 0.007) in overall patients. Interestingly, in Kaplan-Meier analysis, breast cancer patients with high expression of SALL4 demonstrated a worse OS. Both univariate and multivariate analysis illustrated that examination of SALL4 was of great prognostic value in OS. Thus, our data showed that high cytoplasmic expression of SALL4 was considered to be an independent prognostic indicator for breast invasive ductal carcinoma.

Detection of acute cerebral hemorrhage in rabbits by magnetic induction

Acute cerebral hemorrhage (ACH) is an important clinical problem that is often monitored and studied with expensive devices such as computed tomography, magnetic resonance imaging, and positron emission tomography. These devices are not readily available in economically underdeveloped regions of the world, emergency departments, and emergency zones. We have developed a less expensive tool for non-contact monitoring of ACH. The system measures the magnetic induction phase shift (MIPS) between the electromagnetic signals on two coils. ACH was induced in 6 experimental rabbits and edema was induced in 4 control rabbits by stereotactic methods, and their intracranial pressure and heart rate were monitored for 1 h. Signals were continuously monitored for up to 1 h at an exciting frequency of 10.7 MHz. Autologous blood was administered to the experimental group, and saline to the control group (1 to 3 mL) by injection of 1-mL every 5 min. The results showed a significant increase in MIPS as a function of the injection volume, but the heart rate was stable. In the experimental (ACH) group, there was a statistically significant positive correlation of the intracranial pressure and MIPS. The change of MIPS was greater in the ACH group than in the control group. This high-sensitivity system could detect a 1-mL change in blood volume. The MIPS was significantly related to the intracranial pressure. This observation suggests that the method could be valuable for detecting early warning signs in emergency medicine and critical care units.