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Abstract Scarcity of iron and manganese limits the efficiency of the biological carbon pump over large areas of the Southern Ocean. The importance of hydrothermal vents as a source of these micronutrients to the euphotic zone of the Southern Ocean is debated. Here we present full depth profiles of dissolved and total dissolvable trace metals in the remote eastern Pacific sector of the Southern Ocean (55–60° S, 89.1° W), providing evidence of enrichment of iron and manganese at depths of 2000–4000 m. These enhanced micronutrient concentrations were co-located with 3He enrichment, an indicator of hydrothermal fluid originating from ocean ridges. Modelled water trajectories revealed the understudied South East Pacific Rise and the Pacific Antarctic Ridge as likely source regions. Additionally, the trajectories demonstrate pathways for these Southern Ocean hydrothermal ridge-derived trace metals to reach the Southern Ocean surface mixed layer within two decades, potentially supporting a regular supply of micronutrients to fuel Southern Ocean primary production.

Introduction: ALK-positive lung cancers represent a molecularly diverse disease. With drug exposure, driving selection pressure, and resistance pathways, disease relapse will emerge. There is compelling rationale to investigate novel treatment strategies, informed by dynamic circulating tumor DNA (ctDNA) monitoring. Methods: The single-arm, pilot study ALKTERNATE investigated fixed alternating cycles of lorlatinib intercalated with crizotinib in individuals resistant to second-generation ALK inhibitors. Dynamic ctDNA explored the correlation with disease response and disease recurrence and defined disease resistance. The primary outcome was time-to-treatment failure, a composite of tolerability, feasibility, and efficacy. Secondary outcomes included standard survival measures, toxicity, pharmacokinetic analysis, and patient-reported outcomes. Tertiary outcomes were proteogenomic analyses of tissue and plasma. Results: A total of 15 individuals were enrolled; three encountered primary resistance to lorlatinib induction. There were 12 participants who received alternating therapy, and this approach revealed safety, feasibility, and effectiveness. Patient-reported outcomes were maintained or improved on therapy, and toxicity was consistent with previous reports. The pharmacokinetic measures were similar to the single-arm drug experience. Median time-to-treatment failure was 10 months; overall survival was 23 months. ctDNA profiles indicated inferior survival in those with preexistent TP53 mutations and those without clear or cleared ctDNA at trial induction. The study defined a vastly heterogeneous population with an abundance of ALK coexisting with non-ALK resistance variants. Conclusions: ALKTERNATE revealed feasibility with a novel alternating ALK inhibitor strategy in ALK-positive NSCLC. Results support progressing inquiry into this approach and propose a flexible design with drug(s) selected and alternating time frames, informed by real-time plasma profiling. Moving this concept to treatment naive may also optimize impact.

Abstract The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional “QSAR-ready” forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the “QSAR-ready” workflow to generate “MS-ready structures” to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

Abstract We consider quantum algebras of observables associated with subregions in theories of Einstein gravity coupled to matter in the G N → 0 limit. When the subregion is spatially compact or encompasses an asymptotic boundary, we argue that the algebra is a type II von Neumann factor. To do so in the former case we introduce a model of an observer living in the region; in the latter, the ADM Hamiltonian effectively serves as an observer. In both cases the entropy of states on which this algebra acts is UV finite, and we find that it agrees, up to a state-independent constant, with the generalized entropy. For spatially compact regions the algebra is type II1, implying the existence of an entropy maximizing state, which realizes a version of Jacobson’s entanglement equilibrium hypothesis. The construction relies on the existence of well-motivated but conjectural states whose modular flow is geometric at an instant in time. Our results generalize the recent work of Chandrasekaran, Longo, Penington, and Witten on an algebra of operators for the static patch of de Sitter space.

PurposeTo demonstrate the suitability of optically stimulated luminescence detectors (OSLDs) for accurate simultaneous measurement of the absolute point dose and dose-weighted linear energy transfer (LETD) in an anthropomorphic phantom for experimental validation of daily adaptive proton therapy.MethodsA clinically realistic intensity-modulated proton therapy (IMPT) treatment plan was created based on a CT of an anthropomorphic head-and-neck phantom made of tissue-equivalent material. The IMPT plan was optimized with three fields to deliver a uniform dose to the target volume covering the OSLDs. Different scenarios representing inter-fractional anatomical changes were created by modifying the phantom. An online adaptive proton therapy workflow was used to recover the daily dose distribution and account for the applied geometry changes. To validate the adaptive workflow, measurements were performed by irradiating Al2O3:C OSLDs inside the phantom. In addition to the measurements, retrospective Monte Carlo simulations were performed to compare the absolute dose and dose-averaged LET (LETD) delivered to the OSLDs.ResultsThe online adaptive proton therapy workflow was shown to recover significant degradation in dose conformity resulting from large anatomical and positioning deviations from the reference plan. The Monte Carlo simulations were in close agreement with the OSLD measurements, with an average relative error of 1.4% for doses and 3.2% for LETD. The use of OSLDs for LET determination allowed for a correction for the ionization quenched response.ConclusionThe OSLDs appear to be an excellent detector for simultaneously assessing dose and LET distributions in proton irradiation of an anthropomorphic phantom. The OSLDs can be cut to almost any size and shape, making them ideal for in-phantom measurements to probe the radiation quality and dose in a predefined region of interest. Although we have presented the results obtained in the experimental validation of an adaptive proton therapy workflow, the same approach can be generalized and used for a variety of clinical innovations and workflow developments that require accurate assessment of point dose and/or average LET.

