Selective Fusion Research Articles

Membrane Fusion Mediated by Cationic Helical Peptide L-MMBen through Phosphatidylglycerol Recruitment.

Membrane fusion is the basis for many biological processes, which holds promise in biomedical applications including the creation of engineered hybrid cells and cell membrane functionalization. Extensive research efforts, including investigations into DNA zippers and carbon nanotubes, have been dedicated to the development of membrane fusion strategies inspired by natural SNARE proteins; nevertheless, achieving a delicate balance between membrane selectivity and high fusion efficiency through precise molecular engineering remains unclear. In our recent study, we successfully designed L-MMBen, a cationic helical antimicrobial peptide that exhibits remarkable antimicrobial efficacy while demonstrating moderate cytotoxicity. In this work, we demonstrate the effective and selective induction of fusion between phosphatidylglycerol (PG)-containing membranes by L-MMBen. By combining biophysical assays at the single-vesicle level with computer simulations at the molecular level, we discovered that L-MMBen can stably adsorb onto the surface of PG-containing membranes, leading to the formation of stalk structures between vesicles and ultimately resulting in membrane fusion. Furthermore, the occurrence of fusion is attributed to the unique ability of L-MMBen to recruit PG lipids and bridge adjacent vesicles. In contrast, its nonhelical counterpart DL-MMBen was found to lack this capability despite possessing an identical positive charge. These findings present an alternative molecule for achieving selective membrane fusion and provide insights for designing helical peptides with diverse applications.

Is It Enough to Stop Distal Fusion at L3 in Mild to Moderate Lenke 5C Adolescent Idiopathic Scoliosis Patients?

There has been no definitive conclusion on the selection of the lowest instrumented vertebra (LIV) in Lenke 5C adolescent idiopathic scoliosis (AIS) patients. The purpose of this study was to evaluate whether it is enough to stop distal fusion at L3 in mild to moderate Lenke 5C AIS patients with posterior selective lumbar fusion, Ponte osteotomies and segmental direct vertebra rotation and to analyze the risk factors for postoperative complications in patients selecting L3 as the LIV. A retrospective review was conducted on 106 Lenke 5C AIS patients who underwent corrective surgery in our institution from 2010 to 2021, with a minimum 2-year follow-up. The LIV was L3 or L4. According to the LIV, patients were initially divided into Group I (the LIV was L3) and Group II (the LIV was L4). Then, Group I was further divided into a complication group and a non-complication group. Demographics, radiological parameters, postoperative complications, and clinical outcomes were recorded. Univariate analysis and multivariate logistic analysis were used to identify the risk factors for postoperative complications in patients with L3 as the LIV. There were no significant differences in the demographics, radiological parameters, postoperative complications, or clinical outcomes between Group I and Group II (p > 0.05), and the outcomes were satisfactory in both groups. The main postoperative complications were distal adding-on (11 cases), coronal imbalance (16 cases), proximal junctional kyphosis (2 cases), and internal fixation failure (4 cases). Logistic regression analysis revealed that age and postoperative C7-CSVL were independent predictors of postoperative complications when selecting L3 as the LIV. Terminating the distal fusion level at L3 was practical for mild to moderate Lenke 5C AIS patients. For patients selecting L3 as the LIV, younger patients should be cautious, and maintaining postoperative coronal balance is necessary for avoiding postoperative complications.

Multi-Source Information-Based Bearing Fault Diagnosis Using Multi-Branch Selective Fusion Deep Residual Network.

Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, multi-source information-based fault diagnosis methods have become popular, but the information redundancy between multiple signals is a tough problem that will negatively impact the representational capacity of deep learning algorithms and the precision of fault diagnosis methods. Besides that, the characteristics of various signals are actually different, but this problem was usually omitted by researchers, and it has potential to further improve the diagnosing performance by adaptively adjusting the feature extraction process for every input signal source. Aimed at solving the above problems, a novel model for bearing fault diagnosis called multi-branch selective fusion deep residual network is proposed in this paper. The model adopts a multi-branch structure design to enable every input signal source to have a unique feature processing channel, avoiding the information of multiple signal sources blindly coupled by convolution kernels. And in each branch, different convolution kernel sizes are assigned according to the characteristics of every input signal, fully digging the precious fault components on respective information sources. Lastly, the dropout technique is used to randomly throw out some activated neurons, alleviating the redundancy and enhancing the quality of the multiscale features extracted from different signals. The proposed method was experimentally compared with other intelligent methods on two authoritative public bearing datasets, and the experimental results prove the feasibility and superiority of the proposed model.

