Alignment Of Segments Research Articles

SAMCF: Adaptive global style alignment and multi-color spaces fusion for joint optic cup and disc segmentation

The optic cup (OC) and optic disc (OD) are two critical structures in retinal fundus images, and their relative positions and sizes are essential for effectively diagnosing eye diseases. With the success of deep learning in computer vision, deep learning-based segmentation models have been widely used for joint optic cup and disc segmentation. However, there are three prominent issues that impact the segmentation performance. First, significant differences among datasets collecting from various institutions, protocols, and devices lead to performance degradation of models. Second, we find that images with only RGB information struggle to counteract the interference caused by brightness variations, affecting color representation capability. Finally, existing methods typically ignored the edge perception, facing the challenges in obtaining clear and smooth edge segmentation results. To address these drawbacks, we propose a novel framework based on Style Alignment and Multi-Color Fusion (SAMCF) for joint OC and OD segmentation. Initially, we introduce a domain generalization method to generate uniformly styled images without damaged image content for mitigating domain shift issues. Next, based on multiple color spaces, we propose a feature extraction and fusion network aiming to handle brightness variation interference and improve color representation capability. Lastly, an edge aware loss is designed to generate fine edge segmentation results. Our experiments conducted on three public datasets, DGS, RIM, and REFUGE, demonstrate that our proposed SAMCF achieves superior performance to existing state-of-the-art methods. Moreover, SAMCF exhibits remarkable generalization ability across multiple retinal fundus image datasets, showcasing its outstanding generality.

Unveiling the transition of tumbling and tank-treading dynamics of star-shaped polyelectrolytes in shear flow by modulating the solution's dielectric properties.

The effects of the solution's dielectric properties on the conformation and dynamics of star-shaped polyelectrolytes in shear flow are investigated using a hybrid simulation method coupling multi-particle collision dynamics and molecular dynamics. The simulation results showed that by modulating the dielectric properties of the solution, star-shaped polyelectrolytes showed a three-step dynamic behavior transition from tumbling to tank-treading to tumbling dynamics under shear flow. The analysis indicated that this distinct transition in dynamics could be attributed to the uneven distribution of counterions induced by shear on the chain, resulting in a change in the polyelectrolyte conformation and degree of segmental alignment in arms. These findings contribute to a comprehensive understanding of the non-equilibrium dynamics of star-shaped polyelectrolytes in shear flow and offer a viable approach for controlling the dynamic behavior of star-shaped polyelectrolytes by adjusting the dielectric properties of the solution.

Alignment and fusion for adaptive domain nighttime semantic segmentation

In the field of autonomous driving technology, both daytime and nighttime scenes are common. However, due to the poor illumination and difficulty in manual annotation of nighttime images, semantic segmentation of nighttime scenes is more challenging compared to daytime scenes. Therefore, achieving significant progress in nighttime semantic segmentation would greatly enhance the effectiveness of the application of autonomous driving scenarios. In this work, we investigate the problem of domain-adaptive nighttime semantic segmentation (DANSS). The problem aims to learn semantic segmentation in nighttime scenes by leveraging a labeled Cityscapes dataset and unlabeled but roughly aligned day-night image pairs. To address this, we propose an Align and Fusion Network (AAFnet), a network for adaptive domain nighttime semantic segmentation. AAFnet utilizes a novel DAFormer as the backbone to separately compute features for dynamic and static objects during the training process. It also employs methods such as small category sampling and image blending to improve learning effectiveness. Additionally, we propose utilizing local image patches to enhance the results during the testing process. Experimental results demonstrate a significant improvement of 10.4% compared to the previous method, DANNet. Extensive experiments conducted on the Dark Zurich dataset and Nightdriving dataset validate the effectiveness of the proposed approach. Our method outperforms previous backbones and achieves top-ranking results.

A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

Radiographic Risk Factors for Adjacent Segment Disease Following Anterior Cervical Discectomy and Fusion (ACDF): A Systematic Review and Meta-Analysis.

