Part Of Segment Research Articles

The Application of the Posterior Atlanto-Occipital Membrane Tension Release Technique for Surgical Exposure of the Horizontal Part of the Vertebral Artery's Third Segment: An Anatomical and Clinical Investigation.

This study aims to elucidate the anatomical principles governing the surrounding venous structures (VS) of the horizontal part of the third segment of the vertebral artery (V3h) and develop a safe and bloodless surgical technique for exposing V3h. This study used 10 formalin-infused cadaveric head specimens. The dissections were performed stepwise to simulate the far lateral approach process, exposing the V3h with a novel technique. Additionally, we applied this technique to 10 patients undergoing far or extreme lateral approaches. The VS surrounding V3h is divided into 3 components: the vertebral venous plexus, suboccipital cavernous sinus, and the anastomotic vein. The posterior atlanto-occipital membrane (PAOM), a resilient fascial layer in the craniovertebral junction, extends from the periosteum of the occipital squama to the posterior arch of the atlas. It adheres ventrally to the VS within the suboccipital triangle (SOT), forming a tent-like structure that maintains tension and ensures fullness of the VS around V3h. We discovered that by releasing tension in this membrane and reducing strain on this tent-like structure, the collapse of the venous sinus within the SOT can be achieved, resulting in reduced intraoperative bleeding and improved surgical efficiency. Additionally, we successfully managed 10 clinical cases employing the PAOM tension release technique in clinical cases, with no reported incidents of intraoperative vertebral artery injury. The application of the PAOM tension release technique effectively collapses the tent-like structure within the SOT, significantly reducing bleeding during V3h exposure in craniovertebral junction.

3D neural architecture search to optimize segmentation of plant parts

3D neural architecture search to optimize segmentation of plant parts

Midterm Results of the STABILISE Technique in the Treatment of Aortic Dissection.

Background: Contemporary treatments of acute aortic dissection, including medical, surgical, and endovascular options, are remarkably effective at the management of malperfusion and rupture. Unfortunately, long-term studies indicate that 30%-50% of patients need secondary procedures to treat progressive aneurysmal enlargement of the untreated aorta. The Stent-assisted balloon-induced intimal disruption and relamination in aortic dissection repair (STABILISE) technique was introduced to improve long-term outcomes. Purpose: This study aims to investigate the results of the STABILISE technique in patients with aortic dissection. Methods: This is a single-center, retrospective review of all patients treated with the STABILISE technique. There were 12 de novo type B aortic dissection (TBAD) and 7 residual TBAD following type A aortic dissection (TAAD) repair. Results: There was disruption of the dissection membrane and relamination in all or part of the bare metal stent segment in 100% of cases. The average percent attainment of a uni-luminal aorta in comparison to the length with persistent false lumen was 91 ± 12%. Conclusion: Midterm results suggest that the STABILISE technique may improve aortic remodeling after endovascular treatment of acute dissection.

Point cloud classification and part segmentation of steel structure elements

AbstractThe classification and part segmentation of point clouds have gained significant attention in the field of artificial intelligence (AI), especially in the construction industry. However, addressing the dataset directly in AI models remains a challenge, as most existing methods are not well-suited for processing point cloud data. PointNet has emerged as an AI architecture algorithm. It transforms individual points independently to learn local and global features. This research aims to develop a comprehensive framework for classification and part segmentation for point clouds of steel structure elements. The framework enhances the accuracy of the PointNet algorithm, and it consists of three stages: (1) dataset creation; (2) model classification; and (3) part segmentation. First, the dataset creation procedure encompasses modeling steel columns, beams, and braces using Autodesk Revit software. For the classification dataset, a dataset comprising 580 columns and 920 beams is obtained. In the part segmentation dataset, five categories of steel braced frame elements are generated, yielding a total of 21,870 elements for braced frame structures. Several point cloud experiments have been applied, including adjusting the number of points in the point cloud, altering the batch size, and fine-tuning the number of epochs. These experimental settings were systematically investigated to identify the optimal combination that yields the highest (AI) model accuracy. PointNet model achieved 100% accuracy across all classification experiments, while part segmentation experiments reached up to 97.10% accuracy, with a mean intersection over union (MIOU) of 93.70%. The comprehensive analysis of the point cloud dataset is applied on an actual case study to demonstrate the practical features of the proposed research.

