Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element Modeling (FEM) relying on a patient-specific 3D models generated combining data from preoperative CBCT (hard tissue) scans and MRI scans (muscles and skin). An initial simulation was performed assuming that all the muscles and the other soft tissue had the same material properties (Homogeneous Model). This model was compared with the postoperative CBCT 3D simulation for validation purpose. Design of experiments (DoE) was used to assess the effect of the presence of the muscles considered and of their variation in stiffness. The effect of single muscles was evaluated in specific areas of the midface. The quantitative distance error between the homogeneous model and actual patient surfaces for the midface area was 0.55 mm, standard deviation 2.9 mm. In our experience, including muscles in the numerical simulation of orthognathic surgery, brought an improvement in the quality of the simulation obtained. Orthognathic surgery is a well-established practice aimed to restore both morphology and function in congenital or acquired jaw deformities by means of several types of osteotomies. Successful surgery depends on surgical expertise and accurate pre-surgical planning.Accurate surgical planning requires a precise simulation of osteotomies, bone segment movements as well as the precise prediction of facial soft tissue changes induced by bony movements –3. Due tThe most commonly adopted techniques for simulation of soft tissue changes are the finite element modeling (or finite element method ‐ FEM), mass-spring model (MSM), and mass tensor model (MTM) . CompariThe accuracy of FEM simulation depends on the geometrical fidelity of the FE mesh, on the accuracy of the constitutive tissue mechanical properties and on the adoption of realistic boundary conditions . CommercIn the years several efforts have been made to improve the models and to incorporate more anatomical details with different biomechanical properties –17. MRI In this study, the Authors built on their previous efforts in producing accurate patient specific 3D mid-face models to predict the outcome of orthognathic surgery , 21 and Eight consecutive patients admitted at St Orsola–Malpighi University Hospital (Alma Mater Bologna), Oral and Maxillo-facial Unit and undergoing bimaxillary orthognathic surgery were included in this study. Written informed consent was obtained from all subjects involved in the Study; the Study obtained the approval of the local Ethical Committee (protocol number 1015/2021/Oss/AOUBo ). Throughout the duration of the study (12 months), we enrolled 5 female and 3 males, mean age of 33.9 +/- 16.5 . Our cohort included patients undergoing orthognathic surgery for malocclusion or OSAS and not wearing orthodontic devices (which would pose issues for the MRI scan) prior to surgery, non claustrophobic, aged over 18 who signed the informed consent before the beginning of the study. Patients with high anesthesiology risk, immunosuppressed or immunocompromised, with comorbidities such as diabetes, pregnant women, needing presurgical orthodontics and with extensive facial scarring due to previous trauma and/or intervention, were excluded. Bimaxillary surgery was performed by a single surgeon (GB). Presurgical virtual planning was performed according to the standard protocol using a preoperative planning software . Surgical wafers were produced as usual to allow the jaws repositioning.All the patients underwent standard presurgical evaluation by means of planar x-rays and CBCT scans (which is the standard for orthognathic patients). All CBCT scans were obtained from the same machine NewTom 3000 VGI Evo  with the patient in natural head position and standing. Patient occlusion was ensured by asking them to bite a centric occlusion wax cast. After postoperative discharge, all the patients underwent postoperative CBCT scan (using the same machine as the preoperative scan). Only five postoperative CBCT scans were suitable for validation .In addition, each patient received a preoperative MRI with the same protocol (3D DWI sequence on GE 1.5 Tesla machine), with the patient supine in the gantry.All DICOM data were extracted both from preoperative CBCT and MRI scans and from postoperative CBCT scans. The datasets were separately imported in Simpleware ScanIP software , where each component  was rendered three-dimensionally. The skull and mandible were extracted from the preoperative CBCT white in ; the LeFIn Ansys Workbench, boundary conditions were applied to mimick the tethering of the other tissues  on the FE model: each surface was fully constrained in terms of rotation and translation . The manThe FE software allows to attribute to each component of the model specific biomechanical properties established from the Literature . For eacA first simulation was performed for each patient where all muscles were assumed to have the same material properties as the other soft tissues  software by means of an iterative closest point alignment of the predicted and postoperative model followed by visual inspection and manual refinement of the alignment if needed. The predicted model was sliced in order to remove surface sections unaffected by the displacement. Once correct alignment was achieved, surface distance (i.e. prediction error) between the surfaces was calculated and visualised by means of colormaps. Average prediction error was calculated for each patient.To assess the effect of the presence of the muscles as well as of the physiological variation in muscle stiffness, a design of experiments (DoE ) approacIn order to evaluate the system’s behavior on several facial areas, the geometry of the skin surface was divided into four areas: nose (M), upper lip (UL), left and right cheek . These areas were selected as reference landmarks: the total displacement of the four nodes .max, ULmax, Cmax) and minimum  displacement for each node for each patient were extracted and compared with the values retrieved from the respective H model results .From the DoE results, maximum . Relatively to the cheek area, the masseter muscle showed a large spread of sensitivity (from -78.38% to -3.79%) but it always showed a negative sensitivity (-38.75% +/- 25.92%). The UL landmark displacement was negatively influenced by the stiffness of the masseter (-5.22% +/- 4.52%) but was largely positively influenced by the stiffness of the orbicularis oris (89.84% +/- 6.89%).max, ULmax, Cmax) and minimum  displacement recorded and the respective H model values . While for the UL landmark, ULH was consistently higher than ULmax (p = 0.012) and ULmin (p = 0.012) , for the C landmark the CH displacement values were consistently lower than Cmax (p<0.05) but that didn’t reach statistical difference with Cmin (p>0.05). For the N landmark, no clear trend was observed.Orthognathic surgery aims to reposition patients’ jaws in the most functional and aesthetic way. To achieve this, surgical planning is strongly advisable. Thanks to recent improvements in medical imaging,3D virtual planning provides greater accuracy compared to standard bi-dimensional analysis.In this work, we have presented a methodology to create an anatomically accurate 3D face model by combining information from preoperative CBCT and MRI images. Precise replication of the osteotomies performed during surgery allowed for retrospective simulation of the maxillary advancement and validation in a subset of patients by comparison with postoperative 3D scan allowed for the quantification of the accuracy of the H model simulation . KiFor the following step, a parametric set of simulations (DoE) was carried out to assess the effect of the inclusion of the muscle  and to investigate which muscles have the largest effect on the displacement of the different landmarks. The analysis of the sensitivity of the muscle stiffness on the displacement of the landmarks showed that the masseter has a consistently negative effect on the displacement of all the landmarks, i.e. a stiffer muscle would cause a lower displacement on all the points, possibly due to an internal tethering effect. On the other hand, the orbicularis oris muscle stiffness has a consistent positive effect on the displacement of the UL landmark: in this case, a stiffer muscle exhibits lower compression during the orthognathic advancement and transfers the full segment displacement to the upper lip area. No other muscle exhibited consistent behavior throughout the population, except for the zygomaticus major which has a small but consistently negative effect on the N landmark displacement, probably for the same reasons as the masseter.A further analysis of the results and a comparison of the landmark displacement with the respective H model values showed the effect of introducing muscle stiffness in comparison with assuming a simplified homogeneous behaviour of all the soft tissues .In this study, a modelling framework was provided to produce anatomically accurate face models suitable for performing preoperative planning by means of FEM simulation. The assessment of soft tissue changes was quantified by using facial points which are representative of the areas of the face which are affected by orhognathic surgery, selected in a similar manner to a previous study by our group . Kim et Another limitation for this study is the use of different head positions during MRI (supine) and CBCT  scans, which may affect the relative positions of soft and hard tissues. Although the effect is likely to be minor, as reported in another work by our group where different types of surface scanner outputs were compared, this is yet to be quantified .Furthermore, although the simulation validation was successful, unpredictable postoperative weight loss was noticed which may affect the results. No information on preoperative and postoperative patient weight was recorded, therefore it is difficult to comment how this may have affected change in soft tissue thickness. In this study, BSSO movement was neglected  as thisLimitations of this study are obviously tied to the clinical applicability of the protocol as is: the necessity for a pre-operative MRI limits the applicability to patients who either do not need pre-operative orthodontics or undergo pre-operative orthodontics without fixed appliances.The operator time needed to segment the muscular structures and the time spent to compute each FEM experiment makes it impractical for use in actual pre-operative planning. It is however possible to use the results of these experiments, after an expansion of the study population, to extract an approximation of the muscular structures from CT scans, to possibly integrate in the pre-operative planning in clinical practice, along with the use of Machine Learning models, as already attempted by other researchers –40, to cA further limitation is caused by the biomechanical properties of the cartilaginous structures of the nose, which were not specifically addressed in the FEM experiments.In our experience, relying on this data, it is feasible and recommendable to include different facial anatomical regions (such as the different muscles) in the numerical modelling for the preoperative planning of orthognathic surgery. Particular attention should be paid to the inclusion of the orbicularis oris and masseter muscles, which showed a consistent effect on the landmarks considered and in particular to the upper lip area. Further expansion of the study population could prove useful to refine the results obtained and to move towards clinical applicability.