Background and purpose: Respiratory suppression techniques represent an effective motion mitigation strategy for 4D-irradiation of lung tumors with protons. A magnetic resonance imaging (MRI)-based study applied and analyzed methods for this purpose, including enhanced Deep-Inspiration-Breath-Hold (eDIBH). Twenty-one healthy volunteers (41–58 years) underwent thoracic MR scans in four imaging sessions containing two eDIBH-guided MRIs per session to simulate motion-dependent irradiation conditions. The automated MRI segmentation algorithm presented here was critical in determining the lung volumes (LVs) achieved during eDIBH. Materials and methods: The study included 168 MRIs acquired under eDIBH conditions. The lung segmentation algorithm consisted of four analysis steps: (i) image preprocessing, (ii) MRI histogram analysis with thresholding, (iii) automatic segmentation, (iv) 3D-clustering. To validate the algorithm, 46 eDIBH-MRIs were manually contoured. Sørensen-Dice similarity coefficients (DSCs) and relative deviations of LVs were determined as similarity measures. Assessment of intrasessional and intersessional LV variations and their differences provided estimates of statistical and systematic errors. Results: Lung segmentation time for 100 2D-MRI planes was ∼ 10 s. Compared to manual lung contouring, the median DSC was 0.94 with a lower 95 % confidence level (CL) of 0.92. The relative volume deviations yielded a median value of 0.059 and 95 % CLs of −0.013 and 0.13. Artifact-based volume errors, mainly of the trachea, were estimated. Estimated statistical and systematic errors ranged between 6 and 8 %. Conclusions: The presented analytical algorithm is fast, precise, and readily available. The results are comparable to time-consuming, manual segmentations and other automatic segmentation approaches. Post-processing to remove image artifacts is under development.

Abstract Gravitational subsystems with boundaries carry the action of an infinite-dimensional symmetry algebra, with potentially profound implications for the quantum theory of gravity. We initiate an investigation into the quantization of this corner symmetry algebra for the phase space of gravity localized to a region bounded by a 2-dimensional sphere. Starting with the observation that the algebra sdiff $$ \mathfrak{sdiff} $$ (S 2) of area-preserving diffeomorphisms of the 2-sphere admits a deformation to the finite-dimensional algebra su $$ \mathfrak{su} $$ (N), we derive novel finite-N deformations for two important subalgebras of the gravitational corner symmetry algebra. Specifically, we find that the area-preserving hydrodynamical algebra sdiff S 2 ⊕ L ℝ S 2 $$ \mathfrak{sdiff}\left({S}^2\right){\oplus}_{\mathcal{L}}{\mathbb{R}}^{S^2} $$ arises as the large-N limit of sl N ℂ ⊕ ℝ $$ \mathfrak{sl}\left(N,\mathbb{C}\right)\oplus \mathbb{R} $$ and that the full area-preserving corner symmetry algebra sdiff S 2 ⊕ L sl 2 ℝ S 2 $$ \mathfrak{sdiff}\left({S}^2\right){\oplus}_{\mathcal{L}}\mathfrak{sl}{\left(2,\mathbb{R}\right)}^{S^2} $$ is the large-N limit of the pseudo-unitary group su $$ \mathfrak{su} $$ (N, N). We find matching conditions for the Casimir elements of the deformed and continuum algebras and show how these determine the value of the deformation parameter N as well as the representation of the deformed algebra associated with a quantization of the local gravitational phase space. Additionally, we present a number of novel results related to the various algebras appearing, including a detailed analysis of the asymptotic expansion of the su $$ \mathfrak{su} $$ (N) structure constants, as well as an explicit computation of the full diff $$ \mathfrak{diff} $$ (S 2) structure constants in the spherical harmonic basis. A consequence of our work is the definition of an area operator which is compatible with the deformation of the area-preserving corner symmetry at finite N.

Objectives To investigate trends in the incidence rate and main indication for revision hip replacement (rHR) over the past 15 years in the UK.Design Repeated national cross-sectional study from 2006 to 2020.Setting/participants rHR procedures were identified from the National Joint Registry for England, Wales, Northern Ireland, the Isle of Man and the States of Guernsey. Population statistics were obtained from the Office for National Statistics.Main outcome measures Crude incidence rates of rHR.Results The incidence rate of rHR doubled from 11 per 100 000 adults in 2006 (95% CI 10.7 to 11.3) to a peak of 22 per 100 000 adults (95% CI 22 to 23) in 2012, before falling to 17 per 100 000 adults in 2019 (95% CI 16 to 17) (24.5% decrease from peak). The incidence rate of rHR reduced by 39% in 2020 compared with 2019 (during the COVID-19 pandemic). The most frequent indications for rHR between 2006 and 2019 were loosening/lysis (27.8%), unexplained pain (15.1%) and dislocation/instability (14.7%). There were incremental increases in the annual number and incidence rates of rHR for fracture, infection, dislocation/instability and a decrease in rHR for aseptic loosening/lysis.Conclusions The incidence rate of rHR doubled from 2006 to 2012, likely due to high early failure rates of metal-on-metal hip replacements. The incidence of rHR then decreased by approximately 25% from 2012 to 2019, followed by a large decrease during the COVID-19 pandemic. The decrease in the number of rHR performed for aseptic loosening/lysis may reflect improved wear and implant longevity. Increased healthcare resource will be required to care for the increasing numbers of patients undergoing rHR for fracture and infection.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology, and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Abstract We expand on our results in [1] to present a broad new class of gravitational observables in asymptotically Anti-de Sitter space living on general codimension-zero regions of the bulk spacetime. By taking distinct limits, these observables can reduce to well-studied holographic complexity proposals, e.g., the volume of the maximal slice and the action or spacetime volume of the Wheeler-DeWitt patch. As with the codimension-one family found in [1], these new observables display two key universal features for the thermofield double state: they grow linearly in time at late times and reproduce the switchback effect. Hence we argue that any member of this new class of observables is an equally viable candidate as a gravitational dual of complexity. Moreover, using the Peierls construction, we show that variations of the codimension-zero and codimension-one observables are encoded in the gravitational symplectic form on the semi-classical phase-space, which can then be mapped to the CFT.

Differential sensing aims to mimic senses such as taste and smell through the use of synthetic receptors. Here, the authors show that arrays of de novo designed peptide assemblies can be used as sensor components to distinguish various analytes and complex mixtures.

