Semi-invasive wearable clinic: Solution-processed smart microneedle electronics for next-generation integrated diagnosis and treatment.

The integrated smart electronics for real-time monitoring and personalized therapy of disease-related analytes have been gradually gaining tremendous attention. However, human tissue barriers, including the skin barrier and brain-blood barrier, pose significant challenges for effective biomarker detection and drug delivery. Microneedle (MN) electronics present a promising solution to overcome these tissue barriers due to their semi-invasive structures, enabling effective drug delivery and target-analyte detection without compromising the tissue configuration. Furthermore, MNs can be fabricated through solution processing, facilitating large-scale manufacturing. This review provides a comprehensive summary of the recent three-year advancements in smart MNs development, categorized as follows. First, the solution-processed technology for MNs is introduced, with a focus on various printing technologies. Subsequently, smart MNs designed for sensing, drug delivery, and integrated systems combining diagnosis and treatment are separately summarized. Finally, the prospective and promising applications of next-generation MNs within mediated diagnosis and treatment systems are discussed.

Artificial intelligence-based classification of breast lesion from contrast enhanced mammography: a multicenter study.

PURPOSE: The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism. MATERIALS AND METHODS: This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction. RESULTS: The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization. CONCLUSIONS: The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.

Coupled Electronic and Magnonic Topological States in Two-Dimensional Ferromagnets.

Magnetic materials offer a fertile playground for fundamental physics discovery, with not only electronic but also magnonic topological states intensively explored. However, one natural material with both electronic and magnonic nontrivial topologies is still unknown. Here, we demonstrate the coexistence of first-order topological magnon insulators (TMIs) and electronic second-order topological insulators (SOTIs) in 2D honeycomb ferromagnets, giving rise to the nontrivial corner states being connected by the charge-free magnonic edge states. We show that, with C3 symmetry, the phase factor ± ϕ caused by the next nearest-neighbor Dzyaloshinskii-Moriya interaction breaks the pseudo-spin time-reversal symmetry T, which leads to the split of magnon bands, i.e., the emergence of TMIs with a nonzero Chern number of C=-1, in experimentally feasible candidates of MoI3, CrSiTe3, and CrGeTe3 monolayers. Moreover, protected by the C3 symmetry, the electronic SOTIs characterized by nontrivial corner states are obtained, bridging the topological aspect of fermions and bosons with a high possibility of innovative applications in spintronics devices.

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis.

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Topology-Engineered Orbital Hall Effect in Two-Dimensional Ferromagnets.

Recent advances in the manipulation of the orbital angular momentum (OAM) within the paradigm of orbitronics presents a promising avenue for the design of future electronic devices. In this context, the recently observed orbital Hall effect (OHE) occupies a special place. Here, focusing on both the second-order topological and quantum anomalous Hall insulators in two-dimensional ferromagnets, we demonstrate that topological phase transitions present an efficient and straightforward way to engineer the OHE, where the OAM distribution can be controlled by the nature of the band inversion. Using first-principles calculations, we identify Janus RuBrCl and three septuple layers of MnBi2Te4 as experimentally feasible examples of the proposed mechanism of OHE engineering by topology. With our work, we open up new possibilities for innovative applications in topological spintronics and orbitronics.

Five New Species of the Genus Hymenogaster (Hymenogastraceae, Agaricales) from Northern China.

