Accurate Characterization Research Articles

A Novel Model for Instance Segmentation and Quantification of Bridge Surface Cracks-The YOLOv8-AFPN-MPD-IoU.

Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures' performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as the backbone network to carry out instance segmentation. In addition, an asymptotic feature pyramid network (AFPN) is incorporated to ameliorate feature fusion and overall performance. Thirdly, the minimum point distance (MPD) is introduced as a loss function as a way to better explore the geometric features of surface cracks. Finally, the middle aisle transformation is amalgamated with Euclidean distance to compute the length and width of segmented cracks. Analytical comparisons reveal that this developed deep learning network surpasses several contemporary models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and Mask-RCNN. The YOLOv8s + AFPN + MPDIoU model attains a precision rate of 90.7%, a recall of 70.4%, an F1-score of 79.27%, mAP50 of 75.3%, and mAP75 of 74.80%. In contrast to alternative models, our proposed approach exhibits enhancements across performance metrics, with the F1-score, mAP50, and mAP75 increasing by a minimum of 0.46%, 1.3%, and 1.4%, respectively. The margin of error in the measurement model calculations is maintained at or below 5%. Therefore, the developed model can serve as a useful tool for the accurate characterization and quantification of different types of bridge surface cracks.

Second harmonic generation microscopy, biaxial mechanical tests and fiber dispersion models in human skin biomechanics

Advanced numerical simulations of the mechanical behavior of human skin require thorough calibration of the material’s constitutive models based on experimental ex vivo mechanical tests along with images of tissue microstructure for a variety of biomedical applications. In this work, a total of 14 human healthy skin samples and 4 additional scarred skin samples were experimentally analyzed to gain deep insights into the biomechanics of human skin. In particular, second harmonic generation (SHG) microscopy was used to extract detailed images of the distribution of collagen fibers, which were subsequently processed using a three-dimensional Fourier transform-based method recently proposed by the authors to quantify the distribution of fiber orientations. Mechanical tests under both biaxial and uniaxial loading were performed to calibrate the relevant mechanical parameters of two widely used constitutive models of soft fiber-reinforced biological tissues that account for non-symmetrical fiber dispersion. The calibration of the models allowed us to identify correlations between the mechanical parameters of the constitutive models considered. Statement of significanceConstitutive models for soft collagenous tissues can accurately reproduce the complex nonlinear and anisotropic mechanical behavior of skin. However, a comprehensive analysis of both microstructural and mechanical parameters is still missing for human skin. In this study, these parameters are determined by combining biaxial mechanical tests and SHG stacks of collagen fibers on ex vivo healthy human skin samples. The constitutive parameters are provided for two widely used hyperelastic models and enable accurate characterization of skin mechanical behavior for advanced numerical simulations.

Integration of geospatial-based algorithms for groundwater potential characterization in Keiskamma Catchment of South Africa

Groundwater supports over 2.4 billion people across the globe and is critical to food security. The spatial dynamics of groundwater vary from place to place. The irregularity of groundwater resource exploitation is recognized in drought-prone areas, putting pressure on the resource. Hence, accurate groundwater potential characterization is critical for sustainable development and management of groundwater, particularly in drought-prone environments. Therefore, this study aimed at utilizing remote sensing satellite data and geospatial-based (analytical hierarchy process (AHP) and frequency ratio (FR)) algorithms to characterize groundwater potential zones (GWPZs) in the Keiskamma Catchment of South Africa. Seven (7) selected factors, including geology, soil type, slope, rainfall, drainage density, lineament density, and land use land cover, were assigned weights based on the AHP and FR algorithms. The validation results showed that the FR model performed better than the AHP, with the area under curve (AUC) accuracies of 62% and 50%, respectively. Based on the findings of this study, we infer that FR is more reliable than AHP when characterizing GWPZ. Lastly, GWPZ maps produced will be beneficial for improving efficient planning, management strategies, and decision-making.

