Enhanced glycolysis and GSK3 inactivation promote brain metabolic adaptations following neuronal mitochondrial stress.

We analyzed early brain metabolic adaptations in response to mitochondrial dysfunction in a mouse model of mitochondrial encephalopathy with complex IV deficiency [neuron-specific COX10 knockout (KO)]. In this mouse model, the onset of the mitochondrial defect did not coincide with immediate cell death, suggesting early adaptive metabolic responses to compensate for the energetic deficit. Metabolomic analysis in the KO mice revealed increased levels of glycolytic and pentose phosphate pathway intermediates, amino acids and lysolipids. Glycolysis was modulated by enhanced activity of glycolytic enzymes, and not by their overexpression, suggesting the importance of post-translational modifications in the adaptive response. Glycogen synthase kinase 3 inactivation was the most upstream regulation identified, implying that it is a key event in this adaptive mechanism. Because neurons are thought not to rely on glycolysis for adenosine triphosphate production in normal conditions, our results indicate that neurons still maintain their ability to upregulate this pathway when under mitochondrial respiration stress.

LoRaCELL-Driven IoT Smart Lighting Systems: Sustainability in Urban Infrastructure.

In recent years, the rate of urbanization has increased enormously, precipitating an escalating demand for improved services and applications in urban areas to improve the quality of life. In the Internet of Things (IoT)era, cities are transforming into smart urban centers. These cities incorporate connected devices, such as intelligent public lighting systems, to enhance their urban infrastructure. Therefore, this work explores the transformative potential of an IoT-enabled smart lighting system in urban environments, emphasizing its essential role in enhancing safety, economy, and sustainability. In this sense, LoRaCELL (Long-Range Cell) is introduced. LoRaCELL is an innovative system that utilizes edge devices for data collection, such as light intensity, humidity, temperature, air quality, solar ultraviolet radiation, ammeter, and voltmeter. It stands as a pioneering solution for intelligent public lighting systems, contributing to advancing IoT-driven urban development. The outcomes showed that the proposed system could successfully synchronize the devices with each other and send IoT sensing data at a low cost compared to traditional technologies such as LoRaWAN.

Photobiomodulation therapy on skeletal muscles exposed to diabetes mellitus: a systematic review of animal studies.

Diabetes-related muscle damage has been overlooked despite its known association with increased morbidity and mortality in DM individuals. PBMT is a recognized alternative to improve skeletal muscle health in other populations, but its effectiveness in DM is still unclear. To address this issue, we reviewed preclinical studies, available in any language and period, in ten sources of information. The methods were previously registered at PROSPERO (CRD42021271041), based on PRISMA recommendations. Studies in murine models of T1DM or T2DM that reported quantitative analyses of skeletal muscles treated with low-level light therapy could be included after a blind selection process. Most of the seven included studies focus on decompensated T1DM rats with acute muscle injury (cryoinjury or contusion). In these five studies, PBMT improved muscle regeneration, by reducing inflammation and stimulating factors pro-angiogenesis and pro-myogenesis. Some positive effects could also be observed in two studies on muscles without acute injury: control of oxidative stress (T1DM) and reduction of myosteatosis (T2DM). Although infrared laser applied locally appears to be a promising approach, optimal parameters are undefined due to the heterogeneity of outcomes and high risk of bias, which prevented a quantitative synthesis. Several aspects of this growing field have yet to be investigated, particularly regarding the DM model (e.g., aged animals, T2DM), intervention (e.g., comparison with LED), and outcomes (e.g., muscle mass, strength, and function). Future research should aim to improve the internal validity by following guidelines for animal studies and enhance the translatability to clinical trials by using animal models that closely mimic patients with DM in rehabilitation settings.

Attitude Control of Ornithopter Wing by Using a MIMO Active Disturbance Rejection Strategy.

