Search Results (267)

Electroencephalography (EEG) is the only brain imaging method light enough and with the temporal precision to assess electrocortical dynamics during human locomotion. However, head motion during whole-body movements produces artifacts that contaminate the EEG and reduces ICA decomposition quality. We compared commonly used motion artifact removal approaches for reducing the motion artifact from the EEG during running and identifying stimulus-locked ERP components during an adapted flanker task. EEG was recorded from young adults during dynamic jogging and static standing versions of the Flanker task. Motion artifact removal approaches were evaluated based on their ICA’s component dipolarity, power changes at the gait frequency and harmonics, and ability to capture the expected P300 ERP congruency effect. Preprocessing the EEG using either iCanClean with pseudo-reference noise signals or artifact subspace reconstruction (ASR) led to the recovery of more dipolar brain independent components. In our analyses, iCanClean was somewhat more effective than ASR. Power was significantly reduced at the gait frequency after preprocessing with ASR and iCanClean. Finally, preprocessing using ASR and iCanClean also produced ERP components similar in latency to those identified in the standing flanker task. The expected greater P300 amplitude to incongruent flankers was identified when preprocessing using iCanClean. ASR and iCanClean may provide effective preprocessing methods for reducing motion artifacts in human locomotion studies during running. Full article

Pedicle screw fixation is a critical technique for stabilizing lumbar fractures in canines, yet the biomechanical implications of insertion angles remain underexplored. This study aims to identify optimal screw trajectories by analyzing stress distribution and deformation patterns in beagle lumbar segments (L6-L7) using finite element analysis (FEA). A 3D finite element model was reconstructed from CT scans of a healthy beagle, incorporating cortical/cancellous bone, intervertebral disks, and cartilage. Pedicle screws (2.4 mm diameter, 22 mm length) were virtually implanted at angles ranging from 45° to 65°. A 10 N vertical load simulated standing conditions. Equivalent stress and total deformation were evaluated under static loading. The equivalent stress occurred at screw–rod junctions, with maxima at 50° (11.73 MPa) and minima at 58° (3.25 MPa). Total deformation ranged from 0.0033 to 0.0064 mm, with the highest at 55° and the lowest at 54°. The 58° insertion angle demonstrated optimal biomechanical stability with minimal stress concentration, with 56–60° as a biomechanically favorable range for pedicle screw fixation in canine lumbar fractures, balancing stress distribution and deformation control. Future studies should validate these findings in multi-level models and clinical settings. Full article

The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%. Full article

Background/Objectives: In recent years, scientific literature has illustrated the growing interest in telerehabilitation after ACL reconstruction. The aim of this study is to compare the effectiveness of remotely supervised rehabilitation with traditional supervised rehabilitation after ACLR, focusing on objective postoperative functional assessment outcomes. Methods: A retrospective analysis of prospectively collected data was carried out, selecting patients that underwent arthroscopic ACLR by a single surgeon. Functional assessments of the patients were carried out at 1 and 2 weeks and 1, 2 and 3 months after surgery, including range of motion (ROM), maximal voluntary isometric contractions (MVICs) of extensor and flexor muscles, the sit-to-stand test and the countermovement jump. Intergroup statistics were carried out using a non-inferiority hypothesis. Results: A total of 251 patients were included in this study (supervised rehabilitation n = 165; remotely supervised rehabilitation n = 86). Functional assessment improved over time in both groups. The extension ROM deficit decreased to 0 difference 30 days after surgery. The median flexion ROM ILD at 60 days was significantly different among the groups, with a residual 10° ILD in the Group R compared with 0° ILD in group S (p = 0.01). All other assessments did not achieve statistical significance. Conclusions: The results support the integration of a digital rehabilitation tool in post-ACLR recovery programs. The results suggest that remotely supervised rehabilitation can be a viable alternative to traditional supervised rehabilitation for early-stage recovery. However, more research is needed to optimize protocols and to identify patients who may benefit most from this approach. Full article

