Efficacy and cost-effectiveness of a digital guided self-management intervention to support transition from intensive care to community care in anorexia nervosa (TRIANGLE): pragmatic multicentre randomised controlled trial and economic evaluation.

Background: There is uncertainty regarding how best to support patients with anorexia nervosa following inpatient or day care treatment. This study evaluated the impact of augmenting intensive treatment with a digital, guided, self-management intervention (ECHOMANTRA) for patients with anorexia nervosa and their carers. Methods: In this pragmatic multicentre randomised controlled trial and economic evaluation, patients with a diagnosis of anorexia nervosa or atypical anorexia nervosa, aged 16+ and attending one of the 31 inpatient or day-patient services in the UK were randomised with one of their carers to receive ECHOMANTRA plus treatment as usual (TAU), or TAU alone. ECHOMANTRA was hosted on a digital platform and included a workbook, recovery-oriented video-clips and online facilitated groups (patients only, carers only, joint patient-carer). Participants were randomised on a 1:1 ratio using a minimisation algorithm to stratify by site (N = 31) and severity (defined by BMI <15 and ≥ 15 kg/m2 at baseline). The primary outcome was patient depression, anxiety, and stress at 12 months. Primary and secondary outcomes were compared between trial arms on an intention-to-treat basis (ITT). This trial is registered with the ISRSTN registry, ISRCTN14644379. Findings: Between July 01, 2017 and July 20, 2020, 371 patient-carer dyads were enrolled and randomly assigned to ECHOMANTRA + TAU (N = 185) or TAU alone (N = 186). There were no significant differences between trial arms with regards to the primary outcome (completed by N = 143 patients in the TAU group, Mean = 61.7, SD = 29.4 and N = 109 patients in the ECHOMANTRA + TAU group, Mean = 58.3, SD = 26.9; estimated mean difference 0.48 points; 95% CI -5.36 to 6.33; p = 0.87). Differences on secondary outcomes were small and non-significant (standardised effect size estimates ≤0.25). Five patients died (2 from suicide and 3 from physical complications) over the course of the trial, and this was unrelated to their participation in the study. Interpretation: ECHOMANTRA added to TAU was not superior to TAU alone in reducing patient depression, anxiety, and stress symptoms. This may be explained by limited engagement with the intervention materials and changes in usual care practices since the beginning of the trial. Funding: National Institute for Health Research (NIHR), under its Health Technology Assessment Programme (HTA) Programme (Grant Reference Number 14/68/09). NIHR Maudsley Biomedical Research Centre (BRC), South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, Psychology and Neuroscience, and King's College London. NIHR Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust.

Transition support for patients admitted to intensive treatment for anorexia nervosa: qualitative study of patient and carer experiences of a hybrid online guided self-help intervention (ECHOMANTRA).

BACKGROUND: Adults with anorexia nervosa experience high levels of relapse following in-patient treatment. ECHOMANTRA is a novel online aftercare intervention for patients and carers, which provides psychoeducation and support to augment usual care. AIMS: To explore patient and carer experiences of receiving the ECHOMANTRA intervention. METHOD: This is part of the process evaluation of the ECHOMANTRA intervention as delivered in the TRIANGLE trial (ISRCTN: 14644379). Semi-structured interviews were conducted with 20 participants randomised to the ECHOMANTRA (ten patients and ten carers). Thematic analysis was used to analyse the interview transcripts. RESULTS: Five major themes were identified: (1) Mixed experience of the intervention; (2) tailoring the intervention to the stage of recovery; (3) involvement of carers; (4) acceptability of remote support; and (5) impact of self-monitoring and accountability. CONCLUSIONS: Participants were mostly positive about the support offered. The challenges of using remote and group support were counterbalanced with ease of access to information when needed. Components of the ECHOMANTRA intervention have the potential to improve care for people with eating disorders.

Independently evolved pollution resistance in four killifish populations is largely explained by few variants of large effect.

