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Purpose This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver. Design/methodology/approach Different models based on DNNs are designed and proposed for the resolution of inverse electromagnetic problems either as fast solvers for the direct problem or as straightforward inverse problem solvers, with reference to the TEAM 25 benchmark problem for the sake of exemplification. Findings Using DNNs as straightforward inverse problem solvers has relevant advantages in terms of promptness but requires a careful treatment of the underlying problem ill-posedness. Originality/value This work is one of the first attempts to exploit DNNs for inverse problem resolution in low-frequency electromagnetism. Results on the TEAM 25 test problem show the potential effectiveness of the approach but also highlight the need for a careful choice of the training data set.

RNA-binding protein (RBP) dysfunction is a fundamental hallmark of amyotrophic lateral sclerosis (ALS) and related neuromuscular disorders. Abnormal neuronal excitability is also a conserved feature in ALS patients and disease models, yet little is known about how activity-dependent processes regulate RBP levels and functions. Mutations in the gene encoding the RBP Matrin 3 (MATR3) cause familial disease, and MATR3 pathology has also been observed in sporadic ALS, suggesting a key role for MATR3 in disease pathogenesis. Here, we show that glutamatergic activity drives MATR3 degradation through an NMDA receptor-, Ca 2+ -, and calpain-dependent mechanism. The most common pathogenic MATR3 mutation renders it resistant to calpain degradation, suggesting a link between activity-dependent MATR3 regulation and disease. We also demonstrate that Ca 2+ regulates MATR3 through a nondegradative process involving the binding of Ca 2+ /calmodulin to MATR3 and inhibition of its RNA-binding ability. These findings indicate that neuronal activity impacts both the abundance and function of MATR3, underscoring the effect of activity on RBPs and providing a foundation for further study of Ca 2+ -coupled regulation of RBPs implicated in ALS and related neurological diseases.

Tau protein accumulation drives toxicity in several neurodegenerative disorders. To better understand the pathways regulating tau homeostasis in disease, we investigated the role of ubiquilins (UBQLNs)—a class of proteins linked to ubiquitin-mediated protein quality control (PQC) and various neurodegenerative diseases—in regulating tau. Cell-based assays identified UBQLN2 as the primary brain-expressed UBQLN to regulate tau. UBQLN2 efficiently lowered wild-type tau levels regardless of aggregation, suggesting that UBQLN2 interacts with and regulates tau protein under normal conditions or early in disease. Moreover, UBQLN2 itself proved to be prone to accumulation as insoluble protein in male and female tau transgenic mice and the human tauopathy progressive supranuclear palsy. Genetic manipulation of UBQLN2 in a tauopathy mouse model demonstrated that a physiological UBQLN2 balance is required for tau homeostasis. UBQLN2 overexpression exacerbated phosphorylated tau pathology and toxicity in mice expressing P301S mutant tau, whereas P301S mice lacking UBQLN2 showed significantly reduced phosphorylated tau. Further studies support the view that an imbalance of UBQLN2 perturbs ubiquitin-dependent PQC and autophagy. We conclude that changes in UBQLN2 levels, whether because of pathogenic mutations or secondary to disease states, such as tauopathy, contribute to proteostatic imbalances that exacerbate neurodegeneration.SIGNIFICANCE STATEMENTWe defined a role for the protein quality control protein Ubiquilin-2 (UBQLN2), in age-related neurodegenerative tauopathies. This group of disorders is characterized by the accumulation of tau protein aggregates, which differ when UBQLN2 levels are altered. Given the lack of effective disease-modifying therapies for tauopathies and the function of UBQLN2 in handling various disease-linked proteins, we explored the role of UBQLN2 in regulating tau. We found that UBQLN2 reduced tau levels in cell models but behaved differently in mouse brain, where it accelerated mutant tau pathology and tau-mediated toxicity. A better understanding of the diverse functions of regulatory proteins like UBQLN2 can elucidate some of the causative factors in neurodegenerative disease and outline new routes to therapeutic intervention.

In this contribution, the authors focus on the use of a metasurface (physically implemented as a 2D array of spiral resonators) as an additional component of a two-coil Wireless Power Transfer (WPT) system, with the aim of increasing the robustness to misalignment between the transmitter and the receiver coils. Resonator arrays have been proven to have a positive effect on WPT systems’ performance since they produce a focusing effect on the magnetic field; at the same time, they contribute to the reduction of the electric near field. In addition, we herein demonstrate how proper control over the metasurface’s unit cells can contribute to making a WPT system more tolerant to misalignment. In particular, the comparison between metasurfaces of different sizes (keeping the same transmitting and receiving coils) and their optimization performed to improve misalignment robustness is proved by numerical simulations.