Systematic review and meta-analysis. To assess the radiographic risk factors for adjacent segment disease (ASD) following anterior cervical discectomy and fusion (ACDF) for degenerative cervical spine pathologies. PubMed, Embase and the Cochrane Library databases were searched up to December 2023. The primary inclusion criteria were degenerative spinal conditions treated with ACDF, comparing radiological parameters in patients with and without postoperative ASD. The radiographic parameters included intervertebral disc height, cervical sagittal alignment, sagittal segmental alignment, range of motion, segmental height, T1 slope, sagittal vertical axis (SVA), thoracic inlet angle (TIA), and plate to disc distance (PPD). Risk of bias was assessed for all studies. The Cochrane Review Manager was utilized to perform the meta-analysis. From 7044 articles, 13 retrospective studies were included in the final analysis. Three studies had "not serious" bias and the other 10 studies had serious or very serious bias. The total number of patients in the included studies was 1799 patients. Five studies included single-level ACDF, 2 studies included multi-level ACDF, and 6 studies included single or multi-level ACDF. On meta-analysis, the significant risk factors associated with ASD development were reduced postoperative cervical lordosis (mean difference [MD] = 3.35°, P = .002), reduced last-follow-up cervical lordosis (MD = -3.02°, P = .0003), increased preoperative to postoperative cervical sagittal alignment change (MD = -3.68°, P = .03), and the presence of developmental cervical canal stenosis (Odds ratio [OR] = 4.17, P < .001). Decreased postoperative cervical lordosis, greater change in cervical sagittal alignment and developmental cervical canal stenosis were associated with an increased risk of ASD following ACDF.

Fusion with and without lever reduction in degenerative lumbar spondylolisthesis: a retrospective study.

The reduction of slipped vertebra is often performed during surgery for degenerative lumbar spondylolisthesis (DLS). This approach, while potentially improving clinical and radiological outcomes, also carries a risk of increased complications due to the reduction process. To address this, we introduced an innovative lever reduction technique for DLS treatment. This study aims to investigate the clinical efficacy, radiological outcomes, and complications of fusion with or without lever reduction. We conducted a retrospective review of prospectively collected data from a registry of patients who underwent lumbar fusion surgery for DLS, with a follow-up of at least 24months. Self-reported measures included visual analog scale (VAS) for back or leg pain, Oswestry Disability Index (ODI), and the achievement of minimal clinically important difference (MCID). Radiological assessments encompassed spondylolisthesis percentage (SP), focal lordosis (FL), and lumbar lordosis (LL). Complications were categorized using the modified Clavien-Dindo classification (MCDC) scheme. Patients were assigned to the reduction group (RG) and non-reduction group (NRG) based on the application of the lever reduction technique. Clinical and radiological outcomes at baseline, immediately after surgery, and at the last follow-up were compared. A total of 281 patients were analyzed (123 NRG, 158 RG). Baseline patient demographics, comorbidities, and surgical characteristics were similarly distributed between groups except for operating time (NRG 129.25min, RG 138.04min, P = .009). Both groups exhibited significant clinical improvement after surgery (all, P = .000), with no substantial difference between groups (VAS, ODI, or the ability to reach MCID). Patients in RG showed statistically lower SP and higher FL during follow-up (all, P = .000). LL was comparable at different time points within each group or at the same time point between the two groups (all, P > .050). The overall complication rate (NRG 38.2%, RG 27.2%, P = .050) or specific complication rates per MCDC were similar between groups (all, P > .050). Patients in RG were predisposed to a lower risk of adjacent segment degeneration (ASDeg) (NRG 9.8%, RG 6.3%, P = .035). There were no significant differences in postoperative measures such as VAS scores for back and leg pain, ODI, the ability to reach MCID, overall complication rate, or specific complication rates per MCDC between surgical approaches. However, fusion with lever reduction demonstrated a notable advantage in restoring segmental spinal sagittal alignment and reducing the occurrence of ASDeg compared to in situ fusion.

The role of surgical navigation in computer-assisted mandibular reconstruction: is it necessary?

