Improve Alignment Accuracy Research Articles

The Role of Robotics in Enhancing Precision and Outcomes in Orthopedic Surgery

The advent of robotics has significantly transformed orthopedic surgery, addressing challenges inherent in traditional manual techniques, such as variability in outcomes and precision limitations. Robotics enables millimeter-level accuracy, minimizes human error, and allows for personalized, minimally invasive procedures. Historical milestones, from the pioneering ROBODOC system in 1992 to advanced platforms like MAKO and ROSA, illustrate the progressive adoption of robotic systems, particularly in joint replacement and spine surgeries. Comparative studies demonstrate enhanced precision and reduced recovery times for robotic-assisted surgeries compared to conventional methods, with quantifiable improvements in alignment accuracy and postoperative outcomes. Key applications include joint arthroplasties, spinal surgeries, and complex trauma cases, where robotic assistance ensures accurate implant positioning, improved functional outcomes, and higher patient satisfaction rates. However, barriers such as cost, access disparities, and the learning curve persist. Emerging technologies, including artificial intelligence, augmented reality, and haptic feedback, are anticipated to further enhance robotic precision and broaden accessibility. As robotic systems become integral to orthopedic practice, ongoing advancements promise to elevate surgical standards, offering reliable, patient-specific solutions that improve both procedural consistency and quality of life.

Enhancing total knee arthroplasty outcomes: the role of individualized femoral sagittal alignment in robotic-assisted surgery - A randomized controlled trial

BackgroundOptimal sagittal alignment of the femoral prosthesis is critical to the success of total knee arthroplasty (TKA). While robotic-assisted TKA can improve alignment accuracy, the efficacy of default femoral alignment versus individualized alignment remains under scrutiny. This study aimed to compare the differences in prosthetic alignment, anatomical restoration, and clinical outcomes between individualized femoral sagittal alignment and default sagittal alignment in robotic-assisted TKA.MethodsIn a prospective randomised controlled trial, 113 patients (120 knees) underwent robotic-assisted TKA were divided into two groups: 61 with individualized femoral flexion (individualized alignment group) and 59 with default 3–5° flexion (default alignment group). The individualized alignment was based on the distal femoral sagittal anteverted angle (DFSAA), defined as the angle between the mechanical and distal anatomical axes of the femur. The radiographic and clinical outcomes were compared.ResultsDespite similar postoperative femoral flexion angles between groups (P = 0.748), the individualized alignment group exhibited significantly lower incidences of femoral prosthesis extension and higher rates of optimal 0–3° prosthesis flexion (9.8% vs. 27.1%, P = 0.014,78.7% vs. 55.9%, p = 0.008, respectively). The individualized alignment group also demonstrated more favourable changes in sagittal anatomy, with higher maintenance of postoperative anterior femoral offset within 1 mm (54.1% vs. 33.9%, P = 0.026) and posterior condylar offset within 1 mm and 2 mm (44.3% vs. 25.4%, p = 0.031,73.8% vs. 50.8%, p = 0.010, respectively). Although slight improvement in the Hospital for Special Surgery Knee Score (HSS) at three months was observed (P = 0.045), it did not reach a minimal clinically important difference.ConclusionIndividualized tailoring of femoral sagittal alignment in robotic-assisted total knee arthroplasty (TKA) enhances prosthetic alignment and anatomical restoration, suggesting potential improvements in postoperative outcomes.

Full Waveform Inversion Based on Dynamic Time Warping and Application to Reveal the Crustal Structure of Western Yunnan, Southwest China

AbstractWe develop a 3D full waveform inversion (FWI) method based on dynamic time warping (DTW) to address the issue of cycle‐skipping, which can prohibit the convergence of conventional FWI methods. DTW globally compares data samples at different time steps in 2D matrices against the time shifts of waveforms. We introduce the concept of shape descriptors into softDTW, creating a soft‐shapeDTW objective function within our waveform inversion process to improve alignment accuracy. Additionally, including constraints from Sakoe‐Chiba bands in the inversion further enhances efficiency and overall performance. A synthetic test has shown that the soft‐shapeDTW inversion outperforms conventional waveform inversions in overcoming the cycle‐skipping challenges that arise from poor initial models. This method was applied to fit observed seismograms to reveal western Yunnan's crustal structure. Seismic waveforms were recorded by 88 broadband stations from 10 local earthquakes, which were then denoised using a continuous wavelet transform method. Generalized cut and paste waveform inversions were used to determine the source parameters of these seismic events. Our inversion well‐aligned various seismic phases in the selected time windows of seismograms, and the resolved velocity models well associate with local geological structure. Results suggest that the soft‐shapeDTW inversion offers a robust alternative to FWI, reducing the reliance on accurate starting models.