RNA-binding protein (RBP) dysfunction is a fundamental hallmark of amyotrophic lateral sclerosis (ALS) and related neuromuscular disorders. Abnormal neuronal excitability is also a conserved feature in ALS patients and disease models, yet little is known about how activity-dependent processes regulate RBP levels and functions. Mutations in the gene encoding the RBP Matrin 3 (MATR3) cause familial disease, and MATR3 pathology has also been observed in sporadic ALS, suggesting a key role for MATR3 in disease pathogenesis. Here, we show that glutamatergic activity drives MATR3 degradation in a NMDAR-, Ca2+-, and calpain-dependent mechanism. The most common pathogenic MATR3 mutation renders it resistant to calpain degradation, suggesting a link between activity-dependent MATR3 regulation and disease. We also demonstrate that Ca2+ regulates MATR3 through a non-degradative process involving the binding of Ca2+/calmodulin (CaM) to MATR3 and inhibition of its RNA-binding ability. These findings indicate that neuronal activity impacts both the abundance and function of MATR3, and provide a foundation for further study of Ca2+-coupled regulation of RBPs implicated in ALS and related neurological diseases.

The computational cost of topology optimization based on the binary particle swarm optimization is greatly reduced by the use of deep neural networks (DNNs). A first convolutional neural network (CNN) is trained with data coming from finite-element analysis (FEA) with the aim of correctly estimating the output quantity (a motor torque in the proposed case study). A second CNN is trained to give as output the boundary conditions [(BCs), expressed in terms of fields or potentials] to be used as BC of a reduced finite-element method (FEM) model, created in order to still be able to give the correct value of the output quantity. In the optimization phase, the torque properties are evaluated by the trained CNN, and only a reduced percentage of cases are reevaluated by either the full FEM model or the reduced FEM model. The overall computational time of the optimization procedure is significantly reduced.

Lightning electromagnetic (EM) fields are typically computed by assuming the current propagation within a straight vertical channel of negligible cross section. However, it is well known that the lightning path is tortuous and this can significantly affect the radiated fields and the lightning-induced voltages on power lines. Lupò et al. (2000) proposed an analytical method evaluating the EM fields radiated by a tortuous channel assuming a step current source; to consider an arbitrary (realistic) current source, a slow-converging convolution integral was applied. The aim of this article is to reduce the computation time of such procedure by extending to the tortuous channel case the approach proposed by Brignone et al. (2021) for the evaluation of the lightning EM fields with arbitrary current source, originally developed for a straight vertical channel. The performances of the present method are evaluated by means of comparison with the solution proposed by Lupò et al. (2000), showing an excellent agreement with a significant reduction of the CPU effort. This in principle will allow the computation of lightning-induced overvoltages at both individual and statistical level without a prohibitive computational effort.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with no cure or effective treatment in which TAR DNA Binding Protein of 43 kDa (TDP-43) abnormally accumulates into misfolded protein aggregates in affected neurons. It is widely accepted that protein misfolding and aggregation promote proteotoxic stress. The molecular chaperones are the body’s primary line of defense against proteotoxic stress and there has been long-standing interest in understanding the relationship between chaperones and aggregated protein in ALS. Of particular interest are the heat shock protein of 70 kDa (Hsp70) family of chaperones; however, defining which of the 13 human Hsp70 isoforms is critical for ALS, has presented many challenges. To gain insight into the specific Hsp70 that modulates TDP-43, we investigated the relationship between TDP-43 and the Hsp70s using proximity-dependent biotin identification (BioID) and discovered several Hsp70 isoforms associated with TDP-43 in the nucleus, raising the possibility of an interaction with native TDP-43. We further found that HspA5 bound specifically to the RNA-binding domain of TDP-43 using recombinantly expressed proteins. HspA5 is increased in prefrontal cortex neurons of ALS patients. Finally, overexpression of HspA5 in Drosophila rescued TDP-43-induced toxicity, suggesting that upregulation of HspA5 may have a compensatory role in ALS pathobiology.

The use of behavioral models based on deep learning (DL) to accelerate electromagnetic field computations has recently been proposed to solve complex electromagnetic problems. Such problems usually require time-consuming numerical analysis, while DL allows achieving the topologically optimized design of electromagnetic devices using desktop class computers and reasonable computation times. An unparametrized bitmap representation of the geometries to be optimized, which is a highly desirable feature needed to discover completely new solutions, is perfectly managed by DL models. On the other hand, optimization algorithms do not easily cope with high dimensional input data, particularly because it is difficult to enforce the searched solutions as feasible and make them belong to expected manifolds. In this work, we propose the use of a variational autoencoder as a data regularization/augmentation tool in the context of topology optimization. The optimization was carried out using a gradient descent algorithm, and the DL neural network was used as a surrogate model to accelerate the resolution of single trial cases in the due course of optimization. The variational autoencoder and the surrogate model were simultaneously trained in a multi-model custom training loop that minimizes total loss—which is the combination of the two models’ losses. In this paper, using the TEAM 25 problem (a benchmark problem for the assessment of electromagnetic numerical field analysis) as a test bench, we will provide a comparison between the computational times and design quality for a “classical” approach and the DL-based approach. Preliminary results show that the variational autoencoder manages regularizing the resolution process and transforms a constrained optimization into an unconstrained one, improving both the quality of the final solution and the performance of the resolution process.