Experimental determination of mechanical properties of rubber composites, such as tensile strength and hardness, involves complex multistage preparation procedures that are laborious and expensive. In this study, a hybrid filler of carbon black (CB) along with a sustainable filler of lignin is added for reinforcement in the nitrile butadiene rubber (NBR) matrix, with the total filler content varying from 10 parts per hundred rubber (phr) to 80 phr. This work aims to develop a data-driven predictive model for the mechanical properties of rubber composites. An artificial neural network (ANN) model using multilayer feed-forward back-propagation has been created to forecast the tensile strength ( Ts ) and hardness ( Hd ) of rubber composites. The model predicts the uniaxial tensile response and hardness using input parameters that include total filler and lignin loading levels. The effectiveness of the suggested prediction method was demonstrated by statistical analysis using confidence intervals, showing a prediction error between 5.47% and 3.23% for the Ts and between 3.03% and 1.85% for Hd at 95% confidence intervals. A sustainable green band could be defined in the developed model, which is handy for designers to replace CB with lignin in various NBR based products, such as hoses, seals, etc., without compromising on tensile strength and hardness.

Exposure science is evolving from its traditional “after the fact” and “one chemical at a time” approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency’s Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound’s corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Abstract We recently mapped a genetic susceptibility locus on chromosome 6q22.33 for type 1 diabetes (T1D) diagnosed below the age of 7 years between the PTPRK and thymocyte-selection-associated (THEMIS) genes. As the thymus plays a central role in shaping the T cell repertoire, we aimed to identify the most likely causal genetic factors behind this association using thymocyte genomic data. In four thymocyte populations, we identified 253 DNA sequence motifs underlying histone modifications. The G insertion allele of rs138300818, associated with protection from diabetes, created thymocyte motifs for multiple histone modifications and thymocyte types. In a parallel approach to identifying variants that alter transcription factor binding motifs, the same variant disrupted a predicted motif for Rfx7, which is abundantly expressed in the thymus. Chromatin state and RNA sequencing data suggested strong transcription overlapping rs138300818 in fetal thymus, while expression quantitative trait locus and chromatin conformation data associate the insertion with lower THEMIS expression. Extending the analysis to other T1D loci further highlighted rs66733041 affecting the GATA3 transcription factor binding in the AFF3 locus. Taken together, our results support a role for thymic THEMIS gene expression and the rs138300818 variant in promoting the development of early-onset T1D.

Abstract Recently, Ciambelli, Leigh, and Pai (CLP) [ arXiv:2111.13181 ] have shown that nonzero charges integrating Hamilton’s equation can be defined for all diffeomorphisms acting near the boundary of a subregion in a gravitational theory. This is done by extending the phase space to include a set of embedding fields that parameterize the location of the boundary. Because their construction differs from previous works on extended phase spaces by a covariant phase space ambiguity, the question arises as to whether the resulting charges are unambiguously defined. Here, we demonstrate that ambiguity-free charges can be obtained by appealing to the variational principle for the subregion, following recent developments on dealing with boundaries in the covariant phase space. Resolving the ambiguity produces corrections to the diffeomorphism charges, and also generates additional obstructions to integrability of Hamilton’s equation. We emphasize the fact that the CLP extended phase space produces nonzero diffeomorphism charges distinguishes it from previous constructions in which diffeomorphisms are pure gauge, since the embedding fields can always be eliminated from the latter by a choice of unitary gauge. Finally, we show that Wald-Zoupas charges, with their characteristic obstruction to integrability, are associated with a modified transformation in the extended phase space, clarifying the reason behind integrability of Hamilton’s equation for standard diffeomorphisms.

Background: Azo dyes are used in textiles and leather clothing. Human exposure can occur from wearing textiles containing azo dyes. Since the body’s enzymes and microbiome can cleave azo dyes, potentially resulting in mutagenic or carcinogenic metabolites, there is also an indirect health concern on the parent compounds. While several hazardous azo dyes are banned, many more are still in use that have not been evaluated systematically for potential health concerns. This systematic evidence map (SEM) aims to compile and categorize the available toxicological evidence on the potential human health risks of a set of 30 market-relevant azo dyes. Methods: Peer-reviewed and gray literature was searched and over 20,000 studies were identified. These were filtered using Sciome Workbench for Interactive computer-Facilitated Text-mining (SWIFT) Review software with evidence stream tags (human, animal, in vitro) yielding 12,800 unique records. SWIFT Active (a machine-learning software) further facilitated title/abstract screening. DistillerSR software was used for additional title/abstract, full-text screening, and data extraction. Results: 187 studies were identified that met populations, exposures, comparators, and outcomes (PECO) criteria. From this pool, 54 human, 78 animal, and 61 genotoxicity studies were extracted into a literature inventory. Toxicological evidence was abundant for three azo dyes (also used as food additives) and sparse for five of the remaining 27 compounds. Complementary search in ECHA’s REACH database for summaries of unpublished study reports revealed evidence for all 30 dyes. The question arose of how this information can be fed into an SEM process. Proper identification of prioritized dyes from various databases (including U.S. EPA’s CompTox Chemicals Dashboard) turned out to be a challenge. Evidence compiled by this SEM project can be evaluated for subsequent use in problem formulation efforts to inform potential regulatory needs and prepare for a more efficient and targeted evaluation in the future for human health assessments.

Purpose: This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods: IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results: Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion: Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.

Measurement(s) chemical use information Technology Type(s) manual and automated literature collection

Measurement(s) occurrence of chemicals in environmental and biological media Technology Type(s) mass spectrometry (curation of public data) Sample Characteristic - Organism Homo sapiens • aquatic invertebrates • aquatic vertebrates/mammals • birds • fish • terrestrial invertebrates/worms • terrestrial vertebrates • livestock Sample Characteristic - Environment surface water • drinking water • groundwater • ambient air • indoor air • indoor dust • landfill leachate • personal air • precipitation • wastewater • soil • sediment • sludge • consumer products • food products • raw agricultural commodities

Measurement(s) biosolids • chemicals Technology Type(s) digital curation Sample Characteristic - Location United States of America

Using low trophic marine resources such as sugar kelp (Saccharina latissimi) is of great interest to increase the circular food production in the ocean. Sugar kelp does, however, contain high levels of carbohydrates and iodine and does not have considerable levels of protein and lipids, which may make it less suitable as a feeding ingredient. A 10-week feeding trial was done to investigate the effect of graded dietary inclusion levels of fermented sugar kelp (FSK), on growth performance, digestibility, retention of nutrients, and mineral composition in postsmolt Atlantic salmon (Salmo salar L.). The experimental diets were made to simulate a standard grower feed for salmon postsmolts in SW with ∼63% plant-based ingredients vs ∼34% marine ingredients and increasing concentrations of FSK between 0% (control feed) and 4% of the diet. During the feeding trial, the weight gain and specific growth rate (SGR) decreased linearly with increasing dietary FSK levels, where the SGR was slightly reduced from 1.2% for the fish given the control feed to 1.1% in the fish given feeds containing 3% and 4% FSK. This resulted in a lower weight gain of up to 9% in the fish given 4% FSK compared to the control. Feed intake and feed conversion ratio were, however, similar in all diet groups, and FSK inclusion did not influence the digestibility of macronutrients or minerals, except for lipid. The reduced growth is likely related to a lower digestible energy level in the diets, and the retention of both lipids and energy was affected by FSK inclusion. Inclusion of FSK also influenced iodine availability and retention, as well as increasing iodine status in whole body and muscle in a dose-dependent manner until reaching a plateau, which corresponds to 124 mg I kg−1 WW (135 mg I kg−1 DW), at 3% FSK inclusion.

Abstract Monitoring is needed to assess conservation success and improve management, but naïve or simplistic interpretation of monitoring data can lead to poor decisions. We illustrate how to counter this risk by combining decision‐support tools and quantitative counterfactual analysis. We analyzed 20 years of egg rescue for tara iti (Sternula nereis davisae) in Aotearoa New Zealand. Survival is lower for rescued eggs; however, only eggs perceived as imminently threatened by predators or weather are rescued, so concluding that rescue is ineffective would be biased. Equally, simply assuming all rescued eggs would have died if left in situ is likely to be simplistic. Instead, we used the monitoring data itself to estimate statistical support for a wide space of uncertain counterfactuals about decisions and fate of rescued eggs. Results suggest under past management, rescuing and leaving eggs would have led to approximately the same overall fledging rate, because of likely imperfect threat assessment and low survival of rescued eggs to fledging. Managers are currently working to improve both parameters. Our approach avoids both naïve interpretation of observed outcomes and simplistic assumptions that management is always justified, using the same data to obtain unbiased quantitative estimates of counterfactual support.

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency’s ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some–but not all–cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Abstract Background The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of chemical-protein interactions. Next, we trained binary classifiers on MCF7 human breast cancer cell line derived gene expression profiles and chemical-protein labels using six classification algorithms to identify optimal analysis parameters. To validate classifier accuracy, we used holdout data sets, training-excluded reference chemicals, and empirical significance testing of null models derived from permuted chemical-protein associations. To identify classifiers that have variable predicting performance across training data derived from different cellular contexts, we trained a separate set of binary classifiers on the PC3 human prostate cancer cell line. Results We trained classifiers using expression data associated with chemical treatments linked to 51 molecular initiating events. This analysis identified and validated 9 high-performing classifiers with empirical p-values lower than 0.05 and internal accuracies ranging from 0.73 to 0.94 and holdout accuracies of 0.68 to 0.92. High-ranking predictions for training-excluded reference chemicals demonstrating that predictive accuracy extends beyond the set of chemicals used in classifier training. To explore differences in classifier performance as a function of training data cellular context, MCF7-trained classifier accuracies were compared to classifiers trained on the PC3 gene expression data for the same molecular initiating events. Conclusions This methodology can offer insight in prioritizing candidate perturbagens of interest for targeted screens. This approach can also help guide the selection of relevant cellular contexts for screening classes of candidate perturbagens using cell line specific model performance.

We present the commissioning and quality assurance of our clinical protocol for respiratory gating in pencil beam scanning proton therapy for cancer patients with moving targets. In a novel approach, optical tracking has been integrated in the therapy workflow and used to monitor respiratory motion from multiple surrogates, applied on the patients’ chest. The gating system was tested under a variety of experimental conditions, specific to proton therapy, to evaluate reaction time and reproducibility of dose delivery control. The system proved to be precise in the application of beam gating and allowed the mitigation of dose distortions even for large (1.4 cm) motion amplitudes, provided that adequate treatment windows were selected. The total delivered dose was not affected by the use of gating, with measured integral error within 0.15 cGy. Analysing high-resolution images of proton transmission, we observed negligible discrepancies in the geometric location of the dose as a function of the treatment window, with gamma pass rate greater than 95% (2%/2 mm) compared to stationary conditions. Similarly, pass rate for the latter metric at the 3%/3 mm level was observed above 97% for clinical treatment fields, limiting residual movement to 3 mm at end-exhale. These results were confirmed in realistic clinical conditions using an anthropomorphic breathing phantom, reporting a similarly high 3%/3 mm pass rate, above 98% and 94%, for regular and irregular breathing, respectively. Finally, early results from periodic QA tests of the optical tracker have shown a reliable system, with small variance observed in static and dynamic measurements.

Optimal recovery of arm function following stroke requires patients to perform a large number of functional arm movements in clinical therapy sessions, as well as at home. Technology to monitor adherence to this activity would be helpful to patients and clinicians. Current approaches to monitoring arm movements are limited because of challenges in distinguishing between functional and non-functional movements. Here, we present an Arm Rehabilitation Monitor (ARM), a device intended to make such measurements in an unobtrusive manner. The ARM device is based on a single Inertial Measurement Unit (IMU) worn on the wrist and uses machine learning techniques to interpret the resulting signals. We characterized the ability of the ARM to detect reaching actions in a functional assessment dataset (functional assessment tasks) and an Activities-of-Daily-Living (ADL) dataset (pizza-making and walking task) from 12 participants with stroke. The Convolutional Neural Network (CNN) and Random Forests (RF) classifiers had a Matthews Correlation Coefficient score of 0.59 and 0.58 when trained and tested on the functional dataset, 0.50 and 0.49 when trained and tested on the ADL dataset, and 0.37 and 0.36 when trained on the functional dataset and tested on the ADL dataset, respectively. The latter is the most relevant scenario for the intended application of training during a clinical visit for monitoring movements in the in-home setting. The classifiers showed good performance in estimating the time spent reaching and number of reaching gestures and showed low sensitivity to irrelevant arm movements produced during walking. We conclude that the ARM has sufficient accuracy and robustness to merit being used in preliminary studies to monitor arm activity in rehabilitation or home applications.