Selective and multi-scale fusion Mamba for medical image segmentation

Given the high variability in the morphology and size of lesion areas in medical images, accurate medical image segmentation requires both precise positioning of global contours and careful processing of local boundaries. This emphasizes the importance of fusing multi-scale local and global features. However, existing CNN and Transformer-based models are often limited by high parameter counts and complex calculations, making it difficult to efficiently integrate these features. To address this challenge, we proposed two innovative optimization architectures: Selective Fusion Mamba (SF-Mamba) and Multi-Scale Fusion Mamba (MF-Mamba). SF-Mamba can flexibly and dynamically adjust the fusion strategy of local and global features according to the characteristics of the lesions, effectively handling segmentation tasks with variable morphology. MF-Mamba enhances the model’s segmentation ability for lesions of different sizes by capturing global information across scales. Based on these two structures, we constructed a lightweight SMM-UNet model, which not only significantly reduces the computational burden (with only 0.038M parameters) but also demonstrates excellent generalization ability and can efficiently adapt to various types of medical images. Extensive tests on the ISIC2017, ISIC2018, and BUSI public datasets show that SMM-UNet achieves excellent segmentation performance with an extremely low parameter cost.

Recent Global Trends and Hotspots in Occipitocervical Fusion: A Bibliometric Analysis and Visualization Study

Occipitocervical arthrodesis has a variety of indications to treat craniocervical and atlantoaxial pathologies for which a selective cervical fusion would not provide sufficient stability. Over time, the indications for occipitocervical fusions (OCF) have evolved, as new technologies and surgical techniques were developed. In this bibliometric analysis, we aim to explore the progression of OCF literature over time, analyzing the trends in publications and citations, publishing countries and authors, keywords and topics. The Web of Science database was used for data retrieval on July 3rd, 2024, with the search "occipitocervical fusion" OR "occipito-cervical fusion" OR "occipitocervical arthrodesis" OR "occipital cervical fusion" OR "occipital cervical arthrodesis" OR ("OCF" AND "spine surgery"). Excel was used to create the citation analysis and publication trend figures, along with the publishing countries and author analysis. The bibliometric software VosViewer was used to generate the keyword co-occurrence network visualizations. Overall, 762 articles were extracted. The number of pertinent publications and citations increased until 2020 before beginning to decrease. We found that Ehlers Danlos syndrome (EDS) has become a more prevalent topic, as the association between EDS and craniocervical instability has received further scrutiny. "Dysphagia" continues to be a commonly cited topic, while, conversely, rheumatoid arthritis has decreased in publication frequency, possibly related to advances in medical management and surgical techniques. Overall, the United States of America, China, and Japan are the top publishing countries. This analysis of OCF literature provides a helpful overview of emerging trends and clinician concerns, especially as seen through the perspective of time.

Excessive posterior placement of upper instrumented vertebra relative to lower instrumented vertebra as a predictor of proximal junction kyphosis after selective spinal fusion for adolescent idiopathic scoliosis Lenke type 5C curves.