The aim of the manuscript is to present a feasibility analysis for misalignment recovering purposes in a two coils Wireless Power Transfer system with the presence of a Spiral Resonators Array. The misalignment is faced by opportunely varying the values of capacitive loads with an ad-hoc optimization procedure. In a previous work, the authors have already presented a methodology able to compensate the system efficiency drop by applying a postprocessing approach based on an S-parameters matrix circuit representation of the entire system, and by changing the values of the capacitors which opportunely load each element of the SRA structure. Here, an optimization procedure followed by a Montecarlo method have been used in order to investigate the effect of the components tolerances and the sensitivity of the system. In this way, a more realistic modelization can be accomplished, guiding the designer to the realization of a more robust system.

In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

Purpose The lightning phenomenon is one of the main threats in photovoltaic (PV) applications. Suitable protection systems avoid major damages from direct strikes but also nearby strikes may induce overvoltage transients in the module itself and in the power conditioning circuitry, which can permanently damage the system. The effects on the PV system sensibly depend on the converter topology and on the adopted power switch. In the present study, a comparative analysis of the transient response due to a nearby lightning strike (LS) is carried out for three PV systems, each equipped with a different converter, namely, boost, buck and buck–boost, based on either silicon carbide metal oxide semiconductor field effect transistors (SiC MOSFET) or insulated gate bipolar transistors controlled power switch devices, allowing in this way an analysis at different switching frequencies. The purpose of this paper is to present the results of the numerical analysis to help the design of suited protection systems. Design/methodology/approach Using a recently introduced three-dimensional semi-analytical method to simulate the electromagnetic transients caused in PV modules by nearby LSs, we investigate numerically the effect of a LS on the electronic circuits connecting the module to the alternate current (AC) power systems. This study adopts numerical simulations because experimental analyses are not easy to perform and does not grant a sufficient coverage of all statistically relevant aspects. The approach was validated in a previous paper against available experimental data. Findings It is found that the load voltage is not severely interested by the strike effects, thanks to the low pass filters present at the converter output, whereas a relatively high overvoltage develops between the negative pin of the inner circuitry and the “ground” voltage reference. The overcurrent present in the active switches is hardly comparable because of the different topologies and working frequencies; however, the highest overcurrent is observed in the buck converter topology, with SiC MOSFET technology, although it shows the fastest decay. Originality/value This research proposes, to the best of the authors’ knowledge, a comprehensive comparison of the indirect lighting strike effects on the converter connected to PV panels. A proper design of the lightning and surge protection system should take into account such aspects to reduce the risk of induced overvoltage and overcurrent transients.

In the present work the flexibility of an array of resonators applied to a simple 2 coils Wireless Power Transfer system has been presented with the aim of simultaneous Power and Data Transfer.

In this work, a topology optimization procedure is proposed and applied to the TEAM 25 problem, i.e., a model of a die press with an electromagnet for orientation of magnetic powder. The shape of the press is described as a free discretized profile, and its relation to the flux density in the cavity is simulated by finite element analysis (FEA) and learned by a deep neural network (DNN) model. The DNN is used as a surrogate model for optimization, aiming to obtain a desired flux density distribution in the cavity. Results are promising, as better accuracy is obtained with respect to the full FEA-based optimization approach with the reduced time cost. Once trained, the surrogate model can be used to efficiently solve a whole family of problems where a different target field distribution is defined.

Frontotemporal dementia (FTD) therapy development is hamstrung by a lack of susceptibility, diagnostic, and prognostic biomarkers. Blood neurofilament light (NfL) shows promise as a biomarker, but studies have largely focused only on core FTD syndromes, often grouping patients with different diagnoses. To expedite the clinical translation of NfL, we avail ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) study resources and conduct a comprehensive investigation of plasma NfL across FTD syndromes and in presymptomatic FTD mutation carriers. We find plasma NfL is elevated in all studied syndromes, including mild cases; increases in presymptomatic mutation carriers prior to phenoconversion; and associates with indicators of disease severity. By facilitating the identification of individuals at risk of phenoconversion, and the early diagnosis of FTD, plasma NfL can aid in participant selection for prevention or early treatment trials. Moreover, its prognostic utility would improve patient care, clinical trial efficiency, and treatment outcome estimations.

In this contribution, we show that a properly designed Spiral Resonators Array (SRA) is capable of reducing the electric field between the coils of a typical Wireless Power Transfer system (WPT), while retaining its basic performance (in terms of transmitted power, efficiency, available net space between the coils). Several SRA configurations, characterized by a different number of spirals, have been considered and a set of simulations been performed. The results demonstrate that a tradeoff among the number of resonators, the WPT system efficiency and the reduction of electric field must be considered. A prototype has been fabricated and a preliminary experimental validation of the results has been presented.