Abstract The Brown-York stress tensor provides a means for defining quasilocal gravitational charges in subregions bounded by a timelike hypersurface. We consider the generalization of this stress tensor to null hypersurfaces. Such a stress tensor can be derived from the on-shell subregion action of general relativity associated with a Dirichlet variational principle, which fixes an induced Carroll structure on the null boundary. The formula for the mixed-index tensor T i j takes a remarkably simple form that is manifestly independent of the choice of auxiliary null vector at the null surface, and we compare this expression to previous proposals for null Brown-York stress tensors. The stress tensor we obtain satisfies a covariant conservation equation with respect to any connection induced from a rigging vector at the hypersurface, as a result of the null constraint equations. For transformations that act covariantly on the boundary structures, the Brown-York charges coincide with canonical charges constructed from a version of the Wald-Zoupas procedure. For anomalous transformations, the charges differ by an intrinsic functional of the boundary geometry, which we explicity verify for a set of symmetries associated with finite null hyper-surfaces. Applications of the null Brown-York stress tensor to symmetries of asymptotically flat spacetimes and celestial holography are discussed.

How head patterning is regulated in vertebrates is yet to be understood. In this study, we show that frog embryos injected with Noggin at different blastula and gastrula stages had their head development sequentially arrested at different positions. When timed BMP inhibition was applied to BMP-overexpressing embryos, the expression of five genes: xcg-1 (a marker of the cement gland, which is the front-most structure in the frog embryo), six3 (a forebrain marker), otx2 (a forebrain and mid-brain marker), gbx2 (an anterior hindbrain marker), and hoxd1 (a posterior hindbrain marker) were sequentially fixed. These results suggest that the vertebrate head is patterned from anterior to posterior in a progressive fashion and may involve timed actions of the BMP signaling.

Abstract Background Using two large datasets from Dorset, we previously reported an internally validated multivariable risk model for predicting the risk of GI malignancy in IDA—the IDIOM score. The aim of this retrospective observational study was to validate the IDIOM model using two independent external datasets. Methods The external validation datasets were collected, in a secondary care setting, by different investigators from cohorts in Oxford and Sheffield derived under different circumstances, comprising 1117 and 474 patients with confirmed IDA respectively. The data were anonymised prior to analysis. The predictive performance of the original model was evaluated by estimating measures of calibration, discrimination and clinical utility using the validation datasets. Results The discrimination of the original model using the external validation data was 70% (95% CI 65, 75) for the Oxford dataset and 70% (95% CI 61, 79) for the Sheffield dataset. The analysis of mean, weak, flexible and across the risk groups’ calibration showed no tendency for under or over-estimated risks in the combined validation data. Decision curve analysis demonstrated the clinical value of the IDIOM model with a net benefit that is higher than ‘investigate all’ and ‘investigate no-one’ strategies up to a threshold of 18% in the combined validation data, using a risk cut-off of around 1.2% to categorise patients into the very low risk group showed that none of the patients stratified in this risk group proved to have GI cancer on investigation in the validation datasets. Conclusion This external validation exercise has shown promising results for the IDIOM model in predicting the risk of underlying GI malignancy in independent IDA datasets collected in different clinical settings.

Abstract Dhole (Cuon alpinus) is threatened with extinction across its range due to habitat loss and prey depletion. Despite this, no previous study has investigated the distribution and threat of the species at a regional scale. This lack of knowledge continues to impede conservation planning for the species. Here we modeled suitable habitat using presence‐only camera trap data for dhole and dhole prey species in mainland Southeast Asia and assessed the threat level to dhole in this region using an expert‐informed Bayesian Belief Network. We integrated prior information to identify dhole habitat strongholds that could support populations over the next 50 years. Our habitat suitability model identified forest cover and prey availability as the most influential factors affecting dhole occurrence. Similarly, our threat model predicted that forest loss and prey depletion were the greatest threats, followed by local hunting, non‐timber forest product collection, and domestic dog incursion into the forest. These threats require proactive resource management, strong legal protection, and cross‐sector collaboration. We predicted

Background: Systematic evidence maps (SEMs) are gaining visibility in environmental health for their utility to serve as problem formulation tools and assist in decision-making, especially for priority setting. SEMs are now routinely prepared as part of the assessment development process for the US Environmental Protection Agency (EPA) Integrated Risk Information System (IRIS) and Provisional Peer Reviewed Toxicity Value (PPRTV) assessments. SEMs can also be prepared to explore the available literature for an individual chemical or groups of chemicals of emerging interest. Objectives: This document describes the typical methods used to produce SEMs for the IRIS and PPRTV Programs, as well as “fit for purpose” applications using a variety of examples drawn from existing analyses. It is intended to serve as an example base template that can be adapted as needed for the specific SEM. The presented methods include workflows intended to facilitate rapid production. The Populations, Exposures, Comparators and Outcomes (PECO) criteria are typically kept broad to identify mammalian animal bioassay and epidemiological studies that could be informative for human hazard identification. In addition, a variety of supplemental content is tracked, e.g., studies presenting information on in vitro model systems, non-mammalian model systems, exposure-level-only studies in humans, pharmacokinetic models, and absorption, distribution, metabolism, and excretion (ADME). The availability of New Approach Methods (NAMs) evidence is also tracked (e.g., high throughput, transcriptomic, in silico, etc.). Genotoxicity studies may be considered as PECO relevant or supplemental material, depending on the topic and context of the review. Standard systematic review practices (e.g., two independent reviewers per record) and specialized software applications are used to search and screen the literature and may include the use of machine learning software. Mammalian bioassay and epidemiological studies that meet the PECO criteria after full-text review are briefly summarized using structured web-based extraction forms with respect to study design and health system(s) assessed. Extracted data is available in interactive visual formats and can be downloaded in open access formats. Methods for conducting study evaluation are also presented which is conducted on a case-by-case basis, depending on the usage of the SEM.

