Damage Proxies Research Articles

In thermotolerance tests of tropical tree leaves, the chlorophyll fluorescence parameter Fv/Fm measured soon after heat exposure is not a reliable predictor of tissue necrosis.

Tropical rainforests are hot and may be particularly sensitive to ongoing anthropogenic global warming. This has led to increased interest in the thermotolerance of tropical trees. Thermotolerance of leaves of two tropical tree species, Terminalia catappa and Coccoloba uvifera, was determined by exposing leaf samples to 15-min heat treatments, followed by measurements of potential photosystem II quantum yield (dark-adapted value of variable/maximum chlorophyll a fluorescence, Fv/Fm) after 24 h and 14 days, and visible damage (necrosis) after 14 days. T50 (24 h), the temperature at which Fv/Fm declined by 50% 24 h after heat treatments, was associated with only ~10% leaf area damage in C. uvifera and no damage in T. catappa. In neither species was leaf necrosis observed at T5 (24 h), the temperature at which Fv/Fm declined by 5%. In both species, temperatures significantly higher than T50 (24 h) were required for 50% leaf area necrosis to occur. T50 (14 days) was a better proxy of visible leaf damage than T50 (24 h). The relationship between heat-induced Fv/Fm decline and tissue necrosis varies among species. In species surveys of leaf thermal tolerances, calibration of the Fv/Fm assay against the necrosis test is recommended for each species under investigation. Fv/Fm measurements soon after heat exposure do not reliably predict irreversible heat damage and may thus not be suitable to model and predict the thermostability of tropical forest trees.

A Disparate Disaster: Spatial Patterns of Building Damage Caused by Hurricane Ian and Associated Socio-Economic Factors

The literature shows that communities under different socio-economic conditions suffer different levels of damage in disasters. In addition to the physical intensity of hazards, such differences are also related to the varying abilities of communities to prepare for and respond to disasters. This study analyzes the spatial patterns of building damage in Hurricane Ian in 2022 and investigates the socio-economic disparities related to the damage. Specifically, this study employs NASA’s Damage Proxy Map (DPM2) to analyze spatial patterns of building damage caused by the hurricane. Then, it uses statistical analysis to assess the relationships between building damage and hurricane intensity, building conditions, and socio-economic variables at the building and census tract levels. Furthermore, the study applies geographically weighted regression (GWR) to examine the spatial variation of the damage factors. The results provide valuable insights into the potential factors related to building damage and the spatial variation in the factors. The results also reveal the uneven distribution of building damage among different population groups, implying socio-economic inequalities in disaster adaptation and resilience. Moreover, the study provides actionable information for policymakers, emergency responders, and community leaders in formulating strategies to mitigate the impact of future hurricanes by identifying vulnerable communities and population groups.

Acute Changes in Hamstring Injury Risk Factors After a Session of High-Volume Maximal Sprinting Speed Efforts in Soccer Players.

Maximal sprinting speed (MSS) overexposure is associated with increased risk of injury. This study aimed to describe changes in sprint performance-related factors and hamstring strain injury (HSI) risk factors after a high-volume sprinting session in soccer players. A high-volume sprinting session can induce acute changes in several sprint performance-related factors (sprint time and mechanical properties) and HSI risk factors (posterior chain muscle strength, hamstring range of motion, and dynamic lumbo-pelvic control [LPC], measured as changes in anterior pelvic tilt [APT] during maximal speed sprinting). Prospective observational case series. Level 4. Fifteen active male amateur soccer players participated. Changes in sprint performance-related factors and HSI risk factors were examined for 72 hours after high-volume MSS efforts (H-VMSSE) using a soccer-contextualized multifactorial approach. Muscle damage proxy markers (hamstring perceived soreness and creatine kinase) were also examined. H-VMSSE induced decrements in sprint performance-related factors. Significant reductions in theoretical maximal horizontal velocity (P < 0.01; effect size [ES], -0.71) and performance (P = 0.02; ES, -0.59) were observed for 48 and 72 hours after H-VMSSE. Small but significant reductions in posterior chain muscle force-generating capacity were detected for 48 and 72 hours after H-VMSSE for the nondominant (P < 0.03; ES, -0.60) and dominant (P < 0.04; ES, -0.40) leg. Finally, players exhibited persistent small, albeit nonsignificant (P = 0.06; ES, 0.53), decreases in dynamic LPC (APT increases) for 72 hours after H-VMSSE. H-VMSSE induced declines in both sprint performance-related factors and HSI risk factors. Sprinting can alter a player's anatomic structure by increasing APT during the maximum speed phase of the sprint. A soccer-contextualized multifactorial approach might allow for the regulation of MSS dosage depending on individual HSI risk factor status, thereby serving as a tailored "vaccine" for sprinting needs.

