Semi-automated Workflow Research Articles

Characterization of Radiographic Changes Following Lung Stereotactic Body Radiotherapy – A Dose-Based Radiomic Analysis

<h3>Purpose/Objective(s)</h3> Evaluation of diagnostic imaging following lung stereotactic body radiotherapy (SBRT) can be challenging. We hypothesize that radiation dose maps can be used to characterize post-SBRT changes utilizing radiomics. <h3>Materials/Methods</h3> We retrospectively identified patients with early stage (T1-T2, N0) non-small cell lung cancer treated at a single center with SBRT to a dose of 48-55 Gy in 3-5 fractions between 2017 and 2020. Pre-SBRT and the 1-year post-SBRT diagnostic CT scans with IV contrast and 1mm slice thickness were imported into MIM (MIM Software Inc. Cleveland OH). A semi-automated workflow consisted of biologically effective dose (BED) maps generation, rigid fusions of planning CT with each diagnostic scan prioritizing alignment of the gross tumor volume, contouring the lung, and lung volumes of interest (VOI) generation corresponding to high-dose (BED10 >= 100 Gy), intermediate-dose (BED10>=24.48 Gy), and low-dose (BED10<24.48 Gy). Using open-source software and a bin width of 25 Hounsfield units, dose-based VOI masks were used for radiomic analysis of pre- and post-SBRT diagnostic scans. Changes in radiomic features were evaluated with paired 2-sample t-tests and p<0.05 indicating significance Comparing pre- and post-SBRT scans, feature differences were considered treatment related if a significant difference was detected in the intermediate or high dose VOI but not the low dose VOI. <h3>Results</h3> Among 28 patients, 86% were stage T1a-T1c, with the most common dose of 50 Gy in 5 fractions. Diagnostic CT scans were an average of 3.6 months before SBRT and follow up scans were an average of 13.1 months after completion of SBRT. Of 106 radiomic features extracted from each image set, 8 were significant in both intermediate and high dose VOI and 5 were significant in the high dose VOI only (see Table). <h3>Conclusion</h3> We demonstrate the feasibility of a semi-automated workflow for dose related radiomic analysis of diagnostic CT scans in patients treated with SBRT and identify possible radiomic features pertinent to treatment related changes. The workflow can be leveraged to investigate predictive capabilities and assist interpretation of post-SBRT radiologic changes.

Realizing FAIR Digital Objects for the German Helmholtz Association of Research Centres

Realizing FAIR Digital Objects for the German Helmholtz Association of Research Centres

A holistic digital twin simulation framework for industrial facilities: BIM-based data acquisition for building energy modeling

Energy and resource efficiency as well as reduction of emissions are nowadays significant objectives for production companies. Industry 4.0, through extensive digitalization along the value chain, enables the achievement of these objectives not only in the construction of new facilities but also in existing facilities as well. This requires an interdisciplinary approach, extending over production and logistic processes as well as the building, technical building services, and energy supply systems, consolidated through integrated modeling and simulation-based optimization. The research question this study addresses is how to digitally couple these subsystems and optimize the overall system’s performance in terms of energy and resource efficiency, by distancing from silo-field thinking while using an integrated analysis approach. The article briefly presents a holistic modeling and simulation framework, utilizing modular digital twins (DTs) of all elements that may constitute a given industrial unit. The integration of multiple DTs of these subsystems in a hybrid (continuous and discrete) simulation forms a holistic DT ecosystem of an existing facility. The particular focus of the study is the building representation in this DT ecosystem for energy-efficient production. Based on a methodology including hybrid simulation, building information modeling (BIM), and visual programming, a semi-automated data acquisition workflow was proposed. The hybrid simulation is based on Discrete Event System Specification (DEVS) formalism, where the building is incorporated as a building energy model (BEM). Within the abstracted representation of the overall system, the article explores the possibilities of parametrizing the DT of the building, interconnected with the rest of the factory elements, by acquiring information directly from existing BIM models. Through a comparative case study, the proposed workflow is compared to a manual one in terms of integrity and benefits. The study’s contribution lies in: 1) the detection of the required building level of abstraction for a holistic DT ecosystem, 2) the definition of the interconnections between the building-related counterparts and the rest of the virtual environment as well as the data required for their parameterization, and 3) proposing a semi-automated workflow via virtual programming, for BIM-based creation of the building model within a holistic DT ecosystem.

Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence

The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many parts of the world still lack this information or the information is outdated. We created a semi-automated workflow for the development of elements-at-risk (EaR) databases of buildings by detecting building footprints using deep learning and characterising the footprints with building occupancy information using building morphological metrics and open-source auxiliary data. The deep learning model was used to detect building EaR footprints in a city in Kerala (India) with an F1 score of over 76%. The footprints were classified into 13 building occupancy types along with information such as average number of floors, total floor space area, building density, and percentage of built-up area. We analysed the transferability of the approach to a different city in Kerala and obtained an almost similar F1 score of 74%. We also examined the exposure of the buildings and the associated occupancies to floods using the 2018 flood susceptibility map of the respective cities. We notice certain shortcomings in our research particularly, the need for a local expert and good quality auxiliary data to obtain reasonable building occupancy information, however, our research contributes to developing a rapid method for generating a building EaR database in data-scarce regions with attributes of occupancy types, thus supporting regional risk assessment, disaster risk mitigation, risk reduction initiatives, and policy developments.

Automated Immunomagnetic Enrichment and Optomicrofluidic Detection to Isolate Breast Cancer Cells: A Proof-of-Concept towards PoC Therapeutic Decision-Making

In breast cancer research, immunomagnetic enrichment of circulating tumor cells (CTCs) from body fluids has impressively evolved over the last decades. However, there is growing interest in further singularizing these pre-enriched rare cells to decrease signal-to-noise ratio for downstream molecular analysis, e.g., to distinguish between hormone receptor-associated tumor subtypes. This can be done by a combinatory principle to link magnetic cell separation with flow cytometry and single cell dispensing. We have recently introduced an automated benchtop platform with a microfluidic disposable cartridge to immunomagnetically enrich, fluorescence-based detect and dispense single cells from biological samples. Herein, we showcase the fine-tuning of microfluidic cell isolation in dependency of bead-binding on the cell surface. We implemented a gating function for the cytometer subunit of the benchtop platform to selectively dispense cells instead of autofluorescent objects. Finally, we developed a simplified qPCR protocol without RNA purification targeting breast cancer-relevant progesterone and estrogen receptor, Muc-1, Her-2, EpCAM and CXCR4 transcripts. In conclusion, the presented results markedly demonstrate a future diagnostic and therapy-accompanying semi-automated workflow using immunomagnetic enrichment, fluorescence-based isolation and dispensing of circulating tumor cells to achieve tumor subtyping by means of rapid, simple and immediate molecular biological examination of single cells.

Topology-optimized patient-specific osteosynthesis plates

Abstract Patient-specific osteosynthesis plates can be used to reduce complications related to bone fracture treatment, such as infection, malocclusion and fatigue fractures of plates and screws. However, the implant design process is tedious. We propose a semi-automatic workflow to computationally design patient-specific titanium osteosynthesis plates for mandibular angle fractures. In this process, the plate stiffness is maximized while the mass is reduced. Two plate designs with different numbers of screw holes (implant #1 with four holes, implant #2 with eight holes) were generated with identical topology optimization settings and compared in a finite element model simulating various biomechanical masticatory loads. Differences in von Mises stresses in the implants and screws were observed. The load case of clenching the jaw on the opposite side of the fracture showed the highest stress distribution in implant #1 and higher peak stresses in implant #2. Stress concentrations were observed in sharp corners of the implant and could be reduced using local stress-based topology optimization. We conclude that the design process is an effective method to generate patientspecific implants.

SOC-VI-10 Development of a semi-automated workflow including (Q)SAR models to support the risk assessment of non-evaluated food contact material substances

SOC-VI-10 Development of a semi-automated workflow including (Q)SAR models to support the risk assessment of non-evaluated food contact material substances

Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry

Abstract. The monitoring of glaciers in Switzerland has a long tradition, yet glacier changes during the 20th century are only known through sparse observations. Here, we estimate a halving of Swiss glacier volumes between 1931 and 2016 by mapping historical glacier elevation changes at high resolution. Our analysis relies on a terrestrial image archive known as TerrA, which covers about 86 % of the Swiss glacierised area with 21 703 images acquired during the period 1916–1947 (with a median date of 1931). We developed a semi-automated workflow to generate digital elevation models (DEMs) from these images, resulting in a 45 % total glacier coverage. Using the geodetic method, we estimate a Swiss-wide glacier mass balance of −0.52 ± 0.09 m w.e. a−1 between 1931 and 2016. This equates to a 51.5 ± 8.0 % loss in glacier volume. We find that low-elevation, high-debris-cover, and gently sloping glacier termini are conducive to particularly high mass losses. In addition to these glacier-specific, quasi-centennial elevation changes, we present a new inventory of glacier outlines with known timestamps and complete attributes from around 1931. The fragmented spatial coverage and temporal heterogeneity of the TerrA archive are the largest sources of uncertainty in our glacier-specific estimates, reaching up to 0.50 m w.e. a−1. We suggest that the high-resolution mapping of historical surface elevations could also unlock great potential for research fields other than glaciology.

AN OPEN-SOURCE-BASED WORKFLOW FOR DEM GENERATION FROM SENTINEL-1 FOR LANDSLIDE VOLUME ESTIMATION

Abstract. Digital elevation models (DEMs) serve as a basis for various geomorphological applications, such as, landform mapping, natural hazard assessment, and landslide characterisation. The inter-comparison of DEMs can be used to provide estimates of topographic change, for example, volume changes. Sentinel-1 synthetic aperture radar (SAR) data can be used to generate multitemporal topographic datasets using interferometric SAR (InSAR). Such analyses are often conducted using commercial software; however, a well-structured workflow based on free and open-source software (FOSS) increases the applicability and transferability of the DEM generation method and can facilitate the use of multi-temporal topographic analysis within the Earth Sciences. In this study, we aim to develop an open-source-based workflow for the generation of multi-temporal DEMs from Sentinel-1 data, and to explore their potential for landslide volume estimation using FOSS. We implemented this semi-automated and transferable workflow bundled in the open-source Python package “SliDEM”, which relies on SNAP, SNAPHU, and several other open-source software for geospatial and geomorphological applications, all distributed within a Docker image. Two major landslides in Austria and Norway were selected to test, evaluate, and validate the workflow in terms of reliability, performance, reproducibility, and transferability. Even if the quality of the Sentinel-1-based DEMs varies among the study areas, the preliminary results are promising. However, there is a need for improvement and thorough analysis of the causes of elevation errors. A comprehensive evaluation of the influence of the perpendicular and temporal baselines, topography, land use/land cover, and other environmental conditions can be systematically assessed within the “SliDEM” workflow.

Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC

BackgroundHigh-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed.ResultsWe present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htb1 concentrations decrease with replicative age.ConclusionsCell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis.Source code: https://github.com/SchmollerLab/Cell_ACDC

DASCO: A workflow to downscale alien species checklists using occurrence records and to re-allocate species distributions across realms

Information about occurrences of alien species is often provided in so-called checklists, which represents lists of reported alien species in a region. In many cases, available checklists cover whole countries, which is too coarse for many analyses and limits capabilities of assessing status and trends of biological invasions. Information about point-wise occurrences is available in large quantities at online facilities such as GBIF and OBIS, which, however, do not provide information about the invasion status of individual populations. To close this gap, we here provide a semi-automated workflow called DASCO to downscale regional checklists using occurrence records obtained from GBIF and OBIS. Within the workflow, coordinate-based occurrence records for species listed in the provided regional checklists are obtained from GBIF and OBIS, and the status of being an alien population is assigned using the information in the provided checklists. In this way, information in checklists is made available at the local scale, which can then be re-allocated to any other spatial categorisation as provided by the user. In addition, habitats of species are determined to distinguish between marine, brackish, terrestrial, and freshwater species, which allows splitting the provided checklists to the respective realms and ecoregions. By using checklists of global databases, we showcase the usage of the DASCO workflow and revealed &amp;gt; 35 million occurrence records of alien populations in terrestrial and marine regions worldwide, which were back-transformed to terrestrial and marine regions for comparison. DASCO has the potential to be used as a basis for the widely applied species distribution models or assessments of status and trends of biological invasions at large geographic scales. The workflow is implemented in R and in full compliance with the FAIR data principles of open science.