Due to the lack of samples in the industrial defect dataset and the serious class imbalance, deep learning technology cannot be directly applied to industrial defect visual inspection to meet the real application needs. Transfer learning is a good choice to deal with insufficient samples. However, cross-dataset bias is unavoidable during simple knowledge transfer. We noticed that the appearance defects of industrial products are similar, and most defects can be classified as stains or texture jumps, which provides a research basis for building a universal and adaptive industrial defect detection model. In this article, based on the idea of model-agnostic meta-learning (MAML), we propose an adaptive industrial defect detection model through learning from multiple known industrial defect datasets and then transfer it to the novel anomaly detection tasks. In addition, the Siamese network is used to extract differential features to minimize the influence of defect types on model generalization, and can also highlight defect features and improve model detection performance. At the same time, we add a coordinate attention mechanism to the model, which realizes the feature enhancement of the region of interest in terms of two coordinate dimensions. In the simulation experiments, we construct and publish a visual defect dataset of injection molded bottle cups, termed BC defects, which can complement existing industrial defect visual data benchmarks. Simulation results based on BC defects dataset and other public datasets have demonstrated the effectiveness of the proposed general visual detection model for industrial defects. The dataset and code are available at  Industrial surface defect detection is intuitively important for industrial production, which can not only help enterprises to improve product quality to meet the growing needs of consumers, but also help enterprises to locate problems in a timely manner to reduce production costs . Only reetc.) for defect identification  has achieved extensive development and progress in the field of computer vision and pattern recognition. Using the learned features to replace the handcrafted features, a CNN algorithm can map the pixel space features to high-layer semantic representation based on a series of operations consisting of convolution and pooling. There are already many deep neural network models of deep learning, like AlexNet, VggNet, GoogLeNet, ResNet, etc. , which letc. .Industrial defect datasets present a serious sample non-equilibrium phenomenon. In the production process of industrial products, the occurrence of defects is a small probability event. Therefore, the number of sample classes is unbalanced. This phenomenon can easily lead to model overfitting.Industrial defect detection belongs to the multi-objective and multi-scale detection tasks. There are various types of defects and multi-scale differences between the same types. From the computer vision perspective, such multi-objective and multi-scale detection tasks require higher demand on the performance of the detection model.The occurrence of industrial defects has strong randomness. The type, location, size, and severity of industrial defects cannot be predetermined, presenting great randomness, and the collected samples are difficult to meet the marginal effect of the defect feature data, which seriously affects the performance of the detection model.The tasks of visual detection of defects in industrial products have some unique characteristics that lead us to fully consider these factors when designing CNN based detection models:etc. Additionally, there are some works arguing that it is more reasonable to implement knowledge transfer based on similar industrial defect datasets , is proposed based on MAML training strategy. MeDection model takes the Siamese network as input, followed by the 4Conv  backboneThe structure of this article is as follows. Section 1 presents the related work. Section 2 introduces the defect detection model proposed in this article. Section 3 introduces the BC defects dataset. Section 4 shows the simulation results and Section 5 is the conclusion.The texture feature-based methods: The texture feature-based methods are mainly based on the grayscale distribution of pixels and their spatial neighborhoods for anomaly detection, which can be further classified into three categories: statistical methods, signal processing methods, and model methods. For statistical methods  (der (AE)  and geneder (AE) . The semder (AE) , PADIM (der (AE) .Deep learning applies the end-to-end training strategy to enable the model to learn the defect features automatically, which can enhance the model’s generalization performance. Deep learning based methods for industrial defect detection can be divided into two main categories: supervised learning and semi-supervised or unsupervised learning.Industrial defect detection models pursue application generalization and rapidity. The strong adaptive feature extraction network for industrial defects is the key. To the best of our knowledge, there is currently no research focusing on general-purpose feature extraction models for industrial defects.D feature maps of size e.g., Stochastic Gradient Descent or Adam) can lead CNN network gradually converge and obtain the satisfied learning parameters.With the increase in the amount of data and the improvement of computing power, deep neural networks (DNNs) have shown strong performance in the field of machine learning. CNN is the most typical representative of DNNs, which can allow image-level input and realize local feature extraction of images. Feature extraction based on convolution operation is the most important component in CNN. In a specific layer of convolution operation, the weight-shared convolution kernel traverses the entire image, and serves as a feature extractor to highlight local target features. The training process of CNN model is the process of determining parameters of convolution function, which is composed of the trainable parameters Meta learning is a type of transfer learning that aims to learn a general feature extractor from multiple datasets. The models with CNN structure generally belong to the task-driven training methods, which seek to achieve satisfactory fitting accuracy under specific tasks. In contrast, the input of MAML is multiple task sets, and the training of MAML based models requires the balance of performance on multiple target tasks. MAML is the main training strategy of the proposed MeDetection model in this article, and the trained feature extraction network has a strong ability to extract general features for industrial defects.The MeDetection model as shown in It can be seen from Unlike task-driven pattern recognition models, the proposed MeDetection model is trained on multiple industrial defect visual inspection tasks. The types of industrial defects can be roughly divided into stains and texture defects, and the feature difference extraction module further improves the consistency of defect expression. We hope that the MeDetection model has generalized feature extraction capabilities and can adapt to different types of industrial defect recognition tasks.The MAML-based training strategy is applied in MeDetection model. MAML seeks to achieve a balance of model performance on multiple task sets, that is, MAML does not seek to achieve the optimal model on a specific task, but hopes that the model can achieve comprehensive optimality on multiple task sets. The optimization function of MAML is as follows:N is the number of tasks.MAML consists of two loops: Inner loop and Outer loop. The optimal parameters for each task are calculated iteratively in the inner loop, while the outer loop updates the learning parameters for the entire model by computing the gradients relative to the optimal parameters in each new task. The pseudocode is shown in ng tasks . The leaInner loop: We select K samples Then the updated gradients of model for the task Outer loop: During the outer loop, the model parameters The defect features extracted from the SN module may contain redundant information in the fusion process, which is not conducive to the recognition of defects by the model. CA  construcThe above two transformations enables the model to capture long-distance relationships in one direction while retaining spatial information in the other direction, which can help the network to identify defects more accurately. Then, CA module maps The attention responses of CA module contain inter-channel information, horizontal spatial information and vertical spatial information, which can help the network to obtain the location information of defects more accurately and enhance the ability of feature extraction.The MeDetection model is able to learn common representations of defect features from multiple industrial defect tasks, and then implement knowledge transfer on new tasks. Therefore, we need to first prepare a cluster of industrial defect visual inspection task sets. In the experiments, DAGM  and MVTeIn addition, in the previous work , we publThe experiments run on a computer with NVIDIA DGX A100 SXM4 40G GPU. The experimental framework is pytorch1.9.0 with cuda 11.2. The image size is set to via adversarial learning, which point out the unknown defect data lead to a larger distance metric from the learned (known) data distribution at inference time. One-NN (In order to show the performance of MeDetection model more clearly, some excellent industrial defect detection algorithms are added to the comparative experiment. GANomaly  belongs . One-NN  represen. One-NN  is short. One-NN  is a lon. One-NN  belongs In addition, challenges are attached to the MeDetection model, and only 20 images in each training task participate in model training. The simulation results are shown in the last column of In general, MeDetection has two advantages over other models. MeDetection has the highest accuracy and good detection capability for various industrial defects. MeDetection can use a small number of samples to train the model, which reduces the training cost and has a good detection accuracy at the same time.In order to verify the effectiveness of the MAML training training strategy in the proposed MeDetection model, we make comparison of the recognition accuracy based on random initialization of weights and MAML based weights transfer strategy. For industrial defect recognition tasks, we want to reduce the missed detection rate, that is, to avoid the flow of problematic products to the market. The recall rate can clearly evaluate the missed detection of the model. Overall, the proposed MeDetection model exhibits very advanced recognition performance for industrial defect detection. The MAML based training strategy is the corner stone of MeDetection model, which brings two advantages: MAML can accelerate the convergence speed of the model well; MAML can improve the performance of the model greatly.The Grad-CAM algorithm  can be uThe CA module is embedded into the MeDetection model for further refinement of defect features. However, whether the location and number of CA modules have an impact on model performance is an important issue. Two feature refinement modules help the MeDetection model improve performance. The SN module further strengthens the defect features while unifying the defect representation. The CA module enhances the feature extraction ability of the model from the attention perspective. The ablation experiment is carried out to verify the roles of the two modules, and The MeDetection model proposed in this article focuses on the rapid and adaptive visual recognition for industrial appearance defects. The biggest advantage of the MeDetection model is that the MAML training strategy is applied to the optimization of the CNN based visual detection model, so that the model can achieve satisfactory recognition performance even under the premise of limited industrial defect data sets. Simulation results  show thaAn important research topic to further improve the performance of MeDetection model exists. 10.7717/peerj-cs.1264/supp-1Supplemental Information 1Click here for additional data file.