Neurological Damage Measured by S-100b and Neuron-Specific Enolase in Patients Treated with Electroconvulsive Therapy.

Electroconvulsive therapy (ECT) is considered one of the most effective treatments for psychiatric disorders. ECT has proven effective in the treatment of depression, mania, catatonia and psychosis. It is presumed that seizures induced during ECT administration cause toxicity and potentially neuronal and glial cell death. A broad range of neurological disorders increase cerebrospinal fluid and serum levels of neuron-specific enolase (NSE) and S-100b protein. This study aims to investigate the effect of ECT on NSE and S-100b levels, which, together, serve as a proxy for neuronal cell damage. Serum concentrations of S-100b and NSE of adult patients who received ECT were measured by immunoluminometric analysis before and after treatment. A two-way ANOVA test was used to estimate the statistical differences in marker concentrations between the subgroups of the study population. Results: A total of 55 patients were included in the analysis: 52.73% (n = 29) were diagnosed with depression, 21.82% (n = 12) with schizophrenia or other psychosis, 16.36% (n = 9) with mania and 9.09% (n = 5) with catatonia. There were no statistically significant changes in NSE (p = 0.288) and S-100b (p = 0.243) levels. We found no evidence that ECT induced neuronal damage based on NSE and S-100b protein levels measured in the serum of patients before and after treatment.

Large-scale building damage assessment based on recurrent neural networks using SAR coherence time series: A case study of 2023 Turkey–Syria earthquake

The Turkey–Syria earthquakes that occurred on February 6, 2023, have caused significant human casualties and economic damage. Emergency services require quick and accurate assessments of widespread building damage in affected areas. This can be facilitated by using remote sensing methods, specifically all-day and all-weather Synthetic Aperture Radar (SAR). In this study, we aimed to improve the detection of building anomalies in earthquake-affected areas using SAR images. To achieve this, we employed Recurrent Neural Network (RNN) to train coherence time series and predict co-seismic coherence. This approach allowed us to generate a Damage Proxy Map (DPM) for building damage assessment. The results of our study indicated that the estimated proportion of building damage in Kahramanmaras was approximately 24.08%. These findings were consistent with the actual damage observed in the field. Moreover, when utilizing the mean and standard deviation of coherence time series, our method achieved higher accuracy (0.761) and a lower false alarm rate (0.136) compared to directly using coherence with only two views of SAR data. Overall, our study demonstrates that this method provides an accurate and reliable approach for post-earthquake building damage assessment.

Computer vision tools for early post-disaster assessment: Enhancing generalizability

Remote sensing data, particularly satellite imagery, have made early, post-hazard aerial damage assessment possible due to its fast availability and extensive coverage. Despite breakthroughs in using deep computer vision with satellite image inputs, achieving high generalizability across diverse hazards and locations remains the main obstacle to effectively deploying early assessment tools in real-world scenarios, as the same hazard can manifest differently across various landscapes and urban textures. This challenge may be overlooked when working with curated datasets with minimal (test-to-train) shifts in urban textures and hazard damage features (e.g., due to undersized study regions), leaving models ill-prepared for real-world scenarios. The primary objective of this study was to understand non-trivial generalizability challenges by taking the 2023 Turkiye earthquake and the Maui wildfire incidents as “training” and “unseen test” events that simulated a demanding scenario. Subsequently, strategies such as augmenting image channels with damage proxy maps, data fusion, deep ensemble learning, and Test Time Augmentation were exclusively designed and implemented to address those challenges. These measures significantly improved the damage detection, with or without severity classification, with F1 scores increased from 0.71 to 0.82 and 0.40 to 0.87, respectively. Furthermore, and through data fusion, the proposed framework accommodates estimating socioeconomic loss metrics at the individual building level, supporting both response and recovery phases. This research has the potential to enhance the effectiveness of rapid aerial damage assessment models, ultimately aiding in more efficient and targeted disaster response and recovery efforts. Data, models, and codes are available at https://github.com/TRG-AI4Good/Lahaina_Generalizability.