Accurate mandibular reconstruction following tumor ablation is important yet challenging. While computer-assisted surgery and surgical navigation have been applied widely in maxillofacial reconstruction, the accuracy and the efficacy remain debatable due to the native mobile nature. This study aimed to evaluate the surgical outcomes and accuracy of mandibular reconstruction aided by different types of adjunctive computer-assisted techniques with or without intraoperative navigation. Patients with anterior and/or lateral mandibular defects who underwent microvascular mandibular reconstruction aided by adjunct computer-assisted techniques, with or without intraoperative navigation were assessed. The deviations in spatial alignment of the bony segments between the pre-operative and post-operative datasets were measured to evaluate the overall surgical outcomes and accuracy. Independent t-test was performed and p-value less than 0.05 was statistically significant. A total of 93 patients with L/LC or LCL defects who underwent microvascular mandibular reconstruction aided by adjunct computer-assisted techniques with or without surgical navigation were assessed. No significant difference was observed when comparing the mean differences between the preoperative and postoperative intercondylar and intergonial distance in both navigation-assisted and computer-aided design/computer-aided manufacturing (CAD/CAM) groups. There were also no significant differences noted among the different mandibular defects, osteosynthesis plates and types of free flap. Accurate mandibular reconstruction following tumor resection can be achieved by incorporating intraoperative navigation and adjunctive methods such as computer-assisted techniques and our innovative device, mandibular fixation device.

CADS: A Self-supervised Learner via Cross-modal Alignment and Deep Self-distillation for CT Volume Segmentation.

Self-supervised learning (SSL) has long had great success in advancing the field of annotation-efficient learning. However, when applied to CT volume segmentation, most SSL methods suffer from two limitations, including rarely using the information acquired by different imaging modalities and providing supervision only to the bottleneck encoder layer. To address both limitations, we design a pretext task to align the information in each 3D CT volume and the corresponding 2D generated X-ray image and extend self-distillation to deep self-distillation. Thus, we propose a self-supervised learner based on Cross-modal Alignment and Deep Self-distillation (CADS) to improve the encoder's ability to characterize CT volumes. The cross-modal alignment is a more challenging pretext task that forces the encoder to learn better image representation ability. Deep self-distillation provides supervision to not only the bottleneck layer but also shallow layers, thus boosting the abilities of both. Comparative experiments show that, during pre-training, our CADS has lower computational complexity and GPU memory cost than competing SSL methods. Based on the pre-trained encoder, we construct PVT-UNet for 3D CT volume segmentation. Our results on seven downstream tasks indicate that PVT-UNet outperforms state-of-the-art SSL methods like MOCOv3 and DiRA, as well as prevalent medical image segmentation methods like nnUNet and CoTr. Code and pre-trained weight will be available at https://github.com/yeerwen/CADS.

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

Improving Automatic Forced Alignment for Phoneme Segmentation in Quranic Recitation

The effect of degenerative scolioisis on segmental thoracolumbar sagittal alignment compared to age- and pelvic incidence-matched reference values

The effect of degenerative scolioisis on segmental thoracolumbar sagittal alignment compared to age- and pelvic incidence-matched reference values

Acute and prolonged hip kinematic changes after three different Kinesio Taping® interventions: A randomized placebo-controlled trial

Abstract Study aim: This study aimed to compare the acute and prolonged effects of three different Kinesio Taping (KT) techniques (underactive/overactive muscle correction [UMC]/[OMC] and functional correction [FC]) on hip ROM in the sagittal plane and compare them to those of the placebo Kinesio Taping (PKT) application. Materials and methods: The sample consisted of 56 young healthy participants of both genders who were randomly divided into four equal groups. Measurements were performed using Kinovea software for video analysis during the performance of an active straight leg raise and the modified Thomas test at three timepoints: before the intervention and 60 min and 48 h after the intervention. Results: Mixed-model ANOVA revealed a statistically significant interaction between group and timepoint (F = 6.210; p &lt; 0.001; ηp 2 = 0.285 for ipsilateral hip extension ROM; F = 2.064; p = 0.036; ηp 2 = 0.117 for contralateral hip flexion ROM). The results revealed acute and prolonged ROM changes after the application of three different KT techniques compared to those after the control procedure, but these changes were not significant. Conclusions: These findings support the use of KT as a modality for enhancing ROM. In muscle techniques, the factor of the anchor placement direction, with the same percentage of Kinesio tape tension, generates different effects. These effects were greater after 48 h than immediately after application. In changing the segmental posture or joint alignment, FC proved to be more intense than the KT muscle techniques, with an immediate effect that persisted even after two days.

Alignment of multimodal rigid cardiac angiography images with an improved particle swarm algorithm

Abstract In this paper, we conduct a preliminary study on the current development status in medical image alignment and build up a basic framework for image alignment. The feature space, search space, similarity measure, and search strategy of cardiac angiography images are calculated and studied. The DGVF model is utilized to process the traditional snake model for optimization search and is combined with B-splines to construct the B-spline DGVF model. Optimize the traditional MsFCM algorithm by using the PSO algorithm and propose an MsFCM-PSO image segmentation method. It is applied together with the B-spline DGVF model to segment the vascular lumen in cardiac angiography ultrasound images. Finally, the model of this paper is analyzed in terms of segmentation performance, alignment stability, and evaluation of alignment results. The mean values of Dice, IoU, and HD of this paper’s MsFCM-PSO model in image segmentation of cardiac vessels are 94.27%, 92.60%, and 1.06, respectively (all optimal performances). In the ablation experiments, the MsFCMPSO model in this paper shows an increase of 6.02% and 5.47% in the mean values of Dice and IoU compared to the benchmark model. The stability calibration percentage of this paper’s MsFCM-PSO algorithm is 31.13% when the Gaussian factor is 0.5, which is significantly better than other algorithms. The algorithm in this paper is better than other methods in terms of alignment stability and alignment results.

Role of magnetic resonance urethrography in evaluation of male urethral stricture

Background: Urethral stricture in male is a common urological disease causing lower urinary tract symptoms like thin stream of urine with straining to urinate, may be associated with dysuria or acute urinary retention. RGU is gold standard in male urethral stricture evaluation. In this study we compare magnetic resonance urethrography (MRU) with retrograde urethrography (RGU) in diagnosing male urethral stricture. Methods: The 40 male patients with the diagnosis of stricture urethra were taken for this study. Evaluation done first by RGU then after 7 days MRU. Final corroboration of above findings with the surgery (endoscopic or open). Results: Data of 40 male patients with urethral stricture are analysed with computer software SPSS v 25. mean age of stricture diagnosis is 43 years (range is 31-63 years). Mean length of stricture is 1.90 cm (with SD 0.85), 1.89 cm (with SD 0.92) and 1.91 cm (with SD 0.94) in RGU, MRU and Surgery respectively. sensitivity and specificity of RGU is 84.2% and 50% and MRU is 92.1% and 100% respectively. accuracy of RGU and MRU is 82% and 92% respectively. 3 patients out of 16 patients with long segment bulbar urethral stricture (&gt;1.5 cm) without any spongiofibrosis managed with BMG urethroplasty. Two patients out of 24 patients with short segment bulbar urethral stricture (&lt;1.5 cm) with dense spongiofibrosis managed with PEEAU. Conclusions: Both RGU and MRU can detect stricture lengths accurately but MRU has advantage of detection of spongifibrosis in cases of anterior urethral stricture. MRU detects orientation and alignment of both proximal and distal urethral segments in case of PFUI.

Analysis of Personalized Interbody Implants in the Surgical Treatment of Adult Spinal Deformity.

Multicenter cohort. A report from the International Spine Study Group (ISSG) noted that surgeons failed to achieve alignment goals in nearly two-thirds of 266 complex adult deformity surgery (CADS) cases. We assess whether personalized interbody spacers are associated with improved rates of achieving goal alignment following adult spinal deformity (ASD) surgery. ASD patients were included if their surgery utilized 3D-printed personalized interbody spacer(s) and they met ISSG CADS inclusion criteria. Planned alignment was personalized by the surgeon during interbody planning. Planned vs achieved alignment was assessed and compared with the ISSG CADS series that used stock interbodies. For 65 patients with personalized interbodies, 62% were women, mean age was 70.3 years (SD = 8.3), mean instrumented levels was 9.9 (SD = 4.1), and the mean number of personalized interbodies per patient was 2.2 (SD = .8). Segmental alignment was achieved close to plan for levels with personalized interbodies, with mean difference between goal and achieved as follows: intervertebral lordosis = .9° (SD = 5.2°), intervertebral coronal angle = .1° (SD = 4.7°), and posterior disc height = -0.1mm (SD = 2.3mm). Achieved pelvic incidence-to-lumbar lordosis mismatch (PI-LL) correlated significantly with goal PI-LL (r = .668, P < .001). Compared with the ISSG CADS cohort, utilization of personalized interbodies resulted in significant improvement in achieving PI-LL <5° of plan (P = .046) and showed a significant reduction in cases with PI-LL >15° of plan (P = .012). This study supports use of personalized interbodies as a means of better achieving goal segmental sagittal and coronal alignment and significantly improving achievement of goal PI-LL compared with stock devices.

