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Journal articles on the topic 'Inter-dataset variability'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Zhao, Kunkun, Zhisheng Zhang, Haiying Wen, and Alessandro Scano. "Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements." Biomimetics 6, no. 4 (2021): 63. http://dx.doi.org/10.3390/biomimetics6040063.

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Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for q

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Kanagasundaram, Ahilan. "Improving the performance of GPLDA speaker verification using unsupervised inter-dataset variability compensation approaches." International Journal of Speech Technology 21, no. 3 (2018): 533–44. http://dx.doi.org/10.1007/s10772-018-9513-x.

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Rai, Taranpreet, Ambra Morisi, Barbara Bacci, et al. "Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours." Cancers 16, no. 3 (2024): 644. http://dx.doi.org/10.3390/cancers16030644.

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Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process

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Setiawan, Fachruddin Ari, Dio Alif Pradana, and Iim Nandang. "Analysis of Inter-Subject and Session Variability using Brain Topographic Map." Journal of Applied Intelligent System 9, no. 1 (2025): 101–8. https://doi.org/10.62411/jais.v9i1.10051.

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The study described investigates the application of Brain-Computer Interface (BCI) technology, focusing on Motor Imagery (MI) signals which enable individuals to control movements through mental visualization. A major challenge in this field is accurately distinguishing between different movements, particularly when dealing with data from multiple subjects and recording sessions, known as inter-subject and inter-session variability. To address this, the authors employ the Wavelet Packet Transform-Common Spatial Patterns (WPT-CSP) method to enhance the resolution of MI signals. They visualize t

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Hannouche, A., G. Chebbo, G. Ruban, B. Tassin, B. J. Lemaire, and C. Joannis. "Relationship between turbidity and total suspended solids concentration within a combined sewer system." Water Science and Technology 64, no. 12 (2011): 2445–52. http://dx.doi.org/10.2166/wst.2011.779.

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This article confirms the existence of a strong linear relationship between turbidity and total suspended solids (TSS) concentration. However, the slope of this relation varies between dry and wet weather conditions, as well as between sites. The effect of this variability on estimating the instantaneous wet weather TSS concentration is assessed on the basis of the size of the calibration dataset used to establish the turbidity – TSS relationship. Results obtained indicate limited variability both between sites and during dry weather, along with a significant inter-event variability. Moreover,

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Fernández, Néstor, Jacinto Román, and Miguel Delibes. "Variability in primary productivity determines metapopulation dynamics." Proceedings of the Royal Society B: Biological Sciences 283, no. 1828 (2016): 20152998. http://dx.doi.org/10.1098/rspb.2015.2998.

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Temporal variability in primary productivity can change habitat quality for consumer species by affecting the energy levels available as food resources. However, it remains unclear how habitat-quality fluctuations may determine the dynamics of spatially structured populations, where the effects of habitat size, quality and isolation have been customarily assessed assuming static habitats. We present the first empirical evaluation on the effects of stochastic fluctuations in primary productivity—a major outcome of ecosystem functions—on the metapopulation dynamics of a primary consumer. A uniqu

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Yang, Shih-Hung, Yao-Mao Cheng, Jyun-We Huang, and Yon-Ping Chen. "RFaNet: Receptive Field-Aware Network with Finger Attention for Fingerspelling Recognition Using a Depth Sensor." Mathematics 9, no. 21 (2021): 2815. http://dx.doi.org/10.3390/math9212815.

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Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existing methods, regular convolutional kernels, which have limited receptive fields (RFs) and often cannot detect subtle discriminative details, are applied to learn features. In this study, we propose a receptive field-aware network with finger attention (RFaNet) that highlights the finger regions and builds inter-finger relations. To highlight the dis

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Hasson, S., V. Lucarini, and S. Pascale. "Hydrological cycle over South and Southeast Asian river basins as simulated by PCMDI/CMIP3 experiments." Earth System Dynamics 4, no. 2 (2013): 199–217. http://dx.doi.org/10.5194/esd-4-199-2013.

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Abstract. We investigate how the climate models contributing to the PCMDI/CMIP3 dataset describe the hydrological cycle over four major South and Southeast Asian river basins (Indus, Ganges, Brahmaputra and Mekong) for the 20th, 21st (13 models) and 22nd (10 models) centuries. For the 20th century, some models do not seem to conserve water at the river basin scale up to a good degree of approximation. The simulated precipitation minus evaporation (P − E), total runoff (R) and precipitation (P) quantities are neither consistent with the observations nor among the models themselves. Most of the

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Watson, S. J., P. Kritharas, and G. J. Hodgson. "Wind speed variability across the UK between 1957 and 2011." Wind Energy 18, no. 1 (2013): 21–42. http://dx.doi.org/10.1002/we.1679.

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ABSTRACTBy using surface data from 57 UK meteorological stations, a national [British Atmospheric Data Centre (BADC)‐57] and regional wind index for the UK has been calculated for the period 1983–2011. For a subset of seven stations, an additional national index (BADC‐7) has been calculated for the period 1957–2011. The indices show an annual variability of 4% over their respective periods, corresponding to a variation in typical wind turbine capacity factor of 7%. These indices are compared with indices calculated from other sources, namely an index generated using a gridded dataset of observ

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Cammarota, Camillo, and Mario Curione. "Trend Extraction in Functional Data of Amplitudes of R and T Waves in Exercise Electrocardiogram." Fluctuation and Noise Letters 16, no. 02 (2017): 1750014. http://dx.doi.org/10.1142/s0219477517500146.

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The amplitudes of R and T waves of the electrocardiogram (ECG) recorded during the exercise test show both large inter- and intra-individual variability in response to stress. We analyze a dataset of 65 normal subjects undergoing ambulatory test. We model the dataset of R and T series in the framework of functional data, assuming that the individual series are realizations of a non-stationary process, centered at the population trend. We test the time variability of this trend computing a simultaneous confidence band and the zero crossing of its derivative. The analysis shows that the amplitud

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Kellogg, Ryan Thomson, Jan Vargas Machaj, Matthew Nguyen, Anthony Nwanko, Sohil Patel, and Xue Feng. "326 Prediction of Shunt Malfunction Using Automated Ventricular Volume Analysis and Radiomics." Neurosurgery 71, Supplement_1 (2025): 78. https://doi.org/10.1227/neu.0000000000003360_326.

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INTRODUCTION: The assessment of ventricle size is crucial in diagnosing hydrocephalus and detecting shunt malfunctions. Current methods primarily involve 2 dimensional measurements, which can be time-consuming and subject to inter-observer variability. METHODS: We identified patients that underwent placement or revision of a VP shunt between January of 2018 and August of 2020 and collected head CTs from patients which were imaged with a functional shunt, a shunt malfunction, or prior to the placement of their shunt. A convolutional neural network (CNN) was used to automate ventricle segmentati

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Kim, J., and S. K. Park. "Uncertainties in calculating precipitation climatology in East Asia." Hydrology and Earth System Sciences Discussions 12, no. 8 (2015): 7765–83. http://dx.doi.org/10.5194/hessd-12-7765-2015.

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Abstract. This study examines the uncertainty in calculating the fundamental climatological characteristics of precipitation in the East Asia region from multiple fine-resolution gridded analysis datasets based on in-situ rain gauge observations. Five observation-based gridded precipitation datasets are used to derive the long-term means, standard deviations in lieu of interannual variability and linear trends over the 28-year period from 1980 to 2007. Both the annual and summer (June–July–August) mean precipitation is examined. The agreement amongst these precipitation datasets are examined u

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Rentier, Eline S., Ondřej Mottl, L. Camila Pacheco-Riaño, et al. "Global variability in LGM cooling amongst paleoclimate datasets affects biome reconstructions in mountains." Frontiers of Biogeography 18 (May 29, 2025): e135871. https://doi.org/10.21425/fob.18.135871.

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Downscaled paleoclimate datasets are widely used in biogeographical research, aiding our understanding of past environmental shifts and species' responses to climate change. Numerous datasets exist, varying in spatiotemporal resolution and underlying methodologies, resulting in variation in estimated temperature. Understanding this variability is important for accurately reconstructing past biogeographical dynamics, especially in complex regions like mountains. We compare the Mean Annual Temperature (MAT) at the Last Glacial Maximum (LGM) from five different downscaled paleoclimate datasets —

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Odinaev, Ismoil, Kwan Long Wong, Jing Wei Chin, Raghav Goyal, Tsz Tai Chan, and Richard H. Y. So. "Robust Heart Rate Variability Measurement from Facial Videos." Bioengineering 10, no. 7 (2023): 851. http://dx.doi.org/10.3390/bioengineering10070851.

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Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess n

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Capar, Abdulkerim, Dursun Ali Ekinci, Mucahit Ertano, et al. "An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study." PLOS ONE 19, no. 12 (2024): e0314450. https://doi.org/10.1371/journal.pone.0314450.

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Breast cancer, a widespread and life-threatening disease, necessitates precise diagnostic tools for improved patient outcomes. Tumor-Infiltrating Lymphocytes (TILs), reflective of the immune response against cancer cells, are pivotal in understanding breast cancer behavior. However, inter-observer variability in TILs scoring methods poses challenges to reliable assessments. This study introduces a novel and interpretable proof-of-principle framework comprising two innovative inter-observer agreement measures. The first method, Boundary-Weighted Fleiss’ Kappa (BWFK), addresses tissue segmentati

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Sperna Weiland, F. C., L. P. H. van Beek, J. C. J. Kwadijk, and M. F. P. Bierkens. "The ability of a GCM-forced hydrological model to reproduce global discharge variability." Hydrology and Earth System Sciences 14, no. 8 (2010): 1595–621. http://dx.doi.org/10.5194/hess-14-1595-2010.

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Abstract. Data from General Circulation Models (GCMs) are often used to investigate hydrological impacts of climate change. However GCM data are known to have large biases, especially for precipitation. In this study the usefulness of GCM data for hydrological studies, with focus on discharge variability and extremes, was tested by using bias-corrected daily climate data of the 20CM3 control experiment from a selection of twelve GCMs as input to the global hydrological model PCR-GLOBWB. Results of these runs were compared with discharge observations of the GRDC and discharges calculated from m

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Stefanakos, Christos. "Global Wind and Wave Climate Based on Two Reanalysis Databases: ECMWF ERA5 and NCEP CFSR." Journal of Marine Science and Engineering 9, no. 9 (2021): 990. http://dx.doi.org/10.3390/jmse9090990.

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In the present work, the global wind and wave climate is studied on the basis of two well-known reanalysis products, namely ERA5 and CFSR-W (WW3 hereafter). Several statistical features of the datasets are assessed, such as seasonal variability, quantiles of the probability distribution, monthly, annual and inter-annual variability, and several error metrics. The time span covers a period of 31 years (1979–2009), a fact that assures that most of the long-scale features are equally present in both datasets. The analysis performed is depicted both on a global and regional scale. The results are

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Inatsu, Masaru, Tsubasa Nakayama, Yoshie Maeda, and Hirotaka Matsuda. "Dynamical Downscaling for Assessment of the Climate in Ghana." Journal of Disaster Research 9, no. 4 (2014): 412–21. http://dx.doi.org/10.20965/jdr.2014.p0412.

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Dynamical downscaling (DDS), in which a regional atmospheric model (RAM) experiment nested into coarser-resolution data provides a spatio-temporal fine dataset for a particular region, was performed to assess the present climate in Ghana. The DDS successfully evaluated realistic seasonal march and inter-annual variability in rainfall, in comparison with gauge and satellite observation. The DDS also indicated that land-lake and land-sea circulation interacted with the West African monsoon likely characterized the local climate in Ghana.

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Graeff, Philipp, Regina Stacheneder, Laura Alt, and Ruth Ruscheweyh. "The Contribution of Psychological Factors to Inter-Individual Variability in Conditioned Pain Modulation Is Limited in Young Healthy Subjects." Brain Sciences 12, no. 5 (2022): 623. http://dx.doi.org/10.3390/brainsci12050623.