We defined sagittal S-line tilt (SSLT) as the tilt of the line connecting the upper instrumented vertebra and the lower instrumented vertebra. This study aimed to: (1) examine the correlation between SSLT and proximal junctional angle (PJA) change values, and (2) determine the cut-off value of SSLT with respect to proximal junctional kyphosis (PJK) occurrence. Eighty-six consecutive patients (81 female and 5 male; mean age: 15.8years) with Lenke 5C AIS who underwent posterior selective spinal fusion. Pearson's correlation coefficients were used to examine the relationship between preoperative SSLT and changes in PJA from preoperative to 2years postoperative. The impact of SSLT on PJK at 2years after surgery was assessed using a receiver operating characteristic (ROC) curve. We observed a moderate positive correlation between preoperative SSLT and change in PJA (R = 0.541, P < 0.001). We identified 18 patients (21%) with PJK at 2years postoperative. Mean preoperative SSLT in the PJK group and the non-PJK group differed significantly at 23.3 ± 4.1° and 16.1 ± 5.0°, respectively (P < 0.001). The cut-off value of preoperative SSLT for PJK at 2years postoperative was 18° in ROC curve analysis, with a sensitivity of 94%, specificity of 68%, and area under the ROC curve of 0.868. In selective lumbar fusion for AIS Lenke type 5C curves, preoperative SSLT was significantly correlated with PJA change from preoperative to 2years postoperative. SSLT was a predictor of PJK occurrence, with a cut-off value of 18°.

Efficient Selective Image Fusion: A PCB Diagnosis Approach and Implementation

Efficient Selective Image Fusion: A PCB Diagnosis Approach and Implementation

Environment Reconstruction Based on Multi-User Selection and Multi-Modal Fusion in ISAC

Environment Reconstruction Based on Multi-User Selection and Multi-Modal Fusion in ISAC

Postoperative Coronal Imbalance in Lenke 5C Adolescent Idiopathic Scoliosis: Evolution, Risk Factors, and Clinical Implications.

To explore the changes in coronal imbalance (CIB) in Lenke 5C adolescent idiopathic scoliosis (AIS) after posterior selective fusion surgery and determine their implications for surgical decision-making. One hundred twenty patients were categorized according to the preoperative coronal pattern (type A, coronal balance distance [CBD]<20 mm; type B, CBD≥20 mm and coronal C7 plumbline [C7PL] shifted to the concave side of the curve; type C, CBD≥20 mm and C7PL shifted to the convex side of the curve). CIB group (CIB+) was defined as having a CBD≥20 mm at the 2-year follow-up. Compared to type A patients, the prevalence of postoperative CIB was higher in type C patients both immediately postoperative (22% vs. 38%, p<0.05) and at the final follow-up (5% vs. 29%, p<0.05), whereas type A patients showed a greater improvement in CBD (9 of 12 vs. 6 of 24, p<0.05) at the final follow-up. The majority of patients in all groups had recovered to type A at the final follow-up (96 of 120). The proximal Cobb-1 strategy reduced the incidence of postoperative CIB (1 of 38) at the 2-year follow-up, especially in preoperative type C patients. Multivariate logistic regression analysis revealed that type C and overcorrection of the thoracolumbar curve were risk factors for CIB at the 2-year follow-up (p=0.007 and p=0.026, respectively). Patients with type C CIB in AIS exhibited unsatisfactory restoration, with 29% of them exhibiting CIB at the final follow-up. The selective fusion strategy of proximal Cobb-1 may reduce the risk of postoperative CIB especially when the preoperative coronal pattern is type C.

The Impact of the Lowest Instrumented Vertebra on the Correction of the Minor Curve During Selective Fusion in Patients With Adolescent Idiopathic Scoliosis.

Retrospective single-center data analysis. The aim of this investigation was to give advises for choosing the LIV in selective fusion to reach the best correction of the minor curve and sagittal profile. Scoliotic curves can be classified as structural or nonstructural. If selective fusion is performed, the nonstructural curves are not instrumented. The choice of the lowest instrumented vertebra (LIV) and the impact of different levels of the LIV on the correction of the minor curve in the frontal profile and on the sagittal balance is under debate. Forty-seven consecutive patients treated by posterior instrumented fusion were included in this retrospective investigation. Impact of the level of the LIV with regard to distance to end vertebra (EV), to the stable vertebra (StV), to the sagittal infliction point (IP), and to the apex of the lumbar lordosis on the correction of the minor curve was analyzed. Distance of LIV to EV was significant with regard to correction of the minor curve if it was more than 5 levels (P<0.001). Distance of LIV to StV was significant with regard to correction of the minor curve if it was more than 4 levels (P<0.01). Distance of LIV to IP was significant with regard to correction of the minor curve if it was more than 2 levels (P<0.01). Choosing a LIV that was more than 2 levels higher or lower than the sagittal infliction point showed a significantly higher correction of the minor curve. We therefore recommend to keep that distance when LIV is chosen.