This work proposes a mathematical solution for the attitude control problem of an ornithopter wing. An ornithopter is an artificial bird or insect-like aerial vehicle whose flight and lift movements are produced and maintained by flapping wings. The aerodynamical drag forces responsible for the flying movements are generated by the wing attitude and torques applied to its joints. This mechanical system represents a challenging problem because its dynamics consist of MIMO nonlinear equations with couplings in the input variables. For dealing with such a mathematical model, an Active Disturbance Rejection Control-based (ADRC) method is considered. The cited control technique has been studied for almost two decades and its main characteristics are the use of an extended state observer to estimate the nonmeasurable signals of the plant and a state-feedback control law in standard form fed by that observer. However, even today, the application of the basic methodology requires the exact knowledge of the plant's control gain which is difficult to measure in the case of systems with uncertain parameters. In addition, most of the related works apply the ADRC strategy to Single Input Single Output (SISO) plants. For MIMO systems, the control gain is represented by a square matrix of general entries but most of the reported works consider the simplified case of uncoupled inputs, in which a diagonal matrix is assumed. In this paper, an extension of the ADRC SISO strategy for MIMO systems is proposed. By adopting such a control methodology, the resulting closed-loop scheme exhibits some key advantages: (i) it is robust to parametric uncertainties; (ii) it can compensate for external disturbances and unmodeled dynamics; (iii) even for nonlinear plants, mathematical analysis using Laplace's approach can be always used; and (iv) it can deal with system's coupled input variables. A complete mathematical model for the dynamics of the ornithopter wing system is presented. The efficiency of the proposed control is analyzed mathematically, discussed, and illustrated via simulation results of its application in the attitude control of ornithopter wings.

An Edge-Fog Architecture for Distributed 3D Reconstruction and Remote Monitoring of a Power Plant Site in the Context of 5G.

It is well known that power plants worldwide present access to difficult and hazardous environments, which may cause harm to on-site employees. The remote and autonomous operations in such places are currently increasing with the aid of technology improvements in communications and processing hardware. Virtual and augmented reality provide applications for crew training and remote monitoring, which also rely on 3D environment reconstruction techniques with near real-time requirements for environment inspection. Nowadays, most techniques rely on offline data processing, heavy computation algorithms, or mobile robots, which can be dangerous in confined environments. Other solutions rely on robots, edge computing, and post-processing algorithms, constraining scalability, and near real-time requirements. This work uses an edge-fog computing architecture for data and processing offload applied to a 3D reconstruction problem, where the robots are at the edge and computer nodes at the fog. The sequential processes are parallelized and layered, leading to a highly scalable approach. The architecture is analyzed against a traditional edge computing approach. Both are implemented in our scanning robots mounted in a real power plant. The 5G network application is presented along with a brief discussion on how this technology can benefit and allow the overall distributed processing. Unlike other works, we present real data for more than one proposed robot working in parallel on site, exploring hardware processing capabilities and the local Wi-Fi network characteristics. We also conclude with the required scenario for the remote monitoring to take place with a private 5G network.

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications.

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers.

Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID's good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.

Circular economy in Brazilian construction industry: Current scenario, challenges and opportunities.

The effective implantation of the circular economy (CE) presents a great challenge to the industrial sectors, mostly in those of greater environmental impact, such as construction industry. In Brazil, this industry has been growing over the last 20 years; however, such growth was based on an extractivist economy without any consideration to the end of its products lifespan. Only in 2017, 45 million tonnes of construction and demolition waste were generated in the country. This paper presents the current context of public policies in the Brazilian construction industry and proposes initiatives to introduce principles of CE. An exploratory study was developed, and all the 45 references presented in Appendix A of the review paper of Benachio et al. were considered to draw new ideas. These ideas are related and compared to key points in Brazilian regulations. Twelve opportunities for the improvement are listed and organized by construction stages (planning and design, materials manufacturing, construction processes, use and demolition). The ones related to design and planning using Green building information modelling, and materials manufacturing with recycled waste are highlighted due to the existing actions, standards, and Federal legislation. It was found that some sectors and the government already have actions related to CE (based on the 3R principle), but still there are several initiatives needed for its effective implementation in the Brazilian construction industry.

Beverages Rich in Resveratrol and Physical Activity Attenuate Metabolic Changes Induced by High-Fat Diet.

