Anchor Positions Research Articles

Improved DV-Hop algorithm based on geometric Brownian motion model under communication interference

Abstract In a mobile sensor network, a traditional positioning algorithm is unable to locate unknown nodes when losing anchor positions caused by communication interference. To solve this problem, an improved DV-Hop algorithm based on a geometric Brownian motion (GBM) model was proposed including two main stages: location of sink node (LSN) and location of blind node (LBN). In the LSN stage, if the signal transmission of anchors is normal, the GBM model records the moving positions of the anchors. If not, the GBM model predicts the estimated average positions of the anchors using recorded data. Then, the trial count of the GBM model is optimized to further improve the prediction accuracy and computational overhead. In the LBN stage, the unknown nodes’ positions are obtained by the DV-Hop algorithm. In a traditional DV-Hop algorithm, the approximate minimum hop number and average hop distance may lead to huge deviation between true position and estimated position. To improve the positioning accuracy in the LBN stage, the strategies of multi-communication radius and hop distance weighting were adopted. The simulation results demonstrated that the proposed algorithm has the capability to resist communication interference and adaptability at different node speeds , maintaining a relatively high accuracy in locating unknown nodes.

Bias and Deviation Map-Based Weighted Graph Search for NLOS Indoor RTLS Calibration

Recently, UWB-based technology providing centimeter-level accuracy has been developed and widely utilized in indoor real-time location tracking systems. However, location accuracy varies due to factors such as frequency interference, collisions, reflected signals, and whether line-of-sight (LOS) conditions are met, and it can be challenging to ensure high accuracy in specific environments. Fortunately, when anchor positions are fixed, the locations of large obstacles such as columns or furniture remain relatively stable, leading to similar patterns of positioning bias at specific points. This study proposes an algorithm that corrects inaccurate positioning to more closely reflect the actual location based on bias and deviation maps generated using natural neighbor interpolation. Initially, positioning bias and deviations at specific points are sampled, and bias and deviation maps are created using natural neighbor interpolation. During location tracking, the algorithm detects candidate clusters and determines the centroid to estimate the actual location by applying the bias and deviation maps to the measured positions derived through trilateration. To validate the proposed algorithm, experiments were conducted in a non-LOS (NLOS) indoor environment. The results demonstrate that the proposed algorithm can reduce the positioning bias of a UWB-based RTLS by approximately 71.34% compared to an uncalibrated system.

Crossing cable design optimization of multi-tower cable-stayed bridge based on joint analytical method and genetic algorithm

As the length of the main span increases, the stiffness of multi tower cable-stayed bridges continues to decrease. The performance of the structure can be improved by setting crossing cables at the main span. The setting of crossing cables is a key issue in the overall design of bridges. This study presents a new optimization strategy to achieve the optimal setting of crossing cables. The developed technique combines analytical algorithm, optimization algorithm and finite element method. The anchor position, number, cross-sectional area and cable force of crossing cable are taken as variables. Firstly, an analytical algorithm is used to determine the feasible interval of the number and area of crossing cables, and then the genetic algorithm is used to search for the optimal solution within the feasible interval to obtain the crossing cable layout with minimum steel. Based on the analytical method, the feasible interval is determined, so that the population is close to the convergence point from the initial state, and the optimization efficiency is improved. The effectiveness of the proposed method is verified by four optimization problems, and the influence of the deck stiffness on the anchor position of crossing cable is discussed, which provides a reference for the setting of the crossing cables in the preliminary design stage.

Influence of bolt preload of anchors on bond-slip behavior of fiber fabric-steel interface

Bond-slip behavior is a common failure mode in steel structures reinforced with fiber fabric. Premature debonding of the fiber fabric significantly impacts the effectiveness of steel structure reinforcement. In this study, a gripping anchor is proposed to enhance the reliability of the interface between fiber fabric and steel plates. Experimental investigations were conducted to analyze the influence of anchor bolt preload on the double shear joint between fiber fabric and steel plates. 41 specimens were prepared and tested to explore the effects of parameters such as bolt preload, anchor position, number and type of fiber fabric layers, and bond length on load-bearing capacity. Comparative analyses were conducted on failure modes, ultimate loads, and stress-strain distributions of the specimens. Experimental results indicate that the position of the anchor has a minimal impact on the ultimate bearing capacity of the specimen. However, anchors positioned closer to the joint end exhibit lower ductility upon failure. A novel predictive model was developed to characterize the bond-slip behavior of preload applied to carbon fiber fabric and steel plates. The theoretical model of the ultimate bearing capacity in the yield stage fits well with the experimental results of specimens with three layers of carbon fiber fabric, with a difference range of 0.34 % to 14.97 % compared to actual results. This indicates that the model has high accuracy in predicting stress-strain and bearing capacity.