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Conditioned pain modulation (CPM) describes the decrease in pain perception of a test stimulus (TS) when presented together with a heterotopic painful conditioning stimulus (CS). Inter-individual differences in CPM are large and have been suggested to reflect differences in endogenous pain modulation. In a previous analysis, we demonstrated that in young, healthy participants, inter-individual differences account for about one-third of CPM variance, with age and sex together explaining only 1%. Here, we investigated if psychological factors explain significant amounts of inter-individual varia

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Kulberg, Nikolas S., Roman V. Reshetnikov, Vladimir P. Novik, et al. "Inter-observer variability between readers of CT images: all for one and one for all." Digital Diagnostics 2, no. 2 (2021): 105–18. http://dx.doi.org/10.17816/dd60622.

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BACKGROUND: The markup of medical image datasets is based on the subjective interpretation of the observed entities by radiologists. There is currently no widely accepted protocol for determining ground truth based on radiologists reports. AIM: To assess the accuracy of radiologist interpretations and their agreement for the publicly available dataset CTLungCa-500, as well as the relationship between these parameters and the number of independent readers of CT scans. MATERIALS AND METHODS: Thirty-four radiologists took part in the dataset markup. The dataset included 536 patients w

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Boelders, S. M., W. De Baene, G. J. M. Rutten, K. Gehring, and L. L. Ong. "P18.08.B Fully automatic meningioma segmentation using T1-weighted contrast-enhanced MR images only." Neuro-Oncology 24, Supplement_2 (2022): ii95—ii96. http://dx.doi.org/10.1093/neuonc/noac174.335.

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Abstract Background Manual segmentation of brain tumors requires expertise, is time-consuming, and is subject to inter-rater variability. Fully automatic brain tumor segmentation is possible for glioma and meningioma when volumetric T1, T1 contrast-enhanced (T1c), T2, and Fluid-attenuated inversion recovery (FLAIR) MRIs are available. In clinical care of meningiomas, however, often only volumetric T1c scans are available. In this work, we trained a deep learning network to segment meningiomas using only T1c scans for use in clinical research. Material and Methods NnU-Net, a deep learning model

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Halloran, P. R., T. G. Bell, and I. J. Totterdell. "Can we trust empirical marine DMS parameterisations within projections of future climate?" Biogeosciences 7, no. 5 (2010): 1645–56. http://dx.doi.org/10.5194/bg-7-1645-2010.

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Abstract. Dimethylsulphide (DMS) is a globally important aerosol precurser. In 1987 Charlson and others proposed that an increase in DMS production by certain phytoplankton species in response to a warming climate could stimulate increased aerosol formation, increasing the lower-atmosphere's albedo, and promoting cooling. Despite two decades of research, the global significance of this negative climate feedback remains contentious. It is therefore imperative that schemes are developed and tested, which allow for the realistic incorporation of phytoplankton DMS production into Earth System mode

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Voicu, Ioan Paul, Francesco Dotta, Antonio Napolitano, et al. "Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study." Cancers 16, no. 14 (2024): 2578. http://dx.doi.org/10.3390/cancers16142578.

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Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffus

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Schilling, Marcel P., Niket Ahuja, Luca Rettenberger, Tim Scherr, and Markus Reischl. "Impact of Annotation Noise on Histopathology Nucleus Segmentation." Current Directions in Biomedical Engineering 8, no. 2 (2022): 197–200. http://dx.doi.org/10.1515/cdbme-2022-1051.

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Abstract Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains for experts such as pathologists. The variability within an annotator (intra) and the variability between annotators (inter) are current challenges. In clinical practice or biology, instance segmentation is a common task, but a comprehensive and quantitative study regarding the impact of noisy annotations lacks. In this paper, we present a c

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Sebastian, Ganguly, Krishnaswamy, Duffy, Nemani, and Ghosh. "Multi-Scale Association between Vegetation Growth and Climate in India: A Wavelet Analysis Approach." Remote Sensing 11, no. 22 (2019): 2703. http://dx.doi.org/10.3390/rs11222703.

