Specific Class Research Articles

An adaptive, provable correct simplex architecture

Abstract Simplex architectures optimize performance and safety by switching between an advanced controller and a base controller. We propose an approach to synthesize the switching logic and extensions of the base controller in the Simplex architectures to achieve high performance and provable correctness for a rich class of temporal specifications by maximizing the time the advanced controller is active. We achieve provable correctness by performing static verification of the baseline controller. The result of this verification is a set of states that is proven to be safe, called the recoverable region. We employ proofs on demand to ensure that the base controller is safe in those states that are visited during runtime, which depends on the advanced controller. Verification of hybrid systems is often overly conservative, resulting in smaller recoverable regions that cause unnecessary switches to the baseline controller. To avoid these switches, we invoke targeted reachability queries to extend the recoverable region at runtime. In case the recoverable region cannot be extended using the baseline controller, we employ a repair procedure. This tries to synthesize a patch for the baseline controller and can further extend the recoverable region. Our offline and online verification relies upon reachability analysis since it allows observation-based extension of the known recoverable region. We implemented our methodology on top of the state-of-the-art tool HyPro which allowed us to automatically synthesize verified and performant Simplex architectures for advanced case studies, like safe autonomous driving on a race track.

Satisfiability thresholds for regular occupation problems

Abstract In the last two decades the study of random instances of constraint satisfaction problems (CSPs) has flourished across several disciplines, including computer science, mathematics and physics. The diversity of the developed methods, on the rigorous and non-rigorous side, has led to major advances regarding both the theoretical as well as the applied viewpoints. Based on a ceteris paribus approach in terms of the density evolution equations known from statistical physics, we focus on a specific prominent class of regular CSPs, the so-called occupation problems, and in particular on $r$ -in- $k$ occupation problems. By now, out of these CSPs only the satisfiability threshold – the largest degree for which the problem admits asymptotically a solution – for the $1$ -in- $k$ occupation problem has been rigorously established. Here we determine the satisfiability threshold of the $2$ -in- $k$ occupation problem for all $k$ . In the proof we exploit the connection of an associated optimization problem regarding the overlap of satisfying assignments to a fixed point problem inspired by belief propagation, a message passing algorithm developed for solving such CSPs.

Seaweed-Based Bioplastics: Data Mining Ingredient–Property Relations from the Scientific Literature

Automated analysis of the scientific literature using natural language processing (NLP) can accelerate the identification of potentially unexplored formulations that enable innovations in materials engineering with fewer experimentation and testing cycles. This strategy has been successful for specific classes of inorganic materials, but their general application in broader material domains such as bioplastics remains challenging. To begin addressing this gap, we explore correlations between the ingredients and physicochemical properties of seaweed-based biofilms from a corpus of 2000 article abstracts from the scientific literature since 1958, using a supervised word co-occurrence analysis and an unsupervised approach based on the language model MatBERT without fine-tuning. Using known relations between ingredients and properties for test scenarios, we discuss the potential and limitations of these NLP approaches for identifying novel combinations of polysaccharides, plasticizers, and additives that are related to the functionality of seaweed biofilms. The model demonstrates a valuable predictive ability to identify ingredients associated with increased water vapor permeability, suggesting its potential utility in optimizing formulations for future research. Using the model further revealed alternative combinations that are underrepresented in the literature. This automated method facilitates the mapping of relationships between ingredients and properties, guiding the development of seaweed bioplastic formulations. The unstructured and heterogeneous nature of the literature on bioplastics represents a particular challenge that demands ad hoc fine-tuning strategies for state-of-the-art language models for advancing the field of seaweed bioplastics.