Evaluating Danish Breast Cancer Group locoregional radiotherapy guideline adherence in clinical treatment data 2008–2016: The DBCG RT Nation study

Background and purposeGuideline adherence in radiotherapy is crucial for maintaining treatment quality and consistency, particularly in non-trial patient settings where most treatments occur. The study aimed to assess the impact of guideline changes on treatment planning practices and compare manual registry data accuracy with treatment planning data. Materials and methodsThis study utilised the DBCG RT Nation cohort, a collection of breast cancer radiotherapy data in Denmark, to evaluate adherence to guidelines from 2008 to 2016. The cohort included 7448 high-risk breast cancer patients. National guideline changes included, fractionation, introduction of respiratory gating, irradiation of the internal mammary lymph nodes, use of the simultaneous integrated boost technique and inclusion of the Left Anterior Descending coronary artery in delineation practice. Methods for structure name mapping, laterality detection, detection of temporal changes in population mean lung volume, and dose evaluation were presented and applied. Manually registered treatment characteristic data was obtained from the Danish Breast Cancer Database for comparison. ResultsThe study found immediate and consistent adherence to guideline changes across Danish radiotherapy centres. Treatment practices before guideline implementation were documented and showed a variation among centres. Discrepancies between manual registry data and actual treatment planning data were as high as 10% for some measures. ConclusionNational guideline changes could be detected in the routine treatment data, with a high degree of compliance and short implementation time. Data extracted from treatment planning data files provides a more accurate and detailed characterisation of treatments and guideline adherence than medical register data.

APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features

Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through autoproteolytic reaction. At present, APLs are grouped into 10 families belonging to six different clans of proteases. Recognizing their critical roles in many biological processes including virus maturation, and virulence, accurate identification and characterization of APLs is indispensable. Experimental identification and characterization of APLs is laborious and time-consuming. Here, we developed APLpred, a novel support vector machine (SVM) based predictor that can predict APLs from the primary sequences. APLpred was developed using Boruta-based optimal features derived from seven encodings and subsequently trained using five machine learning algorithms. After evaluating each model on an independent dataset, we selected APLpred (an SVM-based model) due to its consistent performance during cross-validation and independent evaluation. We anticipate APLpred will be an effective tool for identifying APLs. This could aid in designing inhibitors against these enzymes and exploring their functions. The APLpred web server is freely available at https://procarb.org/APLpred/.

KGCF: Social relationship-aware graph collaborative filtering for recommendation

With the increasing popularity of recommendation techniques and social networks, social network recommendation has become a significant research field, i.e., predicting a user's preferences based on her or his historical interaction data, because the social relationships of users can not only enrich their interaction information, but also imply the accurate characterization of their preferences. Although there are some existing studies that predict users' preferences by using the characteristics of social relationships, their time complexity and hardware resource consumption are often very expensive, which limits the feasibility in large-scale real-world scenarios. To address these issues, in this paper, we propose a simple and effective K-core Graph Collaborative Filtering (KGCF) model to incorporate the user's social features into a recommendation framework. The user's interaction information is auto-encoded linearly by incorporating the social relationship graph into the user's interaction information and constructing a multi-layer graph filter. The linear autoencoder graph filter alleviates the highly sparse data problem and dramatically reduces the training time and hardware resource consumption. Experimental results on several real-world datasets show the superior effectiveness and spatiotemporal efficiency compared with the existing baselines, especially the training speed is significantly accelerated for large-scale datasets.

Structural characterization of sphingomyelins from tissue using electron-induced dissociation.