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Monsoon climate over India has high degree of spatio-temporal heterogeneity characterized by the existence of multi-climatic zones along with strong intra-seasonal, seasonal, and inter-annual variability. Vegetation growth of Indian forests relates to this climate variability, though the dependence structure over space and time is yet to be explored. Here, we present a comprehensive analysis of this association with quality-controlled satellite-based remote sensing dataset of vegetation greenness and radiation along with station based gridded precipitation datasets. A spatio-temporal time-freq

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Ziyambe, Blessed, Abid Yahya, Tawanda Mushiri, et al. "A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women." Diagnostics 13, no. 10 (2023): 1703. http://dx.doi.org/10.3390/diagnostics13101703.

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Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, div

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Le Cunff, Yann, Laurent Chesneau, Sylvain Pastezeur, et al. "Unveiling inter-embryo variability in spindle length over time: Towards quantitative phenotype analysis." PLOS Computational Biology 20, no. 9 (2024): e1012330. http://dx.doi.org/10.1371/journal.pcbi.1012330.

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How can inter-individual variability be quantified? Measuring many features per experiment raises the question of choosing them to recapitulate high-dimensional data. Tackling this challenge on spindle elongation phenotypes, we showed that only three typical elongation patterns describe spindle elongation in C. elegans one-cell embryo. These archetypes, automatically extracted from the experimental data using principal component analysis (PCA), accounted for more than 95% of inter-individual variability of more than 1600 experiments across more than 100 different conditions. The two first arch

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dos Santos, Vinicius, Didier Gastmans, Ana María Durán-Quesada, et al. "Isotopic composition of convective rainfall in the inland tropics of Brazil." Atmospheric Chemistry and Physics 24, no. 11 (2024): 6663–80. http://dx.doi.org/10.5194/acp-24-6663-2024.

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Abstract. Strong convective systems characterize the tropical central-southern region of Brazil. These systems provide abundant water supply for agroindustrial activities and pose flood risks to large cities. Here, we present high-frequency (2–10 min; inter- and intra-event) rainfall isotopic compositions (n = 90 samples) to reveal regional and local atmospheric processes controlling the isotopic variability of convective systems between 2019–2021. Inter-event rainfall weighted-average (δwgd) values were low (δ18Owgd ≤ −10.0 ‰) due to the higher rainfall along the Hybrid Single-Particle Lagran

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Shible, Hussein, Fabrice Hollender, Dino Bindi, et al. "GITEC: A Generalized Inversion Technique Benchmark." Bulletin of the Seismological Society of America 112, no. 2 (2022): 850–77. http://dx.doi.org/10.1785/0120210242.

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ABSTRACT Generalized inversion techniques (GITs) have become popular for determining seismological parameters (e.g., source, attenuation, and site response), particularly in low-to-moderate seismicity regions. Indeed, GITs can potentially provide reliable site-response estimates when a minimum number of recordings is available, as well as valuable information about source parameters and regional attenuation characteristics. Significant advances have been made on GITs in which different approaches and hypotheses were investigated, such as the application of “nonparametric” and “parametric” inve

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Li, Changsheng, Minyou Chen, and Li Zhang. "A Domain Adaptation-Based Method for Classification of Motor Imagery EEG." Mathematics 10, no. 9 (2022): 1588. http://dx.doi.org/10.3390/math10091588.

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Non-stationarity of EEG signals lead to high variability across sessions, which results in low classification accuracy. To reduce the inter-session variability, an unsupervised domain adaptation method is proposed. Arithmetic mean and covariance are exploited to represent the data distribution. First, overall mean alignment is conducted between the source and target data. Then, the data in the target domain is labeled by a classifier trained with the source data. The per-class mean and covariance of the target data are estimated based on the predicted labels. Next, an alignment from the source

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Ramos-Cooper, Solange, Erick Gomez-Nieto, and Guillermo Camara-Chavez. "VGGFace-Ear: An Extended Dataset for Unconstrained Ear Recognition." Sensors 22