Alterations in cortical thickness and volumes of subcortical structures in pediatric patients with complete spinal cord injury.

AIMS: To study the changes in cortical thickness and subcortical gray matter structures in children with complete spinal cord injury (CSCI), reveal the possible causes of dysfunction beyond sensory motor dysfunction after CSCI, and provide a possible neural basis for corresponding functional intervention training. METHODS: Thirty-seven pediatric CSCI patients and 34 age-, gender-matched healthy children as healthy controls (HCs) were recruited. The 3D high-resolution T1-weighted structural images of all subjects were obtained using a 3.0 Tesla MRI system. Statistical differences between pediatric CSCI patients and HCs in cortical thickness and volumes of subcortical gray matter structures were evaluated. Then, correlation analyses were performed to analyze the correlation between the imaging indicators and clinical characteristics. RESULTS: Compared with HCs, pediatric CSCI patients showed decreased cortical thickness in the right precentral gyrus, superior temporal gyrus, and posterior segment of the lateral sulcus, while increased cortical thickness in the right lingual gyrus and inferior occipital gyrus. The volume of the right thalamus in pediatric CSCI patients was significantly smaller than that in HCs. No significant correlation was found between the imaging indicators and the injury duration, sensory scores, and motor scores of pediatric CSCI patients. CONCLUSIONS: These findings demonstrated that the brain structural reorganizations of pediatric CSCI occurred not only in sensory motor areas but also in cognitive and visual related brain regions, which may suggest that the visual processing, cognitive abnormalities, and related early intervention therapy also deserve greater attention beyond sensory motor rehabilitation training in pediatric CSCI patients.

Altered functional connectivity of insular subregions in subjective cognitive decline.

Objective: Recent research has highlighted the insula as a critical hub in human brain networks and the most susceptible region to subjective cognitive decline (SCD). However, the changes in functional connectivity of insular subregions in SCD patients remain poorly understood. The present study aims to clarify the altered functional connectivity patterns within insular subregions in individuals with SCD using resting-state functional magnetic resonance imaging (rs-fMRI). Methods: In this study, we collected rs-fMRI data from 30 patients with SCD and 28 healthy controls (HCs). By defining three subregions of the insula, we mapped whole-brain resting-state functional connectivity (RSFC). We identified several distinct RSFC patterns of the insular subregions. Specifically, for positive connectivity, three cognitive-related RSFC patterns were identified within the insula, suggesting anterior-to-posterior functional subdivisions: (1) a dorsal anterior zone of the insula that shows RSFC with the executive control network (ECN); (2) a ventral anterior zone of the insula that shows functional connectivity with the salience network (SN); and (3) a posterior zone along the insula that shows functional connectivity with the sensorimotor network (SMN). Results: Compared to the controls, patients with SCD exhibited increased positive RSFC to the sub-region of the insula, demonstrating compensatory plasticity. Furthermore, these abnormal insular subregion RSFCs are closely correlated with cognitive performance in the SCD patients. Conclusion: Our findings suggest that different insular subregions exhibit distinct patterns of RSFC with various functional networks, which are affected differently in patients with SCD.

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.

The possible neural mechanism of neuropathic pain evoked by motor imagery in pediatric patients with complete spinal cord injury: A preliminary brain structure study based on VBM.

In this study, we observed pediatric complete spinal cord injury (CSCI) patients receiving MI training and divided them into different groups according to the effect of motor imagery (MI) training on neuropathic pain (NP). Then, we retrospectively analysed the differences in brain structure of these groups before the MI training, identifying brain regions that may predict the effect of MI on NP. Thirty pediatric CSCI patients were included, including 12 patients who experienced NP during MI and 18 patients who did not experience NP during MI according to the MI training follow-up. The 3D high-resolution T1-weighted images of all subjects were obtained using a 3.0 T MRI system before MI training. A two-sample t-test was performed to evaluate the differences in gray matter volume (GMV) between patients who experienced NP and those who did not experience NP during MI. Receiver operating characteristic (ROC) analysis was performed to compute the sensitivity and specificity of the imaging biomarkers for the effect of MI on NP in pediatric CSCI patients. MI evoked NP in some of the pediatric CSCI patients. Compared with patients who did not experience NP, patients who experienced NP during MI showed larger GMV in the right primary sensorimotor cortex (PSMC) and insula. When using the GMV of the right PSMC and insula in combination as a predictor, the area under the curve (AUC) reached 0.824. Our study demonstrated that MI could evoke NP in some pediatric CSCI patients, but not in others. The individual differences in brain reorganization of the right PSMC and insula may contribute to the different effects of MI on NP. Moreover, the GMV of the right PSMC and insula in combination may be an effective indicator for screening pediatric CSCI patients before MI training therapy.

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint.