Sphingomyelins (SMs) and resulting metabolic products serve important functional and cell signaling roles and can act as potential biomarkers and therapeutic targets in many pathological disorders. SMs each contain a sphingoid base, an amide-linked fatty acyl chain, and a phosphocholine headgroup. Despite these simple building blocks, variations and modifications of both the sphingoid base and the fatty acyl chain result in a diverse array of structurally complicated SM compounds. Conventional tandem mass spectrometry (MS/MS) using the collision-induced dissociation (CID) method only provides limited structural information, necessitating other tools to unravel the structural complexity of these lipids. We utilize electron-induced dissociation (EID) and sequential CID/EID approaches to elucidate detailed structural features of SMs. Integrating the CID/EID method into an imaging MS workflow enables accurate identification of SMs directly from kidney tissue. The application of EID enables identification of SMs at the molecular species level, identifying the sphingosine base and the amide-linked fatty acyl chains. Furthermore, removal of the phosphocholine headgroup via CID followed by sequential EID in an MS3 analysis (CID/EID) enhances the structural information obtained. CID/EID provides diagnostic fragmentation patterns revealing the hydroxylation site and double bond position in both the sphingosine base and amide-linked fatty acyl chains. Detailed structural information of SMs from synthetic standards and biological tissue samples is obtained using an alternative electron-based dissociation method. Accurate characterization of SMs promises to better inform studies of tissue biochemistry, lipid metabolism, and molecular pathology.

Stimuli-Responsive Ion Transport Regulation in Nanochannels by Adhesion-Induced Functionalization of Macroscopic Outer Surface.

Responsive regulation of ion transport through nanochannels is crucial in the design of smart nanofluidic devices for sequencing, sensing, and water-energy nexus. Functionalization of the inner wall of the nanochannel enhances interaction with ions and fluid but restricts versatile chemical approaches and accurate characterizations of fluidic interfaces. Herein, we reveal a responsive regulating mechanism of ion transport through nanochannels by polydopamine (PDA)-induced functionalization on the macroscopic outer surface of nanochannels. Responsive molecules were codeposited with PDA on the outer surface of nanochannels and formed a valve of nanometer thickness to manually manipulate ion transport by changing its gap spacing, surface charge, and wettability under external stimulus. The response ratio can be up to 100-fold by maximizing the proportion of responsive molecules on the outer surface. Laminating the codepositions of different responsive molecules with PDA on the channel's outer surface produces multiple responses. A nearly universal adhesion of PDA with responsive molecules on the open outer surface induces nanochannels responsive to different external stimuli with variable response ratios and arbitrary combinations. The results challenge the primary role of functionalization on the nanoconfined interface of nanofluidics and open opportunities for developing new-style nanofluidic devices through the functionalization of macroscopic interface.

Limited Parallelism in Genetic Adaptation to Brackish Water Bodies in European Sprat and Atlantic Herring.

The European sprat is a small plankton-feeding clupeid present in the northeastern Atlantic Ocean, in the Mediterranean Sea, and in the brackish Baltic Sea and Black Sea. This species is the target of a major fishery and, therefore, an accurate characterization of its genetic population structure is crucial to delineate proper stock assessments that aid ensuring the fishery's sustainability. Here, we present (i) a draft genome assembly, (ii) pooled whole genome sequencing of 19 population samples covering most of the species' distribution range, and (iii) the design and test of a single nucleotide polymorphism (SNP)-chip resource and use this to validate the population structure inferred from pooled sequencing. These approaches revealed, using the populations sampled here, three major groups of European sprat: Oceanic, Coastal, and Brackish with limited differentiation within groups even over wide geographical stretches. Genetic structure is largely driven by six large putative inversions that differentiate Oceanic and Brackish sprats, while Coastal populations display intermediate frequencies of haplotypes at each locus. Interestingly, populations from the Baltic and the Black Seas share similar frequencies of haplotypes at these putative inversions despite their distant geographic location. The closely related clupeids European sprat and Atlantic herring both show genetic adaptation to the brackish Baltic Sea, providing an opportunity to explore the extent of genetic parallelism. This analysis revealed limited parallelism because out of 125 independent loci detected in the Atlantic herring, three showed sharp signals of selection that overlapped between the two species and contained single genes such as PRLRA, which encodes the receptor for prolactin, a freshwater-adapting hormone in euryhaline species, and THRB, a receptor for thyroid hormones, important both for metabolic regulation and the development of red cone photoreceptors.