Abstract 1374: Discovery of MHC class I and class II neoantigens in lung cancer in needle biopsy tissue samples using an optimized high-throughput workflow

Abstract Human leukocyte antigen-associated peptides, known as immunopeptides, play an essential role in adaptive immunity by activating and ensuring the specificity of T-cells. The identification and quantification of the immunopeptidome bear the potential to enable personalized treatments, especially in cancers, vaccines, infectious, and autoimmune diseases. Mass spectrometry is currently the only technology that can reliably measure and identify immunopeptide profiles of biological samples on a large scale. However, the usually high sample input amount and poor scalability are limiting. Here, we introduce a semi-automated workflow to robustly identify immunopeptides from low amounts of cultured cells and tissue samples by systematically optimizing each step of the sample preparation and acquisition. We optimized the native lysis and immunoprecipitation workflow while ensuring scalability and reproducibility. Leveraging the magnetic properties of the beads, 1,000 samples can be processed within a week by a single operator. The established sample preparation offers high reproducibility and identifications of good quality. For class-I immunopeptides, &amp;gt60% of the peptides identified are 9-mers, &amp;gt80% predicted strong binders, and the expected amino acids are enriched at the anchor positions. For class-II, &amp;gt50% of the peptides identified are 14-to-16-mers, and &amp;gt50% are predicted strong binders. Furthermore, the pipeline is highly sensitive as we could still identify over 2,800 class-I immunopeptides when processing as little as 2.5 mg fresh frozen tissue and &amp;gt9,000 class-I and &amp;gt12,000 class-II immunopeptides when preparing 10 million JY cells. Overall, the pipeline is scalable, highly reproducible, and results in high-quality identifications while supporting very limited sample input. Finally, we measured a cohort of 12 cancerous and matched healthy lung tissues from as little as 15 mg tissue, whereby we could identify &amp;gt11,000 class-I immunopeptides and &amp;gt9,000 class-II on average. For class-I, matched samples clustered together, while &amp;gt3,000 immunopeptides were upregulated in the cancer tissues, with a significant enrichment for proteins related to lung cancer. Overall, we established a scalable, efficient pipeline for cell line and tissues immunopeptidomics for class-I and II that generates high-quality identifications and that only requires small amounts of input material and is ready to shed light into immunopeptidomics heterogeneity through large-scale profiling of patients. Citation Format: Ilja E. Shapiro, Luca Raess, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Yuehan Feng, Roland Bruderer, Lukas Reiter. Discovery of MHC class I and class II neoantigens in lung cancer in needle biopsy tissue samples using an optimized high-throughput workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1374.

Uncertainty estimation strategies for quantitative non-targeted analysis.

Non-targeted analysis (NTA) methods are widely used for chemical discovery but seldom employed for quantitation due to a lack of robust methods to estimate chemical concentrations with confidence limits. Herein, we present and evaluate new statistical methods for quantitative NTA (qNTA) using high-resolution mass spectrometry (HRMS) data from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Experimental intensities of ENTACT analytes were observed at multiple concentrations using a semi-automated NTA workflow. Chemical concentrations and corresponding confidence limits were first estimated using traditional calibration curves. Two qNTA estimation methods were then implemented using experimental response factor (RF) data (where RF = intensity/concentration). The bounded response factor method used a non-parametric bootstrap procedure to estimate select quantiles of training set RF distributions. Quantile estimates then were applied to test set HRMS intensities to inversely estimate concentrations with confidence limits. The ionization efficiency estimation method restricted the distribution of likely RFs for each analyte using ionization efficiency predictions. Given the intended future use for chemical risk characterization, predicted upper confidence limits (protective values) were compared to known chemical concentrations. Using traditional calibration curves, 95% of upper confidence limits were within ~tenfold of the true concentrations. The error increased to ~60-fold (ESI+) and ~120-fold (ESI-) for the ionization efficiency estimation method and to ~150-fold (ESI+) and ~130-fold (ESI-) for the bounded response factor method. This work demonstrates successful implementation of confidence limit estimation strategies to support qNTA studies and marks a crucial step towards translating NTA data in a risk-based context.