Energy landscapes of peptide-MHC binding.

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response.

Efficient Solution Resilient to Noise and Anchor Position Error for Joint Localization and Synchronization Using One-Way Sequential TOAs

Efficient Solution Resilient to Noise and Anchor Position Error for Joint Localization and Synchronization Using One-Way Sequential TOAs

Mechanical Stability and Unfolding Pathways of Parallel Tetrameric G-Quadruplexes Probed by Pulling Simulations.

Guanine quadruplex (GQ) is a noncanonical nucleic acid structure formed by guanine-rich DNA and RNA sequences. Folding of GQs is a complex process, where several aspects remain elusive, despite being important for understanding structure formation and biological functions of GQs. Pulling experiments are a common tool for acquiring insights into the folding landscape of GQs. Herein, we applied a computational pulling strategy─steered molecular dynamics (SMD) simulations─in combination with standard molecular dynamics (MD) simulations to explore the unfolding landscapes of tetrameric parallel GQs. We identified anisotropic properties of elastic conformational changes, unfolding transitions, and GQ mechanical stabilities. Using a special set of structural parameters, we found that the vertical component of pulling force (perpendicular to the average G-quartet plane) plays a significant role in disrupting GQ structures and weakening their mechanical stabilities. We demonstrated that the magnitude of the vertical force component depends on the pulling anchor positions and the number of G-quartets. Typical unfolding transitions for tetrameric parallel GQs involve base unzipping, opening of the G-stem, strand slippage, and rotation to cross-like structures. The unzipping was detected as the first and dominant unfolding event, and it usually started at the 3'-end. Furthermore, results from both SMD and standard MD simulations indicate that partial spiral conformations serve as a transient ensemble during the (un)folding of GQs.

Untethered Ultra-Wideband-Based Real-Time Locating System for Road-Worker Safety.

In order to reduce the accident risk in road construction and maintenance, this paper proposes a novel solution for road-worker safety based on an untethered real-time locating system (RTLS). This system tracks the location of workers in real time using ultra-wideband (UWB) technology and indicates if they are in a predefined danger zone or not, where the predefined safe zone is delimited by safety cones. Unlike previous works that focus on road-worker safety by detecting vehicles that enter into the working zone, our proposal solves the problem of distracted workers leaving the safe zone. This paper presents a simple-to-deploy safety system. Our UWB anchors do not need any cables for powering, synchronisation, or data transfer. The anchors are placed inside safety cones, which are already available in construction sites. Finally, there is no need to manually measure the positions of anchors and introduce them to the system thanks to a novel self-positioning approach. Our proposal, apart from automatically estimating the anchors' positions, also defines the limits of safe and danger zones. These features notably reduce the deployment time of the proposed safety system. Moreover, measurements show that all the proposed simplifications are obtained with an accuracy of 97%.

Abstract 5376: Quantitative profiling of HLA class I and class II antigens and neoantigens in tissue biopsy and PBMC samples using an optimized mass spectrometry-based workflow