Tibetan Speech Synthesis Based on Pre-Traind Mixture Alignment FastSpeech2

Most current research in Tibetan speech synthesis relies primarily on autoregressive models in deep learning. However, these models face challenges such as slow inference, skipped readings, and repetitions. To overcome these issues, we propose an enhanced non-autoregressive acoustic model combined with a vocoder for Tibetan speech synthesis. Specifically, we introduce the mixture alignment FastSpeech2 method to correct errors caused by hard alignment in the original FastSpeech2 method. This new method employs soft alignment at the level of Latin letters and hard alignment at the level of Tibetan characters, thereby improving alignment accuracy between text and speech and enhancing the naturalness and intelligibility of the synthesized speech. Additionally, we integrate pitch and energy information into the model, further enhancing overall synthesis quality. Furthermore, Tibetan has relatively smaller text-to-audio datasets compared to widely studied languages. To address these limited resources, we employ a transfer learning approach to pre-train the model with data from resource-rich languages. Subsequently, this pre-trained mixture alignment FastSpeech2 model is fine-tuned for Tibetan speech synthesis. Experimental results demonstrate that the mixture alignment FastSpeech2 model produces higher-quality speech compared to the original FastSpeech2 model, particularly when pre-trained on an English dataset, resulting in further improvements in clarity and naturalness.

Impact of the superimposition methods on accuracy analyses in complete-arch digital implant investigation

ObjectivesTo measure the impact of the superimposition methods on accuracy analyses in digital implant research using an ISO-recommended 3-dimensional (3D) metrology-grade inspection software. Materials and methodsA six-implant edentulous maxillary model was scanned using a desktop scanner (7Series; DentalWings; Montreal, Canada) and an intraoral scanner (TRIOS 4; 3Shape; Copenhagen, Denmark) to generate a reference and an experimental mesh, respectively. Thirty experimental standard tesselletion language (STL) files were superimposed onto the reference model's STL using the core features of six superimposition methods, creating the following groups: initial automated pre-alignment (GI), landmark-based alignment (G1), partial area-based alignment (G2), entire area-based alignment (G3), and double alignment combining landmark-based alignment with entire model area-based alignment (G4 ) or the scan bodies' surface (G5). The groups underwent various alignment variations, resulting in sixteen subgroups (n = 30). The alignment accuracy between experimental and reference meshes was quantified by using the root mean square (RMS) error as trueness and its fluctuation as precision. The Kruskal-Wallis test with a subsequent adjusted post-hoc Dunn's pairwise comparison test was used to analyze the data (α = 0.05). The reliability of the measurements was assessed using the intraclass correlation coefficient (ICC). ResultsA total of 480 superimpositions were performed. No significant differences were found in trueness and precision among the groups (p > 0.05), except for partial area-based alignment (p < 0.001). Subgroup analysis showed significant differences for partial area-based alignment considering only one scan body (p < 0.001). Initial automated alignment was as accurate as landmark-based, partial, or entire area-based alignments (p > 0.05). Double alignments did not improve alignment accuracy (p > 0.05). The entire area-based alignment of the scan bodies’ surface had the least effect on accuracy analyses. ConclusionsDigital oral implant investigation remains unaffected by the superimposition method when ISO-recommended 3D metrology-grade inspection software is used. At least two scan bodies are needed when considering partial area-based alignments. Clinical significanceThe superimposition method choice within the tested ISO-recommended 3D inspection software did not impact accuracy analyses in digital implant investigation.

Design of a Multi-Position Alignment Scheme.