In a society where the population is aging and chronic diseases are more and more present, social determinants of health occupy a growing place in primary care medicine. As the demand for holistic care is growing, the bio-psycho-social model takes on its full meaning within primary health care.(1) The “social prescribing” model, introduced in many countries in recent years, allows the integration of non-medical care into the primary health care system without overburdening the health professional. (2) Social prescribing enables general practitioners and others primary care professionals to refer people to a range of non clinical services to support their health such as the intervention of physical activity specialists.This study defines social prescribing and discusses successful aspects and challenges of the model in targeted prescription of physical activity. It also evaluates a pilot project set up in Belgium in 2022 to test the model. The pilot project aimed at promoting, through doctors and physiotherapists of the medical center, the orientation of patients to physical literacy counselling meetings leaded by a specialized physical educator. The purpose of this study was then to explore, qualitatively, patients and staff of a medical center about their opinion over a physical literacy counselling program. Interviews of patients and healthcare providers were undertaken during this project.The strengths of the pilot project developed include successful implementation of holistic patient-centered care, strengthening of patients’ mental and physical well-being alongside empowering their health. In addition, the project successfully foster preventive care and demonstrates how primary care may transform into primary health care. However, many challenges remain including the sustainability of the model itself, the availability of the services offered, the financial support and the low commitment of doctors and patients.this study highlights first guidelines to introduce the model into the practice of primary healthcare in Belgium. Although the project shows limitations, the theoretical model remains promising and opens up new potential horizons for the practice of general medicine in Belgium.This study received no external funding.


Macrolide antibiotics are important drugs to combat infections. The pharmacokinetics (PK) of these drugs are essential for the determination of their optimal dose regimens, which affect antimicrobial pharmacodynamics and treatment success. For most drugs, the measurement of their concentrations in plasma/serum is the surrogate for drug concentrations in target tissues for therapy. However, for macrolides, simple reliance on total or free drug concentrations in serum/plasma might be misleading. The macrolide antibiotic concentrations of serum/plasma, interstitial fluid (ISF), and target tissue itself usually yield very different PK results. In fact, the PK of a macrolide antibiotic based on serum/plasma concentrations alone is not an ideal predictor for the in vivo efficacy against respiratory pathogens. Instead, the PK based on drug concentrations at the site of infection or ISF provide much more clinically relevant information than serum/plasma concentrations. This review aims to summarize and compare/discuss the use of drug concentrations of serum/plasma, airway ISF, and tissues for computing the PK of macrolides. A better understanding of the PK of macrolide antibiotics based on airway ISF concentrations will help optimize the antibacterial dose regimen as well as minimizing toxicity and the emergence of drug resistance in clinical practice. Macrolide antibiotics are a family of compounds featured by the existence of a macrocyclic lactone ring of ≥12 members .Pharmacokinetics (PK) describe the chronological movement of drugs within the body, i.e., the time course of the drug concentrations of serum/plasma or tissue fluid. Understanding the PK plays an important role in monitoring the antibiotic exposure in a patient. PK are most frequently evaluated by measuring the drug concentrations of serum/plasma. In addition to PK, the optimal dosing of an antibiotic also relies on the pharmacodynamics (PD) of the drug. The PD of a drug describe the relationship between drug concentration and pharmacological activity. Usually, the measurement of drug exposure is based on serum/plasma concentration–time course data . TraditiThis review aims to address the use of serum/plasma concentrations and pulmonary tissue and ISF concentrations for the PK/PD of macrolide antibiotics.max/MIC, and AUC24/MIC, which rely on the serum/plasma concentration as the PK input value and MIC as the PD input value  ,47 46,47f cattle  48]. Th. Th90 foartments . The equartments ,52. The artments .max and AUC of tilmicosin in foals were higher for PELF than for serum (Similarly, the Cor serum  53]. Th. Thmax aThe summary of the ratio thereof and the selected PK parameters based on the mean lung/PELF and plasma concentrations of several macrolide antimicrobials are shown in Because macrolide antibiotics achieve concentrations in lung tissue and ISF that are many times the plasma/serum concentrations, the plasma-free drug concentrations may never reach the lung ISF level. In addition, the vast majority of bacterial pathogens are found in the ISF of the respiratory tract. These phenomena suggest that the macrolide concentration of the airway ISF is a more appropriate predictor of antibacterial activity than either lung tissue or plasma/serum concentrations. Furthermore, pathophysiological responses to infection, including increased phagocyte trafficking to the ISF and vascular permeability, may facilitate local drug delivery . TherefoHow do pharmaceutical companies determine the optimal dose regimen of macrolide antibiotics? The determination of the optimal dose regimen for antibiotics has a clinical cure as its aim. It is widely acceptable that the optimal dosage for clinical use can only be established in a clinical setting using a dose titration approach . The resSince the ISF of the respiratory tract is the target site for most pathogenic bacteria and both BF and PELF contain much higher macrolide concentrations than plasma/serum, many more studies are warranted to evaluate the relevance of macrolide concentrations in the ISF of the respiratory tract. BF and PELF have been considered the principal target sites of the bacterial infections of the respiratory tract. The methods employed for collecting BF and PELF include the direct collection of BF, the pulmonary tissue homogenization technique, the BAL technique, and the microdialysis technique . The tisAlthough the change in plasma/serum concentration–time and the change in airway ISF–concentration–time of macrolide antibiotics are proportional, macrolide antibiotics in plasma/serum do not reflect the antibacterial activity of the airway ISF. Thus, if the ISF concentrations of a macrolide antibiotic are efficiently and precisely collected using the least invasive method, e.g., microdialysis, practical PK/PD parameters can be obtained for such a study. Nevertheless, the PK data of airway ISF, the site of bacterial infection, are more important in setting the optimal dose regimen of a macrolide than the PK data of plasma/serum.


The largest cell fraction of the human nervous system is composed by glial cells. These non-neuronal cells are responsible for a myriad of functions in the brain including neurogenesis, synaptogenesis, neuronal excitability and synaptic connectivity, which support the maintenance of brain homeostasis. Under pathological circumstances, these cells play a pivotal role in neuroinflammation, a hallmark of several neurological and neurodegenerative disorders such as Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), multiple sclerosis, and Alzheimer's disease (AD). Therefore, unraveling the implication of non-neuronal cells in the development of these diseases and their contribution to neuroinflammation is a priority nowadays in neuroscience research.Over the last decades, research has been focused on understanding the low-grade inflammation occurring during aging in the brain as a key player in various neurological disorders, giving special emphasis on neurodegenerative diseases. It is known that there is a cross-talk between different glial and peripheral immune cells in that complex and dynamic process which has consequences in neuronal function. Despite the huge amount of research investigating this specific topic, it is still unclear how these different cell-types interact with each other and how their responses contribute to the development or progression of a specific neurological disorder.New insights into the role of neuroinflammation and glial cells in the development of neurological disorders” compile four articles, among which three are original research and one is a review, going from AD to ALS always highlighting the central role of glial cells in those disorders.This special issue of Frontiers in Cellular Neuroscience entitled “Allendorf and Brown) pointed out to a specific lysosomal protein, neuraminidase 1 (Neu 1), as important player in the process of microglial reactivity during endotoxin/lipopolysaccharide infection. One of the main findings is the sensibilisation that neurons present to glutamate when Neu 1 is released after microglia reactivity, which could lead to neuronal death, proposing a new mechanism underlying neuroinflammation.The first original research article of this issue , the authors report the importance of sex dimorphism in microglial cells, especially in the development of AD. Using different approaches, ranging from the use of an animal model of AD pathology (APP/PS1 mice), to human brain tissue, they emphasized the distinct morphology of microglial cells between males and females. Microglia were more dystrophic in females, which could potentially explain the higher prevalence of AD. They also point toward iron as one important player in the process of microglial functional lost due to the correlation found between the iron-overload microglial cells in females, their dystrophic appearance, and changes in mitochondrial genes which lead to decreased mitochondrial functionality.In the second manuscript (Sanford and McEwan) focusing on the importance of Type I Interferon in different tauopathies. The authors compiled in this manuscript both protective and pathological responses of this cytokine in neurodegeneration-related tauopathies. The involvement of type I interferon pathways and its role in neuroinflammation have been studied more in AD. They extensively described the implication of Type I Interferon in tauopathies and how aging contributes to its signature. Another interesting section of this review focused on PD prion models.This special issue continues with a mini-review , the authors describe a cell-type specific transcriptome of SOD1 mutant mice related to the development of amyotrophic lateral sclerosis (ALS). Considering astrocytic and microglial reactivity as the main players of the neuroinflammation orchestrated during ALS, the authors gave an extensive report of which genes are involved in the decrease of neurons and the increase of microglial and astrocytic reactivity. By using different enrichment analysis, they unraveled the database generated from the transcriptome of spinal cord tissue concluding that most of the differentially expressed genes in this mouse model are expressed predominantly in microglia. In addition to what has already been described in the literature, this manuscript sheds lights on potential therapeutic targets for SOD1-ALS, based on the combination of information coming from different cell types that are playing together an important role in the development of ALS.In the last original research article of this special issue (In summary, the articles collected in the present special issue emphasize the essential role of glial cells in brain function and dysfunction, underlying the interest of these cells as potential future targets for the treatment of neurodegenerative diseases.VM wrote the manuscript. MP-G, MS, and YW edited the manuscript. All authors contributed to the article and approved the submitted version.