Panoptic-PartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation.

Panoptic Part Segmentation (PPS) unifies panoptic and part segmentation into one task. Previous works utilize separate approaches to handle things, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework, Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we first design a meta-architecture that decouples part features and things/stuff features, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Second, we propose a new metric Part-Whole Quality (PWQ), better to measure this task from pixel-region and part-whole perspectives. It also decouples the errors for part segmentation and panoptic segmentation. Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further. We design a new part-whole interaction method using masked cross attention. Finally, extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results. Our models can serve as a strong baseline and aid future research in PPS.

LWSNet: A lightweight network for automated welding point cloud segmentation

Welding technology is a key manufacturing link in modern industrial steel structures, directly impacting welding quality and efficiency. This paper proposes LWSNet, a novel point cloud part segmentation network aimed at accurately segmenting weld areas across various joints to enhance automation and ensure quality. LWSNet builds upon the PointNet++ model, incorporating depthwise separable convolution and normalization-based attention module in its feature extraction block (FEB) for improved lightweight and feature extraction. A linear bottleneck structure and adaptive maximum pooling in its feature generation block (FGB) significantly reduce information loss during feature transmission. Additionally, a laser vision sensor model is designed for point cloud data acquisition, and the WeldJoint-PCD dataset is constructed using multiple data augmentation methods. Experimental results on WeldJoint-PCD show LWSNet’s superior segmentation effectiveness and lightweight design. On ShapeNetPart, cls.mIoU and ins.mIoU reach 82.9% and 85.5%, respectively, outperforming the original model by 1% and 0.4%, demonstrating LWSNet’s strong generalization capabilities.

Relation Constrained Capsule Graph Neural Networks for Non-Rigid Shape Correspondence

Non-rigid 3D shape correspondence aims to establish dense correspondences between two non-rigidly deformed 3D shapes. However, the variability and symmetry of non-rigid shapes usually lead to mismatches due to shape deformation, topological changes, or data with severe noise. To finding an accurate correspondence between 3D dynamic shapes for the local deformation complexity, this article proposes a Relation Constrained Capsule Graph Network (RC-CGNet), which combines global and local features by encouraging the relation constraints between the embedding feature space and the input shape space based on the functional maps framework. Specifically, we design a Diffusion Graph Attention Network (DGANet) to segment the surface into parts with correct edge boundary between two regions. The Minimum Spanning Tree (MST) of geodesic curves among the singularities obtained from the segmented parts is added as relation constraints, which can compute isometric correspondences in both direct and symmetric directions. Besides that, the relation-and-attention constrained neural networks are designed to learn the shape correspondence via attention-aware CapsNet and functional maps under relation constraints. To improve the convergence speed and matching accuracy, we propose an optimized residual network structure based on the Nesterov Accelerated Gradient (NAG) to extract local features, and use graph convolution structure to extract global features. Moreover, a lightweight Gated Attention Module (GAM) is designed to fuse global and local features to obtain a richer feature representation. Since the capsule network has better spatial reasoning ability than the traditional convolutional neural network, our novel network architecture is a dual-route capsule network based on Routing Attention Fusion Block (RAFB), filtering low-discriminative capsules from a holistic view by exploiting geometric hierarchical relationships of semantic parts. Experiments on open datasets show that our method has excellent accuracy and wide adaptability.