Predicting Musculoskeletal Loading at Common Running Injury Locations Using Machine Learning and Instrumented Insoles.

Wearables have the potential to provide accurate estimates of tissue loads at common running injury locations. Here we investigate the accuracy by which commercially available instrumented insoles (ARION; ATO-GEAR, Eindhoven, The Netherlands) can predict musculoskeletal loading at common running injury locations. Nineteen runners (10 males) ran at five different speeds, four slopes, with different step frequencies, and forward trunk lean on an instrumented treadmill while wearing instrumented insoles. The insole data were used as input to an artificial neural network that was trained to predict the Achilles tendon strain, and tibia and patellofemoral stress impulses and weighted impulses (damage proxy) as determined with musculoskeletal modeling. Accuracy was investigated using leave-one-out cross-validation and correlations. The effect of different input metrics was also assessed. The neural network predicted tissue loading with overall relative percentage errors of 1.95 ± 8.40%, -7.37 ± 6.41%, and -12.8 ± 9.44% for the patellofemoral joint, tibia, and Achilles tendon impulse, respectively. The accuracy significantly changed with altered running speed, slope, or step frequency. Mean (95% confidence interval) within-individual correlations between modeled and predicted impulses across conditions were generally nearly perfect, being 0.92 (0.89 to 0.94), 0.95 (0.93 to 0.96), and 0.95 (0.94 to 0.96) for the patellofemoral, tibial, and Achilles tendon stress/strain impulses, respectively. This study shows that commercially available instrumented insoles can predict loading at common running injury locations with variable absolute but (very) high relative accuracy. The absolute error was lower than the methods that measure only the step count or assume a constant load per speed or slope. This developed model may allow for quantification of in-field tissue loading and real-time tissue loading-based feedback to reduce injury risk.

Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii

ABSTRACT Climate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an essential technique for assessing areas prone to wildfires. In this study, we proposed the combination of the damage proxy map (DPM) and differenced normalized burn ratio (dNBR) method to generate a precise wildfire inventory map and used it to predict areas susceptible to wildfire. The wildfire susceptibility maps were produced using frequency ratio (FR), convolutional neural network (CNN), and long short-term memory (LSTM)-based deep learning and their performances were compared. We implemented the proposed method on Maui Island, Hawaii, where wildfires frequently occur. We started the process by generating a wildfire inventory map from 2019 to 2023 based on the DPM method applied to Sentinel-1 synthetic aperture radar (SAR) data combined with a dNBR map retrieved from Sentinel-2 data. The wildfire inventory was randomly divided into a training dataset (70%) and a testing dataset (30%). Fifteen wildfire-related factors, including topographical, meteorological, land use, environmental, and anthropological factors, were selected to predict wildfires. The wildfire-related factors were selected by conducting study literature and considering spatial correlation analysis based on the FR method, information gain ratio analysis (IGR), and multicollinearity assessment using tolerance (TOL) and variance inflation factor (VIF) metrics. The level of susceptibility of an area to wildfire is divided into five, namely very high, high, moderate, low, and very low. The FR, CNN, and LSTM produced wildfire susceptibility maps with similar patterns, significantly influenced by land use and rainfall factors. The highly susceptible areas are located on gentle slopes covered by agricultural land and unhealthy vegetation, and these areas have low rainfall intensity but receive high levels of solar radiation. Meanwhile, areas with relatively low susceptibility occur in forests with high levels of wet canopy evaporation. The prediction results were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and the CNN performed slightly better than the FR and LSTM, with AUC values of 0.879, 0.877, and 0.870, respectively. Hence, the use of the CNN algorithm in the proposed method is appropriate, specifically for the study area. In addition, the susceptibility map provides information on susceptible areas on Maui Island, Hawaii, to increase public awareness.

The Impacts of Multiple Tropical Cyclone Events and Associated Precipitation on Household Income and Expenditures

Tropical cyclones (TCs) are one of the most destructive natural hazards. Damages arise from strong winds, compounded by associated flood-inducing hazards such as heavy rainfall and storm surge. Recent papers have shown that the modelled TC damage estimates fall short of the observed estimates due to the use of wind speed as a sole damage proxy. Damage estimates may be further confounded by inaccurate representations of vulnerability of people and economic sectors, for example, calling for adjusted damage thresholds in less developed regions. This paper evaluates the impacts of compounded TC hazards on household income and expenditure in the Philippines, with adjustments in vulnerability representation drawn from local information. Our results show that the omission of TC-associated precipitation leads to an underestimation of impacts, as well as the number of areas and economic sectors affected by TCs. We find that households cope through a reallocation of budgets and reliance on alternative income sources. Despite extensive public and private disaster risk reduction and management strategies, we still find significant losses in income and expenditures at any number of TC exposure.

Intelligent assessment of building damage of 2023 Turkey-Syria Earthquake by multiple remote sensing approaches

A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters.

Temperature-based measurement interpretation of the MX3D Bridge

The MX3D Bridge is the world’s first structure produced using metal additive manufacturing (AM, i.e. 3D printing), deposited through wire and arc AM (WAAM). The long-term behaviour of structures built using metal AM is unknown and therefore requires the development of techniques that enable this investigation, to facilitate future widespread adoption of these novel manufacturing processes. The MX3D Bridge is instrumented with a comprehensive sensor network (accelerometers, displacement gauges, inclinometers, load cells, strain gauges and thermistors) to enable condition monitoring through the implementation of data-driven anomaly detection techniques. The structure was exposed to environmental and operational variability (EOV) over an 8-week commissioning period, which demonstrated that its response is predominantly temperature driven. Towards the end of the data collection period the decorative end swirls on the bridge were removed, which is taken as a proxy for damage on a structure built using WAAM. EOV is known to mask the damage-sensitive features used in anomaly detection techniques, such as those produced through the end swirl detachment. This paper explores the use of the temperature-based measurement interpretation (TB-MI) approach to: (i) predict the thermal response of the bridge using the iterative regression-based thermal response prediction (IRBTRP) methodology; (ii) remove the influence of EOV from damage sensitive features; and (iii) detect an anomaly event (e.g. the end swirl removal). Accurate predictions are obtained for the thermal response of the MX3D Bridge across 70 sensor measurement signals through the IRBTRP methodology, with 1.2% and 6.4% average prediction errors for the training/validation and unseen testing subperiods, respectively. Moving principal component analysis and cointegration are used with both the measured and thermal response corrected sensor signals for anomaly detection. The detection of the simulated damage is shown to be significantly improved through the removal of EOV, with damage being detected earlier, with greater certainty and across a wider range of sensor clusters (i.e. groups of sensors). The TB-MI approach and IRBTRP methodology can therefore be used to accurately predict the thermal response and detect damage on components and structures produced using metal AM, enabling the advantages of novel AM techniques to be realised within the built environment.

Macular vessel density in the superficial plexus is not a proxy of cerebrovascular damage in non-demented individuals: data from the NORFACE cohort

IntroductionOptical coherence tomography angiography (OCT-A) is a novel tool that allows the detection of retinal vascular changes. We investigated the association of macular vessel density (VD) in the superficial plexus assessed by OCT-A with measures of cerebrovascular pathology and atrophy quantified by brain magnetic resonance imaging (MRI) in non-demented individuals.MethodsClinical, demographical, OCT-A, and brain MRI data from non-demented research participants were included. We analyzed the association of regional macular VD with brain vascular burden using the Fazekas scale assessed in a logistic regression analysis, and the volume of white matter hyperintensities (WMH) assessed in a multiple linear regression analysis. We also explored the associations of macular VD with hippocampal volume, ventricle volume and Alzheimer disease cortical signature (ADCS) thickness assessed in multiple linear regression analyses. All analyses were adjusted for age, sex, syndromic diagnosis and cardiovascular variables.ResultsThe study cohort comprised 188 participants: 89 with subjective cognitive decline and 99 with mild cognitive impairment. No significant association of regional macular VD with the Fazekas categories (all, p > 0.111) and WMH volume (all, p > 0.051) were detected. VD in the nasal quadrant was associated to hippocampal volume (p = 0.007), but no other associations of macular VD with brain atrophy measures were detected (all, p > 0.05).DiscussionRetinal vascular measures were not a proxy of cerebrovascular damage in non-demented individuals, while VD in the nasal quadrant was associated with hippocampal atrophy independently of the amyloid status.

Predicting the Metal Mixture Toxicity with a Toxicokinetic-Toxicodynamic Model Considering the Time-Dependent Adverse Outcome Pathways.

Chemicals mainly exist in ecosystems as mixtures, and understanding and predicting their effects are major challenges in ecotoxicology. While the adverse outcome pathway (AOP) and toxicokinetic-toxicodynamic (TK-TD) models show promise as mechanistic approaches in chemical risk assessment, there is still a lack of methodology to incorporate the AOP into a TK-TD model. Here, we describe a novel approach that integrates the AOP and TK-TD models to predict mixture toxicity using metal mixtures (specifically Cd-Cu) as a case study. We preliminarily constructed an AOP of the metal mixture through temporal transcriptome analysis together with confirmatory bioassays. The AOP revealed that prolonged exposure time activated more key events and adverse outcomes, indicating different modes of action over time. We selected a potential key event as a proxy for damage and used it as a measurable parameter to replace the theoretical parameter (scaled damage) in the TK-TD model. This refined model, which connects molecular responses to organism outcomes, effectively predicts Cd-Cu mixture toxicity over time and can be extended to other metal mixtures and even multicomponent mixtures. Overall, our results contribute to a better understanding of metal mixture toxicity and provide insights for integrating the AOP and TK-TD models to improve risk assessment for chemical mixtures.

Damage Proxy Mapping with SAR interferometric coherence change

Synthetic aperture radar (SAR) can be combined interferometrically to estimate the coherence of the SAR phase. The coherence of the phase over time depends on the stability of the major objects and surface that are reflecting the SAR signal, at spatial scales near the radar wavelength and larger. When there are major changes in the radar reflection, it causes a loss of interferometric coherence, so we use methods of detecting coherence change as a proxy for damage or other sudden change to buildings, other structures, or the land surface, which we call a damage proxy map (DPM). The most common method for estimating the interferometric coherence is with spatial phase correlation measures over small areas. The simplest damage proxy map method is to calculate the coherence of an interferometric SAR (InSAR) pair before the event and another pair that spans the event and take the difference of the coherence to measure the change likely due to the event. This method can be effective for areas where the InSAR coherence is normally very high at the radar wavelength used. SAR at L-band (24 cm) wavelength has high coherence in a wider variety of environments than the shorter wavelengths. A more advanced, but requiring more computational resources, damage proxy map method involves calculating the coherence of all possible InSAR pairs for an interval of some months or years before the event to develop statistics about the coherence of each ground location as a function of InSAR pair interval and other parameters such as interferometric spatial baseline. Then InSAR pairs that span the event are compared to the pre-event statistics to estimate the likelihood that the co-event coherence has been reduced by damage or other surface changes. We have found that damage proxy maps are useful for detecting likely damage from many different types of events, including earthquakes, hurricanes, tornados, tsunami inundation, large landslides, or other causes.

Discovery of potential plasma protein biomarkers for ventricular fibrillation due to first ST-elevation myocardial infarction via proteomics

Abstract Background Although most sudden cardiac deaths are a consequence of ventricular fibrillation (VF) subsequent to myocardial infarction, the underlying biological mechanisms of VF in the setting of ischemia are largely unknown. We used an unbiased mass-spectrometry (MS)-based proteomic approach to improve our understanding of the molecular VF mechanisms. Purpose We aimed to identify and characterize proteins that are associated with VF during first ST-elevation myocardial infarction (STEMI). Methods We included 230 participants from a Danish ongoing case-control study on patients with first STEMI with VF (case, n=110) and without VF (control, n=120) before guided catheter insertion for primary percutaneous coronary intervention. Using MS-based proteomics on plasma samples collected within 24h of symptom onset, we investigated the plasma proteome. After quality control, one patient was excluded and the difference in the quantitative proteome composition between cases and controls were analysed using t-tests, and further corrected for multiple comparison using the Benjamini-Hochberg procedure (q-value). Cardiac enzymes and anterior infarct location were used in a subset as a proxy for large myocardial damage. Results In 229 STEMI patients (72% men, median age 62 years (interquartile range (IQR): 54-70)), a total of 401 unique proteins were identified with a median of 257 proteins (IQR: 244-281) quantified per patient. A total of 26 proteins were associated with VF, these proteins were involved in several biological processes. The most frequent processes were blood coagulation, hemostasis and immunity. After correcting for multiple testing, two up-regulated proteins remained significantly associated with VF, actin beta-like 2 (ACTBL2, FC 2.25, p&amp;lt;0.001, q=0.023) and coagulation factor XIII-A (F13A1, FC 1.48, p&amp;lt;0.001, q=0.023). The most up-regulated protein was ACTBL2, and the second most up-regulated protein was actin alpha 2 (ACTA2, FC 1.72, p=0.001, q=0.073). None of the three proteins were correlated with cardiac enzymes or anterior infarct location. Conclusion VF due to first STEMI was significantly associated with two up-regulated proteins (ACTBL2 and F13A1). None of these proteins were correlated with infarct size which could reflect novel underlying molecular VF mechanisms.

Empirical Modelling of Stem Cambium Heating Caused by Prescribed Burning in Mediterranean Pine Forest

Little is known about the interactions between the variables involved in the post-fire response of Mediterranean pine species to prescribed burning (PB). Thus, it is essential to develop an empirical model in order to assess the influence of tree and stand attributes, burn season, and fire severity on the probability of stem cambium damage occurring. Prescribed burnings were conducted in different seasons and areas covering a wide climatic and ecological range. Potential explanatory variables were measured. A random effects hurdle model framework was used to evaluate the temperature duration above 60 °C as a proxy for stem cambium damage at tree scale. The results showed significant differences in cambium damage between the PB seasons. Pinus nigra was more resistant than other pine species. Bark thickness was critical for protecting cambium. Volume of crown scorch, percentage of stem scorch, and maximum outer bark temperature were directly related to temperature duration above 60 °C in the cambium. Prescribed burning conducted under tree canopy in Mediterranean pine species generally results in a low level of cambium damage. Empirical models could help managers to predict the effects of PB and thus select the most suitable prescriptions.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions.

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

Preoperative surgeon evaluation of corneal endothelial status: the Viability Control of Human Endothelial Cells before Keratoplasty (V-CHECK) study protocol

IntroductionThe success of keratoplasty strongly depends on the health status of the transplanted endothelial cells. Donor corneal tissues are routinely screened for endothelial damage before shipment; however, surgical teams have...

Social information use in herbivore prey can influence the success of biological control

Predation-risk effects (PEs), i.e. costly consequences of antipredator trait responses in prey, have been the focus of many theoretical and applied studies on predator–prey systems, but we still have limited knowledge on how prey functional traits – such as the ability to acquire and use inadvertent social information (ISI) – influence the susceptibility to strong PEs. In this study, I used an individual-based model to explore how ISI use alters the relative importance of consumptive and predation-risk effects on herbivore prey abundance and total consumption, a proxy for plant damage. I found that ISI use contributed to higher prey abundance by lowering predation-related mortality, but it also decreased prey total consumption through PE irrespective of the relation between prey and predator detection ranges and the type of prey social organisation. Moreover, due to its stronger effect on the non-consumptive component of the total predation effect, ISI use contributed to an overall reduction in prey feeding performance. The estimated strengths of different predator effect components were also comparable to previous experimental findings. These findings provide strong support for the idea that the access to and use of risk-related social information not only result in more prey individuals exhibiting the antipredator response but also leads to strong behavioural PEs with substantial consequences on prey consumption.

A note on peak inelastic displacement as a proxy for structural damage in seismic sequences

Structural reliability assessment for a building that may experience damage accumulation during a seismic sequence can play an important role in decision making for post-earthquake repair operations or demolition and rebuilding. This typically requires the integration of the aftershock ground motion hazard at the site with the probabilistic description of the damaged building's capacity to withstand the shaking of the seismic sequence. This is usually quantified as the conditional probability that the structure, starting from a specific damage state and for given shaking intensity, will reach a more severe one. In sequence-based seismic risk studies, an analytically-derived estimate of the peak inelastic displacement is often used as a proxy for structural damage. This paper investigates the issues behind this choice and the ability of inelastic displacement demand to adequately describe structural damage due to a seismic sequence, when compared with more direct metrics of damage, such as stiffness and strength degradation. To reach this objective, a series of inelastic single-degree-of-freedom systems, having different natural period, backbones and post-elastic behavior, were subjected to sequential dynamic analysis, while considering a series of arbitrarily chosen damage states, conventionally defined by displacement demand thresholds. The investigation showed that maintaining the attractive simplicity of deformation-based damage proxies in sequence-based risk analysis, can lead to some counterintuitive representations of seismic vulnerability. Some results suggest that such problems could be alleviated if one were to consider some dependence of damage state transition thresholds on the current state of the structure.