Semi-automated digital workflow to design and evaluate patient-specific mandibular reconstruction implants

The reconstruction of large mandibular defects with optimal aesthetic and functional outcomes remains a major challenge for maxillofacial surgeons. The aim of this study was to design patient-specific mandibular reconstruction implants through a semi-automated digital workflow and to assess the effects of topology optimization on the biomechanical performance of the designed implants. By using the proposed workflow, a fully porous implant (LA-implant) and a topology-optimized implant (TO-implant) both made of Ti–6Al–4V ELI were designed and additively manufactured using selective laser melting. The mechanical performance of the implants was predicted by performing finite element analysis (FEA) and was experimentally assessed by conducting quasi-static and cyclic biomechanical tests. Digital image correlation (DIC) was used to validate the FE model by comparing the principal strains predicted by the FEM model with the measured distribution of the same type of strain. The numerical predictions were in good agreement with the DIC measurements and the predicted locations of specimen failure matched the actual ones. No statistically significant differences (p < 0.05) in the mean stiffness, mean ultimate load, or mean ultimate displacement were detected between the LA- and TO-implant groups. No implant failures were observed during quasi-static or cyclic testing under masticatory loads that were substantially higher (>1000 N) than the average maximum biting force of healthy individuals. Given its relatively lower weight (16.5%), higher porosity (17.4%), and much shorter design time (633.3%), the LA-implant is preferred for clinical application. This study clearly demonstrates the capability of the proposed workflow to develop patient-specific implants with high precision and superior mechanical performance, which will greatly facilitate cost- and time-effective pre-surgical planning and is expected to improve the surgical outcome.

Antibiotic Drug screening and Image Characterization Toolbox (A.D.I.C.T.): a robust imaging workflow to monitor antibiotic stress response in bacterial cells in vivo.

The search for novel drugs that efficiently eliminate prokaryotic pathogens is one of the most urgent health topics of our time. Robust evaluation methods for monitoring the antibiotic stress response in prokaryotes are therefore necessary for developing respective screening strategies. Besides advantages of common in vitrotechniques, there is a growing demand for in vivoinformation based on imaging techniques that allow to screen antibiotic candidates in a dynamic manner. Gathering information from imaging data in a reproducible manner, robust data processing and analysis workflows demand advanced (semi-)automation and data management to increase reproducibility. Here we demonstrate a versatile and robust semi-automated image acquisition, processing and analysis workflow to investigate bacterial cell morphology in a quantitative manner. The presented workflow, A.D.I.C.T, covers aspects of experimental setup deployment, data acquisition and handling, image processing (e.g. ROI management, data transformation into binary images, background subtraction, filtering, projections) as well as statistical evaluation of the cellular stress response (e.g. shape measurement distributions, cell shape modeling, probability density evaluation of fluorescence imaging micrographs) towards antibiotic-induced stress, obtained from time-course experiments. The imaging workflow is based on regular brightfield images combined with live-cell imaging data gathered from bacteria, in our case from recombinant Shewanellacells, which are processed as binary images. The model organism expresses target proteins relevant for membrane-biogenesis that are functionally fused to respective fluorescent proteins. Data processing and analysis are based on customized scripts using ImageJ2/FIJI, Celltool and R packages that can be easily reproduced and adapted by users. Summing up, our approach aims at supporting life-scientists to establish their own imaging-pipeline in order to exploit their data as versatile as possible and in a reproducible manner.

Comparison of Allplex™ 2019-nCoV and TaqPath™ COVID-19 Assays

The clinical presentation of COVID-19 is non-specific, and to improve and limit the spread of the SARS-CoV-2 virus, an accurate diagnosis with a robust method is needed. A total of 500 nasopharyngeal swab specimens were tested for SARS-CoV-2. Of these, 184 samples were found to be positive with Allplex™ 2019-nCoV Assay, which is fully automated. All the positive samples were retested with TaqPath™ COVID-19 CE-IVD RT-PCR Kit (after this, referred to as TaqPath™ COVID-19), semi-automated. The comparison of RT-qPCR for SARS-CoV-2 genes target points shows only one target point in common, the N gene. Therefore, the N gene was used to compare both assays. We noticed different Ct values between the tests. Therefore, samples were divided into four groups depending to the Ct value results: (1) Ct &lt; 25, (2) Ct 25–30, (3) Ct 30–35, (4) Ct &gt; 35. TaqPath™ COVID-19 Kit reconfirmed the results obtained from Allplex™ 2019-nCoV Assay. In conclusion, both the Allplex™ 2019-nCoV assay and TaqPath™ COVID-19 tests accurately confirm the diagnosis of SARS-CoV-2 infection. Even if TaqPath™ COVID-19 has a semi-automated workflow, it does not introduce bias in the diagnostic screening of SARS-CoV-2, and it supports the indirect identification of variants of concern to undergo sequencing.