Uncovering a Seismogenic Fault in Southern Iran through Co-Seismic Deformation of the Mw 6.1 Doublet Earthquake of 14 November 2021

On 14 November 2021, a doublet earthquake, each event of which had an Mw of 6.1, struck near Fin in the Simply Folded Belt (SFB) in southern Iran. The first quake occurred at 12:07:04 UTC, followed by a second one just a minute and a half later. The SFB is known for its blind thrust faults, typically not associated with surface ruptures. These earthquakes are usually linked to the middle and lower layers of the sedimentary cover. Identifying the faults that trigger earthquakes in the region remains a significant challenge and is subject to high uncertainty. This study aims to identify and determine the fault(s) that may have caused the doublet earthquake. To achieve this goal, we utilized the DInSAR method using Sentinel-1 to detect deformation, followed by finite-fault inversion and magnetic interpretation to determine the location, geometry, and slip distribution of the fault(s). Bayesian probabilistic joint inversion was used to model the earthquake sources and derive the geometric parameters of potential fault planes. The study presents two potential fault solutions—one dipping to the north and the other to the south. Both solutions showed no significant difference in strike and fault location, suggesting a single fault. Based on the results of the seismic inversion, it appears that a north-dipping fault with a strike, dip, and rake of 257°, 74°, and 77°, respectively, is more consistent with the geological setting of the area. The fault plane has a width of roughly 3.6 km, a length of 13.4 km, and a depth of 5.6 km. Our results revealed maximum displacements along the radar line of sight reaching values of up to −360 mm in the ascending orbit, indicating an unknown fault with horizontal displacements at the surface ranging from −144 to 170 mm and maximum vertical displacements between −204 and 415 mm. Aeromagnetic data for Iran were utilized with an average flight-line spacing of 7.5 km. The middle of the data observation period was considered to apply the RTP filter, and the DRTP method was used. We calculated the gradient of the residual anomaly in the N-S direction due to the direction of the existing faults and folds. The gradient map identified the fault and potential extension of the observed anomalies related to a fault with an ENE-WSW strike, which could extend to the ~ E-W. We suggest that earthquakes occur in the sedimentary cover of the SFB where subsurface faulting is involved, with Hormuz salt acting as an important barrier to rupture. The multidisciplinary approach used in this study, including InSAR and magnetic data, underscores the importance of accurate fault characterization. These findings provide valuable insights into the seismic hazard of the area.

A sequence decomposition genetic algorithm for petrophysical inversion with spatial correlation

Predicting petrophysical properties based on seismic attributes is essential for accurate subsurface characterization. Typically, a forward model is first established to relate the petrophysical properties to geophysical measurements, and then the petrophysical properties are inverted from the actual measurements. Imposing prior constraints of the spatial relationship is also desirable to regularize the ill-posed inverse problem. However, petrophysical inversion involving spatial correlation presents a high-dimensional optimization problem with multiple local extrema. This problem can be challenging to solve, particularly when discrete variables (such as lithofacies) and continuous variables (such as porosity and fluid saturation) are considered together. Therefore, we develop a global optimization algorithm that uses the Markov chain decomposition and a genetic algorithm for a joint discrete-continuous petrophysical inversion with spatial correlation. The innovation of this algorithm is to spatially decouple the sequence optimization problem into multiple low-dimensional subproblems and overcome the initial value dependence to converge to the global optimal solution. Furthermore, an approximation operation is introduced to expedite the optimization algorithm. Numerical experiments show that after algorithmic acceleration, the spatially correlated inversion requires the same computational time as a simple spatially uncorrelated inversion and achieves significantly better accuracy than the latter. Finally, we apply the method to a case study in East China, wherein the lithofacies, clay content, porosity, and water saturation are inferred from the data of the wave velocities and density. The inversion results around the wells generally match the well-logging data, verifying the feasibility of the new method.