In this study, five new species from China, Hymenogaster latisporus, H. minisporus, H. papilliformis, H. perisporius, and H. variabilis, are described and illustrated based on morphological and molecular evidence. Hymenogaster latisporus was distinguished from other species of the genus by the subglobose, broad ellipsoidal, ovoid basidiospores (average = 13.7 µm × 11.6 µm) with sparse verrucose and ridge-like ornamentation (1-1.2 µm high); H. minisporus by the ellipsoidal to broadly ellipsoidal and small basidiospores (average = 11.7 µm × 9.5 µm); H. papilliformis was characterized by the whitish to cream-colored basidiomes, and broadly fusiform to citriform basidiospores with a pronounced apex (2-3 µm, occasionally up to 4 µm high), papillary, distinct warts and ridges, and pronounced appendix (2-3 µm long); H. perisporius by the dirty white to pale yellow basidiomes, broad ellipsoidal to ellipsoidal, and yellow-brown to dark-brown basidiospores with warts and gelatinous perisporium; H. variabilis by the peridium with significant changes in thickness (167-351 µm), and broad ellipsoidal to subglobose basidiospores ornamented with sparse warts and ridges. An ITS/LSU-based phylogenetic analysis supported the erection of the five new species. A key for Hymenogaster species from northern China is provided.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles.

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Quantum oscillations evidence for topological bands in kagome metal ScV6Sn6.

Metals with kagome lattice provide bulk materials to host both the flat-band and Dirac electronic dispersions. A new family of kagome metals is recently discovered inAV6Sn6. The Dirac electronic structures of this material needs more experimental evidence to confirm. In the manuscript, we investigate this problem by resolving the quantum oscillations in both electrical transport and magnetization in ScV6Sn6. The revealed orbits are consistent with the electronic band structure models. Furthermore, the Berry phase of a dominating orbit is revealed to be aroundπ, providing direct evidence for the topological band structure, which is consistent with calculations. Our results demonstrate a rich physics and shed light on the correlated topological ground state of this kagome metal.

Joint learning of multi-level dynamic brain networks for autism spectrum disorder diagnosis.

Graph convolutional networks (GCNs), with their powerful ability to model non-Euclidean graph data, have shown advantages in learning representations of brain networks. However, considering the complexity, multilayeredness, and spatio-temporal dynamics of brain activities, we have identified two limitations in current GCN-based research on brain networks: 1) Most studies have focused on unidirectional information transmission across brain network levels, neglecting joint learning or bidirectional information exchange among networks. 2) Most of the existing models determine node neighborhoods by thresholding or simply binarizing the brain network, which leads to the loss of edge weight information and weakens the model's sensitivity to important information in the brain network. To address the above issues, we propose a multi-level dynamic brain network joint learning architecture based on GCN for autism spectrum disorder (ASD) diagnosis. Specifically, firstly, constructing different-level dynamic brain networks. Then, utilizing joint learning based on GCN for interactive information exchange among these multi-level brain networks. Finally, designing an edge self-attention mechanism to assign different edge weights to inter-node connections, which allows us to pick out the crucial features for ASD diagnosis. Our proposed method achieves an accuracy of 81.5 %. The results demonstrate that our method enables bidirectional transfer of high-order and low-order information, facilitating information complementarity between different levels of brain networks. Additionally, the use of edge weights enhances the representation capability of ASD-related features.

High-Precision Atom Interferometer-Based Dynamic Gravimeter Measurement by Eliminating the Cross-Coupling Effect.

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses an AI sensor and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement were ±0.42 mGal and ±0.46 mGal after optimizing the cross-coupling effect, which was improved by factors of 4.18 and 4.21 compared to the cases without optimization.

Association between CT-based adipose variables, preoperative blood biochemical indicators and pathological T stage of clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is corelated with tumor-associated material (TAM), coagulation system and adipocyte tissue, but the relationships between them have been inconsistent. Our study aimed to explore the cut-off intervals of variables that are non-linearly related to ccRCC pathological T stage for providing clues to understand these discrepancies, and to effectively preoperative risk stratification. Methods: This retrospective analysis included 218 ccRCC patients with a clear pathological T stage between January 1st, 2014, and November 30th, 2021. The patients were categorized into two cohorts based on their pathological T stage: low T stage (T1 and T2) and high T stage (T3 and T4). Abdominal and perirenal fat variables were measured based on preoperative CT images. Blood biochemical indexes from the last time before surgery were also collected. The generalized sum model was used to identify cut-off intervals for nonlinear variables. Results: In specific intervals, fibrinogen levels (FIB) (2.63-4.06 g/L) and platelet (PLT) counts (>200.34 × 109/L) were significantly positively correlated with T stage, while PLT counts (<200.34 × 109/L) were significantly negatively correlated with T stage. Additionally, tumor-associated material exhibited varying degrees of positive correlation with T stage at different cut-off intervals (cut-off value: 90.556 U/mL). Conclusion: Preoperative PLT, FIB and TAM are nonlinearly related to pathological T stage. This study is the first to provide specific cut-off intervals for preoperative variables that are nonlinearly related to ccRCC T stage. These intervals can aid in the risk stratification of ccRCC patients before surgery, allowing for developing a more personalized treatment planning.