The edge sharpness of a propeller blade plays a vital role in improving energy transmission efficiency and reducing the power required to propel the vehicle. However, producing finely sharpened edges through casting is challenging due to the risk of breakage. Additionally, the blade profile of the wax model can deform during drying, making it difficult to achieve the required edge thickness. To automate the sharpening process, we propose an intelligent system consisting of a six-DoF industrial robot and a laser-vision sensor. The system improves machining accuracy through an iterative grinding compensation strategy that eliminates material residuals based on profile data from the vision sensor. An indigenously designed compliance mechanism is employed to enhance the performance of robotic grinding which is actively controlled by an electronic proportional pressure regulator to adjust the contact force and position between the workpiece and abrasive belt. The system’s reliability and functionality are validated using three different workpiece models of four-blade propellers, achieving accurate and efficient machining within the required thickness tolerances. The proposed system provides a promising solution for finely sharpened propeller blade edges, addressing challenges associated with the earlier robotic-based grinding studies. Industrial robots are widely regarded as a potent asset in the advancement of industrial automation . In receA plethora of related work have been conducted in the field of robotic-based applications aimed at enhancing grinding accuracy through the implementation of diverse methodologies ,17,18,19Although, employing active force control with a six-axis F/T transducer has been identified as an effective approach to achieve the desired outcome in robotic grinding . HoweverOn the other hand, it is hard to guarantee the machining accuracy for those complex workpieces which are easily deformed due to their large scale, low stiffness, and machining characteristics. Therefore, the robotic machining trajectory planned according to the standard CAD model may not be used directly to achieve the machining requirement. To address the issue of workpiece profile inconsistency, there is a feasible way by employing the vision sensor to identify the practical difference between the standard CAD model and the existing object ,36,37. TRecently introduced iterative compensation methods have revolutionized the machining capabilities of five-axis CNC machines, allowing them to excel in producing complex curved workpieces such as aero-engine blades . HoweverThe rest of the paper is organized as follows: In variety of robotic grinding applications, stable and constant force control is a crucial factor that significantly impacts the quality and stability of the machining process and the workpiece. Extensive research has been conducted in the area of constant force control, exploring both active and passive control methods. The majority of active constant force control studies employ a closed-loop PID force-position control methodology and a six-axis force sensor mounted at the end of an industrial robot. However, this method requires processing and handling the force/torque data and transmitting the control signal in an extremely short time to adjust the robot’s motion and compensate for force and position errors. It is challenging to ensure stability in practical applications due to the grinding limitations in various situations.In contrast, passive compliance force control typically focuses only on one-axis force control, aimed at absorbing direct impact and mechanical vibrations between the workpiece and the grinding equipment. In addition, passive compliance mechanisms offer an autonomous error compensation capability by adjusting the contact interface position. This is particularly useful in situations where a workpiece has a profile error that results in the failure of the planned standard robotic trajectory.The impact of unpredictable abrasive belt wear, the complex relationship between grinding depth and contact force , and theP controlled by the electronic regulator. P presents the pressure inside the pneumatic cylinder. The term A refers to the area in contact with the gas. D is the piston diameter of the pneumatic cylinder and In the compliant force control process, the output force Equation .1)Fc=PFc=PFc ism represents the mass of the moving platform which is marked by red dotted lines, as shown in S and the operating time t of one stroke. The needed minimum P is computed according to Equation Q=nSAchieving the required machining accuracy in the grinding process for workpieces that deviate significantly from the standard CAD model using the planned machining trajectory is challenging. In addition, the relationship between the abrasive belt and grinding depth is not predictable, which leads to challenges in sustaining the grinding stability and efficiency in high-production industries.Therefore, the improved grinding compensation strategy with a designed compliance mechanism is proposed to address the aforementioned issues.The robotic grinding compensation strategy can be divided into two stages: (1) vision sensor detection and (2) grinding compensation. In order to conduct the grinding compensation processes for those workpiece sub-areas which have not met the machining requirement, the laser-vision sensor is employed to determine the practical workpiece’s material residual, as shown in ∇R<RTHLD∇R=Tedge−A of the workpiece gradually becomes bigger after each time the grinding process is completed, as shown in In this section, to validate the functionality of the designed compliance mechanism with the proposed grinding compensation strategy, the robotic grinding workcell is established to carry out the experiments for sharpening the propeller blade. The system architecture and communication relationship between multiple devices are shown in The relative setting parameters used in the robotic grinding experiment are shown in Since the maximum velocity the robot can reach when grinding the propeller blade is around 17 mm/s and the minimum trajectory interval is 2.5 mm, the maximum buffer time for processing the data in the transition between the neighbouring trajectory points is computed as 147 ms. To achieve the real-time control of adjusting the pneumatic cylinder output force, the total data processing time through the robot controller to the electronic regulator must be less than 147 ms when every time the grinding compensation is conducted, otherwise the material residual cannot be removed properly. The data processing time communicating among each device is listed in To verify the reliability of the designed compliance mechanism integrated with the proposed iterative grinding compensation strategy, three different four-blade propeller models were used in the experiments, as shown in The average thickness of the medium model at each detection point was estimated at 0.525, 0.423, 0.397, 0.377 and 0.369 mm with the compliance mechanism, as shown in The average thickness and standard deviations were obtained by analysing 40 experimental findings, comprising 160 data determined at each detection point, and all measured outcomes were within the required machining thresholds. Moreover, the experimental results for the small and large models are shown in This paper proposed an improved iterative grinding compensation strategy which is carried out using a self-designed compliance mechanism to enable autonomous sharpening of propeller blades even in the presence of unexpected profile deformations in the workpiece. To demonstrate the practicality of the intelligent robotic grinding system, a series of experimental trials were conducted on various workpiece models. The primary contribution of this study can be summarized as follows:(1) A compliance mechanism is designed to work in conjunction with a vision-sensor-based iterative grinding compensation strategy, aimed at improving the performance of the robotic-based sharpening of the propeller blades. The feasibility and effectiveness of this methodology are validated through experiments, conducted on three distinct workpiece models.(2) The relationship between the robot velocity and multi-device communication time was analysed to ensure the feasibility of the proposed grinding compensation conducted by the designed compliance mechanism.(3) The implementation of the compliance mechanism not only ensures the attainment of the desired machining precision but also results in a significant improvement in the overall machining efficiency. The average number of iterations required to complete the propeller blade-sharpening task is reduced from 3 to 1.8, resulting in an approximate 40% improvement in overall efficiency.(4) The compliance mechanism plays a crucial role in enhancing the stability of the machining process by preventing any structural deformation of the grinding equipment. Moreover, the effects of contact impact and vibrations are suppressed passively during the grinding process, resulting in a more reliable and consistent machining performance.The experimental results demonstrate a significant improvement in the machining efficiency achieved by incorporating the self-designed compliance mechanism with the improved grinding compensation strategy. This approach offers a competitive advantage for high-volume production applications. It is worth mentioning that the implementation of the compliance mechanism and the improved grinding compensation strategy resulted in a noticeable improvement in machining efficiency. However, there was no significant increase observed in the machining accuracy. Therefore, the optimization of machining parameters and the enhancement of grinding equipment specifications are areas of great interest for future research aimed at improving machining accuracy.


Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength. Concrete is a commonly utilized construction material globally, produced by blending cement with sand, gravel, and water. Additional chemical and mineral components can be incorporated into concrete as needed ,2. FurthOne of the new developments in the field of fiber-reinforced concrete is the usage of basalt fiber (BF), which gives excellent results in improving the compressive and flexural strength of concrete composites. Basalt fiber has high tensile strength and ductility, and its resistance to corrosion under alkali, acidic, and salt effects is better than fiber types such as steel, carbon, and aramid, making it a suitable fiber for bridge, shoreline, and road structures ,17. TablPrevious studies have demonstrated the effectiveness of basalt fiber reinforcement in a structural engineering context. Kabay  studied With the evolution of artificial intelligence (AI), machine learning (ML) algorithms are commonly used because ML can provide fast and economical solutions for civil engineering problems ,40,41,422 = 0.99) and a more reliable result than the LR and SVR algorithms. Li et al. [2 = 0.938) outperformed SVM and ANN, especially in FD prediction. Sensitivity analysis to determine the effect of inputs on MDS and FD response variables showed that fiber length and cell pressure have a significant effect on MDS predictions. Najjar et al. [Hasanzadeh et al.  presentei et al.  predictei et al.  used stai et al.  proposedi et al.  proposedi et al.  investigr et al.  developer et al.  used RanIn this study, ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. A dataset of 135 splitting tensile strength experiments has been curated from the literature. Based on this dataset, the splitting tensile strength has been predicted as a function of 12 different independent input features. Furthermore, the impact of each input feature on the model predictions has been quantified using SHAP analysis and individual conditional expectation (ICE) plots. The features have been ranked according to their importance, and the most impactful features have been determined. The introduction of basalt fibers into concrete is a relatively recent development in structural engineering, and there is a lack of reliable techniques for the prediction of basalt fiber-reinforced-concrete strength. The current study aims at developing novel data-driven methodologies for the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. Due to the well-known weakness of concrete under tensile forces, and the increasing application of fiber-reinforced concrete, the availability of sound methodologies for the assessment of basalt fiber-reinforced concrete tensile strength bears great significance. This section presents the statistical properties of the dataset used in training the machine learning models in detail. The original dataset collected from the literature ,73,74,75The stars in the upper right of the correlation values in The range of each feature in the model is shown in The relationship between the fiber content (d Sawant  are givex.The ensemble learning approach combines the predictions of single learning models such as single decision trees to obtain strong learner models. Ensemble learning methods such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest have been successfully applied in the area of engineering systems response prediction. The output of ensemble learning algorithms can be summarized as in Equation (4) where The ensemble learning models aim to minimize the error between the predictions and the actual values which can be represented as in Equation (5), where L is the loss function, n is the total number of samples in the training set, inimized ,79,80.The predictions of the equations from the literature as well as the ensemble learning techniques are presented in the following section. The predictions are compared with the experimental measurements, and the accuracies of different modeling techniques are quantified using coefficient of determination, root mean squared error and mean absolute error. The calculations of these error metrics are given in The predictions of Equations (2) and (3) have been presented in The predictions of the ensemble learning models have been plotted against the actual measurements in The ensemble models are combinations of iteratively generated decision trees. The accuracies of the ensemble learning models are visualized in radar chart form in F is the set of all input features and S is a subset of F which does not contain the feature with index i. According to Equation (6), the impact of a feature is determined by the differences in the model outputs when a feature is included and withheld from the set of input features [The SHAP methodology effectively visualizes the impact of each input feature on the predictions of an ML model. This technique has its origin in cooperative game theory, where the Shapley values are used to measure the contribution of each player to a coalition. The Shapley values features ,82,83.(The SHAP summary plot in The individual conditional expectation plots in Basalt fibers are finding applications in structural engineering in recent years for reinforcing concrete. The splitting tensile strength is one of the decisive parameters that define the strength of concrete. However, the currently available equations for the prediction of this quantity are not sufficiently developed to include the effects of a large number of variables. The current study presents an effective way of predicting the splitting tensile strength of basalt fiber-reinforced concrete with reasonable accuracy based on data obtained from experiments. A total of 135 experimental results were used in training and testing four different ensemble learning algorithms. Among these algorithms, XGBoost delivered the highest accuracy after hyperparameter tuning with an A limitation of the current study is the size of the dataset used in training and testing the ML models. In order to develop predictive models that could be reliably generalized, large datasets are necessary. Moreover, it should be noted that the database used in the study consists of experimental results with small-scale specimens, and size–scale effects are built into the results. Future research in this area may focus on obtaining larger data sets. These larger datasets could also be used for developing reliable closed-form predictive equations for the splitting tensile strength. Furthermore, additional mechanical properties of basalt fiber-reinforced concrete, such as flexural strength, could be investigated.


The Personality Inventory for DSM-5 (PID-5) is a self-report measure of personality pathology designed to measure pathological personality traits outlined in the DSM-5 alternative model of personality disorders. Within the extensive literature exploring the relationship between personality and disordered eating, there are few that explore the relationship between the PID-5 and disordered eating behaviours in a non-clinical sample of males and females: restrictive eating, binge eating, purging, chewing and spitting, excessive exercising and muscle building.An online survey assessed disordered eating, PID-5 traits and general psychopathology and was completed by 394 female and 167 male participants aged 16–30. Simultaneous equations path models were systematically generated for each disordered eating behaviour to identify how the PID-5 scales, body dissatisfaction and age predicted behaviour.The results indicated that each of the six disordered behaviours were associated with a unique pattern of maladaptive personality traits. The statistical models differed between males and females indicating possible differences in how dimensional personality pathology and disordered eating relate.It was concluded that understanding disordered eating behaviour in the context of personality pathology may assist formulating potentially risky behaviour. The ongoing engagement in disordered eating behaviours such as fasting, binge eating and compensatory behaviours has been found to impair almost every human physiological process  in additTo understand the reasons why an individual may engage in risky behaviours such as disordered eating, several studies that have investigated the relationship between disordered eating and personality or personality pathology e.g., . LilenfeThere is a wealth of research focussed on exploring the relationships between personality and EDs and disordered eating. Broadly, research has indicated that eating pathology is associated with traits related to negative affectivity, detachment and conscientiousness . HoweverWhile research using the FFM provides us with valuable insight into the relationship between personality and disordered eating, it is important to note that as a model of normal personality, there are both adaptive and maladaptive variants of each domain and facet . What isWhile there is robust research literature exploring co-occurring personality disorders and eating disorders  the explDespite the extensive research literature on the relationship between diagnosed EDs and personality or personality pathology, the relationship between disordered eating and the AMPD and the associated PID-5 remains more preliminary. Therefore, the overall aim of the present study is to extend on the limited literature to date by exploring predictive models of six disordered eating behaviours among a sample of non-clinical males and females using PID-5 measure of personality pathology. The PID-5 was selected as the measure of personality pathology for use in this study due to its design as a measurement of DSM-5 personality dimensions  and to aStudies exploring gender differences in disordered eating behaviour have suggested that disordered eating has been increasing at a faster rate among males compared to females . AlthougGiven the exploratory nature of the current study, key variables were included in our analyses. Specifically, personality has been found to predict body image dissatisfaction . In addiThere were three broad aims of the current study:•Aim 1: To explore the relationship between the PID-5 and six disordered eating behaviours . Based on limited past research it was expected that restrictive eating behaviour would be associated with increased PID-5 rigid perfectionism, and that binge eating behaviour would be associated with increased PID-5 anhedonia, emotional lability, impulsivity, anxiousness, and depressivity.•Aim 2: To explore the relationship between PID-5 personality dimensions and disordered eating separately for males and females to identify models of personality that are specific for both genders.