Sampling intestinal microbiota in growing pigs: evaluation of CapSa, an ingestible capsule

This study aims to investigate Capsa, an ingestible capsule designed to collect the contents of the small intestine as it passes through the gastrointestinal tract. Eight Swiss Large White pigs weighing between 52.5 and 71.3 kg were administered two capsules each and monitored for three days before euthanasia for post-mortem sampling. Samples were collected from six equally divided segments of the small intestine, along with separate sampling of the solid and liquid contents of each segment when feasible. Samples were also obtained from the large intestine and faeces to determine CapSa’s sampling location. Fifteen capsules were retrieved from faecal samples (93.75%), with 87.5% recovered on the first day post-administration. Only one capsule was not recovered. Comparative analysis of the bacterial composition of the capsules and post-mortem samples was conducted using a Permutational Multivariate Analysis of Variance (PERMANOVA) model (Adonis), with sample type as a factor. The results revealed significant differences in bacterial composition between capsules and samples from the large intestine and faeces (p < 0.01). However, no significant difference was observed between capsule content and the liquid and solid parts of the fourth segment of the small intestine (p > 0.05). This study provides evidence that CapSa can effectively sample the intestinal microbiota of the middle part of the small intestine in growing pigs.

Domain adaptation of deep neural networks for tree part segmentation using synthetic forest trees

Supervised deep learning algorithms have recently achieved state-of-the-art performance in the classification, segmentation and analysis of 3D LiDAR point cloud data in a wide-range of applications and environments. One of the main downsides of deep learning-based approaches is the need for extensive training datasets, i.e. LiDAR point clouds that have been annotated for target tasks by human experts. One strategy for addressing this issue is the use of simulated/synthetic data (with automatically generated annotations) for training models which can then be deployed on real target data/environments. This paper explores using synthetic data of realistic forest trees to train deep learning models for tree part segmentation from real forest LiDAR data. We develop a new pipeline for generating high-fidelity simulated LiDAR scans of synthetic forest trees and combine this with an unsupervised domain adaptation strategy to adapt models trained on synthetic data to LiDAR data captured in real forest environments.Models were trained for semantic segmentation of tree parts using a PointNet++ architecture and evaluated across a range of medium to high-resolution laser scanning datasets collected across both ground-based and aerial platforms in multiple forest environments. Results of our work indicated that models trained on our synthetic data pipeline were competitive with models trained on real data, in particular when real data came from non-target sites, and our unsupervised domain adaptation method further improved performance. Our approach has implications for reducing the burden required in manual human expert annotation of large LiDAR datasets required to achieve high-performance from deep learning methods for forest analysis. The use of synthetically-trained models shown here provides a potential way to reduce the barriers to the use of deep learning in large-scale forest analysis, with implications to applications ranging from forest inventories to scaling-up in-situ forest phenotyping.

Loss of paired immunoglobin-like type 2 receptor B gene associated with age-related macular degeneration impairs photoreceptor function in mouse retina.

Genome-wide association studies have uncovered mostly non-coding variants at over 60 genetic loci linked to susceptibility for age-related macular degeneration (AMD). To ascertain the causal gene at the PILRB/PILRA locus, we used a CRISPR strategy to produce germline deletions in the mouse paired immunoglobin-like type 2 receptor (Pilr) genes that encode highly related activating (PILRB) and inhibitory (PILRA) receptors. We show that a combined loss of Pilrb1 and Pilrb2, but not Pilra, leads to an early but relatively stationary defect as the electroretinography (ERG) amplitudes of Pilrb1/2-/- mice exhibit a marked reduction as early as postnatal day 15 and do not show additional significant decrease at 3 and 12-months. No alterations are evident in Müller glia, microglia, bipolar, amacrine and horizontal cells based on immunohistochemistry using cell-type specific markers. PILRB immunostaining is specifically detected at the proximal part of photoreceptor outer segment. Reduced expression of select calcium-regulated phototransduction and synapse-associated proteins, including GCAP1 and 2, PDE6b, AIPL1, PSD95, and CTBP1 indicates dysregulation of calcium homeostasis as a possible mechanism of retinal phenotype in Pilrb1/2-/- mice. Our studies suggest a novel function of PILRB in retinal photoreceptors and an association of PILRB, but not PILRA, with AMD pathogenesis.

