Domain Model Research Articles

Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

An Analytical Model for an Anode-Supported Solid Oxide Fuel Cell

This study presents a one-dimensional, non-isothermal, steady-state model to analyze performance of an anode-supported solid oxide fuel cell (SOFC). The modeling domain encompasses components including a NiO-YSZ support and functional layer forming the anode, a YSZ electrolyte, a GDC interlayer, as well as LSCF+GDC and LSCF constituting the cathode. The model distinguishes different types of electrode polarization resistances using experimental impedance data at various temperatures under open circuit voltage (OCV), employing a detailed equivalent circuit model to determine the temperature and partial pressure dependencies of electrode exchange current densities. Moreover, this approach serves as an efficient method for estimating the conductivity of both the anode and cathode. As a result, activation energies and ohmic overpotentials are derived, considering microstructural and geometrical aspects, which allows for accurate quantification of the ohmic resistance associated with ionic conduction.The model incorporates coupled multi-physics elements, such as mass and charge transport, electrochemical reactions, and heat transfer. A notable feature of the model is its consideration of reaction zone layers near the electrolyte, where electrochemical reactions generate electrons, oxide ions, and water vapor. Enhancing prediction accuracy, the model applies multilayer discretization within the electrode and electrolyte to reflect actual material properties and localized conditions more accurately. The model predicts the performance of a single-cell SOFC using pure hydrogen at the anode and varying oxygen concentrations (100%, 50%, 20%, and 10%) at different operating temperatures (700°C, 660°C, 620°C, and 580°C) and design conditions. Simulated results are compared with experimental data, and the impact of various operating and design parameters on SOFC performance is examined. Findings indicate that kinetic and concentration overpotentials in an anode-supported SOFC are lower than ohmic overpotentials, even at higher current densities. Furthermore, ohmic overpotential is identified as the primary contributor to total cell potential loss, highlighting the need for its minimization to improve cell performance.

Factors influencing functional recovery after limb-salvage surgery for lower extremity musculoskeletal tumours: a qualitative study combining patients’ and healthcare professionals’ perspectives

Purpose Survival rates of lower extremity musculoskeletal tumours (LEMTs) have been increasing. However, patients continue to experience functional limitations after LEMT followed by limb-salvage surgery (LSS). This study aimed to identify factors influencing functional recovery after LSS for LEMT. Methods A qualitative study was conducted using semi-structured interviews with a purposive sample of adult patients who underwent LSS for LEMT (n=7) and healthcare professionals (HCPs) with expertise in orthopaedic oncology (n=7). Recruitment continued until data saturation. A combination of deductive and inductive qualitative content analysis was performed to analyse the transcribed data, producing subthemes under the main International Classification of Functioning, disability and health (ICF)-model domains. Results Four themes were described: (1) bodily functions and structures, (2) activities and participation, (3) environmental factors, and (4) expectations. Physical aspects such as larger resection size and complications negatively affected recovery. The importance of communication strategies of tertiary HCPs and the unfamiliarity of the primary physical therapists with the disease, its surgical treatment, and related consequences were emphasised by both the interviewed patients and HCPs. Conclusions Functional recovery after LSS for LEMT is a multifactorial process. To improve patient care, improving (intercollegiate) communication strategies on treatment details and expectations about functional outcomes is warranted. Implications for rehabilitation Healthcare professionals should be aware some patients experience limited functional recovery after limb-salvage surgery (LSS) for lower extremity musculoskeletal tumours. Healthcare professionals should use multiple communication strategies to bridge the gap between the information provided and what the patient recalls. Knowledge exchange and communication between primary and tertiary care should be more extensive. Multidisciplinary consultations and/or case managers are needed to address all aspects of the individual’s functional recovery after LSS.

Feature Model for Pet Health Application Domain using Feature Oriented Domain Analysis

Feature Model for Pet Health Application Domain using Feature Oriented Domain Analysis

Adaptive QSMO-Based Sensorless Drive for IPM Motor with NN-Based Transient Position Error Compensation

In commercial electrical equipment, the popular sensorless drive scheme for the interior permanent magnet synchronous motor, based on the quasi-sliding mode observer (QSMO) and phase-locked loop (PLL), still faces challenges such as position errors and limited applicability across a wide speed range. To address these problems, this paper analyzes the frequency domain model of the QSMO. A QSMO-based parameter adaptation method is proposed to adjust the boundary layer and widen the speed operating range, considering the QSMO bandwidth. A QSMO-based phase lag compensation method is proposed to mitigate steady-state position errors, considering the QSMO phase lag. Then, the PLL model is analyzed to select the estimated speed difference for transient position error compensation. Specifically, a transient position error compensator based on a feedback time delay neural network (FB-TDNN) is proposed. Based on the back propagation learning algorithm, the specific structure and optimal parameters of the FB-TDNN are determined during the offline training process. The proposed parameter adaptation method and two position error compensation methods were validated through simulations in simulated wide-speed operation scenarios, including sudden speed changes. Overall, the proposed scheme fully mitigates steady-state position errors, substantially mitigates transient position errors, and exhibits good stability across a wide speed range.

Surface area and porosity analysis in nanoporous carbons by atomistic pore domain model

We present a new atomistic model for evaluating the surface area and porosity of micro-mesoporous carbons. This method, referred to as the atomistic pore domain model (APDM), advances the adsorption porosity methodology by calculating textural properties of micro-mesoporous carbons without relying on assumptions about pore geometry. A thorough analysis of porosity across eleven porous carbons demonstrates a robust correlation between the surface area accessible to N2 molecules, as computed using APDM and the Brunauer-Emmett-Teller method with Rouquerol criterium. This correlation is observed across a spectrum of nanoporous carbons, ranging from ultramicroporous activated carbon fibers and nanoporous carbon beads to supermicro-mesoporous activated carbons and activated carbon fibers. APDM facilitates the extraction of the information regarding the N2 surface area accessibility and the intrinsic geometric surface area of micro-mesoporous carbons. The N2-to-He surface area accessibility ratio of the investigated porous carbons varies from approximately 57 %–94 %, indicating varying degrees of pore sieving among studied carbon samples. Except for ACF-25 micro-mesoporous activated carbon fiber, the intrinsic geometric surface areas of the studied micro-mesoporous carbons are smaller than the geometrical surface area of a single graphene sheet (2640 m2/g).

Genome-Wide Identification of Glutathione S-Transferase Family from Dendrobium officinale and the Functional Characterization of DoGST5 in Cadmium Tolerance.

Glutathione S-transferases (GSTs) are members of a protein superfamily with diverse physiological functions, including cellular detoxification and protection against oxidative damage. However, there is limited research on GSTs responding to cadmium (Cd) stress. This study classified 46 GST genes in Dendrobium officinale (D. officinale) into nine groups using model construction and domain annotation. Evolutionary analysis revealed nine subfamilies with diverse physical and chemical properties. Prediction of subcellular localization revealed that half of the GST members were located in the cytoplasm. According to the expression analysis of GST family genes responding to Cd stress, DoGST5 responded significantly to Cd stress. Transient expression of DoGST5-GFP in tobacco leaves revealed that DoGST5 was localized in the cytoplasm. DoGST5 overexpression in Arabidopsis enhanced Cd tolerance by reducing Cd-induced H2O2 and O2- levels. These findings demonstrate that DoGST5 plays a critical role in enhancing Cd tolerance by balancing reactive oxygen species (ROS) levels, offering potential applications for improving plant adaptability to heavy metal stress.

Simulating the effects of retreating Urmia Lake and increased evapotranspiration rates on the nearby unconfined aquifer

The unsustainable management of water resources along with climate change impacts have led Urmia Lake to severely shrink since 1995. To quantify the groundwater losses in the surrounding area and to avoid the future worsening of a yet worrying scenario a numerical model is needed. For this purpose, a transient state MODFLOW model (2000–2016) has been calibrated and validated versus hydraulic heads. Good model performances were obtained, allowing to accurately estimate the groundwater budget. MODIS dataset was employed to set up the numerical model to identify temporal and spatial extent of evapotranspiration. Evapotranspiration turned out to play a massive contribution in groundwater outflow showing an increasing trend over time. Several scenarios, considering a different way to implement recharge and evapotranspiration in the modelling routine were assessed. The role of the spatialization of the evapotranspiration extinction depth was investigated by averaging remotely sensed heterogeneous datasets over the model domain. No significant differences in model performance indicators were found. Moreover, a model scenario without human groundwater exploitation and land use change was considered. The results slightly differ from the groundwater budget calculated by the validated model and thus indicating the climate change as the major driver of groundwater depletion in the study area. The implementation of such a model allowed for the assessment of the exchange between the lake and the unconfined aquifer, providing a robust foundation for future sustainable management.

Guiding questions to avoid data leakage in biological machine learning applications.

Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons for this is data leakage, which can be seen as the illicit sharing of information between the training data and the test data, resulting in performance estimates that are far better than the performance observed in the intended application scenario. Data leakage can be difficult to detect in biological datasets due to their complex dependencies. With this in mind, we present seven questions that should be asked to prevent data leakage when constructing machine learning models in biological domains. We illustrate the usefulness of our questions by applying them to nontrivial examples. Our goal is to raise awareness of potential data leakage problems and to promote robust and reproducible machine learning-based research in biology.

Transformers in single-cell omics: a review and new perspectives.

Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.

Factors influencing patient experience in hospital wards: a systematic review

BackgroundPatient experience plays an essential role in improving clinical effectiveness and patient safety. It’s important to identify factors influencing patient experience and to improve quality of healthcare.ObjectiveTo identify factors that influence patient experience in hospital wards.MethodsWe conducted a systematic review including six databases; they were PubMed, CINAHL, Embase, PsycInfo, ProQuest, and Cochrane. Studies were included if they met the inclusion criteria. The JBI checklist was used to perform quality appraisal. We used 5 domains of the ecological model to organize and synthesize our findings to comprehensively understand the multi-level factors influencing the issue.ResultA total of 138 studies were included, and 164 factors were identified. These factors were integrated into 6 domains. All domains but one (survey-related factors) could be mapped onto the attributes of the ecological framework: intrapersonal, interpersonal, institutional, community, and public policy level factors. All factors had mixed effect on patient experience. The intrapersonal level refers to individual characteristics of patients. The interpersonal level refers to interactions between patients and healthcare providers, such as the caring time spent by a nurse. The institutional level refers to organizational characteristics, rules and regulations for operations, such as hospital size and accreditation. The community level refers to relationships among organizations, institutions, and informational networks within defined boundaries, such as a hospital located in a larger population area. Public policy level refers to local, state, national, and global laws and policies, including health insurance policies. The sixth domain, survey-related factors, was added to the framework and included factors such as survey response rate and survey response time.ConclusionThe factors influencing patient experience are comprehensive, ranging from intrapersonal to public policy. Providers should adopt a holistic and integrated perspective to assess patient experience and develop context-specific interventions to improve the quality of care.PROSPERO registration number CRD42023401066

EncChain: Enhancing Large Language Model Applications with Advanced Privacy Preservation Techniques

In response to escalating concerns about data privacy in the Large Language Model (LLM) domain, we demonstrate EncChain , a pioneering solution designed to bolster data security in LLM applications. EncChain presents an all-encompassing approach to data protection, encrypting both the knowledge bases and user interactions. It empowers confidential computing and implements stringent access controls, offering a significant leap in securing LLM usage. Designed as an accessible Python package, EncChain ensures straightforward integration into existing systems, bolstered by its operation within secure environments and the utilization of remote attestation technologies to verify its security measures. The effectiveness of EncChain in fortifying data privacy and security in LLM technologies underscores its importance, positioning it as a critical advancement for the secure and private utilization of LLMs.

Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are crucial for health management and diagnosis. However, most data-driven estimation methods heavily rely on scarce labeled data, while traditional transfer learning faces challenges in handling domain shifts across various battery types. This paper proposes an enhanced vision-transformer integrating with semi-supervised transfer learning for SOH and RUL estimation of lithium-ion batteries. A depth-wise separable convolutional vision-transformer is developed to extract local aging details with depth-wise convolutions and establishes global dependencies between aging information using multi-head attention. Maximum mean discrepancy is employed to initially reduce the distribution difference between the source and target domains, providing a superior starting point for fine-tuning the target domain model. Subsequently, the abundant aging data of the same type as the target battery are labeled through semi-supervised learning, compensating for the source model's limitations in capturing target battery aging characteristics. Consistency regularization incorporates the cross-entropy between predictions with and without adversarial perturbations into the gradient backpropagation of the overall model. In particular, across the experimental groups 13–15 for different types of batteries, the root mean square error of SOH estimation was less than 0.66 %, and the mean relative error of RUL estimation was 3.86 %. Leveraging extensive unlabeled aging data, the proposed method could achieve accurate estimation of SOH and RUL.

Stabilization of interconnected models with Nitsche's interface conditions using the two-grid approach: A finite element study

In this paper, a stabilized Stokes–Stokes system with Nitsche's type interface conditions is presented. These conditions are commonly employed in many multi-physical fields, including fluid–fluid interaction, fluid–structure interaction, oceanographic modeling, and atmospheric forecasting. For multi-physical domain modeling purposes, Nitsche's interface conditions provide useful benefits over classical conditions via addressing the complicated nature of fluid phase interface mathematical modeling, phase boundary tracking, interface interactions, and mass and energy transportation. It is not easy to find analytical and numerical solutions for models with these characteristics. We use more accurate interface conditions to solve the fluid–fluid interaction model to accomplish this numerically. This is achieved by including new terms at the interface and decoupling the domain through the two-grid technique, which ultimately reduces the main issue into several smaller problems. Comparing this method to existing models, we find that it is computationally feasible because it uses less memory and operates with a coarse grid instead of a fine grid and thus improves convergence rates for complex and nonlinear problems. Furthermore, it shows mesh independence, supports potential parallelization, and is crucial for advanced multigrid techniques. The optimality of the error is confirmed both theoretically and numerically. The numerical experimental section validates the model through three types of numerical experiments.

Comparative Analysis of State-of-the-Art Q A Models: BERT, RoBERTa, DistilBERT, and ALBERT on SQuAD v2 Dataset

In the rapidly evolving landscape of natural language processing (NLP) and artificial intelligence, recent years have witnessed significant advancements, particularly in text-based question-answering (QA) systems. The Stanford Question Answering Dataset (SQuAD v2) has emerged as a prominent benchmark, offering diverse language understanding challenges. This study conducts a thorough examination of cutting-edge QA models—BERT, DistilBERT, RoBERTa, and ALBERT—each featuring distinct architectures, focusing on their training and performance on SQuAD v2. The analysis aims to uncover the unique strengths of each model, providing insights into their capabilities and exploring the impact of different training techniques on their performance. The primary objective is to enhance our understanding of text-based QA systems' evolution and their effectiveness in real-world scenarios. The results of this comparative study are poised to influence the utilization and development of these models in both industry and research. The investigation meticulously evaluates BERT, ALBERT, RoBERTa, and DistilBERT QA models using the SQuAD v2 dataset, emphasizing instances of accurate responses and identifying areas where completeness may be lacking. This nuanced exploration contributes to the ongoing discourse on the advancement of text-based question-answering systems, shedding light on the strengths and limitations of each QA model. Based on the results obtained, ALBERT achieved an exact match of 86.85% and an F1 score of 89.91% on the SQuAD v2 dataset, demonstrating superior performance in both answerable ('HasAns') and unanswerable ('NoAns') questions. BERT and RoBERTa also showed strong performance, while DistilBERT lagged slightly behind. This study provides a significant contribution to the advancement of text-based question-answering systems, offering insights that can shape the utilization of these models in both industry and research domains.

Annotation Vocabulary (Might Be) All You Need.

Protein Language Models (pLMs) have revolutionized the computational modeling of protein systems, building numerical embeddings that are centered around structural features. To enhance the breadth of biochemically relevant properties available in protein embeddings, we engineered the Annotation Vocabulary, a transformer readable language of protein properties defined by structured ontologies. We trained Annotation Transformers (AT) from the ground up to recover masked protein property inputs without reference to amino acid sequences, building a new numerical feature space on protein descriptions alone. We leverage AT representations in various model architectures, for both protein representation and generation. To showcase the merit of Annotation Vocabulary integration, we performed 515 diverse downstream experiments. Using a novel loss function and only $3 in commercial compute, our premier representation model CAMP produces state-of-the-art embeddings for five out of 15 common datasets with competitive performance on the rest; highlighting the computational efficiency of latent space curation with Annotation Vocabulary. To standardize the comparison of de novo generated protein sequences, we suggest a new sequence alignment-based score that is more flexible and biologically relevant than traditional language modeling metrics. Our generative model, GSM, produces high alignment scores from annotation-only prompts with a BERT-like generation scheme. Of particular note, many GSM hallucinations return statistically significant BLAST hits, where enrichment analysis shows properties matching the annotation prompt - even when the ground truth has low sequence identity to the entire training set. Overall, the Annotation Vocabulary toolbox presents a promising pathway to replace traditional tokens with members of ontologies and knowledge graphs, enhancing transformer models in specific domains. The concise, accurate, and efficient descriptions of proteins by the Annotation Vocabulary offers a novel way to build numerical representations of proteins for protein annotation and design.

On the Complementarity between CMMN and iStar in Complex Domain Modeling

Case Management Modeling and Notation (CMMN) and iStar are two distinct, multi-purposed modeling techniques that may be used to represent organizational challenges at separate levels of abstraction. CMMN, a flexible process-oriented technique, aims to extract knowledge that enhances the representational capacity of activity flows for a specific case. Conversely, the iStar framework adopts a goal-oriented modeling approach, effectively capturing the interplay of social actors and their influence on the attainment of organizational objectives. While prior studies have explored methods for integrating these techniques to attain a more comprehensive understanding of the organizational landscape, these remain primarily associated with a distinct level of the organizational matrix (i.e., CMMN for operations and tactics, and iStar for more strategic aspects). As such, any effort to evaluate their semantic proximity appears fragmented, as it deals only with the partial association of specific notations and elements. This article describes the conduct of a dual-purposed literature review to identify specific criteria that might accommodate a more holistic assessment of the two modeling techniques; these criteria are then employed as a guiding framework to construct a set of propositions that articulate the areas of complementarity and/or divergence between these two techniques, as identified in previous research. These propositions are subsequently subjected to validation by domain experts, leveraging a real-world case study in the educational domain. The results show that there can be areas of semantic convergence between the two techniques, suggesting their parallel use to effectively model complex domain problems. Overall, the present study aims to crystallize an approach for conducting complex modeling comparisons that transcends technical considerations.

Improving inland precipitation forecast in China through data assimilation of microwave temperature sounding data from a three‐orbit constellation

AbstractMicrowave temperature sounders onboard polar‐orbiting satellites can provide global observation data twice a day, supplying a large amount of temperature information for global data assimilation and serving as a crucial instrument to improve operational numerical forecasts. However, regional numerical forecasts are still subject to a lack of polar‐orbiting satellite data within regional model domains, and even multiple polar‐orbiting satellites may simultaneously miss measurements. Establishing a three‐orbit observation system of polar‐orbiting satellites is crucial to improve the spatiotemporal coverage of polar‐orbiting satellite data. In this study, we investigate the impact of assimilating microwave temperature sounding data from a three‐orbit constellation on precipitation forecasts in inland China based on the data from the US afternoon‐orbit satellite NOAA‐19, the European morning‐orbit satellite Meteorological Operational satellite‐A and the Chinese early‐morning‐orbit satellite Fengyun‐3E (FY‐3E) launched recently. The research results indicate that there are data gaps at 0600 and 1800 UTC in the East Asian region only for the morning‐orbit and afternoon‐orbit satellite observations. The FY‐3E satellite can provide additional microwave temperature sounding observations over the eastern region of China, thus partially compensating for the gap in polar‐orbiting satellite data in China. Moreover, the additional assimilation of the FY‐3E data can further improve numerical forecasts, effectively adjusting the spatial structure and eastward movement of the weather system, thereby considerably increasing the prediction accuracy of rainfall location and intensity. Rolling‐prediction results show that the data from the three‐orbit constellation provide a stable and notable improvement in precipitation forecasts in inland China, especially for forecasts longer than nine hours and amounts of rainfall below 10 mm. These research findings provide valuable insights for optimizing the assimilation application of polar‐orbiting satellite data in regional numerical forecasts.

Frequency response sensitivity to crack for piezoelectric FGM beam subjected to moving load

Since functionally graded material (FGM) is increasingly used in high-tech engineering, free and forced vibrations of FGM structures become an important issue. This report addresses the analysis of frequency response sensitivity to crack for piezoelectric FGM beams subjected to moving load. First, a frequency domain model of a cracked FGM beam with a piezoelectric layer is conducted to derive an explicit expression of the electrical charge produced in the piezoelectric layer under the moving load. It was shown in the previous works of the authors that the electrical charge is a reliable representation of the beam frequency response to moving load and can be efficiently employed as a measured diagnostic signal for structural health monitoring. Then, a damage indicator acknowledged as a spectral damage index (SDI) calculated from the electrical frequency response is introduced and used for sensitivity analysis of the response to crack. Under the sensitivity analysis the effect also of FGM and moving load parameters on the sensitivity is examined and illustrated by numerical results.

Proteostasis as a fundamental principle of Tau immunotherapy.

The microtubule-associated protein Tau is a driver of neuronal dysfunction in Alzheimer's disease and other tauopathies. In this process, Tau initially undergoes subtle changes to its abundance, subcellular localisation and a vast array of post-translational modifications including phosphorylation, that progressively result in the protein's somatodendritic accumulation and dysregulation of multiple Tau-dependent cellular processes. Given the various loss- and gain-of-functions of Tau in disease and the brain-wide changes in the proteome that characterise tauopathies, we asked whether targeting Tau would restore the alterations in proteostasis observed in disease. Therefore, by phage display, we generated a novel pan-Tau antibody, RNJ1, that preferentially binds human Tau and neutralises proteopathic seeding activity in multiple cell lines, and benchmarked it against a clinically tested pan-Tau antibody, HJ8.5 (murine version of tilavonemab). We then evaluated both antibodies, alone and in combination, in the K3 tauopathy mouse model, showing reduced Tau pathology and improvements in neuronal function following 14 weekly treatments, without obtaining synergy for the combination. These effects were more pronounced in female mice. To investigate the molecular mechanisms contributing to improvements in neuronal function, we employed quantitative proteomics, phosphoproteomics and kinase prediction analysis to first establish alterations in K3 mice relative to WT controls at the proteome level. In female K3 mice, we found 342 differentially abundant proteins, which are predominantly involved in metabolic and microtubule-associated processes, strengthening previously reported findings of defects in several functional domains in multiple tauopathy models. We next asked whether antibody-mediated Tau target engagement indirectly affects levels of deregulated proteins in the K3 model. Importantly, both immunotherapies, in particular RNJ1, induced abundance shifts towards a restoration to wild-type levels (proteostasis). A total of 257 of 342 (∼75%) proteins altered in K3 were closer in abundance to WT levels after RNJ1 treatment, and 73% after HJ8.5 treatment. However, the magnitude of these changes was less pronounced than that observed with RNJ1, as reflected by a far smaller number of differentially abundant proteins. Furthermore, analysis of the phosphoproteome showed an even stronger restoration effect with RNJ1, with ∼82% of altered phosphopeptides in K3 showing a shift to WT levels, and 75% with HJ8.5. Gene set over-representation analysis (ORA) further confirmed that proteins undergoing restoration are involved in biological pathways affected in K3 mice. Together, our study suggests that a Tau immunotherapy-induced restoration of proteostasis links target engagement and treatment efficacy.