Breaking the degeneracy in stellar spectral classification from single wide-band images

The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of view. This is particularly important for weak gravitational lensing studies, where precise PSF modelling is essential to get accurate shear measurements. Previous research has demonstrated that the SED of stars can be inferred from low-resolution observations using machine-learning classification algorithms. However, a degeneracy exists between the PSF size, which can vary significantly across the field of view, and the spectral type of stars, leading to strong limitations of such methods. We propose a new SED classification method that incorporates stellar spectral information by using a preliminary PSF model, thereby breaking this degeneracy and enhancing the classification accuracy. Our method involves calculating a set of similarity features between an observed star and a preliminary PSF model at different wavelengths and applying a support vector machine to these similarity features to classify the observed star into a specific stellar class. The proposed approach achieves a 91% top-two accuracy, surpassing machine-learning methods that do not consider the spectral variation of the PSF. Additionally, we examined the impact of PSF modelling errors on the spectral classification accuracy.

Multiple Myeloma: Improved Outcomes Resulting from a Rapidly Expanding Number of Therapeutic Options.

Multiple myeloma (MM) is a bone-marrow-based cancer of plasma cells. Over the last 2 decades, marked treatment advances have led to improvements in the overall survival (OS) of patients with this disease. Key developments include the use of chemotherapy, immunomodulatory drugs, proteasome inhibitors, and monoclonal antibodies. MM remains incurable, with outcomes influenced by many factors, including age, sex, genetics, and treatment response. This review summarizes recent studies regarding monitoring and treatment of MM, emphasizing the efficacy of new therapies, the impact of maintenance treatments, and approaches for managing relapsed or refractory MM. The role of specific drug classes used to treat MM, including immunomodulatory drugs, proteasome inhibitors, monoclonal antibodies, and newer treatments such as chimeric antigen receptor T-cell therapies and bispecific antibodies are discussed. Combination therapies have significantly improved outcomes. Maintenance therapies, particularly with lenalidomide, have been effective in extending OS but lead to an increased risk of secondary cancers. Venetoclax, selinexor, and ruxolitinib have shown potential as new therapeutic options for patients with relapsed or refractory MM. Immune-based treatments, such as chimeric antigen receptor T-cell therapy and bispecific antibodies, mark a major advancement for heavily pretreated patients, although challenges remain related to cost, availability, and side effects. The treatment landscape for patients with MM has seen significant progress, with current therapies providing a longer OS and better quality of life. Future research should focus on optimizing these strategies, personalizing therapies, and exploring new therapeutic targets.

Long Intergenic Non-Coding RNAs and BRCA1 in Breast Cancer Pathogenesis: Neighboring Companions or Nemeses?

Breast cancer is one of the leading causes of mortality among women, primarily due to its complex molecular landscape and heterogeneous nature. The tendency of breast cancer patients to develop metastases poses significant challenges in clinical management. Notably, mutations in the breast cancer gene 1 (BRCA1) significantly elevate breast cancer risk. The current research endeavors employ diverse molecular approaches, including RNA, DNA, and protein studies, to explore avenues for the early diagnosis and treatment of breast cancer. Recent attention has shifted towards long non-coding RNAs (lncRNAs) as promising diagnostic, prognostic, and therapeutic targets in the multifaceted progression of breast cancer. Among these, long intergenic non-coding RNAs (lincRNAs), a specific class of lncRNAs, play critical roles in regulating various aspects of tumorigenesis, including cell proliferation, apoptosis, epigenetic modulation, tumor invasion, and metastasis. Their distinctive expression patterns in cellular and tissue contexts underscore their importance in breast cancer development and progression. Harnessing lincRNAs’ sensitivity and precision as diagnostic, therapeutic, and prognostic markers holds significant promise for the clinical management of breast cancer. However, the potential of lincRNAs remains relatively underexplored, particularly in the context of BRCA1-mutated breast cancer and other clinicopathological parameters such as receptor status and patient survival. Consequently, there is an urgent need for comprehensive investigations into novel diagnostic and prognostic breast cancer biomarkers. This review examines the roles of lincRNAs associated with BRCA1 in the landscape of breast cancer, highlighting the potential avenues for future research and clinical applications.

Fear conditioning modulates the intrinsic excitability of ventral hippocampal CA1 neurons in male rats.