The genetic architecture of phenotypic traits can affect the mode and tempo of trait evolution. Human-altered environments can impose strong natural selection, where successful evolutionary adaptation requires swift and large phenotypic shifts. In these scenarios, theory predicts that adaptation is due to a few adaptive variants of large effect, but empirical studies that have revealed the genetic architecture of rapidly evolved phenotypes are rare, especially for populations inhabiting polluted environments. Fundulus killifish have repeatedly evolved adaptive resistance to extreme pollution in urban estuaries. Prior studies, including genome scans for signatures of natural selection, have revealed some of the genes and pathways important for evolved pollution resistance, and provide context for the genotype-phenotype association studies reported here. We created multiple quantitative trait locus (QTL) mapping families using progenitors from four different resistant populations, and using RAD-seq genetically mapped variation in sensitivity (developmental perturbations) following embryonic exposure to a model toxicant PCB-126. We found that one to two large-effect QTL loci accounted for resistance to PCB-mediated developmental toxicity. QTLs harbored candidate genes that govern the regulation of aryl hydrocarbon receptor (AHR) signaling. One QTL locus was shared across all populations and another was shared across three populations. One QTL locus showed strong signatures of recent natural selection in the corresponding wild population but another QTL locus did not. Some candidate genes for PCB resistance inferred from genome scans in wild populations were identified as QTL, but some key candidate genes were not. We conclude that rapidly evolved resistance to the developmental defects normally caused by PCB-126 is governed by few genes of large effect. However, other aspects of resistance beyond developmental phenotypes may be governed by additional loci, such that comprehensive resistance to PCB-126, and to the mixtures of chemicals that distinguish urban estuaries more broadly, may be more genetically complex.

Differential DNA methylation and metabolite profiling of Atlantic killifish (Fundulus heteroclitus) from the New Bedford Harbor Superfund site.

Atlantic killifish (Fundulus heteroclitus) is a valuable model in evolutionary toxicology to study how the interactions between genetic and environmental factors serve the adaptive ability of organisms to resist chemical pollution. Killifish populations inhabiting environmental toxicant-contaminated New Bedford Harbor (NBH) show phenotypes tolerant to polychlorinated biphenyls (PCBs) and differences at the transcriptional and genomic levels. However, limited research has explored epigenetic alterations and metabolic effects in NBH killifish. To identify the involvement of epigenetic and metabolic regulation in the adaptive response of killifish, we investigated tissue- and sex-specific differences in global DNA methylation and metabolomic profiles of NBH killifish populations, compared to sensitive populations from a non-polluted site, Scorton Creek (SC). The results revealed that liver-specific global DNA hypomethylation and differential metabolites were evident in fish from NBH compared with those from SC. The sex-specific differences were not greater than the tissue-specific differences. We demonstrated liver-specific enriched metabolic pathways (e.g., amino acid metabolic pathways converged into the urea cycle and glutathione metabolism), suggesting possible crosstalk between differential metabolites and DNA hypomethylation in the livers of NBH killifish. Additional investigation of methylated gene regions is necessary to understand the functional role of DNA hypomethylation in the regulation of enzyme-encoding genes associated with metabolic processes and physiological changes in NBH populations.

Connecting Suborganismal Data to Bioenergetic Processes: Killifish Embryos Exposed to a Dioxin-Like Compound.

A core challenge for ecological risk assessment is to integrate molecular responses into a chain of causality to organismal or population-level outcomes. Bioenergetic theory may be a useful approach for integrating suborganismal responses to predict organismal responses that influence population dynamics. We describe a novel application of dynamic energy budget (DEB) theory in the context of a toxicity framework (adverse outcome pathways [AOPs]) to make quantitative predictions of chemical exposures to individuals, starting from suborganismal data. We use early-life stage exposure of Fundulus heteroclitus to dioxin-like chemicals (DLCs) and connect AOP key events to DEB processes through "damage" that is produced at a rate proportional to the internal toxicant concentration. We use transcriptomic data of fish embryos exposed to DLCs to translate molecular indicators of damage into changes in DEB parameters (damage increases somatic maintenance costs) and DEB models to predict sublethal and lethal effects on young fish. By changing a small subset of model parameters, we predict the evolved tolerance to DLCs in some wild F. heteroclitus populations, a data set not used in model parameterization. The differences in model parameters point to reduced sensitivity and altered damage repair dynamics as contributing to this evolved resistance. Our methodology has potential extrapolation to untested chemicals of ecological concern. Environ Toxicol Chem 2023;42:2040-2053. © 2023 Oak Ridge National Laboratory and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Independently evolved pollution resistance in four killifish populations is largely explained by few variants of large effect.