Abstract Background There is growing interest in the use of wearable devices that track upper limb activity after stroke to help determine and motivate the optimal dose of upper limb practice. The purpose of this study was to explore clinicians’ perceptions of a prospective wearable device that captures upper limb activity to assist in the design of devices for use in rehabilitation practice. Methods Four focus groups with 18 clinicians (occupational and physical therapists with stroke practice experience from a hospital or private practice setting) were conducted. Data were analyzed thematically. Results Our analysis revealed three themes: (1) “Quantity and quality is ideal” emphasized how an ideal device would capture both quantity and quality of movement; (2) “Most useful outside therapy sessions” described how therapists foresaw using the device outside of therapy sessions to monitor homework adherence, provide self-monitoring of use, motivate greater use and provide biofeedback on movement quality; (3) “User-friendly please” advocated for the creation of a device that was easy to use and customizable, which reflected the client-centered nature of their treatment. Conclusions Findings from this study suggest that clinicians support the development of wearable devices that capture upper limb activity outside of therapy for individuals with some reach to grasp ability. Devices that are easy to use and capture both quality and quantity may result in greater uptake in the clinical setting. Future studies examining acceptability of wearable devices for tracking upper limb activity from the perspective of individuals with stroke are needed.

Abstract The phase space of general relativity in a finite subregion is characterized by edge modes localized at the codimension-2 boundary, transforming under an infinite-dimensional group of symmetries. The quantization of this symmetry algebra is conjectured to be an important aspect of quantum gravity. As a step towards quantization, we derive a complete classification of the positive-area coadjoint orbits of this group for boundaries that are topologically a 2-sphere. This classification parallels Wigner’s famous classification of representations of the Poincaré group since both groups have the structure of a semidirect product. We find that the total area is a Casimir of the algebra, analogous to mass in the Poincaré group. A further infinite family of Casimirs can be constructed from the curvature of the normal bundle of the boundary surface. These arise as invariants of the little group, which is the group of area-preserving diffeomorphisms, and are the analogues of spin. Additionally, we show that the symmetry group of hydrodynamics appears as a reduction of the corner symmetries of general relativity. Coadjoint orbits of both groups are classified by the same set of invariants, and, in the case of the hydrodynamical group, the invariants are interpreted as the generalized enstrophies of the fluid.

Measurement(s) adverse outcome pathway • gene interactions • Orthologous Gene • chemical gene interactions • molecular pathway • disease gene associations • SNP Technology Type(s) digital curation Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14737557

The shift towards higher inclusion of vegetable oils (VOs) in aquafeeds has resulted in major changes in dietary fatty acid composition, especially increased amounts of monounsaturated fatty acids (MUFAs) and decreased polyunsaturated fatty acids (PUFAs) and saturated fatty acids (SFAs). However, little is known about how this change in fatty acid (FA) profile affects the intracellular fate of these fatty acids in the intestinal cells. To investigate this topic, we used the rainbow trout intestinal epithelial cell line (RTgutGC) as an in vitro model. The cells were incubated with either palmitic acid (16:0, PA), oleic acid (18:1n-9, OA), or arachidonic acid (20:4n-6, ARA), to represent the SFA, MUFA, and PUFA, respectively. In all experiments, the RTgutGC were incubated with either non-labeled or radiolabeled FA (PA, OA, or ARA) for 16 h at 190C. The cells were then analyzed for the occurrence of cytosolic lipid droplets (CLD) with confocal microscopy, transcriptomic analysis (non-labeled FA experiments) and lipid class composition in the cells and serosal media from the basolateral side of the cells (radiolabeled FA experiments). CLD accumulation was higher in RTgutGC exposed to OA compared to cells given PA or ARA. This was coupled with increased volume, diameter, and surface area of CLDs in OA treated cells than with other FAs (PA, ARA). The results from radiolabeled FAs performed on permeable transwell inserts showed that OA increased the triacylglycerides (TAG) synthesis and was primarily stored in the cells in CLDs; whereas a significant amount of ARA was transported as TAG to the basolateral compartment. A significant proportion of free FAs was found to be excreted to the serosal basolateral side by the cells, which was significantly higher for PA and OA than ARA. Although there were clear clusters in differentially expressed genes (DEGs) for each treatment group, results from transcriptomics did not correlate to lipid transport and CLD analysis. Overall, the accumulation of TAG in CLDs was higher for oleic acid (OA) compared to arachidonic acid (ARA) and palmitic acid (PA). To conclude, carbon chain length and saturation level of FA differently regulate their intracellular fate during fatty acid absorption.

Background: Environmental health research has recently undergone a dramatic shift, with ongoing technological advancements allowing for broader coverage of exposure and molecular biology signatures. Approaches to integrate such measures are still needed to increase understanding between systems-level exposure and biology. Objectives: We address this gap by evaluating placental tissues to identify novel chemical-biological interactions associated with preeclampsia. This study tests the hypothesis that understudied chemicals are present in the human placenta and associated with preeclampsia-relevant disruptions, including overall case status (preeclamptic vs. normotensive patients) and underlying transcriptomic/epigenomic signatures. Methods: A non-targeted analysis based on high-resolution mass spectrometry was used to analyze placental tissues from a cohort of 35 patients with preeclampsia (n = 18) and normotensive (n = 17) pregnancies. Molecular feature data were prioritized for confirmation based on association with preeclampsia case status and confidence of chemical identification. All molecular features were evaluated for relationships to mRNA, microRNA, and CpG methylation (i.e., multi-omic) signature alterations involved in preeclampsia. Results: A total of 183 molecular features were identified with significantly differentiated abundance in placental extracts of preeclamptic patients; these features clustered into distinct chemical groupings using unsupervised methods. Of these features, 53 were identified (mapping to 40 distinct chemicals) using chemical standards, fragmentation spectra, and chemical metadata. In general, human metabolites had the largest feature intensities and strongest associations with preeclampsia-relevant multi-omic changes. Exogenous drugs were second most abundant and had fewer associations with multi-omic changes. Other exogenous chemicals (non-drugs) were least abundant and had the fewest associations with multi-omic changes. Conclusions: These global data trends suggest that human metabolites are heavily intertwined with biological processes involved in preeclampsia etiology, while exogenous chemicals may still impact select transcriptomic/epigenomic processes. This study serves as a demonstration of merging systems exposures with systems biology to better understand chemical-disease relationships.