The functional architecture undergoes alterations during the preclinical phase of Alzheimer's disease. Consequently, the primary research focus has shifted towards identifying Alzheimer's disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer's Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network's modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifold-regularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution's proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

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.

Rare HER2 L796P missense mutation promotes the growth and oncogenic signaling in breast cancer cells.

PURPOSE: This research aimed to find potential HER2 mutations that would have an impact on breast cancer and investigate the underlying mechanism. EXPERIMENTAL DESIGN: This study first investigated 238 pairs of breast cancer and para-cancerous tissue samples from patients on the targeted next-generation sequencing (tNGS) platform. CCK-8 and clone formation assay were used to investigate whether the mutation exerts proliferative effects on breast cancer cells. In addition, mass spectrometry-based comparative proteomic and phosphoproteomic analyses of the mutation types and wild types of MCF-7 cell lines were carried out. RESULTS: Among the identified mutations, a new mutation HER2 L796P promoted the proliferation of breast cancer cells and had resistance to lapatinib using CCK-8 cell proliferation assay and clone formation assay. The bioinformatic analysis showed that RAS family proteins and ERK phosphorylated proteins significantly increased in the L796P mutant cells. The Gene Ontology (GO) analysis revealed that L796P mutation affected the function of breast cancer at the level of upstream genes in the MAPK and PI3K-AKT-TOR pathways. CONCLUSIONS AND CLINICAL RELEVANCE: This study demonstrated that a rare mutation HER2 L796P could be a potential therapeutic target for the clinical management of breast cancer.

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.

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.

The gray matter atrophy and related network changes occur in the higher cognitive region rather than the primary sensorimotor cortex after spinal cord injury.

Objective: This study used functional magnetic resonance imaging (fMRI) to explore brain structural and related network changes in patients with spinal cord injury (SCI). Methods: Thirty-one right-handed SCI patients and 31 gender- and age-matched healthy controls (HC) were included. The gray matter volume (GMV) changes in SCI patients were observed using voxel-based morphometry (VBM). Then, these altered gray matter clusters were used as the regions of interest (ROIs) for whole-brain functional connectivity (FC) analysis to detect related functional changes. The potential association between GMV and FC values with the visual analog scale (VAS), the American Spinal Injury Association (ASIA) score, and the course of injuries was investigated through partial correlation analysis. Results: GMV of the frontal, temporal, and insular cortices was lower in the SCI group than in the HC group. No GMV changes were found in the primary sensorimotor area in the SCI group. Besides, the altered FC regions were not in the primary sensorimotor area but in the cingulate gyrus, supplementary motor area, precuneus, frontal lobe, and insular. Additionally, some of these altered GMV and FC regions were correlated with ASIA motor scores, indicating that higher cognitive regions can affect motor function in SCI patients. Conclusions: This study demonstrated that gray matter and related network reorganization in patients with SCI occurred in higher cognitive regions. Future rehabilitation strategies should focus more on cognitive functions.

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.

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.

Structural changes in the thalamus and its subregions in regulating different symptoms of posttraumatic stress disorder.

As a key center for sensory information processing and transmission, the thalamus plays a crucial role in the development of posttraumatic stress disorder (PTSD). However, the changes in the thalamus and its role in regulating different PTSD symptoms remain unclear. In this study, fourteen PTSD patients and eighteen healthy controls (HCs) were recruited. All subjects underwent whole-brain T1-weighted three-dimensional Magnetization Prepared Rapid Gradient Echo Imaging scans. Gray matter volume (GMV) in the thalamus and its subregions were estimated using voxel-based morphometry (VBM). Compared to HCs, PTSD patients exhibited significant GMV reduction in the left thalamus and its subregions, including anterior, mediodorsal, ventral-lateral-dorsal (VLD), ventral-anterior, and ventral-lateral-ventral (VLV). Among the significantly reduced thalamic subregions, we found positive correlations between the GMV values of the left VLD and VLV and the re-experiencing symptoms score, arousal symptoms score, and total CAPS score. When using the symptom-related GMV values of left VLV and VLD in combination as a predictor, receiver operating characteristic (ROC) analysis revealed that the area under the curve (AUC) for binary classification reached 0.813. This study highlights the neurobiological mechanisms of PTSD related to thalamic changes and may provide potential imaging markers for diagnosis and therapy targets.

Prevalence of hypothermia and critical hand temperatures during military cold water immersion training.