Estimation of outer-wall length in optimizing cochlear implantation in malformed inner ears

Estimation of cochlear length is gaining attention in the field of cochlear implants (CIs), mainly for selecting of CI electrode lengths. The currently available tools to estimate the cochlear duct length (CDL) are only valid for normal inner anatomy. However, inner ear malformation (IEM) types are associated with different degrees of cystic apices, limiting the application of CDL equations of normal anatomy inner ear. Therefore, this study aimed to understand the degree to which the outer wall (OW) is observed in different malformation types and to formulate mathematical equations to estimate the OW length (OWL) from cochlear parameters, namely the basal turn diameter (A-value) and width (B-value). Three-dimensional (3D) segmentation of promontory and fluid parts of the inner ear was performed to understand the extent to which the OW is visible to measure the OWL manually. Enlarged vestibular aqueduct syndrome (EVAS) was diagnosed in 37 ears, which consistently showed the extent of the OW to an angular depth of 540°, beyond which the cystic apex starts. Incomplete partition (IP) type I was observed in 30 ears, with the OW extending to only 360° of angular depth. IP type II was observed in 35 ears, with the OW extending to 450° of angular depth. IP type III was identified in 24 ears, with the OW observed for 540° of angular depth. Cavity-type malformations were observed in 36 ears, and circumference was measured in the axial view. A strong positive linear correlation was observed between the manually measured OWL and cochlear parameters for all malformation types analyzed. A multiple linear regression model was applied to formulate mathematical equations, which was further used to create a software application for estimating OWLs in IEM types, using cochlear parameters as inputs.

Body Part Segmentation of Anime Characters

ABSTRACTSemantic segmentation is an important approach to present the perceptual semantic understanding of an image, which is of significant usage in various applications. Especially, body part segmentation is designed for segmenting body parts of human characters to assist different editing tasks, such as style editing, pose transfer, and animation production. Since segmentation requires pixel‐level precision in semantic labeling, classic heuristics‐based methods generally have unstable performance. With the deployment of deep learning, a great step has been taken in segmenting body parts of human characters in natural photographs. However, the existing models are purely trained on natural photographs and generally obtain incorrect segmentation results when applied on anime character images, due to the large visual gap between training data and testing data. In this article, we present a novel approach to achieving body part segmentation of cartoon characters via a pose‐based graph‐cut formulation. We demonstrate the use of the acquired body part segmentation map in various image editing tasks, including conditional generation, style manipulation, pose transfer, and video‐to‐anime.

Deep Plug-and-Play Non-Iterative Cluster for 3D Global Feature Extraction

Efficient and accurate point cloud feature extraction is crucial for critical tasks such as 3D recognition and semantic segmentation. However, existing global feature extraction methods for 3D data often require designing different models for different input types (point clouds, voxels, and maps). This article proposes an efficient plug-and-play non-iterative clustering method (NICM) to establish a unified point cloud global feature extraction paradigm suitable for any input type to solve the above problems. The core idea of the NICM is to construct the connection between a single point and other global points based only on the cosine similarity between center points to achieve global feature extraction, which has linear complexity characteristics and can be combined with any existing feature extraction model. Additionally, to better integrate the features extracted by NICM and the original model, this article designs an adaptive feature fusion module is designed based on the gate unit, which retains similar features and effectively fuses dissimilar features based on their importance to downstream tasks. We have applied our method to downstream tasks such as point cloud recognition, part segmentation and scene segmentation. Sufficient experiments have proven that our method can provide comprehensive and robust features for the original model, and effectively improve the performance of downstream tasks.

Крестьянские миграции в степные области: причинные факторы и способы их преодоления в оценках представителей консервативного направления второй половины XIX – начала XX вв.