The genetic architecture of phenotypic traits can affect the mode and tempo of trait evolution. Human-altered environments can impose strong natural selection, where successful evolutionary adaptation requires swift and large phenotypic shifts. In these scenarios, theory predicts the influence of few adaptive variants of large effect, but empirical studies that have revealed the genetic architecture of rapidly evolved phenotypes are rare, especially for populations inhabiting polluted environments. Fundulus killifish have repeatedly evolved adaptive resistance to extreme pollution in urban estuaries. Prior studies, including genome scans for signatures of natural selection, have revealed some of the genes and pathways important for evolved pollution resistance, and provide context for the genotype-phenotype association studies reported here. We created multiple quantitative trait locus (QTL) mapping families using progenitors from four different resistant populations, and genetically mapped variation in sensitivity (developmental perturbations) following embryonic exposure to a model toxicant PCB-126. We found that a few large-effect QTL loci accounted for resistance to PCB-mediated developmental toxicity. QTLs harbored candidate genes that govern the regulation of aryl hydrocarbon receptor (AHR) signaling, where some (but not all) of these QTL loci were shared across all populations, and some (but not all) of these loci showed signatures of recent natural selection in the corresponding wild population. Some strong candidate genes for PCB resistance inferred from genome scans in wild populations were identified as QTL, but some key candidate genes were not. We conclude that rapidly evolved resistance to the developmental defects normally caused by PCB-126 is governed by few genes of large effect. However, other aspects of resistance beyond developmental phenotypes may be governed by additional loci, such that comprehensive resistance to PCB-126, and to the mixtures of chemicals that distinguish urban estuaries more broadly, may be more genetically complex.

Transitions from intensive eating disorder treatment settings: qualitative investigation of the experiences and needs of adults with anorexia nervosa and their carers.

BACKGROUND: Relapse rates for individuals with anorexia nervosa after intensive hospital treatment (in-patient or full-time day care) are high. Better knowledge about the difficulties and opportunities that arise during this transition is needed to identify factors that support or hinder continued recovery upon discharge. AIMS: The aim of this study was to explore the experiences of adult patients and their chosen carers on the process of transitioning from intensive eating disorder treatment settings to the community. METHOD: Semi-structured interviews were conducted with patients with anorexia nervosa (n = 11) discharged from day or in-patient care from specialised eating disorder units across the UK, and their chosen carers (n = 20). Data were analysed with inductive thematic analysis. RESULTS: Four interrelated themes were identified for both groups. For patients, themes were continuity of care, ambivalence about continued recovery, the value of social support and a call for enhanced transition support. For carers, themes were the impact of the eating disorder on themselves and the family, perceptions of recovery and support post-discharge, the impact of previous treatment and care experiences, and desire to create a supportive transition process. CONCLUSIONS: The study provides an insight into the unique challenges that individuals with anorexia nervosa face upon leaving intensive treatment. A lack of post-discharge planning, support system and identity formation outside of anorexia nervosa were perceived as barriers to continued recovery. Patients and carers advocated for transition support that incorporates a phased, inclusive approach with accessible professional and social support in the community.

A description of virtual skills workshops for supporters of loved ones with eating disorders: Indicators of benefit and acceptability, clinical reflections and consideration of neurodiversity.

