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Abstract Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and human epidermal growth factor receptors and has a poor prognosis as it is resistant to chemotherapy. A new treatment option for this type of cancer may be by putting these malignant cells into dormancy. The oocyte’s embryonic milieu presents a unique tumor reversion microenvironment by inducing growth arrest and changing cells’ phenotypes. We conducted an in-silico study to determine the most likely oocyte extract (OE) proteins involved in inducing dormancy using HDock, CluPro, and molecular dynamic (MD) simulation. Results showed low energy scores for complexes between OE proteins and four surface markers: K1C14, CLD3, CLD4, and ITA6. Apolipoprotein A1 (APOA1) and Apolipoprotein C3 (APOC3) showed the highest stability and affinity with these four surface markers: K1C14, CLD3, CLD4, and ITA6. These proteins are involved in key tumor-related pathways such as angiogenesis, proliferation, apoptosis, and migration. This will pave the way for exploring novel therapeutic options to induce dormancy in TNBC cells.

Abstract Therapies targeting the PD-1/PD-L1 pathway have transformed head and neck squamous cell carcinoma (HNSCC) treatment. However, predicting the response to anti-PD-1 therapy remains a clinical challenge. This study evaluated the functional binding of PD-1 ligands in 29 HNSCC patients and compared it to the standard PD-L1 Combined Positive Score (CPS). The assessment of PD-1 ligands’ functionality advances the current ability to predict the response of HNSCC patients to anti-PD-1 therapy.

Abstract Introduction Intra-articular injection of adipose-derived mesenchymal stromal cells (ASCs) and/or platelet-rich plasma (PRP) have been reported to independently and synergistically improve healing of osteochondral lesions in animal models. However, their independent and combined effects when localized to an osteochondral lesion by encapsulation within a photocrosslinkable methacrylated gelatin hydrogel (GelMA) have not been explored. Herein we investigated a unique combination of allogeneic ASCs and PRP embedded in GelMA as a single-stage treatment for osteochondral regeneration in a rabbit model. Methods Thirty mature rabbits were divided into six experimental groups: (1) Sham; (2) Defect; (3) GelMA; (4) GelMA + ASCs; (5) GelMA + PRP; and (6) GelMA + ASCs + PRP.At 12 weeks following surgical repair, osteochondral regeneration was assessed on the basis of gross appearance, biomechanical properties, histological and immunohistochemical characteristics, and subchondral bone volume. Results In terms of mechanical property reflecting the ability of neotissue to bear stress, PRP only group were significantly lower than the Sham group (p = 0.0098). On the other hand, ASCs only and ASCs combined with PRP groups did not exhibit significantly difference, which suggesting that incorporation of ASCs assists in restoring the ability of the neotissue to bear stresses similarly to native tissue (p = 0.346, p = 0.40, respectively). Safranin O in ASCs combined with PRP group was significantly higher than the Defect and GelMA only groups (p = 0.0009, p = 0.0017, respectively). Additionally, ASCs only and ASCs combined with PRP groups presented especially strong staining for collagen type II. Surprisingly, PRP only and PRP + ASCs groups tended to exhibit higher collagen type I and collagen type X staining compared to ASCs only group, suggesting a potential PRP-mediated hypertrophic effect. Conclusion Regeneration of a focal osteochondral defect in a rabbit model was improved by a single-stage treatment of a photocrosslinked hydrogel containing allogenic ASCs and autologous PRP, with the combination of ASCs and PRP producing superior benefit than either alone. No experimental construct fully restored all properties of the native, healthy osteochondral unit, which may require longer follow-up or further modification of PRP and/or ASCs characteristics.

In the evolving landscape of modern robotics, Visual SLAM (V-SLAM) has emerged over the past two decades as a powerful tool, empowering robots with the ability to navigate and map their surroundings. While these methods are traditionally confined to static environments, there has been a growing interest in developing V-SLAM to handle dynamic and realistic scenes. This survey offers a comprehensive overview of the current state-of-the-art V-SLAM methods, including their strengths and weaknesses. The paper also identifies the limitations of existing techniques and proposes potential research directions for future advancements. In addition, it provides an overview of commonly used datasets to evaluate the performance of V-SLAM methods. This survey sheds valuable insights into areas that need additional research to benefit V-SLAM development, including challenges related to limited scalability for systems with multiple agents, sensitivity to lighting changes, high computational cost, and performance issues in noisy environments.