CRFP Mechanical Properties—Stated Values Versus Experimental Data

Abstract This study thoroughly examines the mechanical properties of carbon fiber-reinforced polymers (CFRPs), motivated by the critical need for accurate composite property data in investigating fracture control measures for structures subjected to barely visible impact damage. We compared experimental results with manufacturer-stated values, focusing on discrepancies in fiber volume fraction and its impact on elastic modulus. Experimental findings showed an increase in elastic modulus to 190 GPa for 0 deg orientation samples, compared to the manufacturer's stated value of 159.27 GPa. The recalculated fiber volume fraction increased from the expected 57% to an actual value of 60.96%. This increase in fiber content, determined through the Voigt modulus equation and corroborated by SEM image analysis, directly contributed to the observed variations in elastic modulus. Tension tests at 0 deg and 90 deg angles exhibited average percentage errors of 14.83% and 11.57%, respectively, while compression tests at 0 deg displayed a deviation of approximately −13.92% after adjusting for values beyond 0.05% compressive strain. The study underscores the critical impact of fiber volume fraction on CFRP properties and highlights the importance of precise empirical evaluation for accurate CFRP characterization in applications such as aerospace engineering.

Exploring the impact of harvest year on the mineral composition of Mimosa scabrella Bentham honeydew honey from Santa Catarina (Brazil) using ICP‐MS and chemometric tools

SummaryThis study investigated the variation in mineral elements in bracatinga (Mimosa scabrella Bentham) honeydew honeys produced biannually over three consecutive harvest years. The aim was to assess the relationship between the year of production and the concentration of fourteen rare earth elements and six trace elements analysed using ICP‐MS. The concentrations of the twenty elements varied (P < 0.001) among all the honey samples. The dominant rare earth elements were Eu, followed by Nd and La, while Pd and Au were the major trace elements. Cluster analysis (CA) and linear discriminant analysis (LDA) were performed using all the elements identified. The LDA categorised (83.3% accuracy) the bracatinga honeydew honey samples according to their harvest year. Discriminant functions, influenced by Ce, Tb, and Th, show distinct elemental patterns in bracatinga honeydew honeys, varying with harvest year. This reflects fluctuations in yearly climatic conditions and is important to guarantee accurate characterisation.

Proton beam spot size and position measurements using a multi-strip ionization chamber

PurposeTo develop and characterize a large-area multi-strip ionization chamber (MSIC) for efficient measurement of proton beam spot size and position at a synchrotron-based proton therapy facility. Methods and MaterialsA 420 mm x 320 mm MSIC was designed with 240 vertical strips and 180 horizontal strips at 1.75 mm pitch. The MSIC was characterized by irradiating a grid of proton spots across 17 energies from 73.5 MeV to 235 MeV and comparing to simultaneous measurements made with a reference Gafchromic EBT3 film. Beam profiles, spot sizes, and positions were analyzed. Short term measurement stability and sensitivity were evaluated. ResultsExcellent agreement was demonstrated between the MSIC and EBT3 film for both spot size and position measurements. Spot sizes agreed within ± 0.18 mm for all energies tested. Measured beam spot positions agreed within ± 0.17 mm. The detector showed good short term measurement stability and low noise performance. ConclusionThe large-area MSIC enables efficient and accurate proton beam spot characterization across the clinical energy range. The results indicate the MSIC is suitable for pencil beam scanning proton therapy commissioning and quality assurance applications requiring fast spot size and position quantification.

A VFM-based identification method for anisotropic thermal-mechanical properties of sheet metals using the digital image correlation and infrared thermography assisted heterogeneous test

The accurate characterization of the anisotropic thermal-mechanical constitutive properties of structural sheet metals at elevated temperatures and under nonuniform stress/strain states is crucial for the precise hot plastic forming and structural behavior evaluation of an engineering sheet part. Traditional thermal-mechanical testing methods rely on the assumption of states homogeneity, leading to a large number of tests required for the characterization of material anisotropy and nonlinearity at various high temperatures. In this work, a highly efficient identification method is proposed that allows the simultaneous characterization of the anisotropic yielding, strain hardening and elasto-plasticity thermal softening material properties using the minimum number of tests, releasing the limitations of traditional identification methods. This is implemented by performing a digital image correlation and infrared thermography assisted heterogeneous high temperature test and processing the full-field measurement data based on the principle of virtual work. A double-notched tensile specimen configuration combined with a center-to-periphery temperature gradient is designed first to enable the high heterogeneity of stress/temperature states. Simulations of the heterogeneous tests are then performed to supply idealized reference data that can be used to evaluate the identification sensitivity of the proposed identification algorithm. After the numerical validation of the methodology, it is then applied to the TC4 titanium alloy sheet specimen using the self-developed experimental setup. The experimental identification results verify that the multiple anisotropic thermal-mechanical elasto-plasticity constitutive parameters can be accurately identified from the heterogeneous test with high computation efficiency, significantly simplifying the testing process in comparison to applying multiple traditional homogeneous tests. The current work provides an effective and convenient alternative to high temperature identification strategies used by the material processing community.