•Aim 3: To explore how body image dissatisfaction may mediate the relationship between the PID-5 and disordered eating, after controlling for the potential confounding influence of age.M = 22.15, SD = 3.84), including 167 Males , 394 females , and 10 individuals who identified as another gender . The sample was mainly White (70%) and/or had a Year 12  or equivalent education (45%).All procedures were approved by (Monash University’s) institutional Ethics Committee prior to study commencement. The current research was conducted as part of a wider study exploring the relationship between personality and disordered eating among young Australians. Another paper using this data set reporting on the relationship between disordered eating and the FFM also includes a summary flowchart of participant engagement in the study The survey completed by participants measured DSM-5 personality traits and eating behaviour. All measures used in the current study have been previously and appropriately used with participants as young as 16 years old .The PID-5 Short-Form  has been developed as a 12-item version of the full scale measuring general eating disorder psychopathology . ParticiThe Depression Anxiety Stress Scale-21 item version (DASS-21) was used to measure negative mood states. The DASS-21 is a shortened version of the original 42-item DASS. The Depression subscale is designed to assess low positive affect . The Anxiety subscale measures physical hyperarousal  and the Stress subscale measures tension or irritability  to account for potential suppressor effects of other scales within the same domain for steps two and three. In order to allow for continuity of analysis across genders, the sexual orientation was excluded as a control variable due to the large difference in proportions between females and males who did not identify as heterosexual. A sensitivity analysis was performed, where models including sexual orientation as a control were compared to those without, confirmed this decision as very few differences were found between the models.The statistical procedure used in the current study has been outlined in post hoc power calculations were conducted for the models based on RMSEA using the R statistical software . Chi-square was reported to assess the absolute fit of each model. In addition, the fit of each model was evaluated using the Root Mean Squared Error of Approximation (RMSEA), the comparative fit index (CFI), and the Tucker Lewis Index (TLI). RMSEA values that approximated 0.06 or lower, and CFI and TLI values that approximated 0.95 or higher, indicated better fitting models . Statistsoftware .The indirect path analysis was calculated using 1,000 bootstrapped samples, and bias-corrected 95% confidence intervals. The direct, indirect and total effects have been reported in this paper, along with the bootstrapped 95% confidence intervals. All statistical calculations were implemented using SPSS version 27  and AMOSt-tests between males and females. The Independent Samples t-tests reported in this study have also been reported elsewhere  and between the PID-5 Anhedonia  or Depressivity  subscales and the DASS-21 Depression scale. The strong correlation coefficients are indicative of multicollinearity which would cause instability in the regression coefficients. Therefore, the EDE-QS and DASS-21 scores were not included in subsequent analyses.The correlation matrix see  indicateAs demonstrated in The results for the six path models predicting disordered eating in males specifically have been displayed as The results for the six path models predicting disordered eating in females have been displayed as We explored three aims to further understand the relationship between disordered eating and the PID-5 for males and females separately, and the influence of body dissatisfaction. The results demonstrate that among young people in Australia, both males and females engage in a range of disordered eating behaviours. When considering the aims of the study, there were differences in statistical models between males and females for each studied disordered eating behaviour. Body image dissatisfaction was found to play a unique role in each model. Our study provides a unique contribution and extension to the previous literature by exploring the relationship between the AMPD traits and a number of disordered eating behaviours that have not been previously explored together. Our findings highlight the importance of understanding disordered eating behaviours in the context of dimensional personality pathology. The key findings of the current study in the context of the first aim of this study are explored below.The results of the study revealed different predictive models for each disordered eating behaviour under investigation. Body dissatisfaction was associated with binge eating, purging and muscle building among males. One key finding was that a tendency to act without thinking was associated with binge eating, which is consistent with the current literature which has linked impulsivity to binge eating both using the PID-5  and moreThe spectrum model as outlined by Second, a tendency to act in a manner that increases personal risk was associated with purging, excessive exercise and muscle building, although the relationship was mediated by body dissatisfaction within the purging and muscle building models. Taken together, the current results may indicate a willingness to disregard the potential risk of disordered eating behaviours  in favouThirdly, in parallel with Finally, as an important extension on the current research literature , our modNone of the personality variables or body dissatisfaction included in the final models were found to be associated with restrictive eating or chewing and spitting among males. Previous research has also not found any relationships between restrictive eating and the PID-5  or body Consistent with the statistical models generated for males in this study in addition to past research , there wThe hypothesis that there would be a relationship between PID-5 Perfectionism and restrictive eating was supported in the current study, along with past research suggesting the same . In the Interpersonal difficulties have previously been linked with disordered eating behaviour among females . If one The current study provided a quantitative example of Body dissatisfaction was found to mediate relationships that have previously been identified in past research, more specifically between a lack of pleasure and restrictive eating ,b and anIn interpreting the results of the current study, there are some limitations that should be considered. As there is variability in ED presentations between ethnic backgrounds  and betwBroadly, the results of our research add to the growing body of research that has suggested that personality traits are related to eating behaviours . TherefoThe results of the present study have identified several areas for further exploration. First, a number of differences were identified between the results reported in this paper, and the results reported in our previous research  which exIn general, as there are very few studies exploring the relationships between the PID-5 and disordered eating and EDs, further research is required to understand the relationships in broader samples. However, some tentative clinical implications can be drawn. Consistent with past research that has identified a relationship between EDs and Borderline Personality Disorder, traits indicative of Borderline Personality Disorder were found to predict binge eating in males (Impulsivity) and restrictive eating in females (Risk taking; The overall aim of our research was to explore the relationship between the PID-5 pathological trait dimensions and six disordered eating behaviours among adolescent and young adult males and females. The findings indicate a series of unique personality profiles that differed dependent on gender and disordered eating behaviour. The current research provides an important extension to existing literature that has indicated disordered eating behaviours may serve as unique expressions of pathological personality traits. Furthermore, the present study has provided a basis for the understanding of less-studied disordered eating behaviours such as chewing and spitting, excessive exercise and muscle building among a community-based sample of males and females. In general, the results of the current study support assertions of the need to understand disordered eating behaviour in the context of personality pathology to enhance the formulation of potentially risky behaviour, and open several avenues for future research.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Monash University Human Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.TG, CG, and GS contributed to the conception and design of the study. TG performed statistical analyses and wrote the first draft of the manuscript. JD assisted with designing a statistical plan of the study. All authors contributed to manuscript revision, read, and approved the submitted version.


Neuroimaging studies conducted in the last three decades have distinguished two frontoparietal networks responsible for the control of visuospatial attention. The present review summarizes recent findings on the neurophysiological mechanisms implemented in both networks and describes the evolution from a model centered on the distinction between top-down and bottom-up attention to a model that emphasizes the dynamic interplay between the two networks based on attentional demands. The role of the dorsal attention network (DAN) in attentional orienting, by boosting behavioral performance, has been investigated with multiple experimental approaches. This research effort allowed us to trace a distinction between DAN regions involved in shifting vs. maintenance of attention, gather evidence for the modulatory influence exerted by the DAN over sensory cortices, and identify the electrophysiological correlates of the orienting function. Simultaneously, other studies have contributed to reframing our understanding of the functions of the ventral attention network (VAN) and its relevance for behavior. The VAN is not simply involved in bottom-up attentional capture but interacts with the DAN during reorienting to behaviorally relevant targets, exhibiting a general resetting function. Further studies have confirmed the selective rightward asymmetry of the VAN, proposed a functional dissociation along the anteroposterior axis, and suggested hypotheses about its emergence during the evolution of the primate brain. Finally, novel models of network interactions explain the expression of complex attentional functions and the emergence and restorations of symptoms characterizing unilateral spatial neglect. These latter studies emphasize the importance of considering patterns of network interactions for understanding the consequences of brain lesions. Neuroimaging research in the last quarter century has gathered evidence for a dual-network architecture involved in these two types of attentional control , which includes bilateral regions of the superior parietal lobule (SPL), the intraparietal sulcus (IPS), and the frontal eye field (FEF), and a ventral attention network (VAN), which includes regions of the temporoparietal junction (TPJ) and the ventral frontal cortex (VFC)  . The twoex (VFC)   . The samex (VFC)  or magneex (VFC) .The scope of the present mini-review is to summarize recent findings on the neurophysiological mechanisms implemented in both networks in normal and pathological conditions. It is now clear that the DAN/VAN distinction does not easily map onto a simple top-down/bottom-up, as initially hypothesized. The DAN exhibits sustained activity when attention is voluntarily oriented to a stimulus’ location or feature based on either endogenous or exogenous cues, consistent with a general role in the control of visuospatial attention. Instead, the VAN is deactivated during the sustained orienting of attention but transiently recruited, along with the DAN, during the reorienting of attention and the detection of a target, especially if salient or unexpected . Several2.attentional set and the appropriate selection of task-relevant stimuli and responses  coherent fluctuations of band-limited power (BLP) recorded with MEG across multiple frequencies . Focusin3.deactivated during the sustained orienting of voluntary attention to a stimulus’ location or feature  of the University of Chieti-Pescara.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Correction:Contracept Reprod Med3, 9 (2018)10.1186/s40834-018-0064-yFollowing publication of the original article , it was The correct Abstract should read:Along with increasing availability and utilization of contraception, it is also important to confirm the effects of contraception use on resumption of fertility after discontinuation. However, current evidence on resumption of fertility after contraception use is inconclusive and practical fertility after termination of contraception remains a big concern for women who are using contraception. This fear poses a negative impact on the utilization and continuation of contraception. Therefore, estimating the rate of pregnancy resumption after contraceptive use from the available reports and identifying the associating factors are important for designing a strategy to overcome the problem.The original article  has been