IntroductionThe aim of this study is to identify factors that may predict the response of locally advanced rectal cancer tumors (LARC) to neoadjuvant chemoradiotherapy (CRT) and to evaluate the effect of circulating lymphocytes on pathological tumor response.MethodsThis retrospective study included neoadjuvant CRT-treated, LARC-diagnosed patients at the Rambam Health Care Campus in Haifa, Israel. CHAID analysis, t-test, χ2 test, and ROC curve analyses were performed to explore the association between pathological complete response (pCR) and several factors including patient demographics, tumor characteristics, type of treatment, and levels of circulating lymphocytes measured on a weekly basis.ResultsOut of 198 patients enrolled in the study, pCR was achieved in 50 patients (25%). ROC curve and CHAID analyses showed that absolute lymphopenia was significantly associated with lower pCR rates (p=0.046 and p=0.001, respectively). Other factors that were found to have a significant impact were radiation therapy type (p=0.033) and tumor distance from the anal verge (p= 0.041).ConclusionAn absolute decrease in the level of circulating lymphocytes during preoperative CRT to LARC is associated with poorer tumor response to treatment and thus may serve as a predictive biomarker for treatment resistance.

Coverage Path Planning (CPP) is an essential capability for autonomous robots operating in various critical applications such as fire fighting, and inspection. Performing autonomous coverage using a single robot system consumes time and energy. In particular, 3D large structures might contain some complex and occluded areas that shall be scanned rapidly in certain application domains. In this paper, a new Hybrid Coverage Path Planning (HCPP) approach is proposed to explore and cover unknown 3D large structures using a decentralized multi-robot system. The HCPP approach combines a guided Next Best View (NBV) approach with a developed Long Short Term Memory (LSTM) waypoint prediction approach to decrease the CPP exploration time at each iteration and simultaneously achieve high coverage. The hybrid approach is the new ML paradigm which fosters intelligence by balancing between data efficiency and generality allowing the exchange of some CPP parts with a learned model. The HCPP uses a stateful LSTM network architecture which is trained based on collected paths that cover different 3D structures to predict the next viewpoint. This architecture captures the dynamic dependencies of adjacent viewpoints in the long term sequences like the coverage paths. The HCPP switches between these methods triggered by either the number of iterations or an entropy threshold. In the decentralized multi-robot system, the proposed HCPP is embedded in each robot where each one of them shares its global 3D map ensuring robustness. The results performed in a realistic Gazebo robotic simulator confirmed the advantage of the proposed HCPP approach by achieving high coverage on different 3D unknown structures in a shorter time compared to conventional NBV.

This paper proposes an innovative hazard identification and risk assessment mapping model for Urban Search and Rescue (USAR) environments, concentrating on a 3D mapping of the environment and performing grid-level semantic labeling to recognize all hazards types found in the scene and to distinguish their risk severity level. The introduced strategy employs a deep learning model to create semantic segments for hazard objects in 2D images and create semantically annotated point clouds that encapsulate occupancy and semantic annotations such as hazard type and risk severity level. After that, a 3D semantic map that provides situational awareness about the risk in the environment is built using the annotated point cloud. The proposed strategy is evaluated in a realistic simulated indoor environment, and the results show that the system successfully generates a risk assessment map. Further, an open-source package for the proposed approach is provided online for testing and reproducibility.

ObjectiveCutaneous squamous cell carcinoma (cSCC) is the second most common non-melanoma skin cancer worldwide. It is usually treated surgically, with very high cure rates. However, in 3%-7% of cases, cSCC metastasizes to lymph nodes or distant organs. Many of the affected patients are elderly with comorbidities who are not candidates for standard-of-care curative-intent treatment with surgery and/or radio-/chemotherapy. Immune checkpoint inhibitors, which target programmed cell death protein 1 (PD-1) pathways, have recently emerged as a potent therapeutic option. The present report presents the Israeli experience with PD-1 inhibitors for the treatment of loco-regionally advanced or metastatic cSCC in a diverse and elderly population, with or without the addition of radiotherapy.Material and methodsThe databases of two university medical centers were retrospectively searched for patients with cSCC treated with the PD-1 inhibitors cemiplimab or pembrolizumab between January 2019 and May 2022. Data on baseline, disease-related, treatment-related, and outcome parameters were collected and analyzed.ResultsThe cohort included 102 patients of a median age 78.5 years. Evaluable response data were available for 93. The overall response rate was 80.6%: complete response in 42 patients (45.2%) and partial response in 33 (35.5%). Stable disease was recorded in 7 (7.5%) and progressive disease in 11 (11.8%). Median progression-free survival was 29.5 months. Radiotherapy was administered to the target lesion during PD-1 treatment in 22.5% of patients. mPFS was not significantly different in patients who treated with RT than patients how did not (NR vs 18.4 months, HR=0.93, 95%CI: 0.39 - 2.17, p