Proof of concept: real-time viability and metabolic profiling of probiotics with isothermal microcalorimetry.

Isothermal microcalorimetry (IMC) is a potent analytical method for the real-time assessment of microbial metabolic activity, which serves as an indicator of microbial viability. This approach is highly relevant to the fields of probiotics and Live Biotherapeutic Products (LBPs), offering insights into microbial viability and growth kinetics. One important characteristic of IMC is its ability to measure microbial metabolic activity separately from cellular enumeration. This is particularly useful in situations where continuous tracking of bacterial activity is challenging. The focus on metabolic activity significantly benefits both probiotic research and industrial microbiology applications. IMC's versatility in handling different media matrices allows for the implementation of viability assessments under conditions that mirror those found in various industrial environments or biological models. In our study, we provide a proof of concept for the application of IMC in determining viability and growth dynamics and their correlation with bacterial count in probiotic organisms. Our findings reinforce the potential of IMC as a key method for process enhancement and accurate strain characterization within the probiotic sector. This supports the broader objective of refining the systematic approach and methods used during the development process, thereby providing detailed insights into probiotics and LBPs.

Temporal Analysis of the Incidence, Mortality and Disability-Adjusted Life Years of Benign Gallbladder and Biliary Diseases in High-Income Nations, 1990-2019.

The aim of this observational study was to analyze trends in the incidence, mortality, and disability-adjusted life years (DALYs) of benign gallbladder and biliary diseases across high-income countries between 1990 and 2019. Benign gallbladder and biliary diseases place a substantial burden on healthcare systems in high-income countries. Accurate characterization of the disease burden may help optimize healthcare policy and resource distribution. Age-standardized incidence rates (ASIRs), age-standardized mortality rates (ASMRs), and DALYs data for gallbladder and biliary diseases in males and females were extracted from the 2019 Global Burden of Disease (GBD) study. A mortality-incidence index (MII) was also calculated. Joinpoint regression analysis was performed. The median ASIRs across the European Union 15+ countries in 2019 were 758/100,000 for females and 282/100,000 for males. Between 1990 and 2019 the median percentage change in ASIR was +2.49% for females and +1.07% for males. The median ASMRs in 2019 were 1.22/100,000 for females and 1.49/100,000 for males with a median percentage change over the observation period of -21.93% and -23.01%, respectively. In 2019, the median DALYs was 65/100,000 for females and 37/100,000 among males, with comparable percentage decreases over the observation period of -21.27% and -19.23%, respectively. International variation in lifestyle factors, diagnostic and management strategies likely account for national and sex disparities. This study highlights the importance of ongoing clinical efforts to optimize treatment pathways for gallbladder and biliary diseases, particularly in the provision of emergency surgical services and efforts to address population risk factors.