One of the characteristic features of Russia’s post-reform development was the migration of part of the agricultural segment to its Asian possessions, which caused a lively socio-political discussion in the country. Supporters of the conservative trend play a significant role in discussing the causes of these migrations, identifying the crisis in peasant farming as the principal driver of mass movement toward the empire’s periphery. This article is based on administrative documentation from the Central State Archive of the Republic of Kazakhstan concerning the movement of agrarian migrants into the Kazakh steppe and the actions undertaken by regional and provincial administrations to accommodate them. It also draws from materials in periodical literature that shaped societal perceptions of typical phenomena leading to the causes, progress, and consequences of popular migrations for the national economy. Following the principles of historicism and systematic analysis, which facilitate the exploration of the viewpoints of conservative discourse regarding the identification of causal factors underlying migratory mobility, this article examines approaches to possible state support for settlers at the imperial level. The analysis centers on the positions of the most prominent representatives of conservative doctrine, who viewed agrarian migration as an integral component of governmental policy aimed at the economic development of the “Kirghiz” steppe, a region that geographically partially fell within the borders of Akmolinsk and Semipalatinsk provinces.

Real-Time Semantic Segmentation of 3D LiDAR Point Clouds for Aircraft Engine Detection in Autonomous Jetbridge Operations

This paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning method were employed to gather surrounding environmental 3D point cloud data. The raw 3D environmental data were then filtered using the 3D RANSAC technique, excluding ground data and irrelevant apron areas. Segmentation was subsequently conducted based on the filtered data, focusing on aircraft sections. For the segmented aircraft engine parts, the centroid of the grouped data was computed to determine the 3D position of the aircraft engine. Additionally, PointNet was applied to identify aircraft engines from the segmented data. Dynamic tests were conducted in various weather and environmental conditions, evaluating the detection performance across different jetbridge movement speeds and object-to-object distances. The study achieved a mean intersection over union (mIoU) of 81.25% in detecting aircraft engines, despite experiencing challenging conditions such as low-frequency vibrations and changes in the field of view during jetbridge maneuvers. This research provides a strong foundation for enhancing the robustness of jetbridge autonomous docking systems by reducing the sensor noise and distortion in real-time applications. Our future research will focus on optimizing sensor configurations, especially in environments where sea fog, snow, and rain are frequent, by combining RGB image data with 3D LiDAR information. The ultimate goal is to further improve the system’s reliability and efficiency, not only in jetbridge operations but also in broader autonomous vehicle and robotics applications, where precision and reliability are critical. The methodologies and findings of this study hold the potential to significantly advance the development of autonomous technologies across various industrial sectors.

Programmable In‐Situ Co‐Assembly of Organic Multi‐Block Nanowires for Cascade Optical Waveguides

AbstractOrganic heterostructures (OHs) with multi‐segments exhibit special optoelectronic properties compared with monomeric structures. Nevertheless, the synthesis of multi‐block heterostructures remains challenging due to compatibility issues between segment parts, which restricts their application in optical waveguides and integrated optics. Herein, we demonstrate programmable in‐situ co‐assembly engineering, combining multi‐step spontaneous self‐assembly processes to promote the synthesis of multi‐block heterostructures with a rational arrangement of three or more segments. The rational design of segments enables exciton manipulation and ensures optical waveguides and proper output among the multi‐segment OHs. This work enables the controllable growth of segments within multi‐block OHs, providing a pathway to construct complex OHs for the rational development of future optical applications.

Programmable In-Situ Co-Assembly of Organic Multi-Block Nanowires for Cascade Optical Waveguides.

Organic heterostructures (OHs) with multi-segments exhibit special optoelectronic properties compared with monomeric structures. Nevertheless, the synthesis of multi-block heterostructures remains challenging due to compatibility issues between segment parts, which restricts their application in optical waveguides and integrated optics. Herein, we demonstrate programmable in-situ co-assembly engineering, combining multi-step spontaneous self-assembly processes to promote the synthesis of multi-block heterostructures with a rational arrangement of three or more segments. The rational design of segments enables exciton manipulation and ensures optical waveguides and proper output among the multi-segment OHs. This work enables the controllable growth of segments within multi-block OHs, providing a pathway to construct complex OHs for the rational development of future optical applications.