Semi-Automated Inversion-Specific Data Selection for Volcano Tomography

Active seismic experiments allow reconstructing the subsurface structure of volcanoes with unprecedented resolution and are vital to improve the interpretation of volcanic processes. They require a quality assessment for thousands of seismic waveforms recorded at hundreds of stations in the shortest amount of time. However, the processing necessary to obtain reliable images from such massive datasets demands signal processing and selection strategies specific to the inversions attempted. Here, we present a semi-automated workflow for data selection and inversion of amplitude-dependent information using the original TOMODEC2005 dataset, recorded at Deception Island (Antarctica). The workflow is built to tomographic techniques using amplitude information, and can be generalised to passive seismic imaging. It first selects data depending on standard attributes, like the presence of zeroes across all seismic waveforms. Then, waveform selections depend on inversion-specific attributes, like the delay of the maximum amplitude of the waveform or the quality of coda-wave decays. The automatic workflow and final visual selections produce a dataset reconstructing anomalies at a node spacing of 2 km, imaging a high-attenuation anomaly in the centre of the Deception Island bay, consistent with previously-published maps. Attenuation models are then obtained at a node spacing of 1 km, highlighting bodies of highest attenuation scattered across the island and a NW-SE trend in the high-attenuation anomaly in the central bay. These results show the effect of the local extension regime on volcanic structures, providing details on the eruptive history and evolution of the shallow magmatic and hydrothermal systems. The selection workflow can be easily generalised to other amplitude-dependent tomographic techniques when applied to active seismic surveys. Image improvements from the original dataset are minor when selecting data using standard attributes, like signal-to-noise ratios. Tomographic maps become drastically more stable and consistent between different frequencies and resolutions when data selection targets attributes specific to the inversion.

Lower crust structures and dynamics of southern California revealed by first P and PmP traveltime data

The lower crust plays an important role in coupling the upper mantle force to the brittle upper crust at transform plate boundary regions. Yet, the tomographic resolution in the lower crust is typically much lower than the upper crust, given that most earthquakes take place at seismogenic depths. Here, we present a new P-wave velocity model of the entire crust in southern California by jointly inverting arrival times of first P and Moho reflected PmP waves. A total of 29,512 robust PmP arrivals are picked by a new semi-automatic workflow, forming the largest earthquake-sourced PmP dataset in southern California to date. Such abundant PmP arrivals remarkably improve the resolution of middle and lower crust in tomographic imaging. Our final velocity model reveals prominent low-velocity anomalies beneath the Eastern California Shear Zone (ECSZ) and south of the Coso Volcanic Field (CVF) in the lower crust. We attribute the low velocities beneath the ECSZ to deep fluid concentration which not only triggers major crustal earthquakes but may also contribute to the long-term development of the shear zone. While the low-velocity anomaly south of the CVF may imply the deep magma source of the CVF is located to its south.

Morphological evolution of creek networks in 10 restored coastal wetlands in the UK

Coastal wetlands provide crucial ecosystem services including flood protection and carbon storage, but are being lost rapidly worldwide to the combined effects of sea-level rise, erosion and coastal urbanisation. Managed Realignment (MR) aims to mitigate for these losses by restoring reclaimed land to tidal influence. Data of creek evolution is critical to assess the performance of design strategies and improve design and implementation practices. This data descriptor provides a dataset of the horizontal morphological evolution of creek systems from various initial conditions in 10 MR schemes across the UK. Using a semi-automated workflow, morphological creek parameters were extracted from 52 lidar datasets at 1 m horizontal resolution spanning 2 to 20 years post-breach. This constitutes the most comprehensive systematic monitoring of MR creek morphology to date. The dataset will assist future MR design and provide baseline morphological information for ecological and biogeochemical surveying.

Responsiveness of the HUG-5 in an outpatient clinic: a 12-month randomised feasibility study protocol

IntroductionGlaucoma is a progressive, chronic condition that can have a significant impact on a patient’s health-related quality of life (HRQoL). Validated, disease-specific HRQoL tools such as the Health Utility for...