Consumption of saturated fat causes deleterious effects on health, which could be minimized through physical activity and foods with functional characteristics consumption. The aim of the study was to evaluate the beverage rich in resveratrol consumption and physical exercise in gut microbiota, body composition, lipid peroxidation, interleukin-6 (IL6) concentration and systolic blood pressure (SBP) of rats to the high-fat diet. Wistar rats were fed with control diet, high-fat diet (HFD), HFD and 15 mL solution of resveratrol, HFD and 15 mL of grape juice, HFD and 10 mL of red wine. All animals performed the physical training protocol five days a week. Grape juice and red wine composition were analyzed, SBP, body mass, consumption, adiposity and body composition, gut microbiota, lipid peroxidation and inflammation were evaluated. The grape juice (114.8 ± 22.5 mmHG) and red wine (129 ± 15.8 mmHg) groups showed lower SBP when compared to HFD (216.8 ± 20.6 mmHg) (p < 0.0001). The grape juice group (GJG) (39.1 ± 7) had a higher number of microbiota bands DNA when compared to the other groups (p = 0.002). The GJG (33.7 ± 6.7 pg/mL) presented lower concentration IL6 when compared to high-fat group (47.3 ± 16 pg/mL) (p = 0.003). GJG (4.7 ± 1.2 nmol/L) presented a lower concentration of TBARS when compared to control group (6.1 ± 1.4 nmol/L) and resveratrol group (6.6 ± 0.9 nmol/L), and the red wine group (7.4 ± 1.2 nmol/L) had a higher concentration of TBARS when compared to control group and GJG (p = 0.0001). The consumption of these beverages, especially grape juice, together with physical exercise, was able to promote beneficial changes even in the presence of a HFD.

Can diet modulate trimethylamine N-oxide (TMAO) production? What do we know so far?

BACKGROUND: Trimethylamine N-oxide (TMAO) is a metabolite that has attracted attention due to its positive association with several chronic non-communicable diseases such as insulin resistance, atherosclerotic plaque formation, diabetes, cancer, heart failure, hypertension, chronic kidney disease, liver steatosis, cardiac fibrosis, endothelial injury, neural degeneration and Alzheimer's disease. TMAO production results from the fermentation by the gut microbiota of dietary nutrients such as choline and carnitine, which are transformed to trimethylamine (TMA) and converted into TMAO in the liver by flavin-containing monooxygenase 1 and 3 (FMO1 and FMO3). Considering that TMAO is involved in the development of many chronic diseases, strategies have been found to enhance a healthy gut microbiota. In this context, some studies have shown that nutrients and bioactive compounds from food can modulate the gut microbiota and possibly reduce TMAO production. OBJECTIVE: This review has as main objective to discuss the studies that demonstrated the effects of food on the reduction of this harmful metabolite. METHODS: All relevant articles until November 2020 were included. The articles were searched in Medline through PubMed. RESULTS: Both the food is eaten acutely and chronically, by altering the nature of the gut microbiota, influencing colonic TMA production. Furthermore, hepatic production of TMAO by the flavin monooxygenases in the liver may also be influenced by phenolic compounds present in foods. CONCLUSION: The evidence presented in this review shows that TMAO levels can be reduced by some bioactive compounds. However, it is crucial to notice that there is significant variation among the studies. Further clinical studies should be conducted to evaluate these dietary components' effectiveness, dose, and intervention time on TMAO levels and its precursors.

Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs.

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection.

Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain's topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation's restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research's main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions' quality observing optimizing photometric and mission time criteria.

Photogrammetric Process to Monitor Stress Fields Inside Structural Systems.

This research employs displacement fields photogrammetrically captured on the surface of a solid or structure to estimate real-time stress distributions it undergoes during a given loading period. The displacement fields are determined based on a series of images taken from the solid surface while it experiences deformation. Image displacements are used to estimate the deformations in the plane of the beam surface, and Poisson's Method is subsequently applied to reconstruct these surfaces, at a given time, by extracting triangular meshes from the corresponding points clouds. With the aid of the measured displacement fields, the Boundary Element Method (BEM) is considered to evaluate stress values throughout the solid. Herein, the unknown boundary forces must be additionally calculated. As the photogrammetrically reconstructed deformed surfaces may be defined by several million points, the boundary displacement values of boundary-element models having a convenient number of nodes are determined based on an optimized displacement surface that best fits the real measured data. The results showed the effectiveness and potential application of the proposed methodology in several tasks to determine real-time stress distributions in structures.

A Robotic Cognitive Architecture for Slope and Dam Inspections.

Big construction enterprises, such as electrical power generation dams and mining slopes, demand continuous visual inspections. The sizes of these structures and the necessary level of detail in each mission requires a conflicting set of multi-objective goals, such as performance, quality, and safety. It is challenging for human operators, or simple autonomous path-following drones, to process all this information, and thus, it is common that a mission must be repeated several times until it succeeds. This paper deals with this problem by developing a new cognitive architecture based on a collaborative environment between the unmanned aerial vehicles (UAVs) and other agents focusing on optimizing the data gathering, information processing, and decision-making. The proposed architecture breaks the problem into independent units ranging from sensors and actuators up to high-level intelligence processes. It organizes the structures into data and information; each agent may request an individual behavior from the system. To deal with conflicting behaviors, a supervisory agent analyzes all requests and defines the final planning. This architecture enables real-time decision-making with intelligent social behavior among the agents. Thus, it is possible to process and make decisions about the best way to accomplish the mission. To present the methodology, slope inspection scenarios are shown.