The hippocampus has a known role in learning and memory, with the ventral subregion supporting many learning tasks involving affective responding, including fear conditioning. Altered neuronal intrinsic excitability reflects experience-dependent plasticity that supports learning-related behavioral changes. Such changes have previously been observed in the dorsal hippocampus following fear conditioning, but little work has examined the effect of fear conditioning on ventral hippocampal intrinsic plasticity. The present study tested the hypothesis that acquisition of trace and context fear conditioning alters intrinsic excitability of specific classes of ventral hippocampal CA1 neurons in male rats. We observed distinct changes in excitability that were specific to cell type as well as learning paradigm. Specifically, regular-spiking ventral hippocampal CA1 neurons demonstrated increased excitability following context fear conditioning, and these changes were correlated with context fear retrieval. In contrast, trace fear conditioning resulted in increased excitability of ventral hippocampal CA1 late-spiking neurons from good learners relative to poor learners. Together, these data demonstrate ventral hippocampal CA1 neuronal excitability is finely tuned to support fear memory in a learning- and firing type-specific manner.

Automated Monitoring of Web User Interfaces

Application development for the modern Web involves sophisticated engineering workflows – including user interface (UI) aspects. Such user interfaces comprise Web elements that are typically created with HTML/CSS markup and JavaScript-like languages, yielding Web documents. Their testing entails performing checks to examine visual and structural parts of the resulting UI software against requirements such as usability, accessibility, performance, or, increasingly, compliance with standards. However, current techniques are largely ad-hoc and tailor-made to specific classes of requirements or Web technologies and extensively require human-in-the-loop qualitative evaluations. Web UI evaluation so far has lacked formal foundations, which would provide assurances of compliance with requirements in an automatic manner. To this end, we devise a methodology and accompanying technical framework for web UIs. In our approach, requirements are formally specified in a spatio-temporal logic able to capture both the layout of visual components as well as how they change over time, as a user interacts with them. The technique we advocate is independent of the underlying technologies a Web application may be developed with, as well as the browser and operating system used. To concretely support the specification and evaluation of UI requirements, our framework is grounded on open-source tools for instrumenting, analyzing, and reporting spatio-temporal behaviors in webpages. We demonstrate our approach in practice over Web accessibility standards posing challenges for automated verification.

BayesBay: A Versatile Bayesian Inversion Framework Written in Python

Abstract The lack of versatile tools for Bayesian inference presents a significant challenge to researchers in geophysics, who often resort to developing bespoke codes to address specific classes of inverse problems. In this study, we present BayesBay, a Python package for generalized transdimensional and hierarchical Markov chain Monte Carlo sampling. Leveraging object-oriented programming principles, BayesBay facilitates the definition of Bayesian sampling problems across a range of applications. This includes joint inversions of multiple data sets with different forward functions and unknown noise properties, as well as complex parameterizations involving multiple parameters with unknown dimensionality and/or spatially varying priors. We illustrate BayesBay from both a technical and a practical perspective. The first two applications are common in geophysics: a 2D tomographic problem and a joint inversion for the 1D subsurface structure. The third involves partition modeling and requires a sophisticated parameterization with two nested levels of transdimensionality. In all cases, BayesBay recovers known solutions, highlighting its potential to address a broad range of inverse problems.

Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition.

Artificial intelligence technology has demonstrated remarkable diagnostic efficacy in modern biomedical image analysis. However, the practical application of artificial intelligence is significantly limited by the presence of similar pathologies among different diseases and the diversity of pathologies within the same disease. To address this issue, this paper proposes a reinforced collaborative-competitive representation classification (RCCRC) method. RCCRC enhances the contribution of different classes by introducing dual competitive constraints into the objective function. The first constraint integrates the collaborative space representation akin to holistic data, promoting the representation contribution of similar classes. The second constraint introduces specific class subspace representations to encourage competition among all classes, enhancing the discriminative nature of representation vectors. By unifying these two constraints, RCCRC effectively explores both global and specific data features in the reconstruction space. Extensive experiments on various biomedical image databases are conducted to exhibit the advantage of the proposed method in comparison with several state-of-the-art classification algorithms.