GLFuse: A Global and Local Four-Branch Feature Extraction Network for Infrared and Visible Image Fusion

Infrared and visible image fusion integrates complementary information from different modalities into a single image, providing sufficient imaging information for scene interpretation and downstream target recognition tasks. However, existing fusion methods often focus only on highlighting salient targets or preserving scene details, failing to effectively combine entire features from different modalities during the fusion process, resulting in underutilized features and poor overall fusion effects. To address these challenges, a global and local four-branch feature extraction image fusion network (GLFuse) is proposed. On one hand, the Super Token Transformer (STT) block, which is capable of rapidly sampling and predicting super tokens, is utilized to capture global features in the scene. On the other hand, a Detail Extraction Block (DEB) is developed to extract local features in the scene. Additionally, two feature fusion modules, namely the Attention-based Feature Selection Fusion Module (ASFM) and the Dual Attention Fusion Module (DAFM), are designed to facilitate selective fusion of features from different modalities. Of more importance, the various perceptual information of feature maps learned from different modality images at the different layers of a network is investigated to design a perceptual loss function to better restore scene detail information and highlight salient targets by treating the perceptual information separately. Extensive experiments confirm that GLFuse exhibits excellent performance in both subjective and objective evaluations. It deserves note that GLFuse effectively improves downstream target detection performance on a unified benchmark.

Early diagnosis of Cladosporium fulvum in greenhouse tomato plants based on visible/near-infrared (VIS/NIR) and near-infrared (NIR) data fusion

Plant diseases can inflict varying degrees of damage on agricultural production. Therefore, identifying a rapid, non-destructive early diagnostic method is crucial for safeguarding plants. Cladosporium fulvum (C. fulvum) is one of the major diseases in tomato growth. This work presents a method of data fusion using two hyperspectral imaging systems of visible/near-infrared (VIS/NIR) and near-infrared (NIR) spectroscopy for the early diagnosis of C. fulvum in greenhouse tomatoes. First, hyperspectral images of samples at health and different times of infection were collected. The average spectral data of the image regions of interest were extracted and preprocessed for subsequent spectral datasets. Then different classification models were established for VIS/NIR and NIR data, optimized through various variable selection and data fusion methods. The principal component analysis-radial basis function neural network (PCA-RBF) model established using low-level data fusion achieved optimal results, achieving accuracies of 100% and 99.3% for calibration and prediction, respectively. Moreover, both the macro-averaged F1 (Macro-F1) values reached 1, and the geometric mean (G-mean) values reached 1 and 1, respectively. The results indicated that it was feasible to establish a PCA-RBF model by using the hyperspectral technique with low-level data fusion for the early detection of C. fulvum in greenhouse tomatoes.

SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion

- Accurate state of health (SOH) of batteries is a crucial prerequisite for ensuring the safety and stable operation of electric vehicles. However, existing conventional prediction methods do not consider the influence of early-stage capacity variations on full-cycle SOH with extensive test data. This paper develops a SOH early prediction method of lithium-ion batteries based on voltage interval selection and features fusion. To identify the battery SOH curves with high similarity under identical charge-discharge conditions, a double correlation based early-stage SOH similarity analysis method is presented. To minimize the amount of voltage training data collected during feature extraction, a voltage interval selection method considering sampling time and features correlation is introduced. Meanwhile, a feature fusion method combining entropy weight and correlation factor is used to reduce the impact of redundant health feature information. To address the problems of low accuracy and computational inefficiency using the least squares support vector machine (LSSVM) model, a grey wolf optimizer-LSSVM-adaptive boosting model is developed for battery SOH prediction. The experimental results show that the coefficients of determination remain above 0.98, indicating high SOH prediction accuracy using the developed method. Compared to other methods, the mean absolute errors based on the developed method are maintained below 1.5 %.

Freshness in Salmon by Hand-Held Devices: Methods in Feature Selection and Data Fusion for Spectroscopy

Freshness in Salmon by Hand-Held Devices: Methods in Feature Selection and Data Fusion for Spectroscopy

MASDF-Net: A Multi-Attention Codec Network with Selective and Dynamic Fusion for Skin Lesion Segmentation.

Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion segmentation algorithms have achieved certain success, challenges remain in accurately delineating the boundaries of lesion regions in dermoscopic images with irregular shapes, blurry edges, and occlusions by artifacts. To address these issues, a multi-attention codec network with selective and dynamic fusion (MASDF-Net) is proposed for skin lesion segmentation in this study. In this network, we use the pyramid vision transformer as the encoder to model the long-range dependencies between features, and we innovatively designed three modules to further enhance the performance of the network. Specifically, the multi-attention fusion (MAF) module allows for attention to be focused on high-level features from various perspectives, thereby capturing more global contextual information. The selective information gathering (SIG) module improves the existing skip-connection structure by eliminating the redundant information in low-level features. The multi-scale cascade fusion (MSCF) module dynamically fuses features from different levels of the decoder part, further refining the segmentation boundaries. We conducted comprehensive experiments on the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets. The experimental results demonstrate the superiority of our approach over existing state-of-the-art methods.

Curve progression following selective and nonselective spinal fusion for adolescent idiopathic scoliosis: are selective fusions stable?

The purpose of this study is to compare postoperative outcomes between selective and non-selective fusions longitudinally over the first five postoperative years. Patient parameters were retrieved from a multicenter, prospective, database. Patients with Lenke 1-6, B and C deformities were included. Patients were stratified into 2 groups: selective fusion (SF), if the last instrumented vertebra (LIV) was at or cranial to the lumbar apex, or non-selective fusion (NSF). Differences in coronal and sagittal radiographic outcomes were assessed with generalized linear models (GLMs) at 1-, 2- and 5- year postoperative outcomes. Five-year postoperative categorical radiographic outcomes, flexibility, scoliosis research society scores (SRS), and reoperation rates were compared between groups. Matched cohorts were created for subgroup analysis. 416 (SF:261, NF:155) patients, including 353 females were included in this study. The mean preoperative thoracic and lumbar Cobb angles were 57.3 ± 8.9 and 45.3 ± 8.0, respectively. GLMs demonstrated greater postoperative coronal deformity in the SF group (p < 0.01); however, the difference between groups did not change overtime (p > 0.05) indicating a relatively stable postoperative deformity correction. The SF group had a greater incidence of lumbar Cobb ≥ 26 degrees (p < 0.01). The NSF group demonstrated worse forward and lateral flexibility at 5-year postoperative outcome (p < 0.05). There was no difference in postoperative SRS scores between the SF and NSF groups. Reoperation rates were similar between groups. Selective fusion results in greater coronal plane deformity; however, this deformity does not progress significantly over time compared to non-selective fusion. Selective spinal fusion may be a beneficial option for a larger subset of patients than previously identified. III.

High-Precision Dichotomous Image Segmentation With Frequency and Scale Awareness.

Dichotomous image segmentation (DIS) with rich fine-grained details within a single image is a challenging task. Despite the plausible results achieved by deep learning-based methods, most of them fail to segment generic objects when the boundary is cluttered with the background. In fact, the gradual decrease in feature map resolution during the encoding stage and the misleading texture clue may be the main issues. To handle these issues, we devise a novel frequency-and scale-aware deep neural network (FSANet) for high-precision DIS. The core of our proposed FSANet is twofold. First, a multimodality fusion (MF) module that integrates the information in spatial and frequency domains is adopted to enhance the representation capability of image features. Second, a collaborative scale fusion module (CSFM) which deviates from the traditional serial structures is introduced to maintain high resolution during the entire feature encoding stage. In the decoder side, we introduce hierarchical context fusion (HCF) and selective feature fusion (SFF) modules to infer the segmentation results from the output features of the CSFM module. We conduct extensive experiments on several benchmark datasets and compare our proposed method with existing state-of-the-art (SOTA) methods. The experimental results demonstrate that our FSANet achieves superior performance both qualitatively and quantitatively. The code will be made available at https://github.com/chasecjg/FSANet.

Spastic Equinovarus Foot Deformity.