A Heterogeneous Edge-Fog Environment Supporting Digital Twins for Remote Inspections.

The increase in the development of digital twins brings several advantages to inspection and maintenance, but also new challenges. Digital models capable of representing real equipment for full remote inspection demand the synchronization, integration, and fusion of several sensors and methodologies such as stereo vision, monocular Simultaneous Localization and Mapping (SLAM), laser and RGB-D camera readings, texture analysis, filters, thermal, and multi-spectral images. This multidimensional information makes it possible to have a full understanding of given equipment, enabling remote diagnosis. To solve this problem, the present work uses an edge-fog-cloud architecture running over a publisher-subscriber communication framework to optimize the computational costs and throughput. In this approach, each process is embedded in an edge node responsible for prepossessing a given amount of data that optimizes the trade-off of processing capabilities and throughput delays. All information is integrated with different levels of fog nodes and a cloud server to maximize performance. To demonstrate this proposal, a real-time 3D reconstruction problem using moving cameras is shown. In this scenario, a stereo and RDB-D cameras run over edge nodes, filtering, and prepossessing the initial data. Furthermore, the point cloud and image registration, odometry, and filtering run over fog clusters. A cloud server is responsible for texturing and processing the final results. This approach enables us to optimize the time lag between data acquisition and operator visualization, and it is easily scalable if new sensors and algorithms must be added. The experimental results will demonstrate precision by comparing the results with ground-truth data, scalability by adding further readings and performance.

Sensors Fusion and Multidimensional Point Cloud Analysis for Electrical Power System Inspection.

Thermal inspection is a powerful tool that enables the diagnosis of several components at its early stages. One critical aspect that influences thermal inspection outputs is the infrared reflection from external sources. This situation may change the readings, demanding that an expert correctly define the camera position, which is a time consuming and expensive operation. To mitigate this problem, this work proposes an autonomous system capable of identifying infrared reflections by filtering and fusing data obtained from both stereo and thermal cameras. The process starts by acquiring readings from multiples Observation Points (OPs) where, at each OP, the system processes the 3D point cloud and thermal image by fusing them together. The result is a dense point cloud where each point has its spatial position and temperature. Considering that each point's information is acquired from multiple poses, it is possible to generate a temperature profile of each spatial point and filter undesirable readings caused by interference and other phenomena. To deploy and test this approach, a Directional Robotic System (DRS) is mounted over a traditional human-operated service vehicle. In that way, the DRS autonomously tracks and inspects any desirable equipment as the service vehicle passes them by. To demonstrate the results, this work presents the algorithm workflow, a proof of concept, and a real application result, showing improved performance in real-life conditions.

Deep Learning Applied to Vegetation Identification and Removal Using Multidimensional Aerial Data.

When performing structural inspection, the generation of three-dimensional (3D) point clouds is a common resource. Those are usually generated from photogrammetry or through laser scan techniques. However, a significant drawback for complete inspection is the presence of covering vegetation, hiding possible structural problems, and making difficult the acquisition of proper object surfaces in order to provide a reliable diagnostic. Therefore, this research's main contribution is developing an effective vegetation removal methodology through the use of a deep learning structure that is capable of identifying and extracting covering vegetation in 3D point clouds. The proposed approach uses pre and post-processing filtering stages that take advantage of colored point clouds, if they are available, or operate independently. The results showed high classification accuracy and good effectiveness when compared with similar methods in the literature. After this step, if color is available, then a color filter is applied, enhancing the results obtained. Besides, the results are analyzed in light of real Structure From Motion (SFM) reconstruction data, which further validates the proposed method. This research also presented a colored point cloud library of bushes built for the work used by other studies in the field.

Lack of Parkin Anticipates the Phenotype and Affects Mitochondrial Morphology and mtDNA Levels in a Mouse Model of Parkinson's Disease.