Salvia, a medicinally and economically important genus, is widely used in traditional medicine, agriculture, and horticulture. This study compares the chloroplast genomes of eight East Asian Salvia species to assess genetic diversity, structural features, and evolutionary relationships. Complete chloroplast genomes were sequenced, annotated, and analyzed for gene content, codon usage, and repetitive sequences. Phylogenetic relationships were reconstructed using Maximum Likelihood, Maximum Parsimony and Bayesian inference. The genomes exhibited a conserved quadripartite structure (151,081–152,678 bp, GC content 37.9–38.1%), containing 114 unique genes with consistent arrangement. Codon usage favored A/T endings, with leucine (Leu) most frequent and cysteine (Cys) least. We identified 281 long sequence repeats (LSRs) and 345 simple sequence repeats (SSRs), mostly in non-coding regions. Comparative analysis revealed five hypervariable regions (trnH-psbA, rbcL-accD, petA-psbJ, rpl32-trnL, ycf1) as potential molecular markers. Phylogenetic analysis confirmed the monophyly of East Asian Salvia, dividing them into five clades, with Sect. Sonchifoliae basal. While G1, G3, and G8 were monophyletic, G5 and G6 were paraphyletic, and the G7-G8 relationship challenged traditional classifications. The genomic evidence provides crucial insights for resolving long-standing taxonomic uncertainties and refining the classification system of Salvia. These findings suggest a complex evolutionary history involving hybridization and incomplete lineage sorting, providing valuable genomic insights for Salvia phylogeny, taxonomy, and conservation. Full article

Habitat fragmentation and loss seriously threaten Ceroxylon palms, a key and vulnerable species in Andean forests. Given the need for efficient tools for their monitoring and conservation, this study aimed to evaluate the effectiveness of deep learning YOLO models for the automatic detection of Ceroxylon individuals in high-resolution UAV images. Three versions of YOLO (v8, v10, and v11) were analyzed, each in nano (“n”), medium (“m”), and extra-high (“x”) configurations, considering both processing time and detection accuracy. Difficulties in orthomosaic reconstruction were addressed by specific adjustments to the photogrammetric software parameters. The nine resulting models were tested in seven study plots, with the YOLOv8-m configuration standing out as the one that best balanced processing speed and accuracy, achieving the following outstanding metrics: F1 = 0.91; mAP50 = 0.98; and mAP50-95 = 0.62. These results demonstrate the practical value of YOLO model automatic detection for the informed management and effective conservation of Ceroxylon in mountain ecosystems. Full article

Three-dimensional CAD reconstruction is a long-standing and important task in fields such as industrial manufacturing, architecture, medicine, film and television, research, and education. Reconstructing CAD models remains a persistent challenge in machine learning. There have been many studies on deep learning in the field of 3D reconstruction. In recent years, with the release of CAD datasets, there have been more and more studies on 3D CAD reconstruction using deep learning. With the continuous deepening of research, deep learning has significantly improved the performance of tasks in the field of CAD reconstruction. However, this task remains challenging due to data scarcity and labeling difficulties, model complexity, and lack of generality and adaptability. This paper reviews both classic and recent research results on 3D CAD reconstruction tasks based on deep learning. To the best of our knowledge, this is the first investigation focusing on the CAD reconstruction task in the field of deep learning. Since there are relatively few studies related to 3D CAD reconstruction, we also investigate the reconstruction and generation of 2D CAD sketches. According to the different input data, we divide all investigations into the following categories: point cloud input to 3D CAD models, sketch input to 3D CAD models, other input to 3D CAD models, reconstruction and generation of 2D sketches, characterization of CAD data, CAD datasets, and related evaluation indicators. Commonly used datasets are outlined in our taxonomy. We provide a brief overview of the current research background, challenges, and recent results. Finally, future research directions are discussed. Full article