Simultaneous alignment and surface regression using hybrid 2D–3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations

Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this task and yield remarkable performance. However, due to the large spatial gap and potential mismatch between the B-scans of an OCT volume, all of them were based on 2D segmentation of individual B-scans, which may lose the continuity and diagnostic information of the retinal layers in 3D space. Besides, most of these methods required dense annotation of the OCT volumes, which is labor-intensive and expertise-demanding. This work presents a novel framework based on hybrid 2D–3D convolutional neural networks (CNNs) to obtain continuous 3D retinal layer surfaces from OCT volumes, which works well with both full and sparse annotations. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement vectors and layer segmentation by two 3D decoders coupled via a spatial transformer module. Two losses are proposed to utilize the retinal layers’ natural property of being smooth for B-scan alignment and layer segmentation, respectively, and are the key to the semi-supervised learning with sparse annotation. The entire framework is trained end-to-end. To the best of our knowledge, this is the first work that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.

Corrective Osteotomies in Severe Non-Idiopathic Lower Limb Alignment Disorders in the Aspect of Future Joint Endoprosthesis-A Perspective Study.

The aim of this study was to retrospectively evaluate the effectiveness of corrective osteotomies in lower limb axis disorders at different levels of non-idiopathic (post traumatic, developmental, post-septic) etiology. A total of 50 patients were divided into three groups: A-thigh segment alignment disorder (24 patients); B-tibia segment alignment disorder (18 patients); C-thigh and tibia segment alignment disorder (8 patients). Radiological evaluation of digital lower limb postural X-ray was performed laterally and for AP, and included mLPFA, mLDFA, MAD, CORA coronal and sagittal plane parameters for the femur segment and mMPTA, mLDTA, MAD, CORA coronal and sagittal plane for the tibia segment. Clinical assessment was based on the LLFI. The mean follow-up was 55.8 months (12-86). Improvements in the radiological parameters and statistical significance were achieved for all measurements in all groups (p < 0.05). The most common plane of deformation was the coronal plane (varus/valgus), followed by the transverse (rotational) and sagittal planes (procurvatum/recurvatum). In this study, we examined 29 post-traumatic deformities and 21 other etiologies. Improvements in the LLFI score performance after corrective osteotomies were observed in all three groups. Corrective osteotomies are a safe and useful but challenging method of preserving joints in cases of post-traumatic, developmental or post-septic lower limb alignment disorders.

The Importance of Body Posture in Adolescence and its Relationship with Overall Well-being

Abstract Adolescence is a phase of life with specific health and developmental needs. This is the time to develop self-image and self-esteem, learn to manage emotions and relationships, acquire attributes and abilities, and also physiologically form body posture. An efficient, erect posture of the human body is defined as the alignment of particular body segments that are not affected by pathological changes. Body posture is influenced by a variety of physiological, occupational, and biomechanical factors. It is very important to know these factors and their influence on posture to prevent the appearance of postural deviations and its consequence in their daily lives. This study is a review of articles related to the formation, defects, and corrective measures of body posture during adolescence and identifying its relationship with different aspects of health. This study identified 207 studies on the various aspects of posture in children and adolescents using search engines such as Google Scholar, Science Direct, and PubMed, of which 11 studies met the eligibility criteria for the review. Postural defects are no longer confined to medicine and affect society as a whole. It plays a significant role in defining the social personality of an individual and is responsible for his upbringing. Since these disorders are prevalent in children, it is necessary to emphasize the importance of prevention programs. Thus, children should be taught the habit of maintaining proper posture along with increasing their physical activity levels and promoting healthy lifestyles.

Developmental and genomic insight into the origin of the tardigrade body plan.

Tardigrada is an ancient lineage of miniaturized animals. As an outgroup of the well-studied Arthropoda and Onychophora, studies of tardigrades hold the potential to reveal important insights into body plan evolution in Panarthropoda. Previous studies have revealed interesting facets of tardigrade development and genomics that suggest that a highly compact body plan is a derived condition of this lineage, rather than it representing an ancestral state of Panarthropoda. This conclusion was based on studies of several species from Eutardigrada. We review these studies and expand on them by analyzing the publicly available genome and transcriptome assemblies of Echiniscus testudo, a representative of Heterotardigrada. These new analyses allow us to phylogenetically reconstruct important features of genome evolution in Tardigrada. We use available data from tardigrades to interrogate several recent models of body plan evolution in Panarthropoda. Although anterior segments of panarthropods are highly diverse in terms of anatomy and development, both within individuals and between species, we conclude that a simple one-to-one alignment of anterior segments across Panarthropoda is the best available model of segmental homology. In addition to providing important insight into body plan diversification within Panarthropoda, we speculate that studies of tardigrades may reveal generalizable pathways to miniaturization.