Complete mitochondrial genome of Striatobalanus tenuis Hoek, 1883 (Balanomorpha: Balanidae) and a novel molecular phylogeny within Cirripedia.

Barnacles are crustaceans that are critical model organisms in intertidal ecology and biofouling research. In this study, we present the first mitochondrial genome of Striatobalanus tenuis which is a circular molecule of 15,067 bp in length. Consistent with most barnacles, the mitochondrial genome of S. tenuis encodes 37 genes, including 13 PCGs, 22 tRNAs and 2 rRNAs. A novel insight into the phylogenetic analysis based on the nucleotide data of 13 PCGs showed that the S. tenuis clusters with Striatobalanus amaryllis (bootstrap value = 100) of the same genus, then groups with other Balanoidea species, the Chelonibiidae, Austrobalanidae and Tetraclitidae cluster together forming superfamily Coronuloidea. The result can help us to understand the novel classification within Balanomorpha.

Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.

BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) metastases largely depend on radiologist experience. PURPOSE: To develop a deep learning-based whole-process system (DLWPS) for segmentation and diagnosis of breast lesions and discrimination of ALN metastasis. STUDY TYPE: Retrospective. POPULATION: 1760 breast patients, who were divided into training and validation sets (1110 patients), internal (476 patients), and external (174 patients) test sets. FIELD STRENGTH/SEQUENCE: 3.0T/dynamic contrast-enhanced (DCE)-MRI sequence. ASSESSMENT: DLWPS was developed using segmentation and classification models. The DLWPS-based segmentation model was developed by the U-Net framework, which combined the attention module and the edge feature extraction module. The average score of the output scores of three networks was used as the result of the DLWPS-based classification model. Moreover, the radiologists' diagnosis without and with the DLWPS-assistance was explored. To reveal the underlying biological basis of DLWPS, genetic analysis was performed based on RNA-sequencing data. STATISTICAL TESTS: Dice similarity coefficient (DI), area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and kappa value. RESULTS: The segmentation model reached a DI of 0.828 and 0.813 in the internal and external test sets, respectively. Within the breast lesions diagnosis, the DLWPS achieved AUCs of 0.973 in internal test set and 0.936 in external test set. For ALN metastasis discrimination, the DLWPS achieved AUCs of 0.927 in internal test set and 0.917 in external test set. The agreement of radiologists improved with the DLWPS-assistance from 0.547 to 0.794, and from 0.848 to 0.892 in breast lesions diagnosis and ALN metastasis discrimination, respectively. Additionally, 10 breast cancers with ALN metastasis were associated with pathways of aerobic electron transport chain and cytoplasmic translation. DATA CONCLUSION: The performance of DLWPS indicates that it can promote radiologists in the judgment of breast lesions and ALN metastasis and nonmetastasis. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 3.

Efficacy of ultrasound-guided radiofrequency ablation of papillary thyroid microcarcinoma after one year.

