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Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Abstract Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Integrated hydrological model (IHM) forecasts provide critical insights into hydrological system states, fluxes, and its evolution of water resources and associated risks, essential for many sectors and stakeholders in agriculture, urban planning, forestry, or ecosystem management. However, the accuracy of these forecasts depends on the data quality of the precipitation forcing data. Previous studies have utilized data-driven methods, such as deep learning (DL) during the preprocessing phase to improve precipitation forcing data obtained from numerical weather prediction simulations. Nonetheless, challenges related to the spatiotemporal variability of hourly precipitation data persist, including issues with ground truth data availability, data imbalance in training DL models, and method evaluation. This study compares three (near) real-time spatiotemporal precipitation datasets to be used in the aforementioned IHM forecast systems: (1) 24 h precipitation forecast data obtained by ECMWF’s 10-day HRES deterministic forecast, (2) H-SAF h61 satellite observations as reference, and (3) DL-based corrected HRES precipitation using a U-Net convolutional neural network (CNN). As high-resolution data, H-SAF is used both as a reference for correcting HRES precipitation data and as a stand-alone candidate for forcing data. These datasets are used as forcing data in high-resolution (~0.6 km) integrated hydrologic simulations using ParFlow/CLM over central Europe from April 2020 to December 2022. Soil moisture (SM) simulations are used as a diagnostic downstream variable for evaluating the impact of forcing data. The DL-based correction reduces the gap between HRES and H-SAF by 49, 33, and 12% in mean error, root mean square error, and Pearson correlation, respectively. However, comparison of SM simulations obtained from the three datasets with ESA CCI SM data reveals better agreement with the uncorrected HRES 24-h forecast data. In conclusion, H-SAF satellite-based precipitation data falls short in representing precipitation used for SM simulations compared to 24 h lead time HRES forecasts. This emphasizes the need for more reliable spatiotemporally continuous high-resolution precipitation observations for using DL correction in improving precipitation forecasts. The study demonstrates the potential of DL methods as a near real-time data pre-processor in quasi-operational water resources forecasting workflows. The quality of the preprocessor is directly proportional to the quality of the applied observation.

Many biomedical engineering degree programs lack substantial immersive clinical experiences for undergraduate students, creating a need for clinical immersion programs that contribute to training objectives that emphasize current clinical needs (Becker in Eur J Eng Educ 31:261-272, 2006; Davis et al. in J Eng Educ 91:211-221, 2002; Dym et al. in J Eng Educ 94:103-120, 2005). Immersive clinical experiences have the potential to bridge the gap between clinical and non-clinical learning objectives in biomedical engineering curriculum. In collaboration with Indiana University Health Methodist Hospital, we have created, executed, and evaluated a two-week cardiovascular clinical immersion program for biomedical engineering undergraduate students at Purdue University. As of August 2022, this program has run 11 times since 2014 with 60 participants to date, exposing students to intensive and non-intensive care environments, facilitating interactions with medical professionals, and encouraging exploration of innovative technologies shaping the training of clinicians with direct patient interaction. The variety of cardiovascular topics discussed and clinical settings observed has provided students with a unique, highly beneficial learning opportunity. Keys to the continued success and growth of similar programs include: recruiting a diverse team, support from administrative staff/clinicians, a funded student intern position, and careful consideration of liability/risk management. Areas of future consideration include, streamlining the order of scheduled events, determining if offering course credit would be beneficial to students, and tracking career trajectories after participations.

Restoration supports the recovery of ecological attributes such as cover, complexity, and diversity to slow the areal decline of natural ecosystems. Restoration activity is intensifying worldwide to combat persistent stressors that are driving global declines to the extent and resilience of coral reefs. However, restoration is disputed as a meaningful aid to reef ecological recovery, often as an expensive distraction to addressing the root causes of reef loss. We contend this dispute partly stems from inferences drawn from small-scale experimental restoration outcomes amplified by misconceptions around cost-based reasoning. Alongside aggressive emissions reductions, we advocate urgent investment in coral reef ecosystem restoration as part of the management toolbox to combat the destruction of reefs as we know them within decades.