OBJECTIVE: Providing information and support to those supporting a loved one with an eating disorder is a key part of evidence-based service provision. We report on how we took our workshops for supporters online during the Covid-19 Pandemic when country-side physical distancing restrictions meant we were unable to work face to face. METHODS: We outline the structure of an eight-session 2-h workshop series delivered fortnightly facilitated by a multidisciplinary team of clinicians, researchers and experts by experience. We use a repeated-measures design to understand the possible benefits of the workshops on supporter skills (n = 76). RESULTS: Measured using the Caregiver Skills Scale, we observed small-sized improvements in the overall skills (D = 0.43) of n = 17 supporters who provided data at the end of the intervention. Supporters gave largely positive feedback on the virtual format. They particularly liked the opportunity to interact with other supporters. As facilitators, we overcome our initial anxiety around workshop delivery using a new platform and reflected that having more time to cover key information and for skills practice over a period of 16 weeks offered opportunities to develop and reflect on new skill together as a group. We were also able to work with larger groups of supporters, as several barriers to access were removed. CONCLUSIONS: As the workshops reached a larger number of supporters than through face to face delivery and were of benefit to those who reported on their skills, we plan to continue offering workshops to supporters online in future.

Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation.

The theory of open quantum systems lays the foundation for a substantial part of modern research in quantum science and engineering. Rooted in the dimensionality of their extended Hilbert spaces, the high computational complexity of simulating open quantum systems calls for the development of strategies to approximate their dynamics. In this Letter, we present an approach for tackling open quantum system dynamics. Using an exact probabilistic formulation of quantum physics based on positive operator-valued measure, we compactly represent quantum states with autoregressive neural networks; such networks bring significant algorithmic flexibility due to efficient exact sampling and tractable density. We further introduce the concept of string states to partially restore the symmetry of the autoregressive neural network and improve the description of local correlations. Efficient algorithms have been developed to simulate the dynamics of the Liouvillian superoperator using a forward-backward trapezoid method and find the steady state via a variational formulation. Our approach is benchmarked on prototypical one-dimensional and two-dimensional systems, finding results which closely track the exact solution and achieve higher accuracy than alternative approaches based on using Markov chain Monte Carlo method to sample restricted Boltzmann machines. Our Letter provides general methods for understanding quantum dynamics in various contexts, as well as techniques for solving high-dimensional probabilistic differential equations in classical setups.

Mitigating the Sign Problem through Basis Rotations.

Quantum Monte Carlo simulations of quantum many-body systems are plagued by the Fermion sign problem. The computational complexity of simulating Fermions scales exponentially in the projection time ß and system size. The sign problem is basis dependent and an improved basis, for fixed errors, leads to exponentially quicker simulations. We show how to use sign-free quantum Monte Carlo simulations to optimize over the choice of basis on large two-dimensional systems. We numerically illustrate these techniques decreasing the "badness" of the sign problem by optimizing over single-particle basis rotations on one- and two-dimensional Hubbard systems. We find a generic rotation which improves the average sign of the Hubbard model for a wide range of U and densities for L×4 systems. In one example improvement, the average sign (and hence simulation cost at fixed accuracy) for the 16×4 Hubbard model at U/t=4 and n=0.75 increases by exp[8.64(6)ß]. For typical projection times of ß⪆100, this accelerates such simulation by many orders of magnitude.

An examination of social group memberships in patients with eating disorders, carers, and healthy controls.

This study compared the quantity and quality of social group memberships in patients with anorexia nervosa (n = 30), carers of patients with anorexia nervosa, unrelated to those patients (n = 30), and two cohorts of healthy controls (n = 60) age-matched to these focal groups. A secondary aim was to examine the associations between the quality of group relationships and severity of eating disorder and depression symptoms in patients; and depression symptoms in carers. Participants completed the online Social Identity Mapping Tool, which was used to measure the quantity and quality of social group memberships (e.g., number of social groups, number of groups rated 'highly positive'). Participants also completed self-report measures of clinical symptoms. Compared to controls, patients reported fewer social groups when eating disorder-related groups were included, and significantly fewer social groups, and community groups in particular, when eating disorder-related groups were excluded. Number of positive groups was negatively associated with severity of eating disorder and depression symptoms in patients when eating disorder-related groups were excluded. Carers reported fewer groups overall, fewer family groups, and fewer positive and supportive groups compared to healthy controls. There was a weak association between the number of positive groups and the severity of depression symptoms in carers. Positive group memberships might play a protective role towards developing more severe eating disorder and depression symptoms.