Per- and polyfluoroalkyl substances (PFAS) are a class of man-made chemicals of global concern for many health and regulatory agencies due to their widespread use and persistence in the environment (in soil, air, and water), bioaccumulation, and toxicity. This concern has catalyzed a need to aggregate data to support research efforts that can, in turn, inform regulatory and statutory actions. An ongoing challenge regarding PFAS has been the shifting definition of what qualifies a substance to be a member of the PFAS class. There is no single definition for a PFAS, but various attempts have been made to utilize substructural definitions that either encompass broad working scopes or satisfy narrower regulatory guidelines. Depending on the size and specificity of PFAS substructural filters applied to the U.S. Environmental Protection Agency (EPA) DSSTox database, currently exceeding 900,000 unique substances, PFAS substructure-defined space can span hundreds to tens of thousands of compounds. This manuscript reports on the curation of PFAS chemicals and assembly of lists that have been made publicly available to the community via the EPA’s CompTox Chemicals Dashboard. Creation of these PFAS lists required the harvesting of data from EPA and online databases, peer-reviewed publications, and regulatory documents. These data have been extracted and manually curated, annotated with structures, and made available to the community in the form of lists defined by structure filters, as well as lists comprising non-structurable PFAS, such as polymers and complex mixtures. These lists, along with their associated linkages to predicted and measured data, are fueling PFAS research efforts within the EPA and are serving as a valuable resource to the international scientific community.

Abstract We revisit the covariant phase space formalism applied to gravitational theories with null boundaries, utilizing the most general boundary conditions consistent with a fixed null normal. To fix the ambiguity inherent in the Wald-Zoupas definition of quasilocal charges, we propose a new principle, based on holographic reasoning, that the flux be of Dirichlet form. This also produces an expression for the analog of the Brown-York stress tensor on the null surface. Defining the algebra of charges using the Barnich-Troessaert bracket for open subsystems, we give a general formula for the central — or more generally, abelian — extensions that appear in terms of the anomalous transformation of the boundary term in the gravitational action. This anomaly arises from having fixed a frame for the null normal, and we draw parallels between it and the holographic Weyl anomaly that occurs in AdS/CFT. As an application of this formalism, we analyze the near-horizon Virasoro symmetry considered by Haco, Hawking, Perry, and Strominger, and perform a systematic derivation of the fluxes and central charges. Applying the Cardy formula to the result yields an entropy that is twice the Bekenstein-Hawking entropy of the horizon. Motivated by the extended Hilbert space construction, we interpret this in terms of a pair of entangled CFTs associated with edge modes on either side of the bifurcation surface.

Perfluorooctanoic acid (PFOA) and related compounds are per- and polyfluorinated alkyl substances (PFASs) of concern from toxicological, environmental, and regulatory perspectives. In 2019, the Conference of the Parties to the Stockholm Convention on Persistent Organic Pollutants listed PFOA, its salts, and PFOA-related compounds in Annex A to the Convention. Additionally, the listing specifically included PFOA branched isomers and compounds containing a perfluoroheptyl (C7F15)C moiety, with some noted exclusions. A draft updated “Indicative List” of 393 PFASs (335 with defined structures), each specified as falling within or outside the listing, was released for comment in 2021. The U.S. Environmental Protection Agency’s CompTox Chemicals Dashboard has published a curated PFAS list containing more than 10,700 structures. Applying the PFOA and related compounds listing definition to screen this list required a structure-based approach capable of discerning salts and branched or linear forms of the (C7F15)C moiety. A PFOA SMILES workflow and associated Excel macro file, developed to address this need, applies a series of text substitution rules to a set of canonicalized SMILES structure representations to convert branched forms of the (C7F15)C moiety to linear forms to aid their detection. The approach correctly classified each Stockholm Convention draft Indicative List structure relative to the PFOA and related compounds definition, and accurately discerned branched and linear forms of the (C7F15)C moiety in over 10,700 PFAS structures with 100% sensitivity (no false negatives) and 99.7% accuracy (35 false positives). Approximately 20% of structures in the large PFAS list fell within the PFOA and related compounds definition, and 10% of those were branched. The present work highlights the need to computationally detect branched forms of PFASs and promotes the use of unambiguous, structure-based definitions, along with tools that are publicly available and easy to use, to support clear communication and regulatory action within the PFAS community.

Abstract Background Traditionally, the transcriptomic and proteomic characterisation of CD4+ T cells at the single-cell level has been performed by two largely exclusive types of technologies: single-cell RNA sequencing (scRNA-seq) and antibody-based cytometry. Here, we present a multi-omics approach allowing the simultaneous targeted quantification of mRNA and protein expression in single cells and investigate its performance to dissect the heterogeneity of human immune cell populations. Methods We have quantified the single-cell expression of 397 genes at the mRNA level and up to 68 proteins using oligo-conjugated antibodies (AbSeq) in 43,656 primary CD4+ T cells isolated from the blood and 31,907 CD45+ cells isolated from the blood and matched duodenal biopsies. We explored the sensitivity of this targeted scRNA-seq approach to dissect the heterogeneity of human immune cell populations and identify trajectories of functional T cell differentiation. Results We provide a high-resolution map of human primary CD4+ T cells and identify precise trajectories of Th1, Th17 and regulatory T cell (Treg) differentiation in the blood and tissue. The sensitivity provided by this multi-omics approach identified the expression of the B7 molecules CD80 and CD86 on the surface of CD4+ Tregs, and we further demonstrated that B7 expression has the potential to identify recently activated T cells in circulation. Moreover, we identified a rare subset of CCR9+ T cells in the blood with tissue-homing properties and expression of several immune checkpoint molecules, suggestive of a regulatory function. Conclusions The transcriptomic and proteomic hybrid technology described in this study provides a cost-effective solution to dissect the heterogeneity of immune cell populations at extremely high resolution. Unexpectedly, CD80 and CD86, normally expressed on antigen-presenting cells, were detected on a subset of activated Tregs, indicating a role for these co-stimulatory molecules in regulating the dynamics of CD4+ T cell responses.