Cold-weather military operations can quickly undermine warfighter readiness and performance. Specifically, accidental cold-water immersion (CWI) contributes to rapid body heat loss and impaired motor function. This study evaluated the prevalence of hypothermia and critical hand temperatures during CWI. One-hundred seventeen (N = 117) military personnel (mean ± SD age: 27 ± 6 yr, height: 176 ± 8 cm, weight: 81.5 ± 11.6 kg) completed CWI and rewarming during cold-weather training, which included a 10-min outdoor CWI (1.3 ± 1.4°C) combined with cold air (-4.2 ± 8.5°C) exposure. Following CWI, students removed wet clothing, donned dry clothing, and entered sleeping systems. Core (Tc) and hand (Thand) temperatures were recorded continuously during the training exercise. Tc for 96 students (mean ± SD lowest Tc = 35.6 ± 0.9°C) revealed that 24 students (25%) experienced Tc below 35.0°C. All of 110 students (100%) experienced Thand below 15.0°C, with 71 students (65%) experiencing Thand at or below 8.0°C. Loss of hand function and hypothermia should be anticipated in warfighters who experience CWI in field settings. Given the high prevalence of low Thand, focus should be directed on quickly rewarming hands to recover function.

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.

Knockdown of KLF5 ameliorates renal fibrosis in MRL/lpr mice via inhibition of MX1 transcription.

OBJECTIVE: This study aims to elucidate the role of Kruppel-like factor (KLF5) and myxovirus resistance 1 (MX1) in the progression of renal fibrosis in lupus nephritis (LN). METHODS: First, the expression of KLF5 and MX1 was assessed in the peripheral blood of LN patients and healthy participants. Next, the pathological changes in renal tissues were evaluated and compared in BALB/c and MRL/lpr mice, by detecting the expression of fibrosis marker proteins (transforming growth factor-ß [TGF-ß] and CTGF) and α-SMA, the content of urine protein, and the levels of serum creatinine, blood urea nitrogen, and serum double-stranded DNA antibody. In TGF-ß1-induced HK-2 cells, the messenger RNA levels of KLF5 and MX1 were tested by qRT-PCR, and the protein expression of α-SMA, type I collagen (Col I), fibronectin (FN), and matrix metalloproteinase 9 (MMP9) was measured by western blot analysis. Moreover, the relationship between KLF5 and MX1 was predicted and verified. RESULTS: In renal tissues of MRL/lpr mice and the peripheral blood of LN patients, KLF5 and MX1 were highly expressed. Pearson analysis revealed that KLF5 was positively correlated with MX1. Furthermore, KLF5 bound to MX1 promoter and promoted its transcription level. MRL/lpr mice showed substantial renal injury, accompanied by increased expression of α-SMA, TGF-ß, CTGF, Col I, FN, and MMP9. Injection of sh-KLF5 or sh-MX1 alone in MRL/lpr mice reduced renal fibrosis in LN, while simultaneous injection of sh-KLF5 and ad-MX1 exacerbated renal injury and fibrosis. Furthermore, we obtained the same results in TGF-ß1-induced HK-2 cells. CONCLUSION: Knockdown of KLF5 alleviated renal fibrosis in LN through repressing the transcription of MX1.

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.

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.

Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. AIMS: We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). METHODS: We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. RESULTS: By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. CONCLUSIONS: The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.

Specific Brain Gray Matter Volume Changes in Pediatric Complete Spinal Cord Injury without Fracture or Dislocation Using Voxel-Based Morphometry Analysis: Preliminary Results.

This study aims to investigate the brain gray matter volume (GMV) alterations of pediatric complete thoracolumbar spinal cord injury (SCI) without fracture or dislocation (SCIWOFD) using voxel-based morphometry (VBM) analysis and assess the sensitive neuroimaging biomarkers that may be surrogate targets to enhance brain plasticity. A total of 52 pediatric subjects (age range, 6-12 years), including 25 pediatric SCIWOFD patients and 27 typically developing (TD) children were recruited. An independent two-sample t test was performed to assess between-group differences of brain GMV. Partial correlation analyses were performed to explore the correlations between GMV values and The International Standards for Neurological Classification of Spinal Cord Injury scores, age at the time of injury, time after initial SCI. Receiver operating characteristic analysis was performed to compute the sensitivity and specificity of the imaging biomarkers for pediatric SCIWOFD diagnosis. As for the results, pediatric SCIWOFD patients showed significantly decreased GMV of bilateral cerebellum lobule VIII, right middle occipital gyrus and putamen (PUT), left pallidum (PAL) and thalamus, and increased GMV of vermis III, right cerebellum lobule VI, and supramarginal gyrus. In addition, GMV of left PAL and right PUT were negatively correlated with the pinprick/light touch sensory scores in pediatric SCIWOFD patients. Finally, when using the GMV values of left PAL and right PUT in combination as the predictor, area under the curve reached the highest-0.93. These findings provided evidence that the brain undergoes GMV changes after pediatric SCIWOFD, which may suggest important targets for functional remodeling after SCI in children and provide valuable information for the development of novel and effective rehabilitation therapies in the future.