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US Geological Survey (USGS) National Seismic Hazard Models (NSHMs) are used extensively for seismic design regulations in the United States and earthquake scenario development, as well as risk assessment and mitigation for both buildings and infrastructure. This 2023 update of the long-term, time-independent Alaska NSHM includes substantial changes to both the earthquake rupture forecast (ERF) and ground motion models (GMMs). The ERF includes numerous additions to the finite-fault model, considers two deformation models, and introduces updated declustering and smoothing algorithms in the gridded background seismicity model. For the Alaska–Aleutian subduction zone, megathrust earthquakes occur on an updated structural and segmentation model, and the moment magnitude (M) 8+ rupture and rate model include a logic tree branch that considers slip rates derived from geodetic models of interface coupling. The megathrust model considers multiple models of down-dip width, and magnitudes are computed using newly developed scaling relations. For subduction intraslab events and subduction interface events with M < 7, the 2023 update uses a smoothed seismicity model with rupture depths derived from Slab2. The 2023 model updates GMMs in all tectonic settings using the recently published Next Generation Attenuation Subduction (NGA-Sub) GMMs for subduction interface and intraslab events, and the NGA-West2 GMMs for active crustal settings. Collectively, additions and updates to the Alaska NSHM result in hazard increases across most of south-central Alaska relative to the previous model, published in 2007. These changes are primarily due to the adoption of updated rate models for the large-magnitude interface events and the NGA-Sub GMMs that have much higher aleatory variability (sigma), consistent with global observations, and that include models of epistemic uncertainty. [ABSTRACT FROM AUTHOR]

Clinical trial participant populations fail to adequately represent target populations that drugs are intended to serve. Improving racial and ethnic diversity of clinical trial participants is essential for generalizable, quality clinical research results and ensuring social and medical equity. Site‐level clinical research professionals (CRPs) have unique insights on diversity improvement strategies for clinical trial enrollment. A survey was distributed to current CRPs working at clinical research sites in the United States to describe current practices and perceptions of the impact these practices have on participant diversity. Subsequently, descriptive quantitative analysis and inductive content analysis were performed. For the practices surveyed, there are discrepancies between frequency of use and perceived impact on diversity enrollment. Common current practices include phone‐based or telemedicine study visits, electronic/digital data collection, and participant compensation. However, we report travel reimbursement and services, translated documents and translator services, and adequate participant compensation as most impactful on diverse enrollment. A multistakeholder approach is necessary to enhance diversity and inclusion (D&I) of study participants. Besides large‐scale solutions such as countering community distrust, actionable steps are needed by sponsors and study sites to improve D&I of trial participants. Study leadership at the sponsor, contract research organization (CRO), and site‐level should create diversity plans prior to study start, and CRO and sponsor budgets should consider D&I strategies during study planning. Planning should incorporate strategies to improve D&I including adequate participant compensation, translated documents and translator services, and travel reimbursements. [ABSTRACT FROM AUTHOR]

The Gray--Scott model is a widely studied autocatalytic model that exhibits a range of interesting pattern formation behavior, as well as a rich structure of dynamics that includes many ideas from a typical undergraduate dynamical systems course, and some from beyond. Gaining an understanding of the solutions to this model is arguably most easily conducted via a bifurcation analysis of corresponding ODE problems within the software XPPAUT. In this paper, we provide an introductory XPPAUT tutorial, through which we begin to expose the range of intricate patterns that the Gray--Scott model emits. [ABSTRACT FROM AUTHOR]

Under certain circumstances, such as during the current COVID-19 outbreak, pregnant women can be a target for respiratory infection, and lung examination may be required as part of their clinical evaluation, ideally while avoiding exposure to radiation. We propose a practical approach for obstetricians/gynecologists to perform lung ultrasound examination, discussing potential applications, semiology and practical aspects, which could be of particular importance in emergency situations, such as the current pandemic infection of COVID-19. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

In standard B mode imaging, a set of ultrasound pulses is used to reconstruct a 2-D image even though some of the assumptions needed to do this are not fully satisfied. For this reason, ultrasound medical images show numerous artifacts which physicians recognize and evaluate as part of their diagnosis since even one artifact can provide clinical information. Understanding the physical mechanisms at the basis of the formation of an artifact is important to identify the physiopathological state of the biological medium which generated the artifact. Ultrasound lung images are a significant example of this challenge since everything that is represented beyond the thickness of the chest wall ($\approx ~2$ cm) is artifactual information. A convincing physical explanation of the genesis of important ultrasound lung artifacts does not exist yet. Physicians simply base their diagnosis on a correlation observed over the years between the manifestation of some artifacts and the occurrence of particular lung pathologies. In this article, a plausible genesis of some important lung artifacts is suggested. [ABSTRACT FROM AUTHOR]

Despite being accepted as a robust assessment of masonry stability, thrust line analysis (TLA) relies on assumptions that can lead to a conservative assessment of stability. This article aims to quantify the extent of these limitations through a comparison of TLA with discrete element modeling (DEM). Two studies are provided. The first study compares TLA with DEM (using fixed input parameters) in assessing the stability of unreinforced masonry arches, semi-circular barrel vaults, multi-ring arches, and groin vaults. The tests demonstrate the types of sliding failures overlooked by the safe theorem due to its assumption of infinite friction. Following these validations, the comparisons between 2D structures and 3D counterparts also give insight into the efficacy of the slicing method. The second study examines the effect of DEM input parameters on the DEM-predicted stability of the considered geometries. While material parameters had limited effect on the determination of stability, for each typology, joint friction angle had a unique impact on stability. These trends are graphically presented and demonstrate how t/R ratios alone are not sufficient to unequivocally confirm stability of the considered vaults. Overall, this research informs the extent of safety for using the geometry-based analysis tool, TLA, for analyzing masonry structures. [ABSTRACT FROM AUTHOR]

Curcumin is a versatile anti-inflammatory and anti-cancer agent known for its low bioavailability, which could be improved by developing materials capable of binding and releasing drug in a controlled fashion. The present study describes the preparation of magnetic nano-sized Molecularly Imprinted Polymers (nanoMIPs) for the controlled delivery of curcumin and their high throughput characterisation using microtitre plates modified with magnetic inserts. NanoMIPs were synthesised using functional monomers chosen with the aid of molecular modelling. The rate of release of curcumin from five polymers was studied under aqueous conditions and was found to correlate well with the binding energies obtained computationally. The presence of specific monomers was shown to be significant in ensuring effective binding of curcumin and to the rate of release obtained. Characterisation of the polymer particles was carried out using dynamic light scattering (DLS) technique and scanning electron microscopy (SEM) in order to establish the relationship between irradiation time and particle size. The protocols optimised during this study could be used as a blueprint for the development of nanoMIPs capable of the controlled release of potentially any compound of interest. [ABSTRACT FROM AUTHOR]

Lung ultrasound examination has been suggested as a useful tool with which to detect lung involvement during the COVID-19 pandemic, desirable particularly for pregnant women[[1], [3]]. GRAPH: Table S1 Pre- and post-training test results of 11 participants in rapid-training program on lung ultrasound examination Footnotes 1 D.B. and F.M. contributed equally to this work. [Extracted from the article]

Automatic target recognition (ATR) is generally the reason why inverse synthetic aperture radar (ISAR) imaging systems are employed. Moreover, the use of fully polarimetric radar systems in radar imaging applications such as SAR and ISAR has enhanced both image quality and classification capabilities. The authors propose a novel technique for ATR using polarimetric ISAR (Pol-ISAR) images. The proposed method is based on a model matching approach. Results are obtained that show the effectiveness of such a technique. [ABSTRACT FROM AUTHOR]