Numerical Evidence for Many-Body Localization in Two and Three Dimensions.

Disorder and interactions can lead to the breakdown of statistical mechanics in certain quantum systems, a phenomenon known as many-body localization (MBL). Much of the phenomenology of MBL emerges from the existence of ℓ bits, a set of conserved quantities that are quasilocal and binary (i.e., possess only ±1 eigenvalues). While MBL and ℓ bits are known to exist in one-dimensional systems, their existence in dimensions greater than one is a key open question. To tackle this question, we develop an algorithm that can find approximate binary ℓ bits in arbitrary dimensions by adaptively generating a basis of operators in which to represent the ℓ bit. We use the algorithm to study four models: the one-, two-, and three-dimensional disordered Heisenberg models and the two-dimensional disordered hard-core Bose-Hubbard model. For all four of the models studied, our algorithm finds high-quality ℓ bits at large disorder strength and rapid qualitative changes in the distributions of ℓ bits in particular ranges of disorder strengths, suggesting the existence of MBL transitions. These transitions in the one-dimensional Heisenberg model and two-dimensional Bose-Hubbard model coincide well with past estimates of the critical disorder strengths in these models, which further validates the evidence of MBL phenomenology in the other two- and three-dimensional models we examine. In addition to finding MBL behavior in higher dimensions, our algorithm can be used to probe MBL in various geometries and dimensionality.

Resistance to Cyp3a induction by polychlorinated biphenyls, including non-dioxin-like PCB153, in gills of killifish (Fundulus heteroclitus) from New Bedford Harbor.

Previous reports suggested that non-dioxin-like (NDL) PCB153 effects on cytochrome P450 3A (Cyp3a) expression in Atlantic killifish (Fundulus heteroclitus) gills differed between F0 generation fish from a PCB site (New Bedford Harbor; NBH) and a reference site (Scorton Creek; SC). Here, we examined effects of PCB153, dioxin-like (DL) PCB126, or a mixture of both, on Cyp3a56 mRNA in killifish generations removed from the wild, without environmental PCB exposures. PCB126 effects in liver and gills differed between populations, as expected. Gill Cyp3a56 was not affected by either congener in NBH F2 generation fish, but was induced by PCB153 in SC F1 fish, with females showing a greater response. PCB153 did not affect Cyp3a56 in liver of either population. Results suggest a heritable resistance to NDL-PCBs in killifish from NBH, in addition to that reported for DL PCBs. Induction of Cyp3a56 in gills may be a biomarker of exposure to NDL PCBs in fish populations that are not resistant to PCBs.

Gauge Equivariant Neural Networks for Quantum Lattice Gauge Theories.

Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Motivated by the desire to efficiently simulate many-body quantum systems with exact local gauge invariance, gauge equivariant neural-network quantum states are introduced, which exactly satisfy the local Hilbert space constraints necessary for the description of quantum lattice gauge theory with Z_{d} gauge group and non-Abelian Kitaev D(G) models on different geometries. Focusing on the special case of Z_{2} gauge group on a periodically identified square lattice, the equivariant architecture is analytically shown to contain the loop-gas solution as a special case. Gauge equivariant neural-network quantum states are used in combination with variational quantum Monte Carlo to obtain compact descriptions of the ground state wave function for the Z_{2} theory away from the exactly solvable limit, and to demonstrate the confining or deconfining phase transition of the Wilson loop order parameter.

Pain phenotypes classified by machine learning using electroencephalography features.

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

Exploring the ways in which COVID-19 and lockdown has affected the lives of adult patients with anorexia nervosa and their carers.