Acute brain injuries are caused by a variety of etiologies, each potentially disrupting neurological function. The neurologic impairments are on a spectrum of severity often creating functional barriers to completing activities of daily living. Initial treatment starts immediately upon diagnosis and requires a multimodal approach working to prevent systemic changes. Therapy, bracing treatment, injections, and pharmacologic treatments are the mainstay of early intervention. Worsening upper motor neurological impairment associated with involuntary muscle hyperactivity can lead to a spastic equinovarus foot deformity. Spastic equinovarus foot deformities secondary to anoxic brain injuries or traumatic brain injury pose a challenging situation for orthopaedic surgeons because of associated cognitive impairment, spastic tone, and extensive soft-tissue contractures prohibiting bracing treatment. Tendon releases and transfers in combination with functional bracing treatment are initially attempted, and selective fusions are performed for severe cases. Surgical indications are primarily focused on obtaining a balanced, braceable, functional lower extremity with a plantigrade foot.

A multiband terahertz metamaterial sensor evaluated using a multiresponse variable fusion framework for the precise detection of trace fluoroquinolone antibiotics

A specific structural multiband terahertz (THz) metamaterial sensor (TMS) was designed herein to improve the sensitivity of detecting multiple trace fluoroquinolone (FQ) antibiotics in water, and a multiple resonance response competition and selection fusion (MRR-CSF) framework was proposed for accurate FQ analysis. The TMS based on three-concentric C4-symmetrical split-ring (TC4) structures generated five bands at 0.797, 0.900, 1.097, 1.198, and 1.777 THz. The multiband resonance signals for three types of FQs, including enrofloxacin (ENR), pefloxacin (PEF), and nadifloxacin (NAD), were detected by the TMS, which increased as the antibiotic concentrations increased (5–140 ng/L). The proposed method can solve the problems of low accuracy and poor robustness of conventional single-response regression by combining selected and fused TMS-effective resonance response variables with three multivariate regression strategies. The calculated comprehensive metrics also provide an objective evaluation of multiband TMS performance. The designed TMS combined with the proposed MRR-CSF framework showed 21 %–27 %, 13 %–17 %, and 12 %–13 % improvements in the detection accuracy for ENR, PEF, and NAD, respectively, according to the univariate regression results. The limit of detection for the ENR, PEF, and NAD antibiotic solutions was achieved at 5 ng/L, and the reliable range of recoveries (100 % ± 25 %), average recoveries (>90 %), and relative standard deviation values (<5 %) were obtained for the concentration prediction of newly prepared FQs. This study demonstrates the promising prospect of applying the multiband TMS combined with the MRR-CSF framework for accurate biochemical sensing and detection.

Evaluation and analysis of visual perception using attention-enhanced computation in multimedia affective computing.

Facial expression recognition (FER) plays a crucial role in affective computing, enhancing human-computer interaction by enabling machines to understand and respond to human emotions. Despite advancements in deep learning, current FER systems often struggle with challenges such as occlusions, head pose variations, and motion blur in natural environments. These challenges highlight the need for more robust FER solutions. To address these issues, we propose the Attention-Enhanced Multi-Layer Transformer (AEMT) model, which integrates a dual-branch Convolutional Neural Network (CNN), an Attentional Selective Fusion (ASF) module, and a Multi-Layer Transformer Encoder (MTE) with transfer learning. The dual-branch CNN captures detailed texture and color information by processing RGB and Local Binary Pattern (LBP) features separately. The ASF module selectively enhances relevant features by applying global and local attention mechanisms to the extracted features. The MTE captures long-range dependencies and models the complex relationships between features, collectively improving feature representation and classification accuracy. Our model was evaluated on the RAF-DB and AffectNet datasets. Experimental results demonstrate that the AEMT model achieved an accuracy of 81.45% on RAF-DB and 71.23% on AffectNet, significantly outperforming existing state-of-the-art methods. These results indicate that our model effectively addresses the challenges of FER in natural environments, providing a more robust and accurate solution. The AEMT model significantly advances the field of FER by improving the robustness and accuracy of emotion recognition in complex real-world scenarios. This work not only enhances the capabilities of affective computing systems but also opens new avenues for future research in improving model efficiency and expanding multimodal data integration.