Light Detection and Ranging (LiDAR), a laser-based technology for environmental perception, finds extensive applications in intelligent transportation. Deployed on roadsides, it provides real-time global traffic data, supporting road safety and research. To overcome accuracy issues arising from sensor misalignment and to facilitate multi-sensor fusion, this paper proposes an adaptive calibration method. The method defines an ideal coordinate system with the road’s forward direction as the X-axis and the intersection line between the vertical plane of the X-axis and the road surface plane as the Y-axis. This method utilizes the Kalman filter (KF) for trajectory smoothing and employs the random sample consensus (RANSAC) algorithm for ground fitting, obtaining the projection of the ideal coordinate system within the LiDAR system coordinate system. By comparing the two coordinate systems and calculating Euler angles, the point cloud is angle-calibrated using rotation matrices. Based on measured data from roadside LiDAR, this paper validates the calibration method. The experimental results demonstrate that the proposed method achieves high precision, with calculated Euler angle errors consistently below 1.7%. The comprehensive perception and real-time monitoring of traffic information throughout the entire highway network are of great significance for optimizing traffic management strategies and alleviating traffic congestion . To datePositioning deviation: Angle deviations in LiDAR detection can lead to spatial deviations in vehicle positions, resulting in a decrease in the positioning accuracy of vehicle trajectories.Trajectory drift: Angle deviations can cause an amplification of the offset error between the LiDAR-perceived vehicle trajectory and the actual trajectory, leading to the presence of trajectory drift.Trajectory shape changes: Due to the presence of angle deviations, the perceived shape of the vehicle trajectory may undergo changes, potentially causing errors in determining the vehicle’s driving state and impacting subsequent traffic information processing and analysis.Negative impact on multi-sensor fusion perception: Angle deviations in LiDAR can result in inconsistencies between the perceived data and the data obtained from other sensors such as cameras and radars. Such inconsistencies make it challenging to accurately integrate and process data in a multi-sensor fusion perception system, affecting the comprehensive understanding and holistic analysis of traffic situations.The commonly used roadside device perception technologies mainly include sensing technologies composed of devices such as Light Detection and Ranging (LiDAR) ,4, MilliThe angle calibration of LiDAR point clouds refers to adjusting the collected point cloud data’s posture or rotation to align or align them with a specific reference coordinate system. To date, a significant amount of research has been conducted by domestic and international experts and scholars on angle calibration methods for LiDAR point clouds. These methods can be roughly classified into two categories: those with fixed reference objects and those with non-fixed reference objects. Methods with fixed reference objects primarily involve identifying known reference objects’ shape, position, and other information to calculate the LiDAR’s angular deviation, achieving the purpose of calibration. For example, Y. Park  proposedIn the field of intelligent transportation, point clouds registration ,21 is a XY plane is essentially parallel to the road surface. This method primarily utilizes the Kalman filter (KF) to improve the smoothness of the trajectory. Afterward, based on a certain number of vehicle trajectory points and the fitted road surface plane, it calculates the projection of the ideal coordinate system XYZ onto the LiDAR system’s coordinate system xyz. In this projection, the road heading direction is taken as the X-axis direction, and the direction of the intersection line perpendicular to the X-axis plane and the road surface plane is taken as the Y-axis direction. Finally, the Euler angles are computed to achieve point cloud calibration using rotation matrices.Based on the above, this paper proposes a method for calibrating the angle deviation of roadside LiDAR point clouds. Unlike traditional calibration methods, this method does not rely on fixed reference objects but instead utilizes traffic elements (road heading direction and ground surface) as reference objects. By establishing an ideal coordinate system, the road heading direction is defined as the X-axis direction, and the direction of the intersection line perpendicular to the X-axis plane and the road surface plane is chosen as the Y-axis direction, thus constructing a reference framework adapted to the road. In this ideal coordinate system, the Adaptive calibration method: This paper proposes a simple and efficient adaptive calibration method to address the accuracy issues of LiDAR sensors in roadside intelligent perception systems. By leveraging the Kalman filter (KF) and the random sample consensus (RANSAC) algorithm, this method ensures real-time calibration without the need for manual intervention.Ideal coordinate system definition: The method defines an ideal coordinate system based on the road’s forward direction as the X-axis and the intersection line between the vertical plane of the X-axis and the road surface plane as the Y-axis. This ideal coordinate system serves as a reference for calibration, enhancing the precision and stability of the calibration process. Additionally, it provides a universally applicable reference for further multi-sensor fusion perception.The primary contributions of this article can be summarized as follows:As mentioned earlier, research on methods for obtaining vehicle trajectories using LiDAR is already quite mature. Therefore, this paper does not provide a further description of vehicle trajectory perception methods using LiDAR. Instead, it primarily focuses on validating the feasibility of the calibration method based on the analysis of vehicle trajectory data derived from measured roadside point cloud data. The results demonstrate that the calculated Euler angles have an error rate consistently below 1.7%, indicating extremely high precision.The rest of the paper Is organized as follows: The paper presents a calibration method for addressing angle deviations in roadside LiDAR. Its application scenario is depicted in The vehicle’s driving direction, based on LiDAR perception, is a crucial reference for calculating the projection of the road’s forward direction onto the coordinate system of the LiDAR system. This paper presents a statistical analysis of the angles between the perceived vehicle driving directions and the road heading directions based on four sets of measured LiDAR data. The LiDAR used in the experiments has been calibrated for angles using fixed reference markers, aligning the X-axis of the system’s coordinate system parallel to the road heading direction. The perception range was set as a 12 m segment, with 6 m in front and 6 m behind the LiDAR, as depicted in The experimental results indicate that the expected value of the angle between the vehicle’s driving direction and the road heading direction is close to zero. Therefore, theoretically, it is possible to estimate the road heading direction based on a certain amount of vehicle trajectory data.Adaptability: The KF demonstrates outstanding adaptability to trajectory smoothing problems in linear systems when the data satisfy the conditions of linearity and Gaussian distribution. Under such conditions, the KF provides accurate and stable estimation results.Low computational cost: The KF exhibits relatively low computational complexity. By processing data using a recursive approach, it does not require a substantial amount of computational resources, making it suitable for real-time applications or scenarios with limited computational capacity.Optimality: Under the assumptions of linearity and Gaussian distribution, the KF represents the optimal solution for minimum mean square error estimation. This implies that under ideal conditions, it can achieve the best trajectory smoothing effect and deliver the most accurate estimation results.Low storage requirement: The KF only necessitates storing the current state and covariance information, without the need to retain historical data. Therefore, it places relatively low demands on storage resources, making it advantageous for applications in resource-constrained environments, such as embedded systems.The Kalman filter (KF) is an efficient recursive filter, also known as an autoregressive filter, which is capable of estimating the state of a dynamic system from a series of incomplete and noisy measurements . Due to In the KF, the state transition and measurement models are expressed as:If the observed value The filtering process can be represented as follows:Taking the driving trajectory of a vehicle with lane-changing behavior from the highD dataset  as an exBased on statistical analysis of vehicle trajectory and the RANSAC algorithm, this paper determines the projection of the ideal coordinate system in the coordinate system of the LiDAR system. First, by performing a statistical analysis of the vehicle trajectory data, it is possible to fit the X-axis direction of the ideal coordinate system. Next, the RANSAC algorithm is utilized to fit the ground and find the intersection between the plane perpendicular to the X-axis and the ground, which serves as the Y-axis direction of the ideal coordinate system. Finally, the cross-product operation between the X-axis and Y-axis is performed to determine the Z-axis direction of the ideal coordinate system. The design of this process ensures that the three coordinate axes of the ideal coordinate system are mutually orthogonal.Preprocessing and data preparation: The original data are preprocessed to filter the vehicle trajectory data within the region of interest.Calculating the direction of travel vector for each vehicle: Calculate the motion direction vector for each vehicle and record it.Statistical analysis of travel direction vectors: Perform statistical analysis on the computed vehicle motion direction vectors to determine the direction of road advancement in the coordinate system.Evaluation and validation: Validate the analysis results to determine if there is a significant deviation from the ideal road advancement direction.This paper utilizes the accumulated direction vectors of vehicle trajectory to calculate the direction of the X-axis of the ideal coordinate system in the coordinate system of the LiDAR system. The algorithm flow for fitting the X-axis can be summarized into the following four steps:Equation (8) represents the formula for vector summation.Robustness: RANSAC is capable of handling data sets containing outliers and noise, as it can ignore these abnormal values and produce better fitting results.No dependency on initial values: RANSAC does not require initial values or prior information. It obtains an initial fitting model through random sampling, eliminating the need for preliminary estimations.Simplicity and efficiency: The basic idea of the RANSAC algorithm is straightforward, making it easy to understand and implement. By randomly selecting samples and iterating to find the best model, it exhibits high computational efficiency in most cases.Adjustable parameters: RANSAC allows setting parameters to control the number of samples and the fitting threshold, enabling fine-tuning according to specific problem requirements and achieving improved fitting results.This paper considers the direction of the intersection between the X-axis vertical plane and the ground plane as the Y-axis direction. The main approach is based on implementing the RANSAC algorithm for fitting the ground plane. Random sample consensus (RANSAC)  is an itThe algorithm flow for fitting a plane using the RANSAC algorithm is as follows:Set the maximum number of iterations, Set the distance threshold, Initialize the parameters for the best-fitting plane: Iterate a.Randomly select the smallest set of points from the input data.b.Use the selected points to estimate plane parameters .c.By calculating the distance between each point and the estimated plane, identify the inliers.d.Calculate the number of inliers with distances below e.If the number of inliers is greater than Re-fit the plane using all inliers from step 4(e) to obtain the final estimated plane parameters.The final result, A, represents the plane obtained via fitting using the RANSAC algorithm.Due to the possibility that Determine the normal vectors of the two planes: The normal vector of Calculate the direction vector of the intersection, Normalize the direction vector of the intersection: The magnitude The normalized The direction of the Z-axis, This paper employs rotation matrices to achieve angle calibration of LiDAR point clouds. A mathematical relationship exists between Euler angles and rotation matrices, where Euler angles are a method of describing the rotational orientation of objects in three-dimensional space using three consecutive angles of rotation. The rotation matrix  is a linxyz represents the reference axes of the reference frame, and XYZ represents the object’s own coordinate axes. The intersection line of the xy plane and the XY plane is denoted by N. For a zxz intrinsic Euler angle convention, the