Partial Sequence Analysis of Commercial Peste des Petits Ruminants Vaccines Produced in Africa

Peste des petits ruminants virus (PPRV), which is the only member of the Morbillivirus caprinae species and belongs to the genus Morbillivirus within the Paramyxoviridae family, causes the highly contagious viral sickness “Peste des petits ruminants (PPR).” PPR is of serious economic significance for small ruminant production, particularly in Africa. Control of this critical disease depends highly on successful vaccination against the PPRV. An in-depth understanding of the genetic evolution of the live-attenuated PPR vaccine Nigeria 75/1 strain used in Africa is essential for the successful eradication of this disease by 2030. Therefore, this study investigated the possible genetic evolution of the PPR vaccine produced by various African laboratories compared with the master seed available at AU-PANVAC. RT-PCR was performed to amplify a segment of the hypervariable C-terminal part of the nucleoprotein (N) from commercial batches of PPR vaccine Nigeria 75/1 strain. The sequences were analyzed, and 100% nucleotide sequence identity was observed between the master seed and vaccines produced. The results of this study indicate the genetic stability of the PPR vaccine from the Nigeria 75/1 strain over decades and that the vaccine production process used by different manufacturers did not contribute to the emergence of mutations in the vaccine strain.

Physiological provocation compared to acetazolamide in the assessment of cerebral hemodynamics: a case report

BackgroundSevere large vessel disease may lead to cerebral hemodynamic failure that critically impairs cerebral blood flow (CBF) regulation elevating the risk of ischemic events. Assessment of the condition is often based on changes in CBF during vasodilatation; however, pharmacologically induced vasodilation does not reflect the physiological condition during an ischemic event caused by hemodynamic failure. We compared a [15O]H2O PET brain scan during vasodilation to a [99mTc]HMPAO SPECT brain scan during an ongoing transient ischemic attack (TIA).Case presentationA single patient presenting with limb-shaking TIA underwent CT, Digital Subtraction Angiography, and two different modalities of cerebral perfusion scans: [15O]H2O PET and [99mTc]HMPAO SPECT. Acetazolamide was used in the PET scan to induce vasodilatation, and during the SPECT scan physiological stress, standing up rapidly, was used to induce limb-shaking TIA. CT-angiography and Digital Subtraction Angiography revealed an occlusion in the distal part of the right A2 segment of the anterior cerebral artery, with a corresponding infarction in the watershed area. Collaterals supplied the main vascular territory of the anterior cerebral artery. During rest, neither perfusion modalities demonstrated reduced perfusion outside of the ischemic core. However, we found a pronounced difference between the PET utilizing acetazolamide and the SPECT during the TIA. The PET scan demonstrated relative hypoperfusion in vascular territory supplied by collaterals, while the area around the ischemic core was not affected. Contrary, the SPECT had only minor relative hypoperfusion in the collateral-supplied area, whereas the watershed area proximal to the infarct core had pronounced relative hypoperfusion.ConclusionsThe observed discrepancy in compromised areas during physiological provocation compared to pharmacological induced vasodilation questions the use of an unphysiological stressor for assessment of cerebrovascular hemodynamics. A physiological provocation test may achieve more clinically relevant evaluation.

On-site burn severity assessment using smartphone-captured color burn wound images

Accurate assessment of burn severity is crucial for the management of burn injuries. Currently, clinicians mainly rely on visual inspection to assess burns, characterized by notable inter-observer discrepancies. In this study, we introduce an innovative analysis platform using color burn wound images for automatic burn severity assessment. To do this, we propose a novel joint-task deep learning model, which is capable of simultaneously segmenting both burn regions and body parts, the two crucial components in calculating the percentage of total body surface area (%TBSA). Asymmetric attention mechanism is introduced, allowing attention guidance from the body part segmentation task to the burn region segmentation task. A user-friendly mobile application is developed to facilitate a fast assessment of burn severity at clinical settings. The proposed framework was evaluated on a dataset comprising 1340 color burn wound images captured on-site at clinical settings. The average Dice coefficients for burn depth segmentation and body part segmentation are 85.12 % and 85.36 %, respectively. The R2 for %TBSA assessment is 0.9136. The source codes for the joint-task framework and the application are released on Github (https://github.com/xjtu-mia/BurnAnalysis). The proposed platform holds the potential to be widely used at clinical settings to facilitate a fast and precise burn assessment.