OBJECTIVE: This study aims to evaluate the feasibility and safety of percutaneous radiofrequency ablation guided by ultrasound for treating papillary thyroid microcarcinoma. METHOD: At our institution, fifty people who had been treated for micropapillary thyroid cancer with ultrasound-guided radiofrequency ablation were chosen. Thyroid function was evaluated after one month, and the volume of the ablation region was assessed immediately, 3, 6, and 12 months after treatment. At the same time, the complications or adverse reactions after treatment were evaluated. RESULTS: As time passed, the volume of the ablation area decreased gradually, showing a regression trend. There was a significant difference in the volume of the ablation area between adjacent groups (P < 0.05), and the tumor volume reduction ratio (VRR) of the ablation area was a statistically significant difference between adjacent groups (P < 0.05). There was no significant difference between the indexes related to thyroid function before and after treatment(P > 0.05). No local recurrence or distant metastasis was found during follow-up; The most common complication after the operation was a slight pain in the neck. A few patients had toothache and neck swelling symptoms, and the above symptoms subsided within 24 h after the operation. CONCLUSION: Ultrasound-guided radiofrequency ablation is safe and effective for treating single-focus micropapillary thyroid carcinoma while retaining thyroid function, with few and minor complications, which can be used as an ideal surgical option.

TNFAIP3 Derived from Skeletal Stem Cells Alleviated Rat Osteoarthritis by Inhibiting the Necroptosis of Subchondral Osteoblasts.

Recent investigations have shown that the necroptosis of tissue cells in joints is important in the development of osteoarthritis (OA). This study aimed to investigate the potential effects of exogenous skeletal stem cells (SSCs) on the necroptosis of subchondral osteoblasts in OA. Human SSCs and subchondral osteoblasts isolated from human tibia plateaus were used for Western blotting, real-time PCR, RNA sequencing, gene editing, and necroptosis detection assays. In addition, the rat anterior cruciate ligament transection OA model was used to evaluate the effects of SSCs on osteoblast necroptosis in vivo. The micro-CT and pathological data showed that intra-articular injections of SSCs significantly improved the microarchitecture of subchondral trabecular bones in OA rats. Additionally, SSCs inhibited the necroptosis of subchondral osteoblasts in OA rats and necroptotic cell models. The results of bulk RNA sequencing of SSCs stimulated or not by tumor necrosis factor α suggested a correlation of SSCs-derived tumor necrosis factor α-induced protein 3 (TNFAIP3) and cell necroptosis. Furthermore, TNFAIP3-derived from SSCs contributed to the inhibition of the subchondral osteoblast necroptosis in vivo and in vitro. Moreover, the intra-articular injections of TNFAIP3-overexpressing SSCs further improved the subchondral trabecular bone remodeling of OA rats. Thus, we report that TNFAIP3 from SSCs contributed to the suppression of the subchondral osteoblast necroptosis, which suggests that necroptotic subchondral osteoblasts in joints may be possible targets to treat OA by stem cell therapy.

EA-Net: Research on skin lesion segmentation method based on U-Net.

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making. This paper proposes a CNN with an added attention mechanism (EA-Net) for more accurate medical image segmentation.EA-Net is based on the U-Net network model framework. Specifically, we added a pixel-level attention module (PA) to the encoder section to preserve the local features of the image during downsampling, making the feature maps input to the decoder more relevant to the ground-truth. At the same time, we added a spatial multi-scale attention module (SA) after the decoding process to increase the spatial weight of the feature maps that are more relevant to the ground-truth, thereby reducing the gap between the output results and the ground-truth. We conducted extensive segmentation experiments on skin lesion images from the ISIC 2017 and ISIC 2018 datasets. The results demonstrate that, when compared to U-Net, our proposed EA-Net achieves an average Dice score improvement of 1.94% and 5.38% for skin lesion tissue segmentation on the ISIC 2017 and ISIC 2018 datasets, respectively. The IoU also increases by 2.69% and 8.31%, and the ASSD decreases by 0.3783 pix and 0.5432 pix, indicating superior segmentation performance. EA-Net can achieve better segmentation results when the original image of skin lesions has an obscure boundary and the segmentation area contains interference factors, which proves that the addition of attention mechanism in the encoder and the application of comprehensive attention mechanism can improve the performance of neural network in the field of skin lesions image segmentation.

Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder.