PARK2 is the most common gene mutated in monogenic recessive familial cases of Parkinson's disease (PD). Pathogenic mutations cause a loss of function of the encoded protein Parkin. ParkinKO mice, however, poorly represent human PD symptoms as they only exhibit mild motor phenotypes, minor dopamine metabolism abnormalities, and no signs of dopaminergic neurodegeneration. Parkin has been shown to participate in mitochondrial turnover, by targeting damaged mitochondria with low membrane potential to mitophagy. We studied the role of Parkin on mitochondrial quality control in vivo by knocking out Parkin in the PD-mito-PstI mouse (males), where the mitochondrial DNA (mtDNA) undergoes double-strand breaks only in dopaminergic neurons. The lack of Parkin promoted earlier onset of dopaminergic neurodegeneration and motor defects in the PD-mito-PstI mice, but it did not worsen the pathology. The lack of Parkin affected mitochondrial morphology in dopaminergic axons and was associated with an increase in mtDNA levels (mutant and wild type). Unexpectedly, it did not cause a parallel increase in mitochondrial mass or mitophagy. Our results suggest that Parkin affects mtDNA levels in a mitophagy-independent manner.SIGNIFICANCE STATEMENT Parkinson's disease is characterized by progressive motor symptoms due to the selective loss of dopaminergic neurons in the substantia nigra. Loss-of-function mutations of Parkin cause some monogenic forms of Parkinson's disease, possibly through its role in mitochondrial turnover and quality control. To study whether Parkin has a role in vivo in the context of mitochondrial damage, we knocked out Parkin in a mouse model in which the mitochondrial DNA is damaged in dopaminergic neurons. We found that the loss of Parkin did not exacerbate the parkinsonian pathology already present in the mice, but it was associated with an increase in mtDNA levels (mutant and wild-type) without altering mitochondrial mass. These results shed new light on the function of Parkin in vivo.

Aryl Hydrocarbon Receptor and Uremic Toxins from the Gut Microbiota in Chronic Kidney Disease Patients: Is There a Relationship between Them?

Recent studies have suggested that uremic toxins such as indoxyl sulfate (IS) and indole-3-acetic acid (IAA) from the metabolism of the gut microbiota may be involved in the inflammatory signaling pathway in chronic kidney disease (CKD) patients through the activation of the aryl hydrocarbon receptor (AhR), a ligand-activated transcription factor. The objective of this study was to investigate the possible relationship between uremic toxins (IS and IAA) and AhR protein expression in CKD patients. A cross-sectional observational study involving 17 hemodialysis (HD) [11 men, 55.5 ± 11.7 years of age, 54.0 (25.5-136.0) months of HD, body mass index (BMI) of 25.8 ± 3.8 kg/m2] and 15 non-dialysis-dependent (NDD) CKD (8 men, 54.1 ± 18.2 years of age, glomerular filtration rate of 34.8 ± 21.0 mL/min/1.73 m2, BMI of 27.4 ± 5.0 kg/m2) patients was conducted. IS and IAA levels were measured by reversed-phase high-performance liquid chromatography, and the protein expression levels of AhR and nuclear factor κ B (NF-κB) were evaluated by a Western blot assay. There was no difference in the expression of either AhR or NF-κB in the patients, and as expected, uremic toxin levels were higher in HD patients than in NDD patients. In the overall analysis, AhR protein expression was positively associated with IAA plasma levels ( r = 0.4; p = 0.03) and NF-κB protein expression ( r = 0.62; p = 0.001). Although the role of AhR in inflammation and CVD in CKD patients is far from being completely understood, the association between IAA and AhR observed in this study suggests a possible role for uremic toxins in the cell signaling pathway involved in inflammation in CKD patients.

Cryptic Amyloidogenic Elements in the 3' UTRs of Neurofilament Genes Trigger Axonal Neuropathy.

Abnormal protein aggregation is observed in an expanding number of neurodegenerative diseases. Here, we describe a mechanism for intracellular toxic protein aggregation induced by an unusual mutation event in families affected by axonal neuropathy. These families carry distinct frameshift variants in NEFH (neurofilament heavy), leading to a loss of the terminating codon and translation of the 3' UTR into an extra 40 amino acids. In silico aggregation prediction suggested the terminal 20 residues of the altered NEFH to be amyloidogenic, which we confirmed experimentally by serial deletion analysis. The presence of this amyloidogenic motif fused to NEFH caused prominent and toxic protein aggregates in transfected cells and disrupted motor neurons in zebrafish. We identified a similar aggregation-inducing mechanism in NEFL (neurofilament light) and FUS (fused in sarcoma), in which mutations are known to cause aggregation in Charcot-Marie-Tooth disease and amyotrophic lateral sclerosis, respectively. In summary, we present a protein-aggregation-triggering mechanism that should be taken into consideration during the evaluation of stop-loss variants.