The representation of groundwater is simplified in most regional climate models (RCMs), potentially leading to biases in the simulations. This study introduces a unique dataset from the regional Terrestrial Systems Modelling Platform (TSMP) driven by the Max Planck Institute Earth System Model at Low Resolution (MPI-ESM-LR) boundary conditions in the context of dynamical downscaling of global climate models (GCMs) for climate change studies. TSMP explicitly simulates full 3D soil and groundwater dynamics together with overland flow, including complete water and energy cycles from the bedrock to the top of the atmosphere. By comparing the statistics of heat events, i.e., a series of consecutive days with a near-surface temperature exceeding the 90th percentile of the reference period, from TSMP and those from GCM–RCM simulations with simplified groundwater dynamics from the COordinated Regional Climate Downscaling EXperiment (CORDEX) for the European domain, we aim to improve the understanding of how groundwater representation affects heat events in Europe. The analysis was carried out using RCM outputs for the summer seasons of 1976–2005 relative to the reference period of 1961–1990. While our results show that TSMP simulates heat events consistently with the CORDEX ensemble, there are some systematic differences that we attribute to the more realistic representation of groundwater in TSMP. Compared to the CORDEX ensemble, TSMP simulates fewer hot days (i.e., days with a near-surface temperature exceeding the 90th percentile of the reference period) and lower interannual variability and decadal change in the number of hot days on average over Europe. TSMP systematically simulates fewer heat waves (i.e., heat events lasting 6 d or more) compared to the CORDEX ensemble; moreover, they are shorter and less intense. The Iberian Peninsula is particularly sensitive with respect to groundwater. Therefore, incorporating an explicit 3D groundwater representation in RCMs may be a key in reducing biases in simulated duration, intensity, and frequency of heat waves in Europe. The results highlight the importance of hydrological processes for the long-term regional climate simulations and provide indications of possible potential implications for climate change projections.

Abstract Extreme precipitation events lead to dramatic impacts on society and the situation will worsen under climate change. Decision-makers need reliable estimates of future changes as a basis for effective adaptation strategies, but projections at local scale from regional climate models (RCMs) are highly uncertain. Here we exploit the km-scale convection-permitting multi-model (CPM) ensemble, generated within the FPS Convection project, to provide new understanding of the changes in local precipitation extremes and related uncertainties over the greater Alpine region. The CPM ensemble shows a stronger increase in the fractional contribution from extreme events than the driving RCM ensemble during the summer, when convection dominates. We find that the CPM ensemble substantially reduces the model uncertainties and their contribution to the total uncertainties by more than 50%. We conclude that the more realistic representation of local dynamical processes in the CPMs provides more reliable local estimates of change, which are essential for policymakers to plan adaptation measures.

Abstract Two‐dimensional ultrasound (2DUS) echocardiography is the main noninvasive method used to evaluate cardiac function in animal models of myocardial infarction (MI). However, 2DUS echocardiography does not capture regional differences in cardiac contractility since it relies on planar images to estimate left ventricular (LV) geometry and global function. Thus, the current study was designed to evaluate the efficacy of a newly developed 4‐dimensional ultrasound (4DUS) method in detecting cardiac functional differences between two models of MI, permanent ligation (PL), and ischemia/reperfusion (I/R) in rats. We found that only 4DUS was able to detect LV global functional differences between the two models and that 4DUS‐derived surface area strain accurately detected infarcted regions within the myocardium that correlated well with histological infarct size analysis. We also found that 4DUS‐derived strain, which includes circumferential, longitudinal, and surface area strain, correlated with the peak positive of the first derivative of left ventricular pressure (+dP/dtmax). In conclusion, 4DUS strain echocardiography effectively assesses myocardial mechanics following experimentally induced ischemia in rats and accurately estimates infarct size as early as 1 day after injury. 4DUS also correlates well with +dP/dtmax, a widely used marker of cardiac contractility.

There are several well-known medical conditions in which posture and gravity interact with natural history, including pregnancy. In this review, we provide a comprehensive overview of interactions between maternal posture and maternal physiology and pathophysiology at rest during pregnancy. We conducted a systematic literature search of the MEDLINE database and identified 644 studies from 1991 through 2021, inclusive, that met our inclusion criteria. We present a narrative review of the resulting literature and highlight discrepancies, research gaps, and potential clinical implications. We organize the results by organ system and, commencing with the neurological system, proceed in our synthesis generally in the craniocaudal direction, concluding with the skin. The circulatory system warranted our greatest and closest consideration–literature concerning the dynamic interplay between physiology (heart rate, stroke volume, cardiac output, blood pressure, and systemic vascular resistance), pathophysiology (e.g., hypertension in pregnancy), and postural changes provide an intricate and fascinating example of the importance of the subject of this review. Other organ systems discussed include respiratory, renal, genitourinary, gastrointestinal, abdominal, and endocrine. In addition to summarizing the existing literature on maternal posture-physiology interactions, we also point out gaps and opportunities for further research and clinical developments in this area. Overall, our review provides both insight into and relevance of maternal posture-physiology interactions vis à vis healthcare’s mission to improve health and wellness during pregnancy and beyond.

The ever-increasing need for coral restoration as a tool available to mitigate reef declines and aid in the recovery of lost ecosystem services requires improving restoration performance over time through an adaptive management framework to evaluate the status of restoration programs using uniform, consistent metrics. An evaluation tool, presented herein, allows restoration practitioners and managers to self-evaluate the robustness of each project and identify successful metrics, those metrics that need special attention, and changes to restoration strategies that can improve performance and aid recovery. This tool is designed to allow programs to track the progress of each key metric over time to assist in improving upon successes and learning from failures. The metrics within this restoration evaluation tool focus on published best-management practices and have resulted from extensive research conducted by restoration experts over the past 20 years. Common metrics of growth and survival are included, in addition to parameters vital to the operational success of restoration programs, such as coral reproduction, recruitment of associated reef taxa, increasing habitat for reef fisheries, and improving overall reef habitat. Five Caribbean restoration programs, each with at least 15 years of restoration experience, are presented as case studies. Each program was evaluated based on six restoration categories including: field-based nurseries, outplantings, programmatic management, education and outreach, event-driven restoration, and socioeconomic restoration. Category-specific metrics were scored with a binary scoring system and summarized using a stop-light indicator framework, where the resulting color/score indicates the operations tatus of the different program components (Scores >75% = green/successful; 50–74.9% = light green > yellow > orange/intermediate; 75% indicating most of these programs are performing well, are versatile, well managed, and sustainable. Outside of environmental factors and large-scale disturbance events, many programs described resource limitations, including funding and staffing, as reasons for scoring low on some metrics. A holistic evaluation rubric incorporated into programmatic self-assessment can ensure that restoration projects and programs are working towards success and sustainability.

Resumo Judith Butler explora produtivamente a ampliação do espectro do conceito de “crítica” em Foucault. O nexo entre crítica e formação e entre autocrítica e autoformação permeia a argumentação dos dois autores, mas Butler indica o quanto a própria crítica como virtude, tematizada por Foucault, precisa da dimensão formativa para poder esclarecer a si mesma num contexto teórico pós-fundacionalista e de normatividade não prescritiva. Pretende-se mostrar que, com esse passo, a reconstrução do problema feita por Butler alcança novo patamar, tanto clareando o significado de autoformação do sujeito quanto indicando, com maior precisão, o novo sentido assumido pela noção foucaultiana de “crítica” como virtude.

In numerous medical conditions, including pregnancy, gravity and posture interact to impact physiology and pathophysiology. Recent investigations, for example, pertaining to maternal sleeping posture during the third trimester and possible impact on fetal growth and stillbirth risk highlight the importance and potential clinical implications of the subject. In this review, we provide an extensive discussion of the impact of maternal posture on fetal physiology from conception to the postpartum period in human pregnancy. We conducted a systematic literature search of the MEDLINE database and identified 242 studies from 1991 through 2021, inclusive, that met our inclusion criteria. Herein, we provide a synthesis of the resulting literature. In the first section of the review, we group the results by the impact of maternal posture at rest on the cervix, uterus, placenta, umbilical cord, amniotic fluid, and fetus. In the second section of the review, we address the impact on fetal-related outcomes of maternal posture during various maternal activities (e.g., sleep, work, exercise), medical procedures (e.g., fertility, imaging, surgery), and labor and birth. We present the published literature, highlight gaps and discrepancies, and suggest future research opportunities and clinical practice changes. In sum, we anticipate that this review will shed light on the impact of maternal posture on fetal physiology in a manner that lends utility to researchers and clinicians who are working to improve maternal, fetal, and child health.