Search Results (9)

Endocytosis in filamentous fungi is spatially restricted to a subapical zone known as the endocytic collar, which plays essential roles in membrane recycling and the maintenance of polarized growth. In this study, we investigated the ontogeny of the endocytic collar in Neurospora crassa by tracking fimbrin-labeled endocytic patches using confocal microscopy during conidial germination, hyphal branching, and regeneration following mechanical injury. We consistently observed an initial accumulation of endocytic patches at the hyphal tip, forming an apical cap, which later reorganized into a subapical collar. This transition was correlated with a significant increase in elongation rate and the appearance of a Spitzenkörper, indicating a link between exocytosis and collar positioning. Although this correlation is robust, our data do not establish causality; rather, collar formation appears to occur after surpassing a critical elongation. Our findings suggest that exocytosis displaces endocytosis from the apex, resulting in the formation of the collar, which is not required for the establishment of polarized growth but is essential for its maintenance. These results support the development of a unified model of collar formation in filamentous fungi and provide new insight into the spatial coordination between endocytic and exocytic processes during hyphal development. Full article

The effective suppression of inflammation using disease-modifying therapies is essential in the treatment of multiple sclerosis (MS). Anti-CD20 monoclonal antibodies are commonly used long-term as maintenance therapies, largely due to the lack of reliable biomarkers to guide dosing and evaluate treatment response. However, prolonged use increases the risk of infections and other immune-mediated side effects. The unique ability of brain-derived blood extracellular vesicles (EVs) to cross the blood–brain barrier and reflect the central nervous system (CNS) immune status has sparked interest in their potential as biomarkers. This study aimed to assess whether blood-derived L1CAM⁺ EVs could serve as biomarkers of treatment response to rituximab (RTX) in patients with relapsing-remitting MS (RRMS). Serum samples (n = 25) from the baseline (month 0) and after 6 months were analyzed from the RTX arm of the ongoing randomized clinical trial OVERLORD-MS (comparing anti-CD20 therapies in RRMS patients) and were compared with serum samples from healthy controls (n = 15). Baseline cerebrospinal fluid (CSF) samples from the same study cohort were also included. EVs from both serum and CSF samples were characterized, considering morphology, size, and concentration, using transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). The immunophenotyping of EV surface receptors was performed using flow cytometry with the MACSPlex exosome kit, while label-free quantitative proteomics of EV protein cargo was conducted using a proximity extension assay (PEA). TEM confirmed the presence of EVs with the expected round morphology with a diameter of 50–150 nm. NTA showed significantly higher concentrations of L1CAM⁺ EVs (p < 0.0001) in serum total EVs and EBNA1⁺ EVs (p < 0.01) in serum L1CAM⁺ EVs at baseline (untreated) compared to in healthy controls. After six months of RTX therapy, there was a significant reduction in L1CAM⁺ EV concentration (p < 0.0001) and the downregulation of TNFRSF13B (p = 0.0004; FC = −0.49) in serum total EVs. Additionally, non-significant changes were observed in CD79B and CCL2 levels in serum L1CAM⁺ EVs at baseline compared to in controls and after six months of RTX therapy. In conclusion, L1CAM⁺ EVs in serum showed distinct immunological profiles before and after rituximab treatment, underscoring their potential as dynamic biomarkers for individualized anti-CD20 therapy in MS. Full article

Hematological analysis is crucial for diagnosing and monitoring blood-related disorders. Nevertheless, conventional hematology analyzers remain confined to laboratory settings due to their high cost, substantial space requirements, and maintenance needs. Herein, we present a portable cell tracking velocimetry (CTV) device for the precise measurement of the magnetic susceptibility of biological entities at the single-cell level, focusing on red blood cells (RBCs) in this work. The system integrates a microfluidic channel positioned between permanent magnets that generate a well-defined magnetic field gradient (191.82 TA/mm²). When the cells are injected into the chamber, their particular response to the magnetic field is recorded and used to estimate their properties and quantify their intracellular hemoglobin (Hb) concentration. We successfully track over 400 RBCs per condition using imaging and trajectory analysis, enabling detailed characterizations of their physical and magnetic properties. A comparison of the mean corpuscular hemoglobin measurements revealed a strong correlation between our CTV system and standard ultraviolet–visible (UV-Vis) spectrophotometry (23.1 ± 5.8 pg vs. 22.4 ± 3.9 pg, p > 0.05), validating the accuracy of our measurements. The system’s single-cell resolution reveals population distributions unobtainable through conventional bulk analysis methods. Thus, this portable CTV technology provides a rapid, label-free approach for magnetic cell characterization, offering new possibilities for point-of-care hematological analysis and field-based research applications. Full article

Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis. Full article

Visualization of the interaction of drugs with biological cells creates new approaches to improving the bioavailability, selectivity, and effectiveness of drugs. The use of CLSM and FTIR spectroscopy to study the interactions of antibacterial drugs with latent bacterial cells localized in macrophages create prospects to solve the problems of multidrug resistance (MDR) and severe cases. Here, the mechanism of rifampicin penetration into E. coli bacterial cells was studied by tracking the changes in the characteristic peaks of cell wall components and intracellular proteins. However, the effectiveness of the drug is determined not only by penetration, but also by efflux of the drugs molecules from the bacterial cells. Here, the efflux effect was studied and visualized using FTIR spectroscopy, as well as CLSM imaging. We have shown that because of efflux inhibition, eugenol acting as an adjuvant for rifampicin showed a significant (more than three times) increase in the antibiotic penetration and the maintenance of its intracellular concentration in E. coli (up to 72 h in a concentration of more than 2 μg/mL). In addition, optical methods have been applied to study the systems containing bacteria localized inside of macrophages (model of the latent form), where the availability of bacteria for antibiotics is reduced. Polyethylenimine grafted with cyclodextrin carrying trimannoside vector molecules was developed as a drug delivery system for macrophages. Such ligands were absorbed by CD206+ macrophages by 60–70% versus 10–15% for ligands with a non-specific galactose label. Owing to presence of ligands with trimannoside vectors, the increase in antibiotic concentration inside macrophages, and thus, its accumulation into dormant bacteria, is observed. In the future, the developed FTIR+CLSM techniques would be applicable for the diagnosis of bacterial infections and the adjustment of therapy strategies. Full article

Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method to deal with multi-target tracking (MTT). However, the traditional GM-PHD filter cannot form a continuous track in the tracking process, and it is easy to produce a large number of redundant invalid likelihood functions in a dense clutter environment, which reduces the computational efficiency and affects the update result of target probability hypothesis density, resulting in excessive tracking error. Therefore, based on the GM-PHD filter framework, the target state space is extended to a higher dimension. By adding a label set, each Gaussian component is assigned a label, and the label is merged in the pruning and merging step to increase the merging threshold to reduce the Gaussian component generated by dense clutter update, which reduces the computation in the next prediction and update. After pruning and merging, the Gaussian components are further clustered and optimized by threshold separation clustering, thus as to improve the tracking performance of the filter and finally realizing the accurate formation of multi-target tracks in a dense clutter environment. Simulation results show that the proposed algorithm can form a continuous and reliable track in dense clutter environment and has good tracking performance and computational efficiency. Full article

In this study, an explicit track continuity algorithm is proposed for multitarget tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. Trajectory maintenance and multitarget state extraction in the GM-PHD filter have not been effectively integrated to date. To address this problem, we propose an improved GM-PHD filter. In this approach, the Gaussian components are classified and labeled, and multitarget state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates, thus realizing the integration of trajectory maintenance with state extraction in the GM-PHD filter. As no additional associated procedures are required, the overall real-time performance of the proposed filter is similar to or slightly lower than that of the basic GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity. Full article

Cetacean monitoring is key to their protection. Understanding their behavior relies on multi-channel and high-sampling-rate underwater acoustic recordings for identifying and tracking them in a passive way. However, a lot of energy and data storage is required, requiring frequent human maintenance operations. To cope with these constraints, an ultra-low power mixed-signal always-on wake-up is proposed. Based on pulse-pattern analysis, it can be used for triggering a multi-channel high-performance recorder only when cetacean clicks are detected, thus increasing autonomy and saving storage space. This detector is implemented as a mixed architecture making the most of analog and digital primitives: this combination drastically improves power consumption by processing high-frequency data using analog features and lower-frequency ones in a digital way. Furthermore, a bioacoustic expert system is proposed for improving detection accuracy (in ultra-low-power) via state machines. Power consumption of the system is lower than 30 μW in always-on mode, allowing an autonomy of 2 years on a single CR2032 battery cell with a high detection accuracy. The receiver operating characteristic (ROC) curve obtained has an area under curve of $85\%$ using expert rules and $75\%$ without it. This implementation provides an excellent trade-off between detection accuracy and power consumption. Focused on sperm whales, it can be tuned to detect other species emitting pulse trains. This approach facilitates biodiversity studies, reducing maintenance operations and allowing the use of lighter, more compact and portable recording equipment, as large batteries are no longer required. Additionally, recording only useful data helps to reduce the dataset labeling time. Full article

In a software tracking system, the bug assignment problem refers to the activities that developers perform during software maintenance to fix bugs. As many bugs are submitted on a daily basis, the number of developers required is quite large, and it therefore becomes difficult to assign the right developers to resolve issues with specific bugs. Inappropriate dispatches results in delayed processing of bug reports. In this paper, we propose an algorithm called ABC-DR to solve the bug assignment problem. The ABC-DR algorithm is a two-part composite approach that includes analysis between bug reports (i.e., B-based analysis) and analysis between developers and bug reports (i.e., D-based analysis). For analysis between bug reports, we use the multi-label k-nearest neighbor (ML-KNN) algorithm to find similar bug reports when compared with the new bug reports, and the developers who analyze similar bug reports recommend developers for the new bug report. For analysis between developers and bug reports, we find developer rankings similar to the new bug report by calculating the relevance scores between developers and similar bug reports. We use the artificial bee colony (ABC) algorithm to calculate the weight of each part. We evaluated the proposed algorithms on three datasets—GCC, Mozilla, and NetBeans—comparing ABC-DR with DevRec, DREX, and Bugzie. The experimental results show that the proposed ABC-DR algorithm achieves the highest improvement of 51.2% and 53.56% over DevRec for recall@5 and recall@10 in the NetBeans dataset. Full article