Consistent Interpretation Research Articles

PunctaFinder: an algorithm for automated spot detection in fluorescence microscopy images.

Fluorescence microscopy has revolutionised biological research by enabling the visualisation of subcellular structures at high resolution. With the increasing complexity and volume of microscopy data, there is a growing need for automated image analysis to ensure efficient and consistent interpretation. In this study, we introduce PunctaFinder, a novel Python-based algorithm designed to detect puncta, small bright spots, in raw fluorescence microscopy images without image denoising or signal enhancement steps. Furthermore, unlike other available spot detectors, PunctaFinder not only detects puncta, but also defines the cytoplasmic region, making it a valuable tool to quantify target molecule localisation in cellular contexts. PunctaFinder is a widely applicable punctum detector and size estimator, as evidenced by its successful detection of Atg9-positive vesicles, lipid droplets, aggregates of a destabilised luciferase mutant, and the nuclear pore complex. Notably, PunctaFinder excels in detecting puncta in images with a relatively low resolution and signal-to-noise ratio, demonstrating its capability to identify dim puncta and puncta of dynamic target molecules. PunctaFinder reliably detects puncta in fluorescence microscopy images where automated analysis was not possible before, providing researchers with an efficient and robust method for punctum quantification in fluorescence microscopy images.

Consistent Interpretation of Time- and Frequency-Domain Traces of Ion Migration in Perovskite Semiconductors

Consistent Interpretation of Time- and Frequency-Domain Traces of Ion Migration in Perovskite Semiconductors

Large multimodal model-based standardisation of pathology reports with confidence and its prognostic significance.

Despite the existence of established standards and guidelines for pathology reporting, many pathology reports are still written in unstructured free text. Extracting information from these reports and formatting it according to a standard is crucial for consistent interpretation. Automated information extraction from unstructured pathology reports is a challenging task, as it requires accurately interpreting medical terminologies and context-dependent details. In this work, we present a practical approach for automatically extracting information from unstructured pathology reports or scanned paper reports utilising a large multimodal model. This framework uses context-aware prompting strategies to extract values of individual fields, such as grade, size, etc. from pathology reports. A unique feature of the proposed approach is that it assigns a confidence value indicating the correctness of the model's extraction for each field and generates a structured report in line with national pathology guidelines in human and machine-readable formats. We have analysed the extraction performance in terms of accuracy and kappa scores, and the quality of the confidence scores assigned by the model. We have also evaluated the prognostic value of the extracted fields and feature embeddings of the raw text. Results showed that the model can accurately extract information with an accuracy and kappa score up to 0.99 and 0.98, respectively. Our results indicate that confidence scores are an effective indicator of the correctness of the extracted information achieving an area under the receiver operating characteristic curve up to 0.93 thus enabling automatic flagging of extraction errors. Our analysis further reveals that, as expected, information extracted from pathology reports is highly prognostically relevant. The framework demo is available at: https://labieb.dcs.warwick.ac.uk/. Information extracted from pathology reports of colorectal cancer cases in the cancer genome atlas using the proposed approach and its code are available at: https://github.com/EtharZaid/Labieb.

Aeromagnetic Data Analysis for Sustainable Structural Mapping of the Missiakat Al Jukh Area in the Central Eastern Desert: Enhancing Resource Exploration with Minimal Environmental Impact

This study integrates aeromagnetic data with geological information to develop a consistent interpretation of both shallow and deep structural frameworks at various depths in the Missiakat Al Jukh area, located in the Central Eastern Desert, Egypt. The research begins by processing reduced-to-the-north magnetic pole (RTP) anomalies, using Fast Fourier Transformation (FFT) techniques to distinguish between local residual structures and broader regional features. This multi-scale approach enables a more detailed understanding of the geological complexity in the region, revealing its subsurface structures. Advanced geophysical methods such as upward continuation, Euler deconvolution, source parameter imaging (SPI), and global particle swarm optimization (GPSO) were applied to further refine the determination of structural depths, offering critical insights into the distribution and orientation of geological features at varying depths. The study reveals dominant structural orientations aligned in the NNW-SSE, ENE-WSW, north–south, and east–west directions, reflecting the region’s complex tectonic history. This research is of great importance in terms of sustainability. By delivering detailed subsurface maps and providing more accurate depth estimates of basement rocks (between 0.6 and 1.3 km), it contributes to sustainable resource exploration in the region. A better understanding of the geological structure helps minimize the environmental impact of exploration by reducing unnecessary drilling and concentrating efforts on areas with higher potential. Additionally, the use of non-invasive geophysical techniques supports the transition toward more environmentally conscious exploration practices. The integration of these advanced methods promotes a more sustainable approach to mineral and resource extraction, which is crucial for balancing economic growth with environmental preservation in geologically sensitive areas. Ultimately, this work provides a thorough geological interpretation that not only aids future exploration efforts but also aligns with the global push for sustainable and eco-friendly resource management.

Translation of MFL and UT data by using generative adversarial networks: A comparative study

Magnetic flux leakage (MFL) and ultrasonic testing (UT) are widely used in-line inspection technologies to detect corrosion defects along pipelines. The integration of MFL and UT data has the potential to provide complementary insights that facilitate a comprehensive assessment of pipeline integrity. However, due to the inherent dissimilarity with their underlying physical principles, these techniques yield notable disparities in signal characteristics, posing challenges in integrating these multimodal data. This study aims to establish a translation mapping between MFL and UT signals to achieve consistent physical interpretations across the two modalities. Thus, this study explored the feasibility of generative adversarial network (GAN) based models encompassing both supervised and unsupervised translation approaches contingent on the availability of aligned data. Furthermore, two translation modes, MFL-UT and UT-MFL, were analyzed, respectively, to understand the effectiveness of the translation direction. The experimental results demonstrate satisfactory performance for both aligned and unaligned data translation, with the UT-MFL translation direction yielding superior results. Overall, the translation approaches pave the way for future applications, especially in subsequent data analysis tasks such as registration, comparison, and fusion of multimodal data.

Are the Carpathians tectonically active?: Geomechanical study in deep boreholes in the outer Carpathians (Poland)

Present-day tectonic stress state was investigated in three deep boreholes located in the eastern segment of the Polish Outer Carpathians (POC). Significant rotations of the maximum horizontal stress (SH) were observed in these boreholes, located at the hinge of the anticlines in the upper part of the nappes. For the deepest borehole, D-1 (5.5 km depth), 1D geomechanical modelling was performed to determine the stress gradient profiles. An optimal solution of the model, validated by numerous compressional and extensional failures (breakouts and drilling-induced fractures, respectively) of the borehole wall, was obtained for variable elastic horizontal strain. The strain varies stepwise across the Main Thrust Fault (MTF) and linearly within its walls. The dominance of a strike-slip faulting stress regime was determined for the Carpathian nappes, with contributions from thrust faulting above the MTF and normal faulting below the MTF. A critical stress state for reactivation of preferentially oriented pre-existing faults and fractures was inferred for the competent strata. A consistent interpretation of the variations in stress orientation and magnitude, suggests a contemporary refolding of the anticline at a shallower structural level, enhanced by the reactivation of the MTF and a lack of reactivation of the Carpathian Bottom Thrust. Integration of these results with measurements from previous studies in the eastern segment of the POC indicates a different regional orientation of SH in the autochthonous basement (N-S) and in the nappes (NE-SW). These results indicate a thin-skinned compressive reactivation of the upper part of the accretionary wedge, with the lower part of the nappes remaining passive, or locally prone to minor strike-slip or normal faulting. These results contradict the hypothesis of a contemporaneous extensional collapse of the POC.

Unraveling the early universe’s equation of state and primordial black hole production with PTA, BBN, and CMB observations* *Qing-Hua Zhu is Supported by the National Natural Science Foundation of China (12305073). Zhi-Chao Zhao is supported by the National Key Research and Development Program of China (2021YFC2203001) and the National Natural Science Foundation of China (12005016). Sai Wang is partially supported

Pulsar timing array (PTA) data releases show strong evidence for a stochastic gravitational-wave background in the nanohertz band. When the signal is interpreted by a scenario of scalar-induced gravitational waves (SIGWs), we encounter overproduction of primordial black holes (PBHs). We wonder if varying the equation of state (EoS) of the early Universe can resolve this issue and thereby lead to a consistent interpretation of the PTA data. Analyzing a data combination of PTA, big-bang nucleosynthesis, and cosmic microwave background, we find that an epoch with EoS between the end of inflation and the onset of radiation domination can significantly suppress the production of PBHs, leading to alleviation of the PBH-overproduction issue. With the inferred interval at 95% confidence level, our scenario can interpret the PTA data just as well as the conventional scenario of SIGWs produced during the radiation domination.

Revisiting the Electrochemical Impedance Spectroscopy of Porous Electrodes in Li‐ion Batteries by Employing Reference Electrode

AbstractElectrochemical impedance spectroscopy (EIS), characterized by its non‐destructive and in situ nature, plays a crucial role in comprehending the thermodynamic and kinetic processes occurring within Li‐ion batteries. However, there is a lack of consistent and coherent physical interpretations for the EIS of porous electrodes. Therefore, it is imperative to conduct thorough investigations into the underlying physical mechanisms of EIS. Herein, by employing reference electrode in batteries, we revisit the associated physical interpretation of EIS at different frequencies. Combining different battery configurations, temperature‐dependent experiments, and elaborated distribution of relaxation time analysis, we find that the ion transport in porous electrode channels and pseudo‐capacitance behavior dominate the high‐frequency and mid‐frequency impedance arcs, respectively. This work offers a perspective for the physical interpretation of EIS and also sheds light on the understanding of EIS characteristics in other advanced energy storage systems.

Deep learning-based ultrasonographic classification of canine chronic kidney disease.

In veterinary medicine, attempts to apply artificial intelligence (AI) to ultrasonography have rarely been reported, and few studies have investigated the value of AI in ultrasonographic diagnosis. This study aimed to develop a deep learning-based model for classifying the status of canine chronic kidney disease (CKD) using renal ultrasonographic images and assess its diagnostic performance in comparison with that of veterinary imaging specialists, thereby verifying its clinical utility. In this study, 883 ultrasonograms were obtained from 198 dogs, including those diagnosed with CKD according to the International Renal Interest Society (IRIS) guidelines and healthy dogs. After preprocessing and labeling each image with its corresponding IRIS stage, the renal regions were extracted and classified based on the IRIS stage using the convolutional neural network-based object detection algorithm You Only Look Once. The training scenarios consisted of multi-class classification, categorization of images into IRIS stages, and four binary classifications based on specific IRIS stages. To prevent model overfitting, we balanced the dataset, implemented early stopping, used lightweight models, and applied dropout techniques. Model performance was assessed using accuracy, recall, precision, F1 score, and receiver operating characteristic curve and compared with the diagnostic accuracy of four specialists. Inter- and intra-observer variabilities among specialists were also evaluated. The developed model exhibited a low accuracy of 0.46 in multi-class classification. However, a significant performance improvement was observed in binary classifications, with the model designed to distinguish stage 3 or higher showing the highest accuracy of 0.85. In this classification, recall, precision, and F1 score values were all 0.85, and the area under the curve was 0.89. Compared with radiologists, whose accuracy ranged from 0.48 to 0.62 in this experimental scenario, the AI model exhibited superiority. Intra-observer reliability among radiologists was substantial, whereas inter-observer variability showed a moderate level of agreement. This study developed a deep-learning framework capable of reliably classifying CKD IRIS stages 3 and 4 in dogs using ultrasonograms. The developed framework demonstrated higher accuracy than veterinary imaging specialists and provided more objective and consistent interpretations. Therefore, deep-learning-based ultrasound diagnostics are potentially valuable tools for diagnosing CKD in dogs.

Open Access Data Repository and Common Data Model for Pulse Oximeter Performance Data.

The OpenOximetry Repository is a structured database storing clinical and lab pulse oximetry data, serving as a centralized repository and data model for pulse oximetry initiatives. It supports measurements of arterial oxygen saturation (SaO2) by arterial blood gas co-oximetry and pulse oximetry (SpO2), alongside processed and unprocessed photoplethysmography (PPG) data and other metadata. This includes skin color measurements, finger diameter, vital signs (e.g., arterial blood pressure, end-tidal carbon dioxide), and arterial blood gas parameters (e.g., acid-base balance, hemoglobin concentration). Data contributions are encouraged. All data, from desaturation studies to clinical trials, are collected prospectively to ensure accuracy. A common data model and standardized protocols for consistent archival and interpretation ensure consistent data archival and interpretation. The dataset aims to facilitate research on pulse oximeter performance across diverse human characteristics, addressing performance issues and promoting accurate pulse oximeters. The initial release includes controlled lab desaturation studies (CLDS), with ongoing updates planned as further data from clinical trials and CLDS become available.

OCIE: Augmenting model interpretability via Deconfounded Explanation-Guided Learning

Deep neural networks (DNNs) often encounter significant challenges related to opacity, inherent biases, and shortcut learning, which undermine their practical reliability. In this study, we address these issues by constructing a causal graph to model the unbiased learning process of DNNs. This model reveals that recurrent background information in training samples acts as a confounder, leading to spurious correlations between model inputs and outputs, causing biased predictions. To mitigate these problems and promote unbiased feature learning, we propose the Object-guided Consistency Interpretation Enhancement (OCIE) methodology. OCIE enhances DNN interpretability by integrating explicit objects and explanations into the model’s learning process. Initially, OCIE employs a graph-based algorithm to identify explicit objects within self-supervised vision transformer-learned features. Subsequently, it constructs class prototypes to eliminate invalid detected objects. Finally, OCIE aligns explanations with explicit objects, directing the model’s attention towards the most distinctive classification features rather than irrelevant backgrounds. Extensive experiments on different image classification datasets, including general (ImageNet), fine-grained (Stanford Cars and CUB-200), and medical (HAM) datasets, using two prevailing network architectures, demonstrate that OCIE significantly enhances explanation consistency across all datasets. Furthermore, OCIE proves particularly advantageous for fine-grained classification, especially in few-shot scenarios, by improving both interpretability and classification performance. Additionally, our findings highlight the impact of centralized explanations on the sufficiency of model decisions, suggesting that focusing explanations on explicit objects improves the reliability of DNN predictions. Our code is available at: https://github.com/DLAIResearch/OCIE.

A New Approach to Breast Specimen Orientation: Avoiding Pitfalls with the Specimen Plate Concept.

Accurate specimen marking is crucial during breast cancer surgery to avoid misorientation, which can lead to inadequate re-excision and tumor recurrence. We studied the marking methods at various breast cancer centers to create a tool that would prevent specimen misorientation. An online questionnaire was used to survey marking procedures at major breast cancer centers in Hungary, and a tool was developed using a troubleshooting method. Twelve out of twenty units responded (60%). Nine use an institutionally standardized marking system. Less than half of the surgical teams found specimen mammograms to be unambiguous. In more than 70% of departments, pathologists were uncertain about breast specimen orientation. Ambiguous marking methods caused orientation errors in half of the cases, while unclear marking directions caused the rest. Most pathologists (85%) and surgeons (75%) believed that coronal plane specimen mammography would help solve the problem. A plastic specimen plate has been developed to anchor breast tissue to a coronal breast scheme as seen in mammography images, providing clear localization information throughout the surgical process. There is a lack of standardization in breast specimen orientation and marking in Hungary. An optimized orientation toolkit is being developed to ensure consistent interpretation of specimen mammograms by surgeons and pathologists.

An empirical appraisal of eLife's assessment vocabulary.

Research articles published by the journal eLife are accompanied by short evaluation statements that use phrases from a prescribed vocabulary to evaluate research on 2 dimensions: importance and strength of support. Intuitively, the prescribed phrases appear to be highly synonymous (e.g., important/valuable, compelling/convincing) and the vocabulary's ordinal structure may not be obvious to readers. We conducted an online repeated-measures experiment to gauge whether the phrases were interpreted as intended. We also tested an alternative vocabulary with (in our view) a less ambiguous structure. A total of 301 participants with a doctoral or graduate degree used a 0% to 100% scale to rate the importance and strength of support of hypothetical studies described using phrases from both vocabularies. For the eLife vocabulary, most participants' implied ranking did not match the intended ranking on both the importance (n = 59, 20% matched, 95% confidence interval [15% to 24%]) and strength of support dimensions (n = 45, 15% matched [11% to 20%]). By contrast, for the alternative vocabulary, most participants' implied ranking did match the intended ranking on both the importance (n = 188, 62% matched [57% to 68%]) and strength of support dimensions (n = 201, 67% matched [62% to 72%]). eLife's vocabulary tended to produce less consistent between-person interpretations, though the alternative vocabulary still elicited some overlapping interpretations away from the middle of the scale. We speculate that explicit presentation of a vocabulary's intended ordinal structure could improve interpretation. Overall, these findings suggest that more structured and less ambiguous language can improve communication of research evaluations.

Steel I-girder geometries for enhanced stability in shear loading

This paper challenges the design provisions that rely on the traditional assumptions of simply supported web boundary conditions for shear. While some literature discuss the flange stiffening effect on the web, the detrimental impact of flexible flanges on the shear strength has not been addressed before. Through a detailed analysis of excessive deformations at the web-flange junctions, this study shows that the assumption of simply supported web boundary conditions may be unsafe, contradicting expectations of conservative design in some girders that comply with current design specifications. Accordingly, minimum flange dimensions are proposed to ensure adequate support at the web edges. In addition to categorizing cross-sections that do not achieve simply supported boundary conditions, the paper proposes a modified elastic shear buckling coefficient, which can enhance the estimate of elastic shear buckling stresses of I-girders by up to 60% compared to simply supported web plates. The improved elastic buckling stress is also shown to improve the overall shear strength predictions of I-girders by means of geometric and material nonlinear analyses. This revised coefficient considers the intricate interplay between the flanges, transverse stiffeners, and the web, which shall enable a more mechanistic and consistent interpretation of the ultimate shear capacities in future studies. The findings presented in this paper have implications for cross-sectional proportioning and shear design of plate girders, given that the ultimate design shear capacities of I-girders are typically estimated by summing the web elastic shear buckling stresses with their post-buckling strengths.

Visualizing thyroid health: a pictorial journey through 2017 ACR TI-RADS and common thyroid pathologies.

With the advent of high-resolution ultrasonography (HRUS), more thyroid nodules are being detected than ever before, and they are being identified at an earlier stage. It poses a challenge for radiologists and clinicians in deciding what to do next. Most nodules are benign and require no follow-up and intervention. Even highly suspicious nodules can be followed up, if the size is small. Variations in HRUS interpretation among radiologists are common, with frequent misidentifications between spongiform and solid-cystic lesions, hypoechoic and very hypoechoic nodules, and microcalcification and hyperechoic foci with comet-tail artifacts. Cystic lesions with echogenic contents are often confused with solid nodules, cystic papillary carcinoma thyroid is often confused with colloid cysts. The 2017 ACR TI-RADS (American College of Radiology Thyroid Imaging Reporting and Data System) aims to standardize the interpretation of thyroid nodules and guide further management. Rather than giving specific diagnosis like colloid cyst, adenomatous nodule and papillary carcinoma; ACR TI-RADS classifies nodules from TI-RADS 1 to TI-RADS 5 based on HRUS characteristics and recommends further management. What the authors often read are textual contents that are theoretical, and in practice, the authors get confused while interpreting the characteristics of thyroid nodules. This review offers a detailed visual overview of the 2017 ACR TI-RADS and common thyroid conditions, explaining key features through imaging data and examples for consistent interpretation. Combining textual explanations with visual aids, this article provides practical guidance for interpreting thyroid nodules for radiologists, and clinicians seeking a clear understanding of thyroid imaging and pathology.

Revisiting the Electrochemical Impedance Spectroscopy of Porous Electrodes in Li-ion Batteries by Employing Reference Electrode.

Electrochemical impedance spectroscopy (EIS), characterized by its non-destructive and in situ nature, plays a crucial role in comprehending the thermodynamic and kinetic processes occurring within Li-ion batteries. However, there is a lack of consistent and coherent physical interpretations for the EIS of porous electrodes. Therefore, it is imperative to conduct thorough investigations into the underlying physical mechanisms of EIS. Herein, by employing reference electrode in batteries, we revisit the associated physical interpretation of EIS at different frequencies. Combining different battery configurations, temperature-dependent experiments, and elaborated distribution of relaxation time analysis, we find that the ion transport in porous electrode channels and pseudo-capacitance behavior dominate the high-frequency and mid-frequency impedance arcs, respectively. This work offers a perspective for the physical interpretation of EIS and also sheds light on the understanding of EIS characteristics in other advanced energy storage systems.

A consensus-based electronic medical record template for pediatric morphea patient visits.

Morphea, also known as localized scleroderma, is an inflammatory sclerosing disorder of uncertain pathogenesis that affects the skin and underlying tissues. In the pediatric population, the disease often runs a chronic course with a high risk for irreversible sequelae; as such, patients often require long-term monitoring. The objective of this study is to develop a multi-center, consensus-based electronic medical record template for pediatric morphea patient visits using a modified Delphi method of iterative surveys. By facilitating consistent data collection and interpretation across medical centers and patient populations, this template may improve patient care for pediatric patients with morphea.

Data Evaluation Framework for Refining PFAS Conceptual Site Models

AbstractContaminated sites with per‐ and polyfluoroalkyl substances (PFASs) are the cause of environmental, health, and financial concerns. Understanding and addressing PFASs at their source areas is important for effective characterization, risk assessment, and remediation. This paper introduces a framework that relies on commonly available PFAS data to assist in identifying PFAS source areas and assess PFAS fate and transport considerations along flowpaths. Currently accepted PFAS physical and chemical behaviors have been incorporated into metrics that can be evaluated geospatially, and/or over time, to build a weight‐of‐evidence approach to refine conceptual site models (CSMs). Graphical representation of data according to a PFAS “family tree” is also proposed for more consistent interpretation and pattern recognition. Combined, these tools create a PFAS data evaluation framework (PFAS Framework) that consists of a tiered analysis approach based on data availability and site complexity. Case studies from real sites are presented to demonstrate the capabilities of the PFAS Framework in identifying source areas.

Technical note: An improved methodology for calculating the Southern Annular Mode index to aid consistency between climate studies

Abstract. The Southern Annular Mode (SAM) strongly influences climate variability in the Southern Hemisphere. The SAM index describes the phase and magnitude of the SAM and can be calculated by measuring the difference in mean sea level pressure (MSLP) between middle and high latitudes. This study investigates the effects of calculation methods and data resolution on the SAM index, and subsequent interpretations of SAM impacts and trends. We show that the normalisation step that is traditionally used in calculating the SAM index leads to substantial differences in the magnitude of the SAM index calculated at different temporal resolutions. Additionally, the equal weighting that the normalisation approach gives to MSLP variability at the middle and high southern latitudes artificially alters temperature and precipitation correlations and the interpretation of climate change trends in the SAM. These issues can be overcome by instead using a natural SAM index based on MSLP anomalies, resulting in consistent scaling and variability in the SAM index calculated at daily, monthly and annual data resolutions. The natural SAM index has improved representation of SAM impacts in the high southern latitudes, including the asymmetric (zonal wave-3) component of MSLP variability, whereas the increased weighting given to mid-latitude MSLP variability in the normalised SAM index incorporates a stronger component of tropical climate variability that is not directly associated with SAM variability. We conclude that an improved approach of calculating the SAM index from MSLP anomalies without normalisation would aid consistency across climate studies and avoid potential ambiguity in the SAM index, including SAM index reconstructions from palaeoclimate data, and thus enable more consistent interpretations of SAM trends and impacts.

Implementation of complex engineering problem solving projects in a Malaysian engineering programme

Accreditation bodies mandate incorporating complex problem-solving (CPS) in engineering programmes on top of outcome-based education. However, its attributes are open to interpretation, leading to a lack of consensus on its implementation in engineering curricula. This paper suggests a solution: having CPS in projects, especially for the courses aligned with CPS-related outcomes. To eliminate ambiguities, the characteristics of complex engineering problems (WP) and complex engineering activities (EA) are restated. This ensures a consistent interpretation of WPs and EAs among educators. CPS projects can be phased into four stages: awareness, adoption, growth, and maturity, followed by continual quality improvement at both the course and programme levels. The entire process can span several years. To stay current with the latest academic and industrial developments, implementation should be completed within one accreditation cycle.