Abstract. Background:. This study aimed to compare the oncological and functional outcomes of primary whole gland cryoablation of the prostate using the variable ice cryoprobe (V-Probe®) and the conventional fixed-size ice probe. Materials and methods:. We reviewed the Cryo On-Line Data Registry for men who were treated with primary whole gland prostate cryoablation from 2000 through 2017. A multivariate Cox proportional hazards model was used to compare timing to biochemical recurrence between the V-Probe® and fixed-size ice probe after adjusting for preoperative prostate-specific antigen (PSA), neoadjuvant androgen deprivation therapy, preoperative Gleason score, and preoperative T stage. Results:. A total of 1124 men were included. Median age, Gleason score, and pretreatment PSA were 70 years (interquartile range [IQR]: 65–74 years), 7 (IQR: 6–7) and 5.9 ng/mL (IQR: 4.6–8.1 ng/mL), respectively. The median follow-up time was 25.0 months (IQR: 11.2–48.6 months). V-Probes® were used in 269 (23.9%) cases and fixed-size ice probes in 858 (76.1%) cases. After adjusting for clinical T stage, PSA, neoadjuvant androgen deprivation therapy and preoperative Gleason score, on the multivariate Cox regression model, we found that there was no significant difference between the type of probe and timing to biochemical recurrence (p = 0.35). On multivariate logistic regression, using the V-Probe® was associated with a 91% increase in postoperative urinary retention compared to the fixed-size ice probe (p = 0.003). Conclusions:. The use of the V-Probe® versus conventional fixed-size ice probe was not associated with a difference in biochemical recurrence in patients undergoing primary cryoablation of the prostate.

The ability to decide if a solution to a pose-graph problem is globally optimal is of high significance for safety-critical applications. Converging to a local-minimum may result in severe estimation errors along the estimated trajectory. In this paper, we propose a graph neural network based on a novel implementation of a graph convolutional-like layer, called PoseConv, to perform classification of pose-graphs as optimal or sub-optimal. The operation of PoseConv required incorporating a new node feature, referred to as cost, to hold the information that the nodes will communicate. A training and testing dataset was generated based on publicly available bench-marking pose-graphs. The neural classifier is then trained and extensively tested on several subsets of the pose-graph samples in the dataset. Testing results have proven the model’s capability to perform classification with 92 – 98% accuracy, for the different partitions of the training and testing dataset. In addition, the model was able to generalize to previously unseen variants of pose-graphs in the training dataset. Our method trades a small amount of accuracy for a large improvement in processing time. This makes it faster than other existing methods by up-to three orders of magnitude, which could be of paramount importance when using computationally-limited robots overseen by human operators.

Memristor devices fabricated using the chalcogenide Ge2Te3 phase change thin films in a metal-insulator-metal structure are characterized using thermal and electrical stimuli in this study. Once the thermal and electrical stimuli are applied, cross-sectional transmission electron microscopy (TEM) and X-ray energy-dispersive spectroscopy (XEDS) analyses are performed to determine structural and compositional changes in the devices. Electrical measurements on these devices showed a need for increasing compliance current between cycles to initiate switching from low resistance state (LRS) to high resistance state (HRS). The measured resistance in HRS also exhibited a steady decrease with increase in the compliance current. High resolution TEM studies on devices in HRS showed the presence of residual crystalline phase at the top-electrode/dielectric interface, which may explain the observed dependence on compliance current. XEDS study revealed diffusion related processes at dielectric-electrode interface characterized, by the separation of Ge2Te3 into Ge- and Te- enriched interfacial layers. This was also accompanied by spikes in O level at these regions. Furthermore, in-situ heating experiments on as-grown thin films revealed a deleterious effect of Ti adhesive layer, wherein the in-diffusion of Ti leads to further degradation of the dielectric layer. This experimental physics-based study shows that the large HRS/LRS ratio below the current compliance limit of 1 mA and the ability to control the HRS and LRS by varying the compliance current are attractive for memristor and neuromorphic computing applications.