Introduction: Autism Spectrum Disorder (ASD) has a significant impact on the health of patients, and early diagnosis and treatment are essential to improve their quality of life. Machine learning methods, including multi-classifier fusion, have been widely used for disease diagnosis and prediction with remarkable results. However, current multi-classifier fusion methods lack the ability to measure the belief level of different samples and effectively fuse them jointly. Methods: To address these issues, a multi-classifier fusion classification framework based on belief-value for ASD diagnosis is proposed in this paper. The belief-value measures the belief level of different samples based on distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples), which is more representative than using a single type of information. Then, the complementary relationships between belief-values are captured via a multilayer perceptron (MLP) network for effective fusion of belief-values. Results: The experimental results demonstrate that the proposed classification framework achieves better performance than a single classifier and confirm that the fusion method used can effectively fuse complementary relationships to achieve accurate diagnosis. Discussion: Furthermore, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to the diagnosis of other neuropsychiatric disorders.

Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.

RATIONALE AND OBJECTIVES: This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data. METHODS: This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data. RESULTS: A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis. CONCLUSION: This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool.

A global database of bird nest traits.

The reproductive success of birds is closely tied to the characteristics of their nests. It is crucial to understand the distribution of nest traits across phylogenetic and geographic dimensions to gain insight into bird evolution and adaptation. Despite the extensive historical documentation on breeding behavior, a structured dataset describing bird nest characteristics has been lacking. To address this gap, we have compiled a comprehensive dataset that characterizes three ecologically and evolutionarily significant nest traits-site, structure, and attachment-for 9,248 bird species, representing all 36 orders and 241 out of the 244 families. By defining seven sites, seven structures, and four attachment types, we have systematically classified the nests of each species using information from text descriptions, photos, and videos sourced from online databases and literature. This nest traits dataset serves as a valuable addition to the existing body of morphological and ecological trait data for bird species, providing a useful resource for a wide range of avian macroecological and macroevolutionary research.

Brain tumor feature extraction and edge enhancement algorithm based on U-Net network.

Background: Statistics show that each year more than 100,000 patients pass away from brain tumors. Due to the diverse morphology, hazy boundaries, or unbalanced categories of medical data lesions, segmentation prediction of brain tumors has significant challenges. Purpose: In this thesis, we highlight EAV-UNet, a system designed to accurately detect lesion regions. Optimizing feature extraction, utilizing automatic segmentation techniques to detect anomalous regions, and strengthening the structure. We prioritize the segmentation problem of lesion regions, especially in cases where the margins of the tumor are more hazy. Methods: The VGG-19 network structure is incorporated into the coding stage of the U-Net, resulting in a deeper network structure, and an attention mechanism module is introduced to augment the feature information. Additionally, an edge detection module is added to the encoder to extract edge information in the image, which is then passed to the decoder to aid in reconstructing the original image. Our method uses the VGG-19 in place of the U-Net encoder. To strengthen feature details, we integrate a CBAM (Channel and Spatial Attention Mechanism) module into the decoder to enhance it. To extract vital edge details from the data, we incorporate an edge recognition section into the encoder. Results: All evaluation metrics show major improvements with our recommended EAV-UNet technique, which is based on a thorough analysis of experimental data. Specifically, for low contrast and blurry lesion edge images, the EAV-Unet method consistently produces forecasts that are very similar to the initial images. This technique reduced the Hausdorff distance to 1.82, achieved an F1 score of 96.1%, and attained a precision of 93.2% on Dataset 1. It obtained an F1 score of 76.8%, a Precision of 85.3%, and a Hausdorff distance reduction to 1.31 on Dataset 2. Dataset 3 displayed a Hausdorff distance cut in 2.30, an F1 score of 86.9%, and Precision of 95.3%. Conclusions: We conducted extensive segmentation experiments using various datasets related to brain tumors. We refined the network architecture by employing smaller convolutional kernels in our strategy. To further improve segmentation accuracy, we integrated attention modules and an edge enhancement module to reinforce edge information and boost attention scores.