P0002 Identification of Infliximab and Adalimumab -derived peptides using MHC class II antibody mediated immunopeptidomics and mass spectrometry: implications for drug re-design

Abstract Background Monoclonal antibodies against tumour necrosis factor (TNF) such as Infliximab (IFX) and Adalimumab (ADA) are used as first line therapy against inflammatory bowel disease (IBD) and have proven efficacy. However, in up to 40% of IBD patients these drugs become ineffective after the first year, principally due to the development of anti-drug immunogenicity. Distinct allelic variants at the HLA DQ1 locus predispose patients to develop immunogenicity to IFX or ADA (1, 2, 3). The aim of this study is to identify peptides derived from IFX and ADA that are presented on class II MHC of antigen presenting cells, in order to determine the epitopes that may play a role in T cell mediated immunogenicity against anti-TNF therapy. This may allow rational drug re-design. Methods Monocyte-derived dendritic cells (MDDCS) from 8 different donors, as well as EBV-transformed B cell lines from 3 different donors of known HLA-type were cultured and pulsed with IFX and ADA. Unpulsed cells were used as a negative control. Cells were processed for sequential immune precipitation with HLA-DQ, HLA-DP and HLA-DR antibodies. Peptides were then purified and analysed by mass spectrometry. These were run against a database of known proteins, including IFX and ADA, using the Peaks analytical software and then mapped to the original protein. Peptides were subjected to a binding prediction algorithm, to determine the likelihood of individual peptides binding to each HLA allele combinations. Peptides were then ranked from highest to lowest binder. Results Analysis of peptides from all three MHC class II antibody fractions showed that the highest number of peptides immunoprecipitated were between 14 to 16 amino acids in length, in line with the predicted length for class II MHC proteins. Additionally, there was an enrichment of peptides predicted to bind to specific class II MHC molecules, with an average of 77% binders versus 23% non-binders for MDDCs, and 84% binders versus 16 % non-binders for B-cells, after immune precipitation. Several overlapping peptides from the variable regions of both the heavy and light chain of IFX and ADA were identified, some of which are predicted to bind to risk alleles for anti-TNF immunogenicity, including DQA1*05:01, DRB1*11:01 and HLA-DQA1*05:05 (Figures 1 and 2). Conclusion We have characterised a series of IFX and ADA derived peptides from MDDCs and B cell lines, that were pulsed with anti-TNF antibody drugs. These peptides represent candidate T cell epitopes that may play a role in eliciting anti-drug antibody responses, and these data will therefore contribute to the understanding of the molecular mechanism of anti-TNF treatment failure. References 1)Doherty R, Liao H, Satsangi J, Ternette N. Extended Analysis Identifies Drug-Specific Association of 2 Distinct HLA Class II Haplotypes for Development of Immunogenicity to Adalimumab and Infliximab. Gastroenterology. 2020, 159: 784-787. 2)Sazonovs A, Kennedy N, Moutsianas L, et al. HLA-DQA1*05 Carriage Associated With Development of Anti-Drug Antibodies to Infliximab and Adalimumab in Patients With Crohn’s Disease Gastroenterology. 2020, 158:189-199 3)Ternette N, Liao H., Satsangi J. Association of the HLA DQA1*05 allelic gene variants with immunogenicity to anti-TNF therapeutics - important differences between infliximab and adalimumab. Journal of Crohn's and Colitis. 2024

Lipid Metabolism Alterations in Hyperlipidemic Dogs with Biliary Tract or Endocrine Diseases.

