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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Backflow Transformations via Neural Networks for Quantum Many-Body Wave Functions.

Obtaining an accurate ground state wave function is one of the great challenges in the quantum many-body problem. In this Letter, we propose a new class of wave functions, neural network backflow (NNB). The backflow approach, pioneered originally by Feynman and Cohen [Phys. Rev. 102, 1189 (1956)10.1103/PhysRev.102.1189], adds correlation to a mean-field ground state by transforming the single-particle orbitals in a configuration-dependent way. NNB uses a feed-forward neural network to learn the optimal transformation via variational Monte Carlo calculations. NNB directly dresses a mean-field state, can be systematically improved, and directly alters the sign structure of the wave function. It generalizes the standard backflow [L. F. Tocchio et al., Phys. Rev. B 78, 041101(R) (2008)10.1103/PhysRevB.78.041101], which we show how to explicitly represent as a NNB. We benchmark the NNB on Hubbard models at intermediate doping, finding that it significantly decreases the relative error, restores the symmetry of both observables and single-particle orbitals, and decreases the double-occupancy density. Finally, we illustrate interesting patterns in the weights and bias of the optimized neural network.

Macroscopically Degenerate Exactly Solvable Point in the Spin-1/2 Kagome Quantum Antiferromagnet.

Frustrated quantum magnets are a central theme in condensed matter physics due to the richness of their phase diagrams. They support a panoply of phases including various ordered states and topological phases. Yet, this problem has defied a solution for a long time due to the lack of controlled approximations which make it difficult to distinguish between competing phases. Here we report the discovery of a special quantum macroscopically degenerate point in the XXZ model on the spin-1/2 kagome quantum antiferromagnet for the ratio of Ising to antiferromagnetic transverse coupling J_{z}/J=-1/2. This point is proximate to many competing phases explaining the source of the complexity of the phase diagram. We identify five phases near this point including both spin-liquid and broken-symmetry phases and give evidence that the kagome Heisenberg antiferromagnet is close to a transition between two phases.

Finding Matrix Product State Representations of Highly Excited Eigenstates of Many-Body Localized Hamiltonians.

A key property of many-body localized Hamiltonians is the area law entanglement of even highly excited eigenstates. Matrix product states (MPS) can be used to efficiently represent low entanglement (area law) wave functions in one dimension. An important application of MPS is the widely used density matrix renormalization group (DMRG) algorithm for finding ground states of one-dimensional Hamiltonians. Here, we develop two algorithms, the shift-and-invert MPS (SIMPS) and excited state DMRG which find highly excited eigenstates of many-body localized Hamiltonians. Excited state DMRG uses a modified sweeping procedure to identify eigenstates, whereas SIMPS applies the inverse of the shifted Hamiltonian to a MPS multiple times to project out the targeted eigenstate. To demonstrate the power of these methods, we verify the breakdown of the eigenstate thermalization hypothesis in the many-body localized phase of the random field Heisenberg model, show the saturation of entanglement in the many-body localized phase, and generate local excitations.

Fixed Points of Wegner-Wilson Flows and Many-Body Localization.

Many-body localization (MBL) is a phase of matter that is characterized by the absence of thermalization. Dynamical generation of a large number of local quantum numbers has been identified as one key characteristic of this phase, quite possibly the microscopic mechanism of breakdown of thermalization and the phase transition itself. We formulate a robust algorithm, based on Wegner-Wilson flow (WWF) renormalization, for computing these conserved quantities and their interactions. We present evidence for the existence of distinct fixed point distributions of the latter: a Gaussian white-noise-like distribution in the ergodic phase, a 1/f law inside the MBL phase, and scale-free distributions in the transition regime.

The Elizabeth River Story: A Case Study in Evolutionary Toxicology.

The Elizabeth River system is an estuary in southeastern Virginia, surrounded by the towns of Chesapeake, Norfolk, Portsmouth, and Virginia Beach. The river has played important roles in U.S. history and has been the location of various military and industrial activities. These activities have been the source of chemical contamination in this aquatic system. Important industries, until the 1990s, included wood treatment plants that used creosote, an oil-derived product that is rich in polycyclic aromatic hydrocarbons (PAH). These plants left a legacy of PAH pollution in the river, and in particular Atlantic Wood Industries is a designated Superfund site now undergoing remediation. Numerous studies examined the distribution of PAH in the river and impacts on resident fauna. This review focuses on how a small estuarine fish with a limited home range, Fundulus heteroclitus (Atlantic killifish or mummichog), has responded to this pollution. While in certain areas of the river this species has clearly been impacted, as evidenced by elevated rates of liver cancer, some subpopulations, notably the one associated with the Atlantic Wood Industries site, displayed a remarkable ability to resist the marked effects PAH have on the embryonic development of fish. This review provides evidence of how pollutants have acted as evolutionary agents, causing changes in ecosystems potentially lasting longer than the pollutants themselves. Mechanisms underlying this evolved resistance, as well as mechanisms underlying the effects of PAH on embryonic development, are also described. The review concludes with a description of ongoing and promising efforts to restore this historic American river.

How Model Organisms and Model Uncertainty Impact Our Understanding of the Risk of Sublethal Impacts of Toxicants to Survival and Growth of Ecologically Relevant Species.

Understanding how sublethal impacts of toxicants affect population-relevant outcomes for organisms is challenging. We tested the hypotheses that the well-known sublethal impacts of methylmercury (MeHg) and a polychlorinated biphenyl (PCB126) would have meaningful impacts on cohort growth and survival in yellow perch (Perca flavescens) and Atlantic killifish (Fundulus heteroclitus) populations, that inclusion of model uncertainty is important for understanding the sublethal impacts of toxicants, and that a model organism (zebrafish Danio rerio) is an appropriate substitute for ecologically relevant species (yellow perch, killifish). Our simulations showed that MeHg did not have meaningful impacts on growth or survival in a simulated environment except to increase survival and growth in low mercury exposures in yellow perch and killifish. For PCB126, the high level of exposure resulted in lower survival for killifish only. Uncertainty analyses increased the variability and lowered average survival estimates across all species and toxicants, providing a more conservative estimate of risk. We demonstrate that using a model organism instead of the species of interest does not necessarily give the same results, suggesting that using zebrafish as a surrogate for yellow perch and killifish may not be appropriate for predicting contaminant impacts on larval cohort growth and survival in ecologically relevant species. Our analysis also reinforces the notion that uncertainty analyses are necessary in any modeling assessment of the impacts of toxicants on a population because it provides a more conservative, and arguably realistic, estimate of impact. Environ Toxicol Chem 2024;00:1-12. © 2024 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 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.

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.