A system hierarchy for brain-inspired computing.

Neuromorphic computing draws inspiration from the brain to provide computing technology and architecture with the potential to drive the next wave of computer engineering1-13. Such brain-inspired computing also provides a promising platform for the development of artificial general intelligence14,15. However, unlike conventional computing systems, which have a well established computer hierarchy built around the concept of Turing completeness and the von Neumann architecture16-18, there is currently no generalized system hierarchy or understanding of completeness for brain-inspired computing. This affects the compatibility between software and hardware, impairing the programming flexibility and development productivity of brain-inspired computing. Here we propose 'neuromorphic completeness', which relaxes the requirement for hardware completeness, and a corresponding system hierarchy, which consists of a Turing-complete software-abstraction model and a versatile abstract neuromorphic architecture. Using this hierarchy, various programs can be described as uniform representations and transformed into the equivalent executable on any neuromorphic complete hardware-that is, it ensures programming-language portability, hardware completeness and compilation feasibility. We implement toolchain software to support the execution of different types of program on various typical hardware platforms, demonstrating the advantage of our system hierarchy, including a new system-design dimension introduced by the neuromorphic completeness. We expect that our study will enable efficient and compatible progress in all aspects of brain-inspired computing systems, facilitating the development of various applications, including artificial general intelligence.

Proteomic Analysis Revealed the Potential Role of MAGE-D2 in the Therapeutic Targeting of Triple-Negative Breast Cancer.

Among all the molecular subtypes of breast cancer, triple-negative breast cancer (TNBC) is the most aggressive one. Currently, the clinical prognosis of TNBC is poor because there is still no effective therapeutic target. Here, we carried out a combined proteomic analysis involving bioinformatic analysis of the proteome database, label-free quantitative proteomics, and immunoprecipitation (IP) coupled with mass spectrometry (MS) to explore potential therapeutic targets for TNBC. The results of bioinformatic analysis showed an overexpression of MAGE-D2 (melanoma antigen family D2) in TNBC. In vivo and in vitro experiments revealed that MAGE-D2 overexpression could promote cell proliferation and metastasis. Furthermore, label-free quantitative proteomics revealed that MAGE-D2 acted as a cancer-promoting factor by activating the PI3K-AKT pathway. Moreover, the outcomes of IP-MS and cross-linking IP-MS demonstrated that MAGE-D2 could interact with Hsp70 and prevent Hsp70 degradation, but evidence for their direct interaction is still lacking. Nevertheless, MAGE-D2 is a potential therapeutic target for TNBC, and blocking MAGE-D2 may have important therapeutic implications.

Specific Alterations in Brain White Matter Networks and Their Impact on Clinical Function in Pediatric Patients With Thoracolumbar Spinal Cord Injury.

BACKGROUND: The alternation of brain white matter (WM) network has been studied in adult spinal cord injury (SCI) patients. However, the WM network alterations in pediatric SCI patients remain unclear. PURPOSE: To evaluate WM network changes and their functional impact in children with thoracolumbar SCI (TSCI). STUDY TYPE: Prospective. SUBJECTS: Thirty-five pediatric patients with TSCI (8.94 ± 1.86 years, 8/27 males/females) and 34 age- and gender-matched healthy controls (HCs) participated in this study. FIELD STRENGTH/SEQUENCE: 3.0 T/DTI imaging using spin-echo echo-planar and T1-weighted imaging using 3D T1-weighted magnetization-prepared rapid gradient-echo sequence. ASSESSMENT: Pediatric SCI patients were evaluated for motor and sensory scores, injury level, time since injury, and age at injury. The WM network was constructed using a continuous tracing method, resulting in a 90 × 90 matrix. The global and regional metrics were obtained to investigate the alterations of the WM structural network. topology. STATISTICAL TESTS: Two-sample independent t-tests, chi-squared test, Mann-Whitney U-test, and Spearman correlation. Statistical significance was set at P < 0.05. RESULTS: Compared with HCs, pediatric TSCI patients displayed decreased shortest path length (Lp = 1.080 ± 0.130) and normalized Lp (λ = 5.020 ± 0.363), and increased global efficiency (Eg = 0.200 ± 0.015). Notably, these patients also demonstrated heightened regional properties in the orbitofrontal cortex, limbic system, default mode network, and several audio-visual-related regions. Moreover, the λ and Lp values negatively correlated with sensory scores. Conversely, nodal efficiency values in the right calcarine fissure and surrounding cortex positively correlated with sensory scores. The age at injury positively correlated with node degree in the left parahippocampal gyrus and nodal efficiency in the right posterior cingulate gyrus. DATA CONCLUSION: Reorganization of the WM networks in pediatric SCI patients is indicated by increased global and nodal efficiency, which may provide promising neuroimaging biomarkers for functional assessment of pediatric SCI. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 5.

Dilated lenticulostriate artery on whole-brain vessel wall imaging differentiates pathogenesis and predicts clinical outcomes in single subcortical infarction.

OBJECTIVES: This study aimed to investigate the dilation of lenticulostriate artery (LSA) identified by whole-brain vessel wall imaging (WB-VWI) in differentiating the etiologic subtypes of single subcortical infarction (SSI) and to determine whether the appearance of dilated LSA was associated with 90-day clinical outcomes in parental atherosclerotic disease (PAD)-related SSI. METHODS: Patients with acute SSI were prospectively enrolled and categorized into PAD-related SSI and cerebral small-vessel disease (CSVD)-related SSI groups. The imaging features of LSA morphology (branches, length, dilation, and tortuosity), plaques (burden, remodeling index, enhancement degree, and hyperintense plaque), and CSVD (white matter hyperintensity, lacunes, cerebral microbleed, and enlarged perivascular space) were evaluated. The logistic regression was performed to determine the association of dilated LSA with PAD-related SSI and 90-day clinical outcomes. RESULTS: In total, 131 patients (mean age, 52.2 ± 13.2 years; 99 men) were included. The multivariate logistic regression analysis revealed that the presence of dilated LSAs (odds ratio (OR), 7.40; 95% confidence interval (CI): 1.88-29.17; p = 0.004)) was significantly associated with PAD-related SSI. Moreover, after adjusting for confounding factors, the association of poor outcomes with the total length of LSAs (OR, 0.94; 95% CI: 0.90-0.99; p = 0.011), dilated LSAs (OR, 0.001; 95% CI: 0.0001-0.08; p = 0.002), and plaque burden (OR, 1.35; 95% CI: 1.11-1.63; p = 0.002) remained statistically significant. CONCLUSION: The dilation of LSA visualized on WB-VWI could differentiate various subtypes of SSI within LSA territory and was a prognostic imaging marker for 90-day clinical outcomes for PAD-related SSI. CLINICAL RELEVANCE STATEMENT: Evaluation of LSA morphology based on WB-VWI can differentiate the pathogenesis and predict clinical outcomes in SSI, providing crucial insights into the etiologic mechanisms, risk stratification, and tailored therapies for these patients. KEY POINTS: The prognosis of SSIs within lenticulostriate territory depend on the etiology of the disease. LSA dilation on WB-VWI was associated with parental atherosclerosis and better 90-day outcomes. Accurately identifying the etiology of SSIs in lenticulostriate territory assists in treatment decision-making.

The relationship between the structural changes in the cervical spinal cord and sensorimotor function of children with thoracolumbar spinal cord injury (TLSCI).

STUDY DESIGN: Cross-sectional study. OBJECTIVES: To study the relationship between the structural changes in the cervical spinal cord (C2/3 level) and the sensorimotor function of children with traumatic thoracolumbar spinal cord injury (TLSCI) and to discover objective imaging biomarkers to evaluate its functional status. SETTING: Xuanwu Hospital, Capital Medical University, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, China. METHODS: 30 children (age range 5-13 years) with TLSCI and 11 typically developing (TD) children (age range 6-12 years) were recruited in this study. Based on whether there is preserved motor function below the neurological level of injury (NLI), the children with TLSCI are divided into the AIS A/B group (motor complete) and the AIS C/D group (motor incomplete). A Siemens Verio 3.0 T MR scanner was used to acquire 3D high-resolution anatomic scans covering the head and upper cervical spinal cord. Morphologic parameters of the spinal cord at the C2/3 level, including cross-sectional area (CSA), anterior-posterior width (APW), and left-right width (LRW) were obtained using the spinal cord toolbox (SCT; https://www.nitrc.org/projects/sct ). Correlation analyses were performed to compare the morphologic spinal cord parameters and clinical scores determined by the International Standard for Neurological Classification of Spinal Cord Injuries (ISNCSCI) examination. RESULTS: CSA and LRW in the AIS A/B group were significantly lower than those in the TD group and the AIS C/D group. LRW was the most sensitive imaging biomarker to differentiate the AIS A/B group from the AIS C/D group. Both CSA and APW were positively correlated with ISNCSCI sensory scores. CONCLUSIONS: Quantitative measurement of the morphologic spinal cord parameters of the cervical spinal cord can be used as an objective imaging biomarker to evaluate the neurological function of children with TLSCI. Cervical spinal cord atrophy in children after TLSCI was correlated with clinical grading; CSA and APW can reflect sensory function. Meanwhile, LRW has the potential to be an objective imaging biomarker for evaluating motor function preservation.

HDAC6 promotes inflammation in lupus nephritis mice by regulating transcription factors MAFF and KLF5 in renal fibrosis.

AIM: This study explored the effect and mechanism of MAFF and HDAC6 on renal fibrosis and inflammation in lupus nephritis (LN). METHODS: IL-33 treated renal epithelial cells and MRL/lpr mice were respectively used for in vitro and in vivo experiments. The expressions of HDAC6, MAFF, and KLF5 were measured in cells and renal tissues. Before and after cell transfection, the morphological changes in renal tissues were observed using Hematoxylin and eosin (H&E) and Masson staining. The proteinuria, serum creatinine (SCr), blood urea nitrogen (BUN), and double-stranded DNA (dsDNA) levels were detected by biochemical analysis. The expressions of fibrosis and inflammation related proteins (including α-SMA, Vimentin, IL-1ß, IL-6, and TNF-α), p65, and iNOS were also detected. The relationship among MAFF, HDAC6, and KLF5 was determined by chromatin immunoprecipitation and dual luciferase reporter gene assay. RESULTS: Renal tissues and cell models had elevated expressions of HDAC6 and KLF5, and decreased MAFF expression. HDAC6 suppression or MAFF overexpression led to suppression of proteinuria, SCr, BUN, and dsDNA levels, as well as attenuation of inflammatory infiltration and collagen deposition. HDAC6 can suppress MAFF expression via deacetylation to abolish its suppression of KLF5 expression, thus increasing KLF5 expression. In vivo and in vitro experiments showed the suppressive effect of HDAC6 suppression on renal fibrosis and inflammation can be abolished by KLF5 overexpression. CONCLUSION: HDAC6 suppresses MAFF expression via deacetylation to elevate KLF5 expression, which consequently enhances fibrosis and inflammatory response in LN.

Altered Brain Function in Pediatric Patients With Complete Spinal Cord Injury: A Resting-State Functional MRI Study.

BACKGROUND: Injury to the spinal cord of children may cause potential brain reorganizations, affecting their rehabilitation. However, the specific functional alterations of children after complete spinal cord injury (CSCI) remain unclear. PURPOSE: To explore the specific functional changes in local brain and the relationship with clinical characteristics in pediatric CSCI patients, clarifying the impact of CSCI on brain function in developing children. STUDY TYPE: Prospective. SUBJECTS: Thirty pediatric CSCI patients (7.83 ± 1.206 years) and 30 age-, gender-matched healthy children as controls (HCs) (8.77 ± 2.079 years). FIELD STRENGTH/SEQUENCE: 3.0 T/Resting-state functional MRI (rs-fMRI) using echo-planar-imaging (EPI) sequence. ASSESSMENT: Amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) were used to characterize regional neural function. STATISTICAL TESTS: Two-sample t-tests were used to compare the ALFF, fALFF, ReHo values of the brain between pediatric CSCI and HCs (voxel-level FWE correction, P < 0.05). Spearman correlation analyses were performed to analyze the associations between the ALFF, fALFF, ReHo values in altered regions and the injury duration, sensory motor scores of pediatric CSCI patients (P < 0.05). Then receiver operating characteristic (ROC) analysis was conducted to identify possible sensitive imaging indicators for clinical therapy. RESULTS: Compared with HCs, pediatric CSCI showed significantly decreased ALFF in the right postcentral gyrus (S1), orbitofrontal cortex, and left superior temporal gyrus (STG), increased ALFF in bilateral caudate nucleus, thalamus, middle cingulate gyrus, and cerebellar lobules IV-VI, and increased ReHo in left cerebellum Crus II and Brodmann area 21. The ALFF value in the right S1 negatively correlated with the pinprick and light touch sensory scores of pediatric CSCI. When the left STG was used as an imaging biomarker for pediatric CSCI, it achieved the highest area under the curve of 0.989. CONCLUSIONS: These findings may provide potential neural mechanisms for sensory motor and cognitive-emotional deficits in children after CSCI. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 5.

A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm.

Wireless sensors networks (WSNs) play an important role in life. With the development of 5G, its security issues have also raised concerns. Therefore, it is an important topic to study the offense and defense confrontation in WSNs. A complete information static game model is established to analyze the offense and defense confrontation problem of WSNs in 5G. An adaptive equilibrium optimizer algorithm (AEO) based on parameter adaptive strategy is proposed, which can jump out of the local optimal solution better. Experiments show that the optimization ability of AEO outperforms other algorithms on at least 80% of the 23 classical test functions of CEC. The convergence speed of AEO is better in the early stage of population iteration. The optimal offensive and defensive strategy under different offense and defense resources through simulation experiments is analyzed. The conclusion shows that when the offensive resources are large, the offender takes an indiscriminate attack. When the defense resources are small, the defender should defend the most important elements, and when the defense resources are large, the defender should allocate the same resources to defend each element to obtain the maximum benefit. This paper provides new solution ideas for the security problems under the offense and defense game in WSNs.

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization.

Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore, we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network.

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model.

Musculoskeletal ultrasound imaging is an important basis for the early screening and accurate treatment of muscle disorders. It allows the observation of muscle status to screen for underlying neuromuscular diseases including myasthenia gravis, myotonic dystrophy, and ankylosing muscular dystrophy. Due to the complexity of skeletal muscle ultrasound image noise, it is a tedious and time-consuming process to analyze. Therefore, we proposed a multi-task learning-based approach to automatically segment and initially diagnose transverse musculoskeletal ultrasound images. The method implements muscle cross-sectional area (CSA) segmentation and abnormal muscle classification by constructing a multi-task model based on multi-scale fusion and attention mechanisms (MMA-Net). The model exploits the correlation between tasks by sharing a part of the shallow network and adding connections to exchange information in the deep network. The multi-scale feature fusion module and attention mechanism were added to MMA-Net to increase the receptive field and enhance the feature extraction ability. Experiments were conducted using a total of 1827 medial gastrocnemius ultrasound images from multiple subjects. Ten percent of the samples were randomly selected for testing, 10% as the validation set, and the remaining 80% as the training set. The results show that the proposed network structure and the added modules are effective. Compared with advanced single-task models and existing analysis methods, our method has a better performance at classification and segmentation. The mean Dice coefficients and IoU of muscle cross-sectional area segmentation were 96.74% and 94.10%, respectively. The accuracy and recall of abnormal muscle classification were 95.60% and 94.96%. The proposed method achieves convenient and accurate analysis of transverse musculoskeletal ultrasound images, which can assist physicians in the diagnosis and treatment of muscle diseases from multiple perspectives.

CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation.

Recent research has shown that visual-text pretrained models perform well in traditional vision tasks. CLIP, as the most influential work, has garnered significant attention from researchers. Thanks to its excellent visual representation capabilities, many recent studies have used CLIP for pixel-level tasks. We explore the potential abilities of CLIP in the field of few-shot segmentation. The current mainstream approach is to utilize support and query features to generate class prototypes and then use the prototype features to match image features. We propose a new method that utilizes CLIP to extract text features for a specific class. These text features are then used as training samples to participate in the model's training process. The addition of text features enables model to extract features that contain richer semantic information, thus making it easier to capture potential class information. To better match the query image features, we also propose a new prototype generation method that incorporates multi-modal fusion features of text and images in the prototype generation process. Adaptive query prototypes were generated by combining foreground and background information from the images with the multi-modal support prototype, thereby allowing for a better matching of image features and improved segmentation accuracy. We provide a new perspective to the task of few-shot segmentation in multi-modal scenarios. Experiments demonstrate that our proposed method achieves excellent results on two common datasets, PASCAL-5i and COCO-20i.

Lossless Image Steganography Based on Invertible Neural Networks.

Image steganography is a scheme that hides secret information in a cover image without being perceived. Most of the existing steganography methods are more concerned about the visual similarity between the stego image and the cover image, and they ignore the recovery accuracy of secret information. In this paper, the steganography method based on invertible neural networks is proposed, which can generate stego images with high invisibility and security and can achieve lossless recovery for secret information. In addition, this paper introduces a mapping module that can compress information actually embedded to improve the quality of the stego image and its antidetection ability. In order to restore message and prevent loss, the secret information is converted into a binary sequence and then embedded in the cover image through the forward operation of the invertible neural networks. This information will then be recovered from the stego image through the inverse operation of the invertible neural networks. Experimental results show that the proposed method in this paper has achieved competitive results in the visual quality and safety of stego images and achieved 100% accuracy in information extraction.

Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks.

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.

Functional Reorganizations Outside the Sensorimotor Regions Following Complete Thoracolumbar Spinal Cord Injury.

BACKGROUND: Studies have shown that loss of sensorimotor function in spinal cord injury (SCI) leads to brain functional reorganization, which may play important roles in motor function recovery. However, the specific functional changes following SCI are still poorly understood. PURPOSE: To investigate whether there are functional reorganizations outside the sensorimotor regions after complete thoracolumbar SCI (CTSCI), and how these reorganizations are associated with clinical manifestations. STUDY TYPE: Prospective. SUBJECTS: Eighteen CTSCI patients (28-67 years of age; 16 men) and 18 age-, gender-matched healthy controls (HCs) (27-64 years of age; 16 men). FIELD STRENGTH/SEQUENCE: Resting-state functional magnetic resonance imaging (RS-fMRI) using echo-planar-imaging (EPI) sequence at 3.0 T. ASSESSMENT: Data preprocessing was performed using Data Processing Assistant for Resting-State fMRI (DPARSF). Amplitude of low-frequency fluctuations (ALFF) was used to characterize regional neural function, and seed-based functional connectivity (FC) was used to evaluate the functional integration of the brain network. STATISTICAL TESTS: Two-sample t-tests were used for ALFF and FC measures (the data conform to the normal distribution), partial correlation analysis was used to analyze the correlation between clinical and imaging indicators, and receiver operating characteristic (ROC) analysis was used to search for sensitive imaging indicators. RESULTS: Compared with HCs, CTSCI patients showed decreased ALFF in right lingual gyrus (LG), increased ALFF in right middle frontal gyrus (MFG), and decreased FC between the right LG and Vermis_3 (cluster-level FWE correction with P < 0.05). Subsequent correlation analyses revealed that decreased FC between the right LG and Vermis_3 positively correlated with the visual analog scale (VAS) (P = 0.043, r = 0.443). Finally, the ROC analysis showed that the area under the curve (AUC) of FC value between right LG and Vermis3 was 0.881. DATA CONCLUSION: These findings suggest a possible theoretical basis of the mechanism of visual-, emotion-, and cognition-related techniques in rehabilitation training for CTSCI.

Efficacy and safety of mycophenolate mofetil in the treatment for IgA nephropathy: a meta-analysis of randomized controlled trials.

AIM: IgA nephropathy is virtually known as the most common glomerulopathy to end-stage renal failure in the world. Mycophenolate mofetil is a selective immunosuppressant widely used in organ transplantation, yet its tolerance and effectiveness in IgAN is controversial. METHODS: This is a systematic review and random-effects meta-analysis, searching PubMed, Embase, Te Cochrane Library, Science Citation Index, Ovid evidence-based medicine, Chinese Biomedical Literature and Chinese Science and Technology Periodicals. Screen out randomized controlled trials on patients with biopsy-proven IgA nephropathy and analysis mycophenolate mofetil treatment regimens used for therapy of IgA nephropathy. Complete remission and partial remission, doubling of creatinine level, proteinuria, incidence of end-stage kidney disease, infection, Cushing syndrome, diabetes, hepatic dysfunction or gastrointestinal symptoms, neurologic or visual ambiguity, acne, and alopecia were observed. RESULTS: Nine relevant trials were conducted with 587 patients enrolled. In Mycophenolate mofetil or plus medium/low-dose steroid comparing full-dose steroid alone or placebo, there was no significant difference. The risk of Cushing syndrome and diabetes had been significantly lowered with Mycophenolate mofetil-treated patients, while the risk of infection had been increased. CONCLUSIONS: Mycophenolate mofetil therapy did not differ in reducing proteinuria and Scr in patients with IgAN who had persistent proteinuria, while having fewer Cushing syndrome and diabetes risk and more infection risk. However, larger randomized studies are needed to reveal these results.

Plaque enhancement in multi-cerebrovascular beds associates with acute cerebral infarction.

BACKGROUND: It is valuable to explore the relationship between plaque characteristics and stroke by using three-dimensional (3D) magnetic resonance imaging (MRI) of the vessel wall. PURPOSE: To investigate the association between plaque enhancement score (PES) of co-existing intracranial and extracranial carotid plaques and ischemic stroke using 3D MRI. MATERIAL AND METHODS: Symptomatic patients were recruited and underwent cerebrovascular 3D MRI of the vessel wall. The number, enhancement degree, and stenosis of plaques in intracranial and extracranial carotid arteries were evaluated. The PES calculated by summing enhancement degree of all detected plaques was compared between patients with and without acute cerebral infarction (ACI) and its association with ACI was determined. RESULTS: Of 157 recruited patients, 118 (75.2%) had co-existing plaques. Patients with ACI had significantly greater PES of co-existing plaques compared with those without ACI (9, interquartile range [IQR] 5-11 vs. 5, IQR 2-7, P<0.001). The odds ratio for PES of co-existing plaques in discriminating ACI was 1.410 (95% confidence interval [CI] 1.146-1.735, P = 0.001) after adjustment for stenosis, intraplaque hemorrhage, and traditional risk factors. Receiver operating characteristic curve analysis showed that, in discriminating ACI, PES had higher area under the curve (AUC 0.693-0.764) than plaque number (AUC 0.625-0.683) and enhancement degree (AUC 0.570-0.706) alone in any vascular bed. The AUC of PES of co-existing plaques combined with stenosis, NIHSS scores, intraplaque hemorrhage, hyperlipidemia, and blood pressure reached 0.847. CONCLUSION: Cerebrovascular plaque enhancement score combining plaque number and enhancement degree is independently associated with ACI. The enhancement score of co-existing plaques has higher strength in discriminating ACI compared with plaques in a single vascular bed.

Effect of Acupuncture Stimulation of Hegu (LI4) and Taichong (LR3) on the Resting-State Networks in Alzheimer's Disease: Beyond the Default Mode Network.

It was reported that acupuncture could treat Alzheimer's disease (AD) with the potential mechanisms remaining unclear. The aim of the study is to explore the effect of the combination stimulus of Hegu (LI4) and Taichong (LR3) on the resting-state brain networks in AD, beyond the default network (DMN). Twenty-eight subjects including 14 AD patients and 14 healthy controls (HCs) matched by age, gender, and educational level were recruited in this study. After the baseline resting-state MRI scans, the manual acupuncture stimulation was performed for 3 minutes, and then, another 10 minutes of resting-state fMRI scans was acquired. In addition to the DMN, five other resting-state networks were identified by independent component analysis (ICA), including left frontal parietal network (lFPN), right frontal parietal network (rFPN), visual network (VN), sensorimotor network (SMN), and auditory network (AN). And the impaired connectivity in the lFPN, rFPN, SMN, and VN was found in AD patients compared with those in HCs. After acupuncture, significantly decreased connectivity in the right middle frontal gyrus (MFG) of rFPN (P = 0.007) was identified in AD patients. However, reduced connectivity in the right inferior frontal gyrus (IFG) (P = 0.047) and left superior frontal gyrus (SFG) (P = 0.041) of lFPN and some regions of the SMN (the left inferior parietal lobula (P = 0.004), left postcentral gyrus (PoCG) (P = 0.001), right PoCG (P = 0.032), and right MFG (P = 0.010)) and the right MOG of VN (P = 0.003) was indicated in HCs. In addition, after controlling for the effect of acupuncture on HCs, the functional connectivity of the right cerebellum crus I, left IFG, and left angular gyrus (AG) of lFPN showed to be decreased, while the left MFG of IFPN and the right lingual gyrus of VN increased in AD patients. These findings might have some reference values for the interpretation of the combination stimulus of Hegu (LI4) and Taichong (LR3) in AD patients, which could deepen our understanding of the potential mechanisms of acupuncture on AD.

Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets.

In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.

A Multi-Omics Study of Human Testis and Epididymis.

The human testis and epididymis play critical roles in male fertility, including the spermatogenesis process, sperm storage, and maturation. However, the unique functions of the two organs had not been systematically studied. Herein, we provide a systematic and comprehensive multi-omics study between testis and epididymis. RNA-Seq profiling detected and quantified 19,653 in the testis and 18,407 in the epididymis. Proteomic profiling resulted in the identification of a total of 11,024 and 10,386 proteins in the testis and epididymis, respectively, including 110 proteins that previously have been classified as MPs (missing proteins). Furthermore, Five MPs expressed in testis were validated by the MRM method. Subsequently, multi-omcis between testis and epididymis were performed, including biological functions and pathways of DEGs (Differentially Expressed Genes) in each group, revealing that those differences were related to spermatogenesis, male gamete generation, as well as reproduction. In conclusion, this study can help us find the expression regularity of missing protein and help related scientists understand the physiological functions of testis and epididymis more deeply.