Abstract Major histocompatibility complex (MHC) molecules play a central role in orchestrating immune responses by presenting antigenic peptides derived from both self and foreign proteins. In the context of cancer, understanding the repertoire of tumor-associated antigens (TAAs) presented by MHC molecules (or HLA molecules in human) is crucial for deciphering how the immune system recognizes and responds to malignant cells. The identification of neoantigens, unique to individual tumors due to somatic mutations, has become a focal point in immunopeptidomic studies. One significant hurdle in systematic immunopeptidomics analysis is the high input material requirement. Here, we present a semi-automated workflow to robustly identify and quantify immunopeptides from reduced amounts of clinical tissue biopsy and peripheral blood mononuclear cell samples. At the core of immunopeptidomics is the enrichment of HLA-associated peptides, followed by identification using mass spectrometry and bioinformatics tools. We optimized the native lysis and a sequential immunoprecipitation workflow for both class I and class II immunopeptides while ensuring scalability and reproducibility. Leveraging the magnetic properties of the beads, 1,000 samples can be processed within a week by a single operator. For both tissue and PBMC samples, we performed a systematic ramping experiment starting with as little as 2.5mg tissue or 5 million PBMCs. In all experiments, The established sample preparation offers high reproducibility and identifications of good quality: 1) Class-I immunopeptides: >60% of the peptides identified are 9-mers, >80% predicted strong binders, and the expected amino acids are enriched at the anchor positions; 2) Class-II immunopeptides, >50% of the peptides identified are 14-to-16-mers, and >50% are predicted strong binders. Furthermore, the pipeline is highly sensitive as we could still identify over 2,800 class-I immunopeptides when processing 2.5 mg fresh frozen tissue and 2,000 - 3,000 class-I immunopeptides when starting from 5 million PBMCs. Furthermore, the developed immunopeptidomics workflow was deployed to profile a cohort of 12 cancerous and matched healthy lung tissue samples. Their Class-I immunopeptidomes clearly displayed a pattern where matched tissues from each subject cluster together, further underlining the fact that the intricate immune-tumor interface is highly personalized. Overall, we established a robust pipeline for for class-I and II immunopeptidome profiling from clinically relevant sample types. Taking advantage of the nature of mass spectrometry-based methods, customized targeted assays can be developed without the need for affinity reagent, allowing specific and absolute quantification of any immunopeptide of interest. Citation Format: Ilja E. Shapiro, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Daniel Redfern, Yuehan Feng, Roland Bruderer, Lukas Reiter. Quantitative profiling of HLA class I and class II antigens and neoantigens in tissue biopsy and PBMC samples using an optimized mass spectrometry-based workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5376.

Experimental-numerical analyses on energy harvesting characteristics of non-conventional T-shaped beams with varied piezoelectric patch location and anchor position

Recently, researchers have studied innovative beam structures in a quest to maximize the energy conversion capability of piezoelectric (PZT) transducers coupled with the shims. A T-shaped beam is one of the structural elements used for harvester design owing to its exceptional geometrical advantage. While a number of research papers have reported its performance characteristics, its potential for harnessing the vibration energy at much lower resonance frequency and damping has not been explored. This paper presents non-conventional T-shaped beam harvesters, namely, the Uniform Tapered T-shaped Beam (UTB) and the Reversed Tapered T-shaped Beam (RTB), with the purpose of enhancing the T-shaped beam functionality. A performance analysis between the UTB, RTB and a conventional T-shaped beam (CTB) harvester of equal mass was conducted, where experimental results show that the RTB harvester has a lower fundamental resonance frequency of 12.3% and 28.3% when compared to the CTB and UTB, respectively. Also, a maximum terminal voltage of 82.8 V and an output peak power of 4.6 mW were produced by the RTB harvester when the PZT patch was placed near its clamped end. In addition, we investigated the effect of the anchor position of the beams on their energy harvesting performance using a finite element analysis (FEA) tool. The numerical results show that the position of the anchor has a considerable effect on the overall characteristics of the configurations.

Optimal Reconfiguration Planning of a 3-DOF Point-Mass Cable-Driven Parallel Robot

Cable-driven parallel robots (CDPRs) have attracted much attention due to their advantages, such as large workspace and excellent load capacity. However, their adaptability to different tasks has been limited because of fixed configurations. To improve this, a novel 3-DOF point-mass reconfigurable CDPR (RCDPR) has been designed, and its configuration can be changed by adjusting the positions of multiple cable anchors. Since wrench feasible workspace (WFW) is an essential criterion that describes the configuration characteristics, an optimal reconfiguration planning method is proposed to schedule the sequence and number of all movable cable anchors for adjusting the WFW range. Based on a two-level optimization process, the method can realize static reconfiguration (SR) or dynamic reconfiguration (DR) of the RCDPR. If SR cannot provide the required WFW by finding a static optimal configuration, the WFW range will be dynamically adjusted by DR. Besides, the number of movable cable anchors is minimized in DR by applying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -norm optimization to the anchor velocity sequences. Simulations and experiments are implemented, and the results show that the proposed method can automatically determine when, which, and how the cable anchors move under physical constraints, and reducing the number of movable cable anchors can indeed save actuator energy effectively.

Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation

In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE.

Efficacy of all-inside devices in reducing gap and step-off in knee extension for ramp lesion repair: A cadaveric study.

The aim of this study is to assess the dynamics of the tear site of meniscal ramp lesions, particularly considering knee flexion angles, and validate anchor fixation using an all-inside device. Eight Thiel-embalmed paired cadaveric knees with their whole bodies were used in this study. The ramp lesions were created arthroscopically, and ramp lesion dynamics were evaluated by gradually extending the knee from 90° of knee flexion. Changes in the gap and step-off (0: no step-off; 1: cross-sectional overlap exists; and 2: tibial articular surface exposed) were evaluated at 90°, 60°, 30°, and 10° of knee flexion. After dynamic evaluation, all-inside repairs of the ramp lesions using all-inside devices were conducted. Dissection was performed to confirm the position of anchor fixation. As the knee was extended, the gap significantly decreased at all knee flexion angles. Similarly, the step-off grade decreased as the knee was extended, and the step-off completely disappeared in all cases when the knee was extended from 30° to 10°. The average knee flexion angle at which the gap and step-off completely disappeared was 22.5°. After suturing the ramp lesion, arthroscopic evaluation showed that the gap had disappeared and the step-off had been repaired in all cases. Anchor fixation locations were not found within the joint but were fixed to the semimembranosus tendon or its surrounding articular capsule. Overall, 31% (5/16) anchors were fixed to the attachment site of the semimembranosus tendon, whereas the remaining were fixed to the articular capsule, located peripherally to the semimembranosus tendon. Suturing with an all-inside device for ramp lesions is a good option, and the repair in knee extension was found to be reasonable, considering the dynamics of ramp lesions in this study. Level IV.

Calibration and Compensation of Anchor Positions for UWB Indoor Localization

Calibration and Compensation of Anchor Positions for UWB Indoor Localization

A high-confidence geometric compensation approach for improving downward surface accuracy

Overhangs are usually inevitable in Additive Manufacturing (AM). Moreover, making their downward surfaces reach higher accuracy after printing has an imperative impact on improving the accuracy of them and their corresponding as-printed parts. Although extensive studies have been devoted to improving the accuracy of as-printed parts, the effective, general, reliable, and less-consumption (in time and material) approach to make an overhang (especially on its downward surfaces) reach its ideal accuracy is still rare. Hence, a new machine-leaning-based geometric compensation approach is proposed in this study. First, based on the Taguchi method, a series of (overhang) benchmarks are designed and printed to collect the (geometric) deviations of the downward surfaces. With these data, a new deviation predictor is established based on the gaussian process regression model. The predictor can effectively predict the deviation(s) (as well as its corresponding quantified uncertainty) of each downward surface (after printing) by using a pointwise manner. Meanwhile, to make the downward surface of an overhang meet its ideal accuracy (after printing) with high confidence, a specific stochastic chance-constrained programming problem is first formulated to evaluate the new and suitable overhang height of each point on the downward surface. Based on the above-mentioned predictor, the programming problem gets solved by developing a new overhang-height optimization method that integrates the Monte Carlo simulation with the particle swarm optimization. After that, a support structure-associated compensation method is presented to make a pointwise compensation (according to the above-evaluated overhang height) on the downward surface and determine the support structures’ optimized number and anchor positions. Finally, experiments on several representative overhangs are also implemented based on a typical material extrusion machine to validate the effectiveness of the proposed approach. The results show that the proposed approach reduces the deviations of the downward surface by up to 63.92% on average, saving up to 40% of material (less support structures). In addition, methodological comparisons with state-of-the-art approaches are also implemented. The comparisons show that the proposed approach has the great potential to reliably improve the accuracy of the overhang’s downward surface(s) after printing in a less-consumption manner.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

Synthetic Approaches towards Peptide‐Conjugates of Pt(II) Compounds with an (O,S) Chelating Moiety

AbstractMetal‐containing peptide (bio−)conjugates have received continuous interest due to their enormous potential for bioinorganic and medicinal research. In many bioconjugates the chemical inertness of the metal‐containing units facilitates synthesis e. g. by copper‐catalyzed alkyne−azide cycloaddition or the formation of peptide bonds. However, when the metal complex contains labile ligands, which are often critical to their biological activity, the synthetic proceeding requires careful planning. Here, we report on the synthesis of a set of peptide bioconjugates with a platinum(II) core, coordinated through widely variable (O,S) chelating β‐hydroxydithiocinnamic ester and two monodentate ligands. We have evaluated the synthetic applicability of metal−peptide bioconjugation techniques between the model peptide Leu5−enkephalin and differently functionalized (O,S)Pt units. Within this, the type and position of anchor used at the β‐hydroxydithiocinnamic unit proved to be crucial for success, but equally important was the synthetic order of conjugation and complexation. In this work, synthetic approaches for the linkage of metal complexes that are coordinated by functionalized ligands towards peptides were explored. Two general methods to link the studied (O,S)Pt pharmacophore to the model peptide, Leu5−enkephalin, have been applied, namely the most prominent “click” reaction, CuAAC, and the linking via amide bonds. Overall, it could be shown that the structural motif of the (O,S)Pt compounds presented here offers multiple possibilities of derivatization, so that bioconjugation towards peptides can be made possible. Depending on the demands made on the resulting metal bioconjugate, both the dithioester and the aromatic unit of β‐hydroxydithiocinnamic esters can be used as anchor for peptides. However, from our results here, it can be concluded that anchoring at the aromatic subsite seems preferable from a synthetic point of view as the spatial distance of the reactive terminal functional group (alkyne or azide) to the metal center may avoid intramolecular reactions. Also, for using azide−alkyne click chemistry as a conjugation strategy, the (O,S) unit should contain the azide function and not the alkyne function, as triple bond hydration may occur. Classical amide‐bond conjugation also proved to be a suitable method in our hands when performed in solution. A coupling of the β‐hydroxydithiocinnamic unit to resin‐bound peptide would not be possible due to the typically harsh cleavage conditions.

Experimental investigation and analytical prediction on bond behaviour of CFRP-to-concrete interface with FRP anchors

Hybrid bonded technology with fiber-reinforced polymer (FRP) anchors can effectively improve the load capacity of carbon fiber-reinforced polymer (CFRP)-to-concrete interface. To investigate the reinforcement effect of FRP anchors, tensile shear tests of seventeen CFRP-concrete bonded joints with additional FRP anchors were conducted in this paper. The effects of the construction sequence, anchor position, and type of FRP anchor were considered in the experimental investigation. It was found that bonding CFRP sheet before installing FRP anchor and construction simultaneously could ensure the integrity of FRP anchor and provide a better anchor effect. The ultimate load-carrying capacity was increased with the increasing of plate length after anchor, that is the effect of anchor position. From the results of specimens with different types of FRP anchors at the same location, the FRP anchor composed of multiple small anchors can take effect earlier. By fitting the test results, the exponential bond-slip model for the CFRP-to-concrete interface and the elastic constitutive model for the FRP anchor were obtained. Further, substituting the two constitutive models, analytic theoretical models for load-slip curves and strain distribution were derived to predict the bond behavior of FRP anchored CFRP-to-concrete interface in this paper. The comparison between the prediction results and experimental data shows the good predictive accuracy of these analytical models.

DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction.

Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a position-wise gated layer and a residual binding interaction convolution layer. This allows the model to control the information flow in peptides to be aware of anchor positions and model the interactions between peptides and the MHC pseudo (binding) sequence directly with multiple convolutional kernels. The performance of DeepMHCI has been thoroughly validated by extensive experiments on four benchmark datasets under various settings, such as 5-fold cross-validation, validation with the independent testing set, external HPV vaccine identification, and external CD8+ epitope identification. Experimental results with visualization of binding motifs demonstrate that DeepMHCI outperformed all competing methods, especially on non-9-mer peptides binding prediction. DeepMHCI is publicly available at https://github.com/ZhuLab-Fudan/DeepMHCI.

Constrained Path Planning for Reconfiguration of Cable-Driven Parallel Robots

Obstacle avoidance is a challenging problem in cable-driven parallel robots (CDPRs) due to the cross-distribution of cables in the workspace. To address this challenge, a novel constrained path planning method is proposed in this article to realize the reconfiguration of CDPRs, where the cable anchor positions and cable lengths are adjusted simultaneously in the reconfiguration to achieve obstacle avoidance. To realize such reconfiguration, the loop-closure constraints are first formulated and the constrained configuration space is established in terms of the implicitly represented manifold. Then, combined with the designed wrench feasible detection and collision detection that are responsible for the positive cable tension guarantee and obstacle avoidance, the bidirectional rapidly exploring random trees are utilized to generate branches in the tangent spaces of the manifold to finally discover a feasible path in the whole constrained configuration space. Finally, simulations and experiments are implemented on the 3-DOF reconfigurable CDPR and the results indicate that the proposed method can coordinate the changes of the cable anchor positions and cable lengths by using the loop-closure constraints, and a collision-free path during the reconfiguration can be successfully found in the presence of regular or irregular obstacles.