angles can be statically defined as follows: N, N and the x-axis. The reference frame composed of xyz (fixed frame) is also known as the laboratory reference frame, which remains stationary. On the other hand, the coordinate system composed of XYZ (body-fixed frame) is fixed to the object and rotates with the object during its rotation.Euler angles are used to describe the rotational orientation of an object in three-dimensional Euclidean space . They coRegarding the order and labeling of angles, as well as the specification of the two axes of the angle, there are no conventions. Therefore, when using Euler angles, it is essential to explicitly define the rotation angles and rotation axes. In practical applications, different proper Euler angles can be used to describe the rotation of an object.This paper utilizes rotation matrices to achieve the angle calibration of point cloud data. The specified rotation matrix is composed of three elementary rotation matrices, as shown in Equation (12):In the equation, from right to left, it represents rotations around the Z-axis  system with 64 channels and the capability of performing three-dimensional high-speed scanning. The LiDAR’s scanning frequency is 10 Hz, with a horizontal resolution of zxz Euler angles: The roadside perception system in the experiment involved mounting an uncalibrated LiDAR system at a height of 3 m on the side of a highway. The system continuously collected data for approximately 15 min, capturing trajectory information for nearly 1200 vehicles. Subsequently, the researchers used fixed reference markers for measurements to calculate the offset angle of the LiDAR system. This allowed them to determine the transformation relationship between the LiDAR system coordinate system and the ideal coordinate system, expressed as This paper validates the proposed calibration method for the LiDAR system. Based on the collected vehicle trajectory data, vehicle trajectories within a 10-m range before and after the LiDAR deployment locations are selected. The trajectory data is categorized into four experimental groups based on the vehicle IDs, with lower ID numbers being assigned to vehicles that entered the recording area earlier. These experimental groups are specifically labeled as Group A, Group B, Group C, and Group D in accordance with academic conventions. The partial calculation results for The experimental results indicate that there is almost no error between the calculated values of In typical scenarios, when deploying a LiDAR within a roadside environment, factors such as the minor vibrations induced during the LiDAR’s operation, suboptimal mounting angles, and vibrations caused by high-speed vehicle passages can collectively result in deviations between the LiDAR’s scanning angles and the ideal scenario. Such deviations in scanning angles may potentially undermine the efficacy of the roadside LiDAR perception system, subsequently increasing the intricacy of post-data processing and analysis. Therefore, the investigation of methods for LiDAR angle deviation calibration holds utmost significance.Cost and resource savings: In remote urban areas, embedding the program into the perception system is sufficient to achieve adaptive calibration. Furthermore, it can automatically calibrate the angle deviation of LiDAR periodically, ensuring the perception system maintains high-precision sensing capabilities.Applicability to multi-LiDAR systems: In scenarios with a linear layout of the perception area and the presence of multiple LiDAR sensors, relying on known LiDAR deployment positions greatly simplifies the challenges of fusing data from multiple sources, thereby enhancing the accuracy of fused perception results.This paper presents a calibration method suitable for roadside LiDAR systems, the essence of which lies in establishing an ideal coordinate system and leveraging traffic elements (including the ground and vehicle trajectories) as reference benchmarks. Calibration is achieved through precise processing of LiDAR point cloud data. The feasibility of this method is validated through a comprehensive experimental design. The experimental results unequivocally demonstrate that this method has achieved remarkable effectiveness in addressing the angle calibration challenges of roadside LiDAR systems. In comparison to conventional calibration methods based on a fixed reference board ,14,15, tHowever, in reality, the method of using fixed reference boards still holds a prominent position in terms of accuracy. Researchers’ analyses reveal that the crucial factor affecting the calibration accuracy of the method lies in the vehicle trajectory detection algorithm. Future research efforts will be concentrated on optimizing this aspect. The method proposed in  suggestsSimplified parameter configuration: As an adaptive calibration method, it requires only the specification of the vehicle trajectory data for fitting, eliminating the need for intricate parameter tuning.Cost-effectiveness: It significantly saves human and material resources.High safety: It enhances operational safety by eliminating the need for manual intervention by operators at the roadside.Non-interference with normal operations: Embedded within the perception system’s program, it does not disrupt the normal sensing functionality of the LiDAR system, accurately capturing vehicle information and trajectory data.Adaptation to multi-LiDAR fusion perception: For continuous placement of multiple LiDAR sensors along long straight road sections, this calibration method simplifies the complexity of multi-source data fusion perception by requiring only the determination of LiDAR deployment positions.Enhancing trajectory data accuracy: By addressing the issue of angular deviation, this approach contributes to the improved perception accuracy of vehicle trajectory data. Consequently, it establishes a more precise foundational dataset for subsequent applications.In summary, addressing the issue of LiDAR angle deviations in roadside LiDAR perception systems, the proposed method in this paper offers a sound solution to this problem, while also streamlining the challenges of multi- LiDAR data fusion and registration. The primary advantages of this method can be summarized as follows:Initial instability: During the initial operational phase of the system, when vehicles exhibit behaviors like lane changes within the region of interest, the limited quantity of fitted vehicle trajectory data might lead to reduced perception accuracy. However, as the number of fitted vehicle trajectories increases, accuracy gradually improves.Real-time impact: In the early stages of system operation, performing the calibration algorithm for almost every perceived vehicle trajectory data point might affect the real-time nature of the perception system, resulting in increased algorithm execution times.Applicability to complex road segments: This calibration method performs optimally on straight road segments; however, it might be challenging to find suitable road segments for calibration in complex road scenarios.The limitations of this method can be summarized as follows:Future research will extensively explore the application of the calibration algorithm to handle complex road segments with varying curvatures. Simultaneously, efforts will be directed toward enhancing algorithm efficiency and robustness.This paper introduces an innovative LiDAR angle calibration method that eliminates the need for a calibration board, effectively tackling the problem of point cloud angle deviation caused by the suboptimal installation poses of roadside LiDAR systems. The proposed method relies solely on perception targets (vehicle trajectories and the ground) for calibration. This paper validates the feasibility of the proposed calibration method based on real-world roadside data. The experimental results demonstrate that when the number of fitted vehicle trajectories exceeds 50, the calculated Euler angles remain relatively stable, with an error rate of less than 1.7%. These experimental results fully demonstrate that the proposed calibration method in this paper possesses extremely high accuracy and reliability.During the initialization phase of roadside perception systems, this method can be utilized to achieve adaptive calibration of point cloud angles. Notably, this approach is robust to the influence of road slopes. By adopting this calibration method, the perception performance can be significantly enhanced, providing more standardized inputs for subsequent data processing and information fusion. With a forward-looking perspective, this technique shows tremendous potential in enhancing the precision and dependability of roadside LiDAR systems, thereby fostering improved safety and efficiency across diverse applications, including autonomous vehicles, traffic monitoring, and environmental sensing. Further research and development in this direction could lead to even more sophisticated calibration techniques, ultimately optimizing the overall performance and applicability of roadside LiDAR perception systems.


Brain-computer interfaces (BCI) can build a novel pathway from the brain to external devices. Advanced developments of BCIs have enabled direct brain control of devices such as computer cursors or robotic arms and constructed neuroprosthesis that converts brain signals into speech and handwriting, which demonstrate great potential in neural rehabilitation (Pan et al., Su et al. showed that a 3D convolutional neural network model with multiscale convolutional kernels could effectively improve emotion recognition performance with EEG signals compared with traditional approaches. The study Dai et al. proposed a novel feature selection method for high-dimensional biomedical data to improve both efficiency and accuracy in biomedical signal processing.The intersection of artificial intelligence and the brain is promising to bring new developments in BCI systems. Machine learning technologies, especially deep neural networks, have demonstrated effectiveness in brain signal processing and analysis, and improved the performance of BCI systems in both accuracy and stability. The study Liu et al. demonstrated that the proposed transfer learning-based approach could maintain stable performance within-subject cross-day even with zero training, which could improve the BCI performance and reduce the calibration burden, facilitating practical applications of BCI systems.Artificial intelligence technology also shows the ability to help BCIs break through the current bottleneck problems. One critical challenge in practical BCI systems lies in that users have to recalibrate the BCI with repetitive training trials every day, which can be tedious and harms the user experience. The study Lu et al. built a video target detection system with the brain-in-the-loop, by detecting video-induced P3 signals when the subjects inspect the video. With the strength of a graph convolutional network, high recognition performance was achieved. The study Du et al. constructed a BCI-based hand rehabilitation system with tactile-enhanced multi-sensory feedback and demonstrated that the vibrotactile enhancement had a reinforcing effect on sensorimotor brain activities and could promote the clinical application of BCIs in the rehabilitation of stroke patients.The convergence of artificial intelligence and the brain also emerges novel forms of BCI systems. The study Singanamalla et al. proposed to learn effective EEG representations with a spiking neural network, and the latent spike representations reflected EEG features and improved the BCI performance with significance. The perspective can be inspiring for the field of neural signal processing.Another novel and inspiring perspective toward the intersection of artificial intelligence and the brain is to use brain-like computing models to analyze brains. Brain-like computing models such as spiking neural networks have great potential in the effective representation and computation of brain signals with the natural biological plausible property. The study Therefore, bringing together artificial intelligence and brain intelligence is promising to emerge novel technologies in BCIs from various perspectives. Artificial intelligence technologies can enable high-performance BCIs with accurate and stable control, and facilitate practical applications of BCIs and Neuroprosthesis.YQ wrote the editorial for the topic. All authors contributed to the article and approved the submitted version.