Spinopelvic sagittal realignment and incidence of adjacent segment disease after single-segment posterior lumbar inter-body fusion using 12° lordotic cages-a 2-year prospective cohort study.

The importance of spinopelvic sagittal alignment for adjacent segment disease (ASD) after lumbar fusion surgery has been reported. However, no longitudinal cohort studies have determined the extent to which segmental alignment and spinopelvic global alignment can be achieved using 12° lordotic cages in posterior lumbar inter-body fusion (PLIF) and the extent to which the development of ASD can be prevented. The purpose of this study was to analyze changes in segmental and spinopelvic sagittal alignment after single-segment PLIF with 12° lordotic cages, to clarify the relationship between changes in segmental and spinopelvic sagittal alignment, and to report the incidence of ASD at 2 years postoperatively. Subjects in this 2-year prospective longitudinal cohort study were 28 patients who had undergone L4/5 PLIF using 12° lordotic cages. Incidence of operative ASD (O-ASD) was evaluated as clinical outcomes. Radiological measurements were examined preoperatively and at 3 months, 1 year and 2 years postoperatively. The following radiographic spinopelvic parameters were measured: segmental lordosis (SL) at L4/5; sagittal vertical axis (SVA); T1 pelvic angle (TPA); thoracic kyphosis (TK); lumbar lordosis (LL); sacral slope (SS); pelvic tilt (PT); and pelvic incidence (PI). With respect to radiological outcomes, changes in SL (ΔSL) and spinopelvic parameters and the incidence of radiological ASD (R-ASD) were evaluated. Correlations of ΔSL and changes in other spinopelvic parameters (ΔSVA, ΔTPA, ΔTK, ΔLL, ΔSS, ΔPT, and ΔPI-LL) between preoperatively and 3 months postoperatively were examined. The follow-up rate was 100% (n=28) at 1 year postoperatively and 96.4% (n=27) at 2 years postoperatively. No cases of O-ASD were seen during 2 years of follow-up. Significant realignment was observed and maintained at 2 years postoperatively in almost all spinopelvic sagittal parameters (SL, SVA, TPA, LL, PT, PI-LL). Regarding the correlation between ΔSL and other parameters, significant correlations were detected with ΔSVA (r=-0.37, P<0.05) and ΔLL (r=0.538, P<0.01). Three cases (11.1%) showed R-ASD at 2 years postoperatively. PLIF with 12° lordotic cages for L4 degenerative spondylolisthesis improved SL and global sagittal realignment, and achieved satisfactory clinical outcomes with a low incidence of ASD during 2 years of follow-up.

The early Cambrian Kylinxia zhangi and evolution of the arthropod head

The early Cambrian Kylinxia zhangi occupies a pivotal position in arthropod evolution, branching from the euarthropod stem lineage between radiodonts (Anomalocaris and relatives) and "great-appendage" arthropods.1,2 Its combination of appendage and exoskeletal features is viewed as uniquely bridging the morphologies of so-called "lower" and "upper" stem-group euarthropods.3,4 Microtomographic study of new specimens of Kylinxia refines and corrects previous interpretation of head structures in this species. Phylogenetic analyses incorporating new data reinforce the placement of Kylinxia in the euarthropod stem group but support new hypotheses of head evolution. The head of Kylinxia is composed of six segments, as in extant mandibulates, e.g., insects.5 In Kylinxia, these are an anterior sclerite associated with an unpaired median eye and paired lateral eyes (thus three rather than five eyes as was previously described1), deutocerebral frontal-most appendages, and four pairs of biramous appendages (rather than two pairs of uniramous appendages). Phylogenetic trees suggest that a six-segmented head in the euarthropod crown group was already acquired by a common ancestor with Kylinxia. The segmental alignment and homology of spinose frontal-most appendages between radiodonts and upper stem-group euarthropods6,7,8,9,10 is bolstered by morphological similarities and inferred phylogenetic continuity between Kylinxia and other stem-group euarthropods.