Background and Objectives: The anterior cruciate ligament (ACL) is crucial for knee stability, preventing anterior displacement of the tibia and rotation relative to the femur. Despite ACL reconstruction (ACLR), residual instability is common, affecting knee function. Anterolateral ligament reconstruction (ALLR) alongside ACLR improves outcomes, as the ALL plays a significant role in rotational stability. This study aims to assess the clinical and functional outcomes of the ACLR+ALLR combination using biomechanical testing in patients with at least ten months of follow-up. Materials and Methods: This cross-sectional comparative cohort study involves patients with ACLR. Inclusion criteria were adult patients who underwent ACLR within the last 3 years, with the same surgical technique performed by a single operator. Patients underwent anamnestic and clinical evaluation and completed Lysholm and KOOS questionnaires. Biomechanical tests included a Unilateral Drop Jump, a Countermovement Jump with knee rotation, and a five-repetition Sit-To-Stand. Force platforms, a camera, and surface electromyography were used to assess biomechanical stability and joint function. Results: This study included 18 subjects, 5 with ACLR and ALLR, and 13 with ACLR alone. The groups showed no significant differences in the KOOS and Lysholm scales and clinical outcomes. Muscle trophism reduction compared to the contralateral limb was noted in both groups. Biomechanical evaluations showed no difference in Quadriceps muscle activity during the landing phase of the Drop Jump. However, the ACL-ALL group exhibited fewer spikes and fewer knee joint angular excursions during ground impact stabilization. In the 5-STS task, a significant difference was observed in the vertical force peak. Differences in muscle activity during foot rotation and force components during the jumping phase were noted in the Countermovement Jump. Conclusions: ACLR combined with ALLR shows similar perceived joint function but improved biomechanical joint stability. Further studies with larger samples and longer follow-ups are needed for validation. Full article

Aortic valve disease accounts for approximately 5% of all congenital heart defects in children. Choosing the optimal valve replacement in this population is challenging, as it must ensure durability, accommodate growth, and minimize the need for long-term anticoagulation. Biological valves do not require anticoagulation but lack durability and growth potential, leading to frequent reoperations. Mechanical valves offer longevity but necessitate lifelong anticoagulation and do not grow with the child. Among the available surgical options, the Ross procedure has emerged as a preferred approach due to its favorable hemodynamic performance, growth potential, and freedom from anticoagulation. First described in 1967, this technique involves replacing the diseased aortic valve with a pulmonary autograft and reconstructing the right ventricular outflow tract using a human or non-human valve substitute. Despite its advantages, the procedure is technically demanding, has a considerable learning curve, and transforms a single-valve pathology into a bivalvular condition. This narrative review provides an updated perspective on the Ross procedure in children, focusing on long-term survival, reoperation rates, and the role of percutaneous valve replacement in delaying surgical reintervention. By synthesizing the latest evidence, we aim to clarify the current standing of the Ross procedure as a durable and effective solution for pediatric aortic valve disease. Full article

Complex-valued neural networks have emerged as an effective instrument in image reconstruction, exhibiting significant advancements compared to conventional techniques. This study introduces an innovative methodology to tackle the difficulties related to image reconstruction within medical microwave imaging. Initially, in the estimation phase, the proposed methodology integrates the Born iterative method with quadratic programming. Subsequently, in the refinement stage, the study explores the application of complex-valued neural networks to enhance the quality of reconstructions. The research emphasizes distinct complex-valued neural network architectures, namely, CV-UNET, CV-CNN, CV-MLP, and their corresponding performances. CV-UNET stands out as the best architecture, surpassing conventional methods and the other complex-valued neural networks variants. The complex-valued neural network improves the fidelity of reconstructions and simplifies the procedure by obviating the need for multiple training steps, a common prerequisite in real-valued neural networks. Full article