Fasting hyperlipidemia results from lipid metabolism defects associated with alterations in specific lipoprotein classes. These changes may originate from genetic predispositions or underlying metabolic disorders, including cholestasis and endocrine diseases. This retrospective study aimed to analyze variations in lipoprotein electrophoresis (LPE) profiles in hyperlipidemic dogs and investigate the associations between biliary tract diseases, endocrine disorders, and lipid metabolism. Sixty-five dogs that underwent LPE were classified into non-hyperlipidemic (NHL) and hyperlipidemic (HL) groups, with further subdivisions based on the presence of biliary tract disease, endocrine disorders, or breed predisposition (Miniature Schnauzers). Dogs in the HL group with biliary tract disease and those with underlying metabolic disorders exhibited significantly higher pre-beta fractions and cholesterol concentrations compared to the NHL group. Additionally, significant linear relationships were observed between serum gamma-glutamyl transpeptidase (GGT) levels and both pre-beta fractions and cholesterol concentrations. The Schnauzer group showed lower alpha fractions and higher triglyceride and cholesterol levels than the NHL group. These findings suggest that LPE is a valuable and useful tool for detecting lipid metabolism abnormalities. Clinicians should consider the potential for altered lipid metabolism when evaluating patients with biliary tract or endocrine diseases in small animal practice.

Mapping neutron biological effectiveness for DNA damage induction as a function of incident energy and depth in a human sized phantom

We present new developments for an ab-initio model of the neutron relative biological effectiveness (RBE) in inducing specific classes of DNA damage. RBE is evaluated as a function of the incident neutron energy and of the depth inside a human-sized reference spherical phantom. The adopted mechanistic approach traces neutron RBE back to its origin, i.e. neutron physical interactions with biological tissues. To this aim, we combined the simulation of radiation transport through biological matter, performed with the Monte Carlo code PHITS, and the prediction of DNA damage using analytical formulas, which ground on a large database of biophysical radiation track structure simulations performed with the code PARTRAC. In particular, two classes of DNA damage were considered: sites and clusters of double-strand breaks (DSBs), which are known to be correlated with cell fate following radiation exposure. Within a coherent modelling framework, this approach tackles the variation of neutron RBE in a wide energy range, from thermal neutrons to neutrons of hundreds of GeV, and reproduces effects related to depth in the human-sized receptor, as well as to the receptor size itself. Besides providing a better mechanistic understanding of neutron biological effectiveness, the new model can support better-informed decisions for radiation protection: indeed, current neutron weighting (ICRP)/quality (U.S. NRC) factors might be insufficient for use in some radiation protection applications, because they do not account for depth. RBE predictions obtained with the reported model were successfully compared to the currently adopted radiation protection standards when the depth information is not relevant (at the shallowest depth in the phantom or for very high energy neutrons). However, our results demonstrate that great care is needed when applying weighting factors as a function of incident neutron energy only, not explicitly considering RBE variation in the target. Finally, to facilitate the use of our results, we propose look-up RBE tables, explicitly considering the depth variable, and an analytical representation of the maximal RBE vs. neutron energy.

Morse Theory, Discrete Morse Theory and Applications

By employing a specific class of smooth functions to study a space, Morse theory establishes deep connections between analysis and topology. It is a classical subject of pure mathematics, originally pioneered by Marston Morse in the 1920s. In this article, we use Morse theory to present a proof of an interesting result on the knots, known as the Fry-Milnor theorem. We also discuss discrete Morse theory, a subject of applied mathematics developed by Robin Forman in the 1990s, and its application. We focus on elucidating especially the inherent similarity between classical Morse theory and discrete Morse theory

Boundedness of maximal functions in Lp spaces

In this paper, we derive suitable Lp bounds for a specific class of maximal operators defined along the surface of revolution, where the kernels belong to Lq(Sn-1) with q > 1. Using these bounds in conjunction with extrapolation techniques, we establish the Lp boundedness of the maximal operator when its kernels lie in the block space Bq(0, s-1)(Sn-1) or in (L log L)s(Sn-1).

Antibiotic Use and Subsequent Cognitive Decline and Dementia Risk in Healthy Older Adults.

Antibiotics rapidly reduce intestinal bacterial diversity, leading to dysbiosis that persists for months to years. Although emerging evidence from retrospective and claims-based studies has linked dysbiosis to cognitive function, prospective data are lacking. We aim to examine the prospective association of antibiotics with cognitive aging among initially healthy older adults. We leveraged data from prospective follow-up and observational extension of ASPirin in Reducing Events in the Elderly, a completed randomized trial of community-based Australian older adults. Among participants whose prescription records were available and without dementia during the first 2 years of follow-up, we identified any or repeated antibiotic use based on the Anatomical Therapeutic Chemical code (J01). We assessed the associations of antibiotic use during the first 2 years with longitudinal changes in standardized composite and domain-specific cognitive scores (global cognition, episodic memory, language and executive function, and psychomotor speed) using linear mixed models, and with incident, clinically adjudicated dementia (Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition criteria) and incident cognitive impairment, no dementia (CIND, without a dementia trigger but with significant, nontransient decline), using Cox proportional hazard models. Over a median of 4.7 years after the second follow-up visit, we documented 461 dementia and 2,576 CIND cases among 13,571 participants (mean age ± SD 75.0 ± 4.1 years, 54.3% female). Compared with nonuse, antibiotic use was not associated with increased risks for dementia (hazard ratio [HR] 1.03; 95% CI 0.84-1.25), CIND (HR 1.02; 95% CI 0.94-1.11), or subsequent declines in cognitive scores, after adjusting for sociodemographic, lifestyle factors, family history of dementia, baseline cognitive function, and medications known to affect cognition. There were also no associations according to the cumulative frequency of antibiotic use, long-term use, specific antibiotic classes (e.g., beta-lactams, tetracyclines, and sulfonamides), and subgroups defined by risk factors. Among initially healthy older adults, any or repeated antibiotic use was not associated with incident dementia, CIND, or accelerated cognitive decline. Although prescription data may not reflect the actual use, we examined the frequency of antibiotics within a defined period to capture the extent and duration of antibiotic exposure. Our results do not support an association between antibiotic-associated gut microbiome disruption and dementia risk.

Issues in the Expansion and Contraction of Operators

This paper explores fundamental issues in the correct contraction and expansion of operators, with a primary focus on the concept of symmetry within operator theory. Special attention is given to how symmetry influences the behavior of operators, particularly regarding their approximation and convergence properties. In the domains of quantum mechanics and condensed matter physics, such operators are essential for modeling phenomena like superconductivity, excitons, and surface states. The symmetric properties of operators have a profound impact on the physical interpretations and predictions these models generate. A rigorous analysis is provided regarding the existence of correct contractions and expansions for a specific class of nonlinear operators, demonstrating how symmetry affects the structural integrity of operators under natural conditions. The study presents a comprehensive description of the set of all correct contractions, expansions, and regular expansions, with an application to a third-order nonlinear differential expression. Additionally, a condition for the unique solvability of a Bitsadze–Samarskii-type problem is derived, showcasing how symmetry plays a crucial role in guiding the solution of complex physical models. Furthermore, the paper emphasizes the importance of preserving symmetry in the construction of operators, ensuring the consistency and accuracy of mathematical models. This has significant implications for both theoretical research and practical applications in various fields, including nuclear physics and quantum theory.

A retrospective analysis of medications associated with pityriasis rosea reported in the FDA adverse events reporting system

Pityriasis rosea (PR) is an acute exanthematous disease with an uncertain physiopathology, increasingly recognized as potentially drug induced. This study aims to investigate medication triggers associated with PR by analyzing cases reported in the FDA Adverse Event Reporting System (FAERS) database. A retrospective review of 343 PR cases reported in the FAERS database from January 1, 1998, to March 31, 2024, was conducted. Reporting odds ratios (ROR) were calculated to assess associations between PR and specific drug classes, including tumor necrosis factor (TNF) inhibitors and angiotensin-converting enzyme (ACE) inhibitors. Logistic regression analysis evaluated the influence of factors such as sex, age group, and seriousness of outcomes on the occurrence of PR. Females represented 56.3% of cases and the 18–64 age group comprised 55.4% of cases. TNF inhibitors were significantly associated with PR (ROR = 4.1881 [3.1970–5.4865], P < 0.0001), particularly infliximab (ROR = 6.5284 [3.9523–10.7837], P < 0.0001), etanercept (ROR = 3.4921 [2.2873–5.3315], P < 0.0001), and adalimumab (ROR = 3.086 [2.0213–4.7115], P < 0.0001). ACE inhibitors were also associated with PR (ROR = 9.9808 [6.0423–16.4864], P < 0.0001), with higher odds in older patients (OR 14.08 [4.2–47.2], P < 0.0001) and those reporting serious outcomes (OR 9.53 [1.24–72.99], P = 0.03). Based on the FAERS, there has been a consistent rise in PR cases, with TNF inhibitors and ACE inhibitors being associated medication classes tied to PR. Given the limited literature on drug-related triggers and patient demographics, we aimed to highlight the characteristics of PR cases that could enhance awareness and inform better clinical outcomes for affected patients.

Probabilistic estimation of rheological properties in subduction zones using sequences of earthquakes and aseismic slip

Abstract Constraining the effective rheology of major faults contributes to improving our understanding of the physics of plate boundary deformation. Geodetic observations over the earthquake cycle are often used to estimate key rheological parameters, assuming specific laboratory-informed classes of viscous or frictional rheological models. However, differentiating between various rheological model classes using only observations of a single earthquake (coseismic and postseismic deformation) is difficult—especially in the presence of coarse spatiotemporal sampling, inherent observational noise, and non-uniqueness of the inverted properties. In this study, we present a framework to estimate key rheological parameters of a subduction zone plate interface using simulations of sequences of earthquakes and aseismic slip, constrained by pre- and postseismic surface displacement timeseries. Our simplified forward model consists of a two-dimensional subduction zone, represented by a discretized planar fault or narrow shear zone, divided into a locked, shallow region (“asperity”) experiencing periodically imposed coseismic events, and a stress-driven creeping section governed by power-law viscoelasticity or rate-dependent friction. Our inverse model fits the rheological parameters of the interface to surface displacement timeseries in a Bayesian probabilistic way. We validate that our proposed framework can successfully recover depth-dependent profiles of effective viscosity using a synthetic dataset of pre- and postseismic observations. Our first set of numerical experiments show that our framework is only mildly sensitive to uncertainties in the rupture history or assumed coseismic slip, making it robust enough to be applied to real observations of subduction zones. Our second set of tests considers the similarities of surface displacement timeseries between synthetic models that model the plate interface either as a shear zone described by power-law viscosity, or a surface described by rate-dependent friction. Here, we find that the ability to fit surface observations using functional or mechanical models assuming frictional behavior does not constitute sufficient evidence to actually infer frictional behavior at depth, as the surface expressions are virtually indistinguishable from deformation generated from models with depth-variable power-law viscous behavior. Based on our numerical experiments, we conclude that studies that aim to infer the mechanical behavior and rheological properties at depth in subduction zones should consider the surface expression from time periods representative of the entire seismic cycle. Graphical Abstract

Dissipation enhancing properties for a class of Hamiltonian flows with closed streamlines

We study the evolution of a passive scalar subject to molecular diffusion and advected by an incompressible velocity field on a 2D bounded domain. The velocity field is u = ∇ ⊥ H , where H is an autonomous Hamiltonian whose level sets are Jordan curves foliating the domain. We focus on the high Péclet number regime ( Pe := ν − 1 ≫ 1 ), where two distinct processes unfold on well separated time-scales: streamline averaging and standard diffusion. For a specific class of Hamiltonians with one non-degenerate elliptic point (including perturbed radial flows), we prove exponential convergence of the solution to its streamline average on a subdiffusive time-scale T ν ≪ ν − 1 , up to a small correction related to the shape of the streamlines. The time-scale T ν is determined by the behavior of the period function around the elliptic point. To establish this result, we introduce a model problem arising naturally from the difference between the solution and its streamline average. We use pseudospectral estimates to infer decay in the model problem, and, in fact, this analysis extends to a broader class of Hamiltonian flows. Finally, we perform an asymptotic expansion of the full solution, revealing that the leading terms consist of the streamline average and the solution of the model problem.