Introduction Inferior vena cava (IVC) filter tilt is a common complication that occurs during and after filter placement. Severe tilting leads to reduced filter efficacy, lower retrieval success, and higher complication rates during retrieval. We present a novel catheter technique to correct severely tilted cone-shaped IVC filters without having to retrieve and replace the existing filter. Methods A retrospective review was performed for patients at our institution over three years who had severely tilted filters and underwent correction with the catheter technique. Indications for filter placement were categorized, and patient age, gender, tilt correction outcome, and complication rates were collected and analyzed. After severe tilting was noted on post-IVC filter deployment venogram, a Sos catheter was passed via the same femoral access site used for the filter placement. The catheter tip was reformed inside the cone of the filter and was used to push the filter tip back toward midline. Completion venogram was taken to document the amelioration of the tilt. Results Out of 28 patients who were found to have severely tilted filters on deployment and underwent correction with the catheter technique, 27/28 (96.4%) had successful correction. One (3.6%) had a minor complication where the filter struts became entangled with the catheter tip; however, simple maneuvering of the catheter and use of a stiff wire to straighten the catheter loop freed up the entanglement. No major complications occurred. Conclusion This technique is safe, effective, obviates filter replacement, and can be considered an additional management option for severe IVC filter tilt during placement.

Abstract The physical interactions between ice sheets and the atmosphere and ocean around them are major factors in determining the state of the climate system, yet many current Earth System models omit them entirely or treat them very simply. In this work we describe how models of the Greenland and Antarctic ice sheets have been incorporated into the global U.K. Earth System model (UKESM1) via substantial technical developments with a two‐way coupling that passes fluxes of energy and water, and the topography of the ice sheet surface and ice shelf base, between the component models. File‐based coupling outside the running model executables is used throughout to pass information between the components, which we show is both physically appropriate and convenient within the UKESM1 structure. Ice sheet surface mass balance is computed in the land surface model using multi‐layer snowpacks in subgrid‐scale elevation ranges and compares well to the results of regional climate models. Ice shelf front discharge forms icebergs, which drift and melt in the ocean. Ice shelf basal mass balance is simulated using the full three‐dimensional ocean model representation of the circulation in ice‐shelf cavities. We show a range of example results, including from simulations with changes in ice sheet height and thickness of hundreds of meters, and changes in ice sheet grounding line and land‐terminating margin of many tens of kilometres, demonstrating that the coupled model is computationally stable when subject to significant changes in ice sheet geometry.

Purpose: To evaluate the suitability of low-dose CT protocols for online plan adaptation of head-and-neck patients. Methods: We acquired CT scans of a head phantom with protocols corresponding to CT dose index volume CTDIvol in the range of 4.2–165.9 mGy. The highest value corresponds to the standard protocol used for CT simulations of 10 head-and-neck patients included in the study. The minimum value corresponds to the lowest achievable tube current of the GE Discovery RT scanner used for the study. For each patient and each low-dose protocol, the noise relative to the standard protocol, derived from phantom images, was applied to a virtual CT (vCT). The vCT was obtained from a daily CBCT scan corresponding to the fraction with the largest anatomical changes. We ran an established adaptive workflow twice for each low-dose protocol using a high-quality daily vCT and the corresponding low-dose synthetic vCT. For a relative comparison of the adaptation efficacy, two adapted plans were recalculated in the high-quality vCT and evaluated with the contours obtained through deformable registration of the planning CT. We also evaluated the accuracy of dose calculation in low-dose CT volumes using the standard CT protocol as reference. Results: The maximum differences in D98 between low-dose protocols and the standard protocol for the high-risk and low-risk CTV were found to be 0.6% and 0.3%, respectively. The difference in OAR sparing was up to 3%. The Dice similarity coefficient between propagated contours obtained with low-dose and standard protocols was above 0.982. The mean 2%/2 mm gamma pass rate for the lowest-dose image, using the standard protocol as reference, was found to be 99.99%. Conclusion: The differences between low-dose protocols and the standard scanning protocol were marginal. Thus, low-dose CT protocols are suitable for online adaptive proton therapy of head-and-neck cancers. As such, considering scanning protocols used in our clinic, the imaging dose associated with online adaption of head-and-neck cancers treated with protons can be reduced by a factor of 40.

For the past six decades, human health risk assessment of chemicals has relied on in vivo data from human epidemiological and experimental animal toxicological studies to inform the derivation of non-cancer toxicity values. The ongoing evolution of this risk assessment paradigm in an environmental landscape of data-poor chemicals has highlighted the need to develop and implement non-testing methods, so-called New Approach Methodologies (NAMs). NAMs include a growing number of in silico and in vitro data streams designed to inform hazard properties of chemicals, including kinetics and dynamics at different levels of biological organization, environmental fate and transport, and exposure. NAMs provide a fit-for-purpose science-basis for human hazard and risk characterization of chemicals ranging from data-gap filling applications to broad evidence-based decision-making. Systematic assembly and delivery of empirical and predicted data for chemicals are paramount to advancing chemical evaluation, and software tools serve an essential role in delivering these data to the scientific community. The CompTox Chemicals Dashboard (from here on referred to as the “Dashboard”) is one such tool and is a publicly available web-based application developed by the US Environmental Protection Agency to provide access to chemistry, toxicity and exposure information for ~900,000 chemicals. The Dashboard is increasingly becoming a valuable resource for assessors tasked with the evaluation of potential human health risks associated with chemical exposures. In this context, the significant amount of information present in the Dashboard facilitates: 1) assembly of information on physicochemical properties and environmental fate and transport and exposure parameters and metrics; 2) identification of cancer and non-cancer health effects from extant human and experimental animal studies in the public domain and/or information not available in the public domain (i.e., “grey literature”); 3) systematic literature searching and review for developing cancer and non-cancer hazard evidence bases; and 4) access to mechanistic information that can aid or augment the analysis of traditional toxicology evidence bases, or potentially, serve as the primary basis for informing hazard identification and dose–response when traditional bioassay data are lacking. Finally, in silico predictive tools developed to conduct structure–activity or read-across analyses are also available within the Dashboard. This practical tutorial is intended to address key questions from the human health risk assessment community dealing with chemicals in both food and in the environment. Perspectives for future development or refinement of the Dashboard highlight foreseen activities to further support the research and risk assessment community in cancer and non-cancer chemical evaluations.