OBJECTIVE: This qualitative study explores the ways in which the coronavirus disease 2019 (COVID-19) pandemic and associated lockdown measures have affected the lives of adult patients with anorexia nervosa (AN) and their carers. METHOD: Semi-structured interviews were conducted with patients with AN (n = 21) and carers (n = 28) from the start of UK Government imposed lockdown. Data related directly to the impact of lockdown and COVID-19 were analysed using thematic analysis. RESULTS: Four broad themes were identified for patients and carers separately. Patients experienced: 1. reduced access to eating disorder (ED) services; 2. disruption to routine and activities in the community; 3. heightened psychological distress and ED symptoms; 4. increased attempts at self-management in recovery. Carer themes included: 1. concern over provision of professional support for patients; 2. increased practical demands placed on carers in lockdown; 3. managing new challenges around patient wellbeing; 4. new opportunities. CONCLUSIONS: Reduced access to ED services, loss of routine and heightened anxieties and ED symptoms resulting from COVID-19 and lockdown measures presented challenges for patients and carers. Increased remote support by ED services enabled the continuation of treatment and self-management resources and strategies promoted self-efficacy in both groups.

Impulsivity, Emotional Dysregulation and Executive Function Deficits Could Be Associated with Alcohol and Drug Abuse in Eating Disorders.

BACKGROUND: Empirical data suggests a high comorbid occurrence of eating disorders (EDs) and substance use disorders (SUDs), as well as neurological and psychological shared characteristics. However, no prior study has identified the neuropsychological features of this subgroup. This study examines the prevalence of alcohol and/or drug abuse (A/DA) symptoms in ED patients. It also compares the clinical features and neuropsychological performance of ED patients with and without A/DA symptoms. METHODS: 145 participants (74.5% females) with various forms of diagnosed EDs underwent a comprehensive clinical (TCI-R, SCL-90-R and EDI-2) and neuropsychological assessment (Stroop, WCST and IGT). RESULTS: Approximately 19% of ED patients (across ED subtypes) had A/DA symptoms. Those with A/DA symptoms showed more impulsive behaviours and higher levels of interoceptive awareness (EDI-2), somatisation (SCL-90-R) and novelty seeking (TCI-R). This group also had a lower score in the Stroop-words measure, made more perseverative errors in the WCST and showed a weaker learning trajectory in the IGT. CONCLUSIONS: ED patients with A/DA symptoms display a specific phenotype characterised by greater impulsive personality, emotional dysregulation and problems with executive control. Patients with these temperamental traits may be at high risk of developing a SUD.

A multidimensional density dependent matrix population model for assessing risk of stressors to fish populations.

Modeling exposure and recovery of fish and wildlife populations after stressor mitigation serves as a basis for evaluating remediation success. Herein, we develop a novel multidimensional density dependent matrix population model that analyzes both size-structure and age class-structure simultaneously. This modeling approach emphasizes application in conjunction with field monitoring efforts (e.g., through effects-based monitoring programs) and/or laboratory analysis to link effects due to stressors to outcomes in populations. We applied the model to investigate Atlantic killifish (Fundulus heteroclitus) exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin with effects on fertility and survival rates. The Atlantic killifish is an important and well-studied model organism for understanding the effects of pollutants and other stressors in estuarine and marine ecosystems. For each exposure concentration, the corresponding plots of total population size, population size structure, and age structure over time were generated. The present study serves as an example of how a multidimensional matrix population model can integrate effects across the life cycle, provide a linkage between endpoints observed in the individual and ecological risk to the population as a whole, and project outcomes for multiple generations.

Deep Learning Enabled Strain Mapping of Single-Atom Defects in Two-Dimensional Transition Metal Dichalcogenides with Sub-Picometer Precision.

Two-dimensional (2D) materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x. We utilize deep learning to mine large data sets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class averages which allow us to measure 2D atomic spacings with up to 0.2 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe2-2xTe2x lattice that correspond to alternating rings of lattice expansion and contraction. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials.