Search Results (67)

This study examines how assessments of coastal extreme sea levels depend on the separability and reconstructability of the astronomical tide in hourly sea-level records. Using a global tide-gauge network, it proposes an ensemble-learning correction framework that integrates a physical-baseline threshold with multi-criteria consistency testing to determine whether machine-learning enhancement is genuinely effective across stations and time windows. The analysis uses hourly records from 528 UHSLC tide gauges, with 31-day short sequences used to reconstruct 180-day sea-level variability. Taking the physical tidal model as the baseline, residuals are corrected using Extremely Randomized Trees, Random Forest, and Gradient Boosting. To avoid false improvement driven solely by error reduction, a hierarchical decision framework is established. Baseline model quality is first screened using NSE and the coefficient of determination, after which mathematical artefacts are identified through diagnostics of peak suppression and variance shrinkage. A five-level classification is then derived from the convergent evidence of twelve performance metrics and four statistical significance tests. The results show a consistent global pattern across all three algorithms. Approximately 57% of stations meet the criterion for genuine improvement, whereas about 42% are associated with an unreliable physical baseline, indicating that the dominant source of failure arises not from the ensemble-learning algorithms themselves, but from spatially varying limitations in the underlying physical baseline. Spatially, the credibility of machine-learning correction is strongly conditioned by baseline quality: stations with effective correction are more continuous along the eastern North Atlantic and European coasts, whereas stations with ineffective correction are more concentrated in the Gulf of Mexico, the Caribbean, and the marginal seas and archipelagic regions of the western Pacific. These results indicate that the observed spatial heterogeneity primarily reflects geographically varying physical and dynamical conditions that control baseline reliability and residual learnability, rather than a standalone difference in the intrinsic capability of ensemble learning itself. Full article

Background: Early and accurate diagnosis remains crucial to improving outcomes in oral cancer. Optical coherence tomography (OCT) offers real-time, high-resolution imaging that may support diagnosis and treatment planning in oral squamous cell carcinoma (OSCC). Methods: In this prospective study, preoperative OCT scans were obtained from 68 histologically confirmed OSCC lesions, with 30 paired adjacent mucosa samples from the same patients as histologically negative comparators (diagnostic dataset: 98 lesions). OCT findings were compared with histopathology for diagnostic performance, OCT biomarker patterns by tumour grade, tumour depth measurement, margin assessment, and subsite-specific performance. Results: OCT demonstrated 98.5% sensitivity, 96.7% specificity, and an AUC of 0.98 for detection of invasive OSCC. OCT biomarkers—including abnormal epithelial architecture with variable epithelial thickness, stratification loss, basement membrane disruption, and increased subepithelial reflectivity—varied systematically with tumour differentiation grade. Tumour depth measurements showed acceptable agreement with histology, while margin definition was correct in 80% of cases. Performance was highest in the tongue and the floor of the mouth, with reduced performance in posterior/keratinised subsites. Image artefacts occurred in 5.1% of scans. Conclusions: OCT provides a reproducible, real-time adjunct for diagnosis, margin planning, and lesion stratification in OSCC, with recognised limitations related to light attenuation and operator-dependent factors. Multicentre validation and integration with digital interpretation platforms are warranted. Full article

Under the intensifying water shortages in the vegetation season, early identification of Ips typographus L. damage is crucial for preventing wide outbreaks, which undermine the economic potential of commercial stands of Norway spruce (Picea abies Karst.) across Europe. For this purpose, remote sensing based on satellite images is considered one of the most efficient methods, particularly in homogenous and wide forested landscapes. However, under highly heterogeneous seminatural managed forest landscapes in lowland Central and Northern Europe, as illustrated by the eastern Baltic region and Latvia in particular, the efficiency of such an approach can lack the desired accuracy. Hence, the identification of smaller damage patches by I. typographus, which can act as a source of wider outbreaks, can be overlooked, and situational awareness can be further aggravated by infrastructure artefacts. In this study, the accuracy of satellite imaging for the identification of I. typographus damage was evaluated, focusing on the occurrence of false positives and particularly false negatives obtained from the comparison with UAV imaging. Across the studied landscapes, correct or partially correct identification of damage patches larger than 30 m² occurred in 73% of cases. Still, the satellite image analysis of the highly heterogeneous landscape resulted in quite a common occurrence of false negatives (up to one-third of cases), which were related to stand and patch properties. The high rate of false negatives, however, is crucial for the prevention of outbreaks, as the sources of outbreaks can be underestimated, burdening prompt and hence effective implication of countermeasures. Accordingly, elaborating an analysis of satellite images by incorporating stand inventory data could improve the efficiency of early detection systems, especially when coupled with UAV reconnaissance of heterogeneous landscapes, as in the eastern Baltic region. Full article

The widespread use of methylsulphonylmethane (MSM) as a dietary supplement highlights the need to understand its fundamental behaviour in aqueous solutions. In this paper, we investigate changes in the MSM band shape as a function of its concentration using Attenuated Total Reflection FTIR (ATR-FTIR) spectroscopy. ATR spectra may be complicated by significant optical artefacts arising from refractive index changes. These can be misinterpreted as genuine vibrational shifts, leading to erroneous conclusions. Here, we systematically investigate aqueous MSM solutions using three different internal reflection elements. Applying a rigorous ATR correction procedure, validated by transmission measurements and PARAFAC (Parallel Factor Analysis) analysis, decouples physical phenomena from optical distortions. The corrected spectra reveal a crucial finding: the primary effect of MSM is not a shift in the sulphone band position, but a distinct change in its shape. This result, supported by DFT (Density Functional Theory) calculations, indicates increased heterogeneity of local hydration environments and demonstrates the criticality of proper ATR correction. Full article

The biophysical characteristics of cellular membranes, particularly their electrical properties in the $\alpha$-dispersion frequency domain, offer valuable insights into cellular states and are increasingly important for cancer diagnostics through epidermal growth factor receptor (EGFR) expression analysis. However, a critical limitation in these electrical measurements is the confounding effect of morphological changes that inevitably occur during prolonged observation periods. These shape alterations significantly impact measured capacitance values, potentially masking true biological responses to epidermal growth factor (EGF) stimulation that are essential for cancer detection. In this study, we attempted to address this fundamental challenge by developing a deep learning method that establishes a direct computational relationship between cellular morphology and electrical properties. We combined optical trapping technology and capacitance measurements to generate a comprehensive dataset of HeLa cells under two different experimental conditions: (i) DPBS treatment and (ii) EGF stimulation. Our convolutional neural network (CNN) architecture accurately predicts 401-point capacitance spectra (0.1–2 kHz) from binary morphological images at low frequencies (0.1–0.8 kHz, < 10% error rate). This capability allows for the identification and subtraction of morphology-dependent components from measured capacitance changes, effectively isolating true biological responses from morphological artefacts. The model demonstrates remarkable prediction performance across diverse cell morphologies in both experimental conditions, validating the robust relationship between cellular shape and electrical characteristics. Our method significantly improves the precision and reliability of EGFR-based cancer diagnostics by providing a computational framework for a morphology-induced measurement error correction. Full article

Backgrounds: Accurate annotation of open reading frames (ORFs) is fundamental for understanding gene function and post-transcriptional regulation. A critical but often overlooked aspect of transcriptome annotation is the selection of authentic translation start sites. Many genome annotation pipelines identify the longest possible ORF in alternatively spliced transcripts, using internal methionine codons as putative start sites. However, this computational approach ignores the biological reality that ribosomes select start codons based on sequence context, not ORF length. Methods: Here, we demonstrate that this practice leads to systematic misannotation of nonsense-mediated decay (NMD) targets in the Arabidopsis thaliana Araport11 reference transcriptome. Using TranSuite software to identify authentic start codons, we reanalyzed transcriptomic data from an NMD-deficient mutant. Results: We found that correct ORF annotation more than doubles the number of identifiable NMD targets with premature termination codons followed by downstream exon junctions, from 203 to 426 transcripts. Furthermore, we show that incorrect ORF annotations can lead to erroneous protein structure predictions, potentially introducing computational artefacts into protein databases. Conclusions: Our findings underscore the importance of biologically informed ORF annotation for accurate assessment of post-transcriptional regulation and proteome prediction, with implications for all eukaryotic genome annotation projects. Full article

Achieving high dimensional accuracy in the build direction remains a critical challenge in laser-based powder bed fusion of metals (PBF-LB/M), particularly for taller components. This study investigates the application of the standardized Z-artefact defined in ISO/ASTM 52902:2023 to evaluate and correct vertical dimensional deviations in AlSi10Mg parts. Benchmark artefacts were produced without Z-scaling and measured using a structured light 3D scanner. A linear trend of increasing undersizing with build height was observed across two build jobs, indicating a systematic Z-error. Based on the reproducible average deviation, a shrinkage compensation factor of 1.0017 was derived and applied in a third build job using the same processing parameters. This correction reduced the root mean square error (RMSE) from over 100 µm to below 25 µm and improved the achievable ISO tolerance grades from IT 9–11 to IT 5–9. The approach proved effective without requiring changes to process parameters. However, local surface features such as elevated edges and roughness remained dominant sources of deviation and are not captured in step height-based evaluations. Overall, this study demonstrates a practical, standard-compliant method to improve vertical dimensional accuracy in PBF-LB/M, with potential applicability to industrial quality assurance and future extension to more complex geometries. Full article

MRI perfusion is used to diagnose and monitor neurological conditions such as brain tumors, stroke, dementia, and traumatic brain injury. Dynamic Susceptibility Contrast (DSC) is the most widely available quantitative MRI technique for perfusion imaging. Even in its most basic implementation, DSC MRI provides critical hemodynamic metrics like cerebral blood flow (CBF), blood volume (CBV), mean transit time (MTT), and time between the peak of arterial input and residue function (Tmax), through the dynamic tracking of a gadolinium-based contrast agent. Notwithstanding its high clinical importance and widespread use, the reproducibility and diagnostic reliability are impeded by a lack of standardized pre-processing protocols and quality controls. A comprehensive literature review and the authors’ aggregated experience identified common DSC MRI artefacts and corresponding pre-processing methods. Pre-processing methods to correct for artefacts were evaluated for their practical applicability and validation status. A consensus on the pre-processing was established by a multidisciplinary team of experts. Acquisition-related artefacts include geometric distortions, slice timing misalignment, and physiological noise. Intrinsic artefacts include motion, B₁ inhomogeneities, Gibbs ringing, and noise. Motion can be mitigated using rigid-body alignment, but methods for addressing B₁ inhomogeneities, Gibbs ringing, and noise remain underexplored for DSC MRI. Pre-processing of DSC MRI is critical for reliable diagnostics and research. While robust methods exist for correcting geometric distortions, motion, and slice timing issues, further validation is needed for methods addressing B₁ inhomogeneities, Gibbs ringing, and noise. Implementing adequate mitigation methods for these artefacts could enhance reproducibility and diagnostic accuracy, supporting the growing reliance on DSC MRI in neurological imaging. Finally, we emphasize the crucial importance of pre-scan quality assurance with phantom scans. Full article

In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval. Full article

In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, including both natural geological features and anthropogenic landforms. The suitability of the derived filtered geomorphic references (FGRs) is evaluated through spatial correlation with ground truths (GTs) extracted from the topographical and geological geodatabases of Santander Bay, Northern Spain. In this study, it is revealed that existing artefacts, derived from vegetation or human infrastructures, pose challenges in the units’ construction, and large physiographic units are better represented using low-pass filters, whereas detailed units are more accurately depicted with high-pass filters. The results indicate a propensity of high-frequency filters to detect anthropogenic elements within the DTM. The quality of GTs used for validation proves more critical than the geodatabase scale. Additionally, in this study, it is demonstrated that the footprint of buildings remains uneliminated, indicating that the model is a poorly refined digital surface model (DSM) rather than a true digital terrain model (DTM). Experiments validate the DTM’s capability to highlight contacts and constructions, with water detection showing high precision (≥60%) and varying precision for buildings. Large units are better captured with low filters, whilst high filters effectively detect anthropogenic elements and more detailed units. This facilitates the design of validation and correction procedures for DEMs derived from LiDAR point clouds, enhancing the potential for more accurate and objective Earth surface representation. Full article

The interdisciplinary study of two reliquary busts from Sarezzano (Piedmont, Italy) is a perfect example of the necessity to provide for material characterisation as a recurring common practice in historical studies and a mandatory step in conservation assessment. Furthermore, the diagnostics of cultural heritage play a crucial role in art historical research, providing relevant information on artefacts’ genesis, production technology, and conservation history. The study of the materials of the reliquary busts was performed by non-invasive (portable X-ray fluorescence spectrometry) and micro-invasive (stereomicroscope, attenuated total reflection Fourier transform infrared spectroscopy and powder X-ray diffraction analysis) methods. According to the results, the busts were found to be made of a tin–lead alloy, a rather unusual material for mediaeval reliquary busts. Moreover, the outcome suggests that the busts were originally silvered, except for the hair and beard which are still gilded. The analysis reveals the use of colophony as an adhesive buffer layer on the busts’ alloy, as well as inside them, to favour the metal working process, since it is found as degraded residue. Finally, even the typology of alloy decay is defined. All this information has enabled us to determine the artistic technique and estimate the value and quality of the material employed. In addition, it has led to the correct choice of materials and methods to be adopted during the restoration, and therefore the usage of more suitable solvents and tools. Full article

Skin shrinkage begins immediately after surgical incision and is an artefact associated with the excision and fixation of a specimen. Skin shrinkage results in important changes in histologic tissue dimensions and can affect the correct quantification of the histologic tumour-free margin (HTFM). Bilateral and symmetrical circular skin samples with a diameter of 60 mm were taken from the lateral thoracic, flank and femoral regions of dog cadavers, with the samples from one side belonging to the study group and the samples from the same animal from the other side belonging to the control group. The radius and diameter of the specimen were measured immediately after the excision and 10 min later for each sample. The measurements of the study group were taken again after manual re-extension and fixation on a cork plate before formalin fixation and 48 h after formalin fixation. A total of 66 (33 study and 33 control group) samples were collected from 11 canine cadavers. The mean diameter shrinkage after formalin fixation was 18.24% for the control group and 0.64% for the study group. A statistically significant difference between the study and the control group was found (p < 0.001). This method of specimen fixation in the study group avoided skin shrinkage and deformation of the specimen in formalin, which we believe improves the diagnostic accuracy of surgical margins and, thus, reduces the number of false-positive or false-negative HTFM. Full article

Spectral photon-counting cone-beam computed tomography (CT) imaging is challenged by individual pixel response behaviours, which lead to noisy projection images and subsequent image artefacts like rings. Existing methods to correct for this either use calibration measurements, like signal-to-thickness calibration (STC), or perform a post-processing ring artefact correction of sinogram data or scan reconstructions without taking the pixel response explicitly into account. Here, we present a novel post-processing method (digital-to-analogue converter (DAC)-shifting) which explicitly measures the current pixel response using flat-field images and subsequently corrects the projection data. The DAC-shifting method was evaluated using a repeat series of the spectral photon-counting imaging (Medipix3) of a phantom with different density inserts and iodine K-edge imaging. The method was also compared against polymethyl methacrylate (PMMA)-based STC. The DAC-shifting method was shown to be effective in correcting individual pixel responses and was robust against detector instability; it led to a 47.4% average reduction in CT-number variation in homogeneous materials, with a range of 40.7–55.6%. On the contrary, the STC correction showed varying results; a 13.7% average reduction in CT-number variation, ranging from a 43.7% increase to a 45.5% reduction. In K-edge imaging, DAC-shifting provides a sharper attenuation peak and more uniform CT values, which are expected to benefit iodine concentration quantifications. Full article

With the rise in popularity of different human-centred applications using 3D reconstruction data, the problem of generating photo-realistic models has become an important task. In a multiview acquisition system, particularly for large indoor scenes, the acquisition conditions will differ along the environment, causing colour differences between captures and unappealing visual artefacts in the produced models. We propose a novel neural-based approach to colour correction for indoor 3D reconstruction. It is a lightweight and efficient approach that can be used to harmonize colour from sparse captures over complex indoor scenes. Our approach uses a fully connected deep neural network to learn an implicit representation of the colour in 3D space, while capturing camera-dependent effects. We then leverage this continuous function as reference data to estimate the required transformations to regenerate pixels in each capture. Experiments to evaluate the proposed method on several scenes of the MP3D dataset show that it outperforms other relevant state-of-the-art approaches. Full article

Thanks to the recent development of innovative instruments and software with high accuracy and resolution, 3D modelling provides useful insights in several sectors (from industrial metrology to cultural heritage). Moreover, the 3D reconstruction of objects of artistic interest is becoming mandatory, not only because of the risks to which works of art are increasingly exposed (e.g., wars and climatic disasters) but also because of the leading role that the virtual fruition of art is taking. In this work, we compared the performance of four 3D instruments based on different working principles and techniques (laser micro-profilometry, structured-light topography and the phase-shifting method) by measuring four samples of different sizes, dimensions and surface characteristics. We aimed to assess the capabilities and limitations of these instruments to verify their accuracy and the technical specifications given in the suppliers’ data sheets. To this end, we calculated the point densities and extracted several profiles from the models to evaluate both their lateral (XY) and axial (Z) resolution. A comparison between the nominal resolution values and those calculated on samples representative of cultural artefacts was used to predict the performance of the instruments in real case studies. Overall, the purpose of this comparison is to provide a quantitative assessment of the performance of the instruments that allows for their correct application to works of art according to their specific characteristics. Full article