The current new type of inertial navigation system, including rotating inertial navigation systems and three-autonomy inertial navigation systems, has been increasingly widely applied. Benefited by the rotating mechanisms of these inertial navigation systems, alignment accuracy can be significantly enhanced by implementing IMU (Inertial Measurement Unit) rotation during the alignment process. The principle of suppressing initial alignment errors using rotational modulation technology was investigated, and the impact of various component error terms on alignment accuracy of IMU during rotation was analyzed. A corresponding error suppression scheme was designed to overcome the shortcoming of the significant scale factor error of fiber optic gyroscopes, and the research content of this paper is validated through corresponding simulations and experiments. The results indicate that the designed alignment scheme can effectively suppress the gyro scale factor error introduced by angular motion and improve alignment accuracy.

Co-linear chaining on pangenome graphs

Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment. Recent works show how the co-linear chaining problem can be efficiently solved for acyclic pangenome graphs by exploiting their small width and how incorporating gap cost in the scoring function improves alignment accuracy. However, it remains open on how to effectively generalize these techniques for general pangenome graphs which contain cycles. Here we present the first practical formulation and an exact algorithm for co-linear chaining on cyclic pangenome graphs. We rigorously prove the correctness and computational complexity of the proposed algorithm. We evaluate the empirical performance of our algorithm by aligning simulated long reads from the human genome to a cyclic pangenome graph constructed from 95 publicly available haplotype-resolved human genome assemblies. While the existing heuristic-based algorithms are faster, the proposed algorithm provides a significant advantage in terms of accuracy. Implementation (https://github.com/at-cg/PanAligner).

Feasibility Study of Using Augmented Mirrors for Alignment Task during Orthopaedic Procedures in Mixed Reality.

Accurate depth estimation poses a significant challenge in egocentric Augmented Reality (AR), particularly for precision-dependent tasks in the medical field, such as needle or tool insertions during percutaneous procedures. Augmented Mirrors (AMs) provide a unique solution to this problem by offering additional non-egocentric viewpoints that enhance spatial understanding of an AR scene. Despite the perceptual advantages of using AMs, their practical utility has yet to be thoroughly tested. In this work, we present results from a pilot study involving five participants tasked with simulating epidural injection procedures in an AR environment, both with and without the aid of an AM. Our findings indicate that using AM contributes to reducing mental effort while improving alignment accuracy. These results highlight the potential of AM as a powerful tool for AR-enabled medical procedures, setting the stage for future exploration involving medical professionals.

E-commerce bookstore user alignment model based on multidimensional feature joint representation and implicit behavior compensation

The accurate identification of the same user across different e-commerce platforms is crucial for cross-platform recommendations. Given the complexity of the feature space of heterogeneous e-commerce platforms and the sparsity of explicit user behavior data, we propose a heterogeneous e-commerce network alignment model based on multidimensional joint representation and implicit behavior compensation. First, in response to the complexity of the feature space of heterogeneous e-commerce platforms, the UBN2vec model was constructed. The model generates independent embeddings for multidimensional features of “user attributes-behaviors-neighborhood”, and integrates them using an attention mechanism. Furthermore, through joint optimization of user multidimensional features, low-rank dense user overall embedding vectors are generated, effectively representing the complex features of e-commerce users. Second, to alleviate the sparsity issue of user behavior data on an e-commerce bookstore platform, we propose a method for compensating implicit user behavior data based on item association. This method predicts potential user behavior information by mining the latent correlations between items. It effectively captures user preferences and improves alignment accuracy. Finally, to reduce the computational complexity of the user-matching process, a user interest group block indexing method based on user interest preferences is developed. This method reduces matching times among users with significantly different interests, thereby reducing the computational complexity of the heterogeneous e-commerce bookstore user alignment algorithm. Experiments show that the proposed model can accurately and efficiently identify the same user across e-commerce bookstore platforms.

A scalable parallel algorithm for global sequence alignment with customizable scoring scheme

SummarySequence alignment is a critical computational problem in various domains, including genomics, proteomics, and natural language processing. The Needleman‐Wunsch (NW) algorithm is a classical dynamic programming approach for finding the optimal global alignment between two sequences. However, its quadratic time and space complexity make it impractical for aligning large‐scale sequences, which are increasingly common in modern applications. In this article, we propose a parallel variation of the NW algorithm that enables scalable global sequence alignment with customizable scoring schemes. Our approach re‐formulates the dependencies in the NW algorithm to enable parallel execution, thereby leveraging the computational power of modern parallel architectures, such as graphics processing unit (GPU). Furthermore, our algorithm supports arbitrary linear scoring schemes, which allows us to use domain‐specific knowledge to improve alignment accuracy. We establish the correctness of our algorithm and evaluate its performance using real DNA and user trajectory sequences on GPUs. Our parallel algorithm has shown impressive results in our experiments, with a peak performance of 27.99 GCUPS (giga cell updates per second) and a maximum speedup of 48.18 times compared to the traditional sequential implementation. Additionally, our algorithm demonstrates remarkable scalability, enabling the alignment of sequences of any length while ensuring balanced work distribution and optimal utilization of resources. Our primary objective is to harness the computational capabilities of a single GPU and fully utilize the processing power of multi‐core CPUs.

Improving performance of medical image alignment through super-resolution.

Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced by the effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, we propose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed the model based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinical and public data. Through all three experiments, we demonstrate that higher resolution data yields better results in the alignment process. To fully constrain similarity and structure, a new loss function is proposed; Pearson correlation coefficient combined with regional mutual information. In all test samples, the newly proposed loss function obtains higher results than the common loss function and improve alignment accuracy. Subsequent experiments verified that, combined with the newly proposed loss function, the super-resolution processed data boosts alignment, can reaching up to 9.58%. Moreover, this boost is not limited to a single model, but is effective in different alignment models. These experiments demonstrate that the unsupervised alignment method with super-resolution preprocessing proposed in this study effectively improved alignment and plays an important role in tracking different patient conditions over time.

Novel wafer-scale adhesive bonding with improved alignment accuracy and bond uniformity

We report a versatile method for improving post-bonding wafer alignment accuracy and BCB thickness uniformity in stacks bonded with soft-baked BCB. It is based on novel BCB-based micro-pillars that act as anchors during bonding. The anchor structures become a natural part of the bonding interface therefore causing minimal interference to the optical, electrical and mechanical properties of the bonded stack. We studied these properties for fixed anchor density and various anchor heights with respect to the adhesive BCB thickness. We demonstrated that the alignment accuracy can be improved by approximately an order of magnitude and approach the fundamental pre-bond alignment accuracy by the tool. We also demonstrated that this technique is effective for a large range of BCB thicknesses of 2–16 μm. Furthermore we observed that the thickness non-uniformities were reduced by a factor of 2–3 × for BCB thicknesses in the 8–16 μm range.

Resolving Power Equipment Data Inconsistency via Heterogeneous Network Alignment

This paper studies the problem of resolving data inconsistency from multiple sources in managing data related to power equipment for China’s state grid corporation. This paper proposes to automatically align inconsistent devices from multiple sources, i.e., the same devices that have multiple entries with different values in each source, by HENGE, a HEtetrogeneous Network GEneration model. HENGE builds multiple data sources into a heterogeneous graph, and captures complex physical and semantic relationships among devices. HENGE combines both feature and relational information and improves alignment accuracy by feature-enhanced residual graph encoder and disentangled representation learning. HENGE is capable to learn from a small amount of labeled data, through a uniformity autoencoder trained on an unsupervised generation task. Experiments on two real-world datasets demonstrate the capability of HENGE in resolving inconsistent device entries in multiple sources.

Comparison of clinical outcomes of VISIONAIRE patient-specific instrumentation with conventional instrumentation in total knee arthroplasty: a systematic literature review and meta-analysis

IntroductionMalalignment and resulting complications are major challenges in total knee arthroplasty (TKA) which patient-specific instrumentation (PSI) is proposed to alleviate. Previous PSI meta-analyses of TKA outcomes typically do not differentiate between PSI systems and assess relatively few outcomes, so the value of their findings is limited. VISIONAIRE™ cutting guides (Smith + Nephew Inc., Memphis, TN, USA) is a PSI system based on preoperative magnetic resonance and X-ray imaging. A systematic literature review (SLR) and meta-analysis, focussed specifically on VISIONAIRE, were conducted to assess TKA accuracy, intraoperative outcomes, and postoperative outcomes, compared with conventional instrumentation (CI).Materials and methodsThe SLR was performed using PubMed, Embase, and Google Scholar databases to identify relevant studies published until March 2022. Depending on statistical heterogeneity, meta-analyses were performed for outcome measures with fixed effect (I2 < 50%) or random-effects models (I2 ≥ 50%). Dichotomous outcomes were reported as odds ratios and continuous outcomes were reported as mean differences. Descriptive analyses were performed for outcomes not amenable to meta-analysis.ResultsOutcomes for VISIONAIRE versus CI were reported in 25 studies. Compared with CI, VISIONAIRE reduced odds of mechanical outliers by 40% (p < 0.0001), with no statistically significant differences in odds of overall coronal, sagittal, or rotational plane component outliers. VISIONAIRE improved surgical efficiency (operating room, turnover, and tourniquet times reduced by 7.3% (p = 0.02), 42% (p = 0.022), and 15.9% (p = 0.01), respectively), lowering the odds of blood transfusion by 53% (p = 0.01) and shortening patients’ hospital stays (11.1% reduction; p < 0.0001). There were no significant differences between groups in incidence of postoperative complications and (descriptively analyzed) return-to-function outcomes.ConclusionOptions for PSI in TKA differ substantially, and it is important to assess the outcomes of individual systems. The current findings suggest that VISIONAIRE guides can lead to improved alignment accuracy and surgical efficiency compared with CI, without compromising postoperative safety and return-to-function outcomes.

A transfer alignment algorithm based on combined double-time observation of velocity and attitude

PurposeThis paper aims to present a novel transfer alignment method based on combined double-time observations with velocity and attitude for ships’ poor maneuverability to address the system errors introduced by flexural deformation and installing which are difficult to calibrate.Design/methodology/approachBased on velocity and attitude matching, redesigning and deducing Kalman filter model by combining double-time observation. By introducing the sampling of the previous update cycle of the strapdown inertial navigation system (SINS), current observation subtracts previous observation are used as measurements for transfer alignment filter, system error in measurement introduced by deformation and installing can be effectively removed.FindingsThe results of simulations and turntable tests show that when there is a system error, the proposed method can improve alignment accuracy, shorten the alignment process and not require any active maneuvers or additional sensor equipment.Originality/valueCalibrating those deformations and installing errors during transfer alignment need special maneuvers along different axes, which is difficult to fulfill for ships’ poor maneuverability. Without additional sensor equipment and active maneuvers, the system errors in attitude measurement can be eliminated by the proposed algorithms, meanwhile improving the accuracy of the shipboard SINS transfer alignment.

WITCH: Improved Multiple Sequence Alignment Through Weighted Consensus Hidden Markov Model Alignment.

Accurate multiple sequence alignment is challenging on many data sets, including those that are large, evolve under high rates of evolution, or have sequence length heterogeneity. While substantial progress has been made over the last decade in addressing the first two challenges, sequence length heterogeneity remains a significant issue for many data sets. Sequence length heterogeneity occurs for biological and technological reasons, including large insertions or deletions (indels) that occurred in the evolutionary history relating the sequences, or the inclusion of sequences that are not fully assembled. Ultra-large alignments using Phylogeny-Aware Profiles (UPP) (Nguyen et al. 2015) is one of the most accurate approaches for aligning data sets that exhibit sequence length heterogeneity: it constructs an alignment on the subset of sequences it considers "full-length," represents this "backbone alignment" using an ensemble of hidden Markov models (HMMs), and then adds each remaining sequence into the backbone alignment based on an HMM selected for that sequence from the ensemble. Our new method, WeIghTed Consensus Hmm alignment (WITCH), improves on UPP in three important ways: first, it uses a statistically principled technique to weight and rank the HMMs; second, it uses HMMs from the ensemble rather than a single HMM; and third, it combines the alignments for each of the selected HMMs using a consensus algorithm that takes the weights into account. We show that this approach provides improved alignment accuracy compared with UPP and other leading alignment methods, as well as improved accuracy for maximum likelihood trees based on these alignments.

Image-Free Robotic-Assisted Total Knee Arthroplasty Improves Implant Alignment Accuracy: A Cadaveric Study.

Improving resection accuracy and eliminating outliers in total knee arthroplasty (TKA) is important to improving patient outcomes regardless of alignment philosophy. Robotic-assisted surgical systems improve resection accuracy and reproducibility compared to conventional instrumentation. Some systems require preoperative imaging while others rely on intraoperative anatomic landmarks. We hypothesized that the alignment accuracy of a novel image-free robotic-assisted surgical system would be equivalent or better than conventional instrumentation with fewer outliers. Forty cadaveric specimens were used in this study. Five orthopedic surgeons performed 8 bilateral TKAs each, using the VELYS Robotic-Assisted System (DePuy Synthes) and conventional instrumentation on contralateral knees. Pre-resection and postresection computed tomography scans, along with optical scans of the implant positions were performed to quantify resection accuracies relative to the alignment targets recorded intraoperatively. The robotic-assisted cohort demonstrated smaller resection errors compared to conventional instrumentation in femoral coronal alignment (0.63° ± 0.50° vs 1.39° ± 0.95°, P < .001), femoral sagittal alignment (1.21° ± 0.90° vs 3.27° ± 2.51°, P < .001), and tibial coronal alignment (0.93° ± 0.72° vs 1.65° ± 1.29°, P= .001). All other resection angle accuracies were equivalent. Similar improvements were found in the femoral implant coronal alignment (0.89° ± 0.82° vs 1.42° ± 1.15°, P= .011), femoral implant sagittal alignment (1.51° ± 1.08° vs 2.49° ± 2.10°, P= .006), and tibial implant coronal alignment (1.31° ± 0.84° vs 2.03° ± 1.44°, P= .004). The robotic-assisted cohort had fewer outliers (errors >3°) for all angular resection alignments. This invitro study demonstrated that image-free robotic-assisted TKA can improve alignment accuracy compared to conventional instrumentation and reduce the incidence of outliers.

Novel Wafer-Scale Adhesive Bonding with Improved Alignment Accuracy and Bond Uniformity

Novel Wafer-Scale Adhesive Bonding with Improved Alignment Accuracy and Bond Uniformity

UAV Image Stitching Using Shape-Preserving Warp Combined With Global Alignment

In this letter, we propose a strategy for unmanned aerial vehicle (UAV) image stitching to generate natural-looking panoramas. Traditional methods using homography to perform alignment cannot account for images with parallax, so they require that the input images should be taken from the same viewpoint or the scene should be near the planar. However, remote sensing images obtained by UAVs usually do not satisfy such an ideal situation, and the stitching results always suffer from artifacts. To overcome these challenges and obtain natural-looking panoramas, a global alignment strategy is proposed to better align the input images. Combined with a shape-preserving warp, the stitching results can achieve better alignment accuracy while maintaining the shape. Meanwhile, locality preserving matching (LPM) is used to eliminate mismatches during feature detection and matching for accurate alignment. In addition, to make the stitching results more natural-looking, we also use multiband blending to eliminate artifacts that may exist in the results due to unmodeled effects. Experiments show that our stitching strategy can effectively improve alignment accuracy and obtain natural-looking results compared to other state-of-the-art methods.

MAGUS+eHMMs: improved multiple sequence alignment accuracy for fragmentary sequences.

SummaryMultiple sequence alignment is an initial step in many bioinformatics pipelines, including phylogeny estimation, protein structure prediction and taxonomic identification of reads produced in amplicon or metagenomic datasets, etc. Yet, alignment estimation is challenging on datasets that exhibit substantial sequence length heterogeneity, and especially when the datasets have fragmentary sequences as a result of including reads or contigs generated by next-generation sequencing technologies. Here, we examine techniques that have been developed to improve alignment estimation when datasets contain substantial numbers of fragmentary sequences. We find that MAGUS, a recently developed MSA method, is fairly robust to fragmentary sequences under many conditions, and that using a two-stage approach where MAGUS is used to align selected ‘backbone sequences’ and the remaining sequences are added into the alignment using ensembles of Hidden Markov Models further improves alignment accuracy. The combination of MAGUS with the ensemble of eHMMs (i.e. MAGUS+eHMMs) clearly improves on UPP, the previous leading method for aligning datasets with high levels of fragmentation.Availability and implementationUPP is available on https://github.com/smirarab/sepp, and MAGUS is available on https://github.com/vlasmirnov/MAGUS. MAGUS+eHMMs can be performed by running MAGUS to obtain the backbone alignment, and then using the backbone alignment as an input to UPP.Supplementary information Supplementary data are available at Bioinformatics online.