Pottery is one of the most common and abundant types of human remains found in archaeological contexts. The analysis of archaeological pottery involves the reconstruction of pottery vessels from their sherds, which represents a laborious and repetitive task. In this work, we investigate a deep learning-based approach to make that process more efficient, accurate, and fast. In that regard, given a sherd’s digital point cloud in a standard, so-called canonical position, the proposed method predicts the geometric transformation, which moves the sherd to its expected normalized position relative to the vessel’s coordinate system. Among the main components of the proposed method, a pair of deep 1D convolutional neural networks trained to predict the 3D Euclidean transformation parameters stands out. Herein, rotation and translation components are treated as independent problems, so while the first network is dedicated to predicting translation moments, the other infers the rotation parameters. In practical applications, once a vessel’s shape is identified, the networks can be trained to predict the target transformation parameter values. Thus, given a 3D model of a complete vessel, it may be virtually broken down countless times for the production of sufficient data to meet deep neural network training demands. In addition to overcoming the scarcity of real sherd data, given a virtual sherd in its original position, that procedure provides paired canonical and normalized point clouds, as well as the target Euclidean transformation. The herein proposed 1D convolutional neural network architecture, the so-called PotNet, was inspired by the PointNet architecture. While PointNet was motivated by 3D point cloud classification and segmentation applications, PotNet was designed to perform non-linear regressions. The method is able to provide an initial estimate for the correct position of a sherd, reducing the complexity of the problem of fitting candidate pairs of sherds, which could be then carried out by a classical adjustment method like ICP, for instance. Experiments using three distinct real vessels were carried out, and the reported results suggest that the proposed method can be successfully used for aiding pottery reconstruction. Full article

The accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-range photogrammetry (CRP) is widely used in the 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, novel view synthesis (NVS) technology, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using imaging devices with different resolutions (cameras on smartphones and UAV). These plots included one with sparse, leafless trees and another with dense foliage and more occlusions. We then performed dense reconstruction of forest stands using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. The photogrammetric method is suitable for relatively simple forest stands, as it is less adaptable to complex ones. This results in tree point cloud models with issues such as excessive canopy noise and wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, yielding tree point clouds of the highest quality that offer more detailed trunk and canopy information. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings confirm that NVS methods have significant application potential for 3D reconstruction of urban forests. Full article

Background/Objectives: Freshwater shrimps of the family Atyidae, particularly the hyperdiverse genus Caridina, are keystone decomposers in tropical aquatic ecosystems and valuable aquaculture resources. However, their evolutionary relationships remain unresolved due to conflicting morphological and molecular evidence. Here, we sequenced and characterized the complete mitochondrial genome of Caridina mariae (Tiger Shrimp), aiming to (1) elucidate its genomic architecture, and (2) reconstruct a robust phylogeny of Caridea using 155 decapod species to address long-standing taxonomic uncertainties. Methods: Muscle tissue from wild-caught C. mariae (voucher ID: KIZ-2023-001, Guangdong, China) was subjected to Illumina NovaSeq 6000 sequencing (150 bp paired-end). The mitogenome was assembled using MITObim v1.9, annotated via MITOS2, and validated by PCR. Phylogenetic analyses employed 13 protein-coding genes under Bayesian inference (MrBayes v3.2.7; 10⁶ generations, ESS > 200) and maximum likelihood (RAxML v8.2.12; 1000 bootstraps), with Harpiosquilla harpax as the outgroup. The best-fit substitution model (MtZoa + F + I + G4) was selected via jModelTest v2.1.10. Results: The 15,581 bp circular mitogenome encodes 37 genes (13 PCGs, 22 tRNAs, and 2 rRNAs) and an A + T-rich control region (86.7%). Notably, trnS1 lacks the dihydrouracil arm—a rare structural deviation in Decapoda. The 13 PCGs exhibit moderate nucleotide skew (AT = 0.030; GC = −0.214), while nad5, nad4, and nad6 show significant GC-skew. Phylogenomic analyses strongly support (PP = 1.0; BS = 95) a novel sister-group relationship between Halocaridinidae and Typhlatyinae, contradicting prior morphology-based classifications. The monophyly of Penaeoidea, Astacidea, and Caridea was confirmed, but Eryonoidea and Crangonoidea formed an unexpected clade. Conclusions: This study provides the first mitogenomic framework for C. mariae, revealing both conserved features (e.g., PCG content) and lineage-specific innovations (e.g., tRNA truncation). The resolved phylogeny challenges traditional Caridea classifications and highlights convergent adaptation in freshwater lineages. These findings offer molecular tools for the conservation prioritization of threatened Caridina species and underscore the utility of mitogenomics in decapod systematics. Full article