CMR-based cardiac phenotyping in different forms of heart failure

Heart failure (HF) is a heterogenous disease requiring precise diagnostics and knowledge of pathophysiological processes. Since structural and functional imaging data are scarce we hypothesized that cardiac magnetic resonance (CMR)-based analyses would provide accurate characterization and mechanistic insights into different HF groups comprising preserved (HFpEF), mid-range (HFmrEF) and reduced ejection fraction (HFrEF). 22 HFpEF, 17 HFmrEF and 15 HFrEF patients as well as 19 healthy volunteers were included. CMR image assessment contained left atrial (LA) and left ventricular (LV) volumetric evaluation as well as left atrioventricular coupling index (LACI). Furthermore, CMR feature-tracking included LV and LA strain in terms of reservoir (Es), conduit (Ee) and active boosterpump (Ea) function. CMR-based tissue characterization comprised T1 mapping as well as late-gadolinium enhancement (LGE) analyses. HFpEF patients showed predominant atrial impairment (Es 20.8%vs.25.4%, p = 0.02 and Ee 8.3%vs.13.5%, p = 0.001) and increased LACI compared to healthy controls (14.5%vs.23.3%, p = 0.004). Patients with HFmrEF showed LV enlargement but mostly preserved LA function with a compensatory increase in LA boosterpump (LA Ea: 15.0%, p = 0.049). In HFrEF LA and LV functional impairment was documented (Es: 14.2%, Ee: 5.4% p < 0.001 respectively; Ea: 8.8%, p = 0.02). This was paralleled by non-invasively assessed progressive fibrosis (T1 mapping and LGE; HFrEF > HFmrEF > HFpEF). CMR-imaging reveals insights into HF phenotypes with mainly atrial affection in HFpEF, ventricular affection with atrial compensation in HFmrEF and global impairment in HFrEF paralleled by progressive LV fibrosis. These data suggest a necessity for a personalized HF management based on imaging findings for future optimized patient management.

Seasonal Patterns and Allergenicity of Casuarina Pollen in Sydney, Australia: Insights from 10 Years of Monitoring and Skin Testing

Casuarina (Australian pine, She-oak) is native to Australia and South East Asia and is known for its abundant wind-borne pollen. Despite not being considered a major aeroallergen, some patients report respiratory symptoms upon exposure, with positive skin prick tests (SPT) to Casuarina pollen extract. This study investigates Casuarina pollen dispersal patterns in Sydney, Australia, over a 10-year period, from 2008 to 2018, revealing a bimodal distribution of pollen from September to October (southern hemisphere spring) and February to March (mid-late summer). Analysis of historical SPT data shows 20% of individuals with respiratory allergies reacting positively to Casuarina pollen extract, with almost 90% of these also reacting to grass pollen, suggesting potential cross-reactivity. Notably, there are no exclusive reactions to Casuarina pollen. Understanding the prolonged pollen season underscores the importance of year-round monitoring for accurate characterization. Currently lacking are commercially available skin test extracts or specific IgE assays for Casuarina sensitization, necessitating challenge studies to confirm clinical symptoms directly attributable to Casuarina pollen. By elucidating the seasonal dynamics and meteorological drivers of Casuarina pollen dispersion, alongside the potential allergenicity suggested by skin prick tests, this study paves the way for improved management of Casuarina-related allergies and highlights the critical need for further research on native Australian plant allergens.

An object-based region-growing phase unwrapping method for mapping vertical displacement in permafrost landscapes

Terrain displacement due to the seasonal thaw of the active layer above permafrost can be sensitive to climate change; however, its accurate characterization remains a challenge. This study aimed to improve the measurement of the subsidence or vertical ground surface displacement using differential synthetic aperture radar interferometry (InSAR). Existing methods for reliable phase unwrapping are hindered by the decorrelation between time-series of SAR acquisitions that can result due to the heterogeneity and structural sensitivity of permafrost landscapes to external conditions. In this study, an advanced phase unwrapping method was proposed, in which three types of regions, namely non-residue, sparse residue, and dense residue objects, were obtained from wrapped interferogram and residue map using a segmentation method. Two variants of Polynomial-Based Region Growing Phase Unwrapping (PBRGPU) were developed, which are sparse-residue Object-based PBRGPU(SOP) and dense-residue Object-based PBRGPU(DOP). The results demonstrated that the proposed method outperformed the existing phase unwrapping methods by partially suppressing the decorrelation phase and enhancing robustness for complex terrain deformation in the absence of measured field data. Both the PBRGPU variants and segmentation strategies compose the object-based unwrapping method for permafrost, and also provide a new framework by combining the segmentations and scenarios for phase unwrapping for permafrost regions.