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective management. Lung ultrasound (LUS) has emerged as a promising tool to evaluate respiratory conditions in neonates. This evaluation is mainly based on the interpretation of visual patterns (horizontal artifacts, vertical artifacts, and consolidations). Automated interpretation of these patterns can assist clinicians in their evaluations. However, developing AI-based solutions for this purpose is challenging, primarily due to the lack of annotated data and inherent subjectivity in expert interpretations. This study aims to propose an automated solution for the reliable interpretation of patterns in LUS videos of newborns. We employed two distinct strategies. The first strategy is a frame-to-video-level approach that computes frame-level predictions from deep learning (DL) models trained from scratch (F2V-TS) along with fine-tuning pre-trained models (F2V-FT) followed by aggregation of those predictions for video-level evaluation. The second strategy is a direct video classification approach (DV) for evaluating LUS data. To evaluate our methods, we used LUS data from 34 neonatal patients comprising of 70 exams with annotations provided by three expert human operators (3HOs). Results show that within the frame-to-video-level approach, F2V-FT achieved the best performance with an accuracy of 77% showing moderate agreement with the 3HOs. while the direct video classification approach resulted in an accuracy of 72%, showing substantial agreement with the 3HOs, our proposed study lays down the foundation for reliable AI-based solutions for newborn LUS data evaluation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Mitochondrial dysfunction is one of the earliest pathological events observed in amyotrophic lateral sclerosis (ALS). The aim of this study is to evaluate the therapeutic efficacy of 2,4-dinitrophenol (DNP), a mild mitochondrial uncoupler, in an ALS mouse model to provide preclinical proof-of-concept evidence of using DNP as a potential therapeutic drug for ALS., Methods: hSOD1 G93A mice were treated with 0.5-1.0 mg/kg DNP through daily oral gavage from presymptomatic stage or disease onset until 18 weeks old. Longitudinal behavioral studies were performed weekly or biweekly from 6 to 18 weeks old. In situ muscle contraction measurements in extensor digitorum longus muscles were conducted to evaluate the preservation of contractile force and motor unit numbers in hSOD1 G93A mice following DNP treatment. Muscle innervation and inflammatory markers were assessed using immunostaining. Extent of protein oxidation and activation of Akt pathway were also examined., Results: DNP delayed disease onset; improved motor coordination and muscle performance in vivo; preserved muscle contractile function, neuromuscular junction morphology, and muscle innervation; and reduced inflammation and protein oxidation at 18 weeks old in hSOD1 G93A mice. Strikingly, symptomatic hSOD1 G93A mice exhibited a period of recovery in running ability at 20 cm/s several weeks after 2,4-dinitrophenol treatment started at disease onset, offering the first observation in disease phenotype reversal using a small molecule., Interpretation: Our results strongly support that micro-dose DNP may be used as a potential novel treatment for ALS patients, with a possibility for recovery, when used at optimal doses and time of intervention. ANN NEUROL 2024., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background & Aims: There is an unmet need for a reliable and reproducible non-invasive measure of fatty liver content (FLC) for monitoring steatotic liver disease in clinical practice. Sonographic FLC assessment is qualitative and operator-dependent, and the dynamic quantification range of algorithms based on a single ultrasound (US) parameter is unsatisfactory. This study aims to develop and validate a new multiparametric algorithm based on B-mode images to quantify FLC using Magnetic Resonance (MR) values as standard reference., Methods: Patients with elevated liver enzymes and/or bright liver at US (N = 195) underwent FLC evaluation by MR and by US. Five US-derived quantitative features [attenuation rate(AR), hepatic renal-ratio(HR), diaphragm visualization(DV), hepatic-portal-vein-ratio(HPV), portal-vein-wall(PVW)] were combined by mixed linear/exponential regression in a multiparametric model (Steatoscore2.0). One hundred and thirty-four subjects were used for training and 61 for independent validations; score-computation underwent an inter-operator reproducibility analysis., Results: The model is based on a mixed linear/exponential combination of 3 US parameters (AR, HR, DV), modelled by 2 equations according to AR values. The computation of FLC by Steatoscore2.0 (mean ± std, 7.91% ± 8.69) and MR (mean ± std, 8.10% ± 10.31) is highly correlated with a low root mean square error in both training/validation cohorts, respectively (R = 0.92/0.86 and RMSE = 5.15/4.62, p < .001). Steatoscore2.0 identified patients with MR-FLC≥5%/≥10% with sensitivity = 93.2%/89.4%, specificity = 86.1%/95.8%, AUROC = 0.958/0.975, respectively and correlated with MR (R = 0.92) significantly (p < .001) better than CAP (R = 0.73)., Conclusions: Multiparametric Steatoscore2.0 measures FLC providing values highly comparable with MR. It is reliable, inexpensive, easy to use with any US equipment and qualifies to be tested in larger, prospective studies as new tool for the non-invasive screening and monitoring of FLC., (© 2024 The Author(s). Liver International published by John Wiley & Sons Ltd.)

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. However, current LUS approaches are based on subjective interpretation of imaging artifacts, which results in poor specificity as quantitative evaluation lacks. The latter could be improved by adopting LUS spectroscopy of vertical artifacts. Indeed, parameterizing these artifacts with native frequency, bandwidth, and total intensity ( [Formula: see text]) already showed potentials in differentiating pulmonary fibrosis (PF). In this study, we acquired radio frequency (RF) data from 114 patients. These data (representing the largest LUS RF dataset worldwide) were acquired by utilizing a multifrequency approach, implemented with an ULtrasound Advanced Open Platform (ULA-OP). Convex (CA631) and linear (LA533) probes (Esaote, Florence, Italy) were utilized to acquire RF data at three (2, 3, and 4 MHz), and four (3, 4, 5, and 6 MHz) imaging frequencies. A multifrequency analysis was conducted on vertical artifacts detected in patients having cardiogenic pulmonary edema (CPE), pneumonia, or PF. These artifacts were characterized by the three abovementioned parameters, and their mean values were used to project each patient into a feature space having up to three dimensions. Binary classifiers were used to evaluate the performance of these three mean features in differentiating patients affected by CPE, pneumonia, and PF. Acquisitions of multifrequency data performed with linear probe lead to accuracies up to 85.43% in the differential diagnosis of these diseases (convex probes' maximum accuracy was 74.51%). Moreover, the results showed high potentials of mean [Formula: see text] (by itself or combined with other features) in improving LUS specificity.

Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings. The wide application of point-of-care ultrasound at the bedside is limited mainly due to a lack of training for data acquisition and interpretation. Artificial Intelligence can serve as a potential tool to automate and improve the LUS data interpretation process, which mainly involves analysis of hyper-echoic horizontal and vertical artifacts, and hypo-echoic small to large consolidations. This paper presents, Fused Lung Ultrasound Encoding-based Transformer (FLUEnT), a novel pediatric LUS video scoring framework for detecting lung consolidations using fused LUS encodings. Frame-level embeddings from a variational autoencoder, features from a spatially attentive ResNet-18, and encoded patient information as metadata combiningly form the fused encodings. These encodings are then passed on to the transformer for binary classification of the presence or absence of consolidations in the video. The video-level analysis using fused encodings resulted in a mean balanced accuracy of 89.3 %, giving an average improvement of 4.7 % points in comparison to when using these encodings individually. In conclusion, outperforming the state-of-the-art models by an average margin of 8 % points, our proposed FLUEnT framework serves as a benchmark for detecting lung consolidations in LUS videos from pediatric pneumonia patients., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Libertario Demi is the co-founder of UltraAI. The rest of authors declare no conflict of interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Static 3-dimensional (3D) printing became attractive for operative planning in cases that involve difficult anatomy. An interactive (low cost, fast) 3D print allowing deliberate surgical practice can be used to improve interventional simulation and planning., Background: Endovascular treatment of complex aortic aneurysms is technically challenging, especially in case of narrow aortic lumen or significant aortic angulation (hostile anatomy). The risk of complications such as graft kinking and target vessel occlusion is difficult to assess based solely on traditional software measuring methods and remain highly dependent on surgeon skills and expertise., Methods: A patient with juxtarenal AAA with hostile anatomy had a 3-dimensional printed model constructed preoperatively according to computed tomography images. Endovascular graft implantation in the 3D printed aorta with a standard T-Branch Cook (Cook® Medical, Bloomington, IN, USA) was performed preoperatively in the simulation laboratory enabling optimized feasibility, surgical planning and intraoperative decision making., Results: The 3D printed aortic model proved to be radio-opaque and allowed simulation of branched endovascular aortic repair (BREVAR). The assessment of intervention feasibility, as well as optimal branch position and orientation was found to be useful for surgeon confidence and the actual intervention in the patient. There was a remarkable agreement between the 3D printed model and both CT and X-ray angiographic images. Although the technical success was achieved as planned, a previously deployed renal stent caused unexpected difficulty in advancing the renal stent, which was not observed in the 3D model simulation., Conclusion: The 3D printed aortic models can be useful for determining feasibility, optimizing planning and intraoperative decision making in hostile anatomy improving the outcome. Despite already offering satisfying accuracy at present, further advancements could enhance the 3D model capability to replicate minor anatomical deformities and variations in tissue density., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.

Automated cardiac segmentation from two-dimensional (2D) echocardiographic images is a crucial step toward improving clinical diagnosis. Anatomical heterogeneity and inherent noise, however, present technical challenges and lower segmentation accuracy. The objective of this study is to propose a method for the automatic segmentation of the ventricular endocardium, the myocardium, and the left atrium, in order to accurately determine clinical indices. Specifically, we suggest using the recently introduced pixel-to-pixel Generative Adversarial Network (Pix2Pix GAN) model for accurate segmentation. To accomplish this, we integrate the backbone PatchGAN model for the discriminator and the UNET for the generator, for building the Pix2Pix GAN. The resulting model produces precisely segmented images, thanks to UNET's capability for precise segmentation and PatchGAN's capability for fine-grained discrimination. For the experimental validation, we use the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset, which consists of echocardiographic images from 500 patients in 2-chamber (2CH) and 4-chamber (4CH) views at the end-diastolic (ED) and end-systolic (ES) phases. Similarly to state-of-the-art studies on the same dataset, we followed the same train-test splits. Our results demonstrate that the proposed GAN-based technique improves segmentation performance for clinical and geometrical parameters compared to the state-of-the-art methods. More precisely, throughout the ED and ES phases, the mean Dice values for the left ventricular endocardium reached 0.961 and 0.930 for 2CH, and 0.959 and 0.950 for 4CH, respectively. Furthermore, the average ejection fraction correlation and Mean Absolute Error obtained were 0.95 and 3.2ml for 2CH, and 0.98 and 2.1ml for 4CH, outperforming the state-of-the-art results.

See also the commentary by Sitek in this issue. Supplemental material is available for this article.