Background: Breast cancer, irrespective of gender, stands as the most prevalent cancer globally, with an annual estimate of 2.3 million new cases. Surgical intervention, including therapeutic mastectomy (excluding prophylactic procedures), is performed on approximately 28% of patients, necessitating subsequent breast reconstruction. Although implant-based breast reconstruction (IBBR) is frequently employed due to its relative ease compared to autologous methods, it presents a notable risk for complications at mid-term such as peri-prosthetic infections. These complications can lead to implant loss and the eventual compromise of the mastectomy pocket. To address these complications, negative pressure wound therapy with instillation and dwell (NPWTi-d) emerges as a promising rescue intervention, known for its capacity to significantly reduce bacterial load and potentially salvage compromised soft tissues. However, the evidence supporting its effectiveness in infected pockets after mastectomy is currently insufficient. This study aims at investigating the efficacy of NPWTi-d in the management of peri-prosthetic mastectomy pocket infection. Methods: A thorough literature search has been concluded through PubMed, Web of Science, and Cochrane databases up until 18th March 2025 on evaluating NPWTi-d’s ability to manage peri-prosthetic infections and preserve mastectomy pockets for subsequent reconstruction. Furthermore, a meta-analysis on the salvage rate of the mastectomy pocket was carried out, while for other outcomes, a descriptive analysis was applied. Results: Nine studies (n = 230 patients) were included, investigating whether the us NPWTi-d was successful in treating peri-prosthetic infection and preserving the mastectomy pocket for subsequent reconstruction by expander or implant. The pooled salvage rate of the implant-based BR due to the use of NPWTi-d was 86.1% (95%CI: 80.6–91.6%). Preservation of the skin envelope avoided secondary reconstruction after a defined time interval, reducing number and complexity of surgeries and related costs. Conclusions: This innovative surgical approach should be considered in selected cases of infected implants after breast reconstruction in breast cancer centers. However, the actual low level of evidence is based on case series, and it is not possible to define generally accepted recommendations for the use of NPWTi-d to save the mastectomy pocket. Full article

Soil nutrient and fertility assessments provide a direct measure for evaluating forest management effects. In this study, we examined soil nutrient content in Chinese fir (Cunninghamia lanceolata) plantations under four reconstruction patterns: pure plantation, introduced broadleaf, introduced needleleaf, and introduced mixed broadleaf-needleleaf. The soil fertility index (SFI) evaluation model was constructed based on partial least squares path modeling (PLS-PM), revealing the influence of stand characteristics on SFI in early stand reconstruction. The results showed that, compared to pure plantations, total nutrient content increased in the introduced needleleaf pattern by 13.94% to 21.15% and available nutrient content by 18.21% to 26.91%. In contrast, both introduced broadleaf and mixed broadleaf-needleleaf exhibited a declining trend. Significant differences were observed among the reconstruction patterns (p < 0.05). In the SFI evaluation model, soil chemistry total nutrient (SCT) and soil chemistry available nutrient (SCA) made significant contributions. The weights of SCT and SCA in SFI were 0.52 and 0.48, respectively. The SFI of four patterns ranged from 0.43 to 0.58, indicating relatively low soil fertility. Compared to pure plantations, introduced trees did not enhance soil fertility in early stand reconstruction. The SFI of the introduced needleleaf was significantly higher than that of the other two reconstruction patterns (p < 0.05). Stand construction (including diameter at breast height, tree density, and tree biomass) explained 14.69% of SFI variation, with a contribution of 31.72% in the surface soil layer (0~20 cm). Tree biomass significantly influenced SFI variation, accounting for over 40% of the total stand factors. Retained tree biomass had a substantially greater effect than introduced tree biomass, contributing twice as much to SFI variation. PLS-PM could effectively reflect the soil nutrient status and accurately estimate the weight of soil fertility. In early stand reconstruction, retained tree biomass might be the major influence on soil fertility variation. We suggest determining reasonable thinning intensity to retain enough Chinese fir and promote the growth of introduced trees. This study introduces a novel approach to soil fertility assessment and provides theoretical support for formulating effective forest management strategies in the early reconstruction of Chinese fir plantations. Full article