In this paper, a real-time dynamic programming (RTDP) approach was developed for the first time to jointly carry a slung load using two unmanned aerial vehicles (UAVs) with a trajectory optimized for time and energy consumption. The novel strategy applies RTDP algorithm, where the journey was discretized into horizons consisting of distance intervals, and for every distance interval, an optimal policy was obtained using a dynamic programming sweep. The RTDP-based strategy is applied for dual-UAV collaborative payload transportation using coordinated motion where UAVs act as actuators on the payload. The RTDP algorithm provides the optimal velocity decisions for the slung load transportation to either minimize the journey time or the energy consumption. The RTDP approach involves minimizing a cost function which is derived after simplifying the combined model of the dual-UAV-payload system. The cost function derivation was also accommodated to dynamically distribute the load/energy between two multi-rotor platforms during a transportation mission. The cost function is used to calculate transition costs for all stages and velocity decisions. A terminal cost is used at the last distance interval during the first phase of the journey when the velocity at the end of the current horizon is not known. In the second phase, the last stage or edge of the horizon includes the destination, hence final velocity is known which is used to calculate the transition cost of the final stage. Once all transition costs are calculated, the minimum cost is traced back from the final stage to the current stage to find the optimal velocity decision. The developed approach was validated in MATLAB simulation, software in the loop Gazebo simulation, and real experiments. The numerical and Gazebo simulations showed the successful optimization of journey time or energy consumption based on the selection of the factor λ. Both simulation and real experiments results show the effectiveness and the applicability of the proposed approach.

Background: The increasing rate of breast cancer globally requires extraordinary efforts to discover new effective sources of chemotherapy with fewer side effects. Glutaminase-free l-asparaginase is a vital chemotherapeutic agent for various tumor malignancies. Microorganisms from extreme sources, such as marine bacteria, might have high l-asparaginase productivity and efficiency with exceptional antitumor action toward breast cancer cell lines. Results: l-Asparaginase-producing bacteria, Bacillus velezensis isolated from marine sediments, were identified by 16S rRNA sequencing. l-Asparaginase production by immobilized cells was 61.04% higher than that by free cells fermentation. The significant productivity of enzyme occurred at 72 h, pH 6.5, 37°C, 100 rpm. Optimum carbon and nitrogen sources for enzyme production were glucose and NH4Cl, respectively. l-Asparaginase was free from glutaminase activity, which was crucial medically in terms of their severe side effects. The molecular weight of the purified enzyme is 39.7 KDa by SDS-PAGE analysis and was ideally active at pH 7.5 and 37°C. Notwithstanding, the highest stability of the enzyme was found at pH 8.5 and 70°C for 1 h. The enzyme kinetic parameters displayed Vmax at 41.49 μmol/mL/min and a Km of 3.6 × 10−5 M, which serve as a proof of the affinity to its substrate. The anticancer activity of the enzyme against breast adenocarcinoma cell lines demonstrated significant activity toward MDA-MB-231 cells when compared with MCF-7 cells with IC50 values of 12.6 ± 1.2 μg/mL and 17.3 ± 2.8 μg/mL, respectively. Conclusion: This study provides the first potential of glutaminase-free l-asparaginase production from the marine bacterium Bacillus velezensis as a prospect anticancer pharmaceutical agent for two different breast cancer cell lines.How to cite: Mostafa Y, Alrumman S, Alamri S, et al. Enhanced production of glutaminase-free L-asparaginase by marine Bacillus velezensis and cytotoxic activity against breast cancer cell lines. Electron J Biotechnol 2019;42. https://doi.org/10.1016/j.ejbt.2019.10.001. Keywords: Asparaginase, Bacillus velezensis, Breast cancer, Chemotherapy, Fermentation, Glutaminase, Immobilization, Marine, MCF-7 cells, Nitrogen, Production, Purification

Bioethanol-derived biomass is a green sustainable source of energy that is highly recommended as an efficient alternative to the replacement of fossil fuels. However, this type of bioethanol production is always expensive with very low bioethanol concentration. Therefore, this work aims to represent a facile and green approach for bioethanol production with high concentration and purity as well as reasonable cost from corn stover (CS). The goal of this study is to characterize CS and its treated samples with maleic acid (CSM) using various characterization analyses, such as proximate and ultimate analysis, HHV, TGA, FTIR, SEM, and CHNS. The bioethanol production stages: Pretreatment, enzymatic degradation, fermentation, and finally bioethanol separation and purification via the pervaporation process, which have been investigated and optimized are associated with the economic analysis. The optimum operating condition of the pretreatment process was 2% maleic acid, 1:20 solid-to-liquid ratio at 45 psi, 120 °C, and 1 h of operation in the autoclave. This process contributes to 53 and 45% lignin and hemicellulose removal, 98% cellulose recovery, and a glucose yield of 741 mg/dL. The yeast isolate succeeded in the production of 1230 mg/dL of bioethanol. This isolated yeast strain was close to Pichia nakasei with a similarity of 98%, and its amplified 18S rRNA gene sequence was deposited in GenBank with the accession number MZ675535. Poly (MMA-co-MA) membrane was synthesized, characterized, and its efficiency for increasing the bioethanol concentration was evaluated using the integrated pervaporation technique. The techno-economic analysis is presented in detail to evaluate the process profitability, which achieves a considerable profit for the whole duration of the project without any losses as it reaches a net profit of USD 1 million in 2023, reaching USD 2.1 million in 2047 for a company with a capacity of 32 thousand tons per year. The sequential strategy offers a promising approach for efficient bioethanol production under mild and environmentally friendly conditions that enable its implication industrially.

Lung cancer is a worldwide prevalent malignancy. This disease has a low survival rate due to diagnosis at a late stage challenged by the involvement of metastatic sites. Non-small-cell lung cancer (NSCLC) is presented in 85% of cases. The last decade has experienced substantial advancements in scientific research, leading to a novel targeted therapeutic approach. The newly developed pharmaceutical agents are aimed towards specific mutations, detected in individual patients inflicted by lung cancer. These drugs have longer and improved response rates compared to traditional chemotherapy. Recent studies were able to identify rare mutations found in pulmonary tumors. Among the gene alterations detected were mesenchymal epithelial transition factor (MET), human epidermal growth factor 2 (HER2), B-type Raf kinase (BRAF), c-ROS proto-oncogene (ROS1), rearranged during transfection (RET) and neurotrophic tyrosine kinase (NTRK). Ongoing clinical trials are gaining insight onto possible first and second lines of medical treatment options intended to enable progression-free survival to lung cancer patients.

The wide distribution of infections-related pathogenic microbes is almost related to the contamination of food and/or drinking water. The current applied treatments face some limitations. In the current study, k-carrageenan polymer was used as supporting material for the proper/unreleased silver nanoparticles that showed strong antimicrobial activity against six pathogenic bacteria and yeast. The bio-extract of the pupa of green bottle fly was used as the main agent for the synthesis of silver nanoparticles. The qualitative investigation of biologically synthesized silver nanoparticles was determined using UV-Vis spectrophotometric analysis; however, the size of nanoparticles was in range of 30–100 nm, as confirmed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and particle size analyzer. The proper integration of silver nanoparticles into the polymeric substrate was also characterized through fourier transform infrared (FT-IR), thermogravimetric analysis (TGA), SEM, and tensile strength. The antimicrobial activity of k-carrageenan/silver nanoparticles against Gram positive, Gram negative, and yeast pathogens was highly effective. These results indicate the probable exploitation of the polymeric/nanoparticles composite as an extra stage in water purification systems in homes or even at water treatment plants.

This paper presents a strategy to autonomously explore unknown indoor environments, focusing on 3D mapping of the environment and performing grid level semantic labeling to identify all available objects. Unlike conventional exploration techniques that utilize geometric heuristics and information gain theory on an occupancy grid map, the work presented in this paper considers semantic information, such as the class of objects, in order to gear the exploration towards environmental segmentation and object labeling. The proposed approach utilizes deep learning to map 2D semantically segmented images into 3D semantic point clouds that encapsulate both occupancy and semantic annotations. A next-best-view exploration algorithm is employed to iteratively explore and label all the objects in the environment using a novel utility function that balances exploration and semantic object labeling. The proposed strategy was evaluated in a realistically simulated indoor environment, and results were benchmarked against other exploration strategies.

Category: Trauma Introduction/Purpose: INTRODUCTION: Talus fractures are not uncommon and one of the serious fractures in foot and ankle. Peroneal tendon dislocation is one of the commonly missed soft tissue injuries which may have significant impact on the outcomes including persistent pain and swelling. AIM: To report the incidence of peroneal tendon dislocation in talus fracture and the significance of fleck sign in the diagnosis of peroneal tendon dislocation. Methods: We retrospectively reviewed 100 consecutive talus fractures in the period between 1/1/2011 to 1/7/2017. Inclusion criteria were: The patient underwent open reduction and internal fixation, had pre-operative CT scan that is available for review and 3 views ankle plain radiographs. 2 independent authors review the radiographs for peroneal tendon dislocation, fleck sign and fracture classification if any. Any dispute was resolved by the senior author. Patient records were reviewed for laterality, age, sex, mode of injury, associated injuries and operative interventions. 43 ankles met inclusion criteria. 42 were males, mean age was 31 year and the predominant modes of injury were fall from height or unknown. Results: Peroneal tendon dislocation was found in 9 patients out of 43. (20%). All dislocations were found in talar neck fractures, and up to 67% of Hawkins type III and IV Talar neck fractures were associated with peroneal tendon dislocation. Risk of dislocation increased with severity of the fracture. 5 cases of peroneal tendon dislocation out of 9 were associated with lateral or bimalleolar fractures. Most of the dislocations were missed and no additional procedures were done to address such injury. Fleck sign sensitivity was 44.4% while specificity was 88%. The Negative Predictive Value (PPV) was 86%. Conclusion: Peroneal tendons dislocation is associated with 20% of talus fractures. The incidence of dislocation increases with neck fracture and severity of these fractures. Fleck sign is highly specific radiographic sign and hence we recommend intra-operative assessment of peroneal tendons in patients with fleck sign.

Urban search and rescue missions require rapid intervention to locate victims and survivors in the affected environments. To facilitate this activity, Unmanned Aerial Vehicles (UAVs) have been recently used to explore the environment and locate possible victims. In this paper, a UAV equipped with multiple complementary sensors is used to detect the presence of a human in an unknown environment. A novel human localization approach in unknown environments is proposed that merges information gathered from deep-learning-based human detection, wireless signal mapping, and thermal signature mapping to build an accurate global human location map. A next-best-view (NBV) approach with a proposed multi-objective utility function is used to iteratively evaluate the map to locate the presence of humans rapidly. Results demonstrate that the proposed strategy outperforms other methods in several performance measures such as the number of iterations, entropy reduction, and traveled distance.

In this paper, a Next Best View (NBV) approach with a profiling stage and a novel utility function for 3D reconstruction using an Unmanned Aerial Vehicle (UAV) is proposed. The proposed approach performs an initial scan in order to build a rough model of the structure that is later used to improve coverage completeness and reduce flight time. Then, a more thorough NBV process is initiated, utilizing the rough model in order to create a dense 3D reconstruction of the structure of interest. The proposed approach exploits the reflectional symmetry feature if it exists in the initial scan of the structure. The proposed NBV approach is implemented with a novel utility function, which consists of four main components: information theory, model density, traveled distance, and predictive measures based on symmetries in the structure. This system outperforms classic information gain approaches with a higher density, entropy reduction and coverage completeness. Simulated and real experiments were conducted and the results show the effectiveness and applicability of the proposed approach.

This paper explores the application of a real-time dynamic programming (RTDP) algorithm to transport a payload using a multi-rotor unmanned aerial vehicle (UAV) in order to optimize journey time and energy consumption. The RTDP algorithm is developed by discretizing the journey into distance interval horizons and applying the RTDP sweep to the current horizon to get the optimal velocity decision. RTDP sweep requires the current state of the UAV to generate the next best velocity decision. To the best of the authors knowledge, this is the first time that such real-time optimization algorithm is applied to multi-rotor based transportation. The algorithm was first tested in simulations and then experiments were performed. The results show the effectiveness and applicability of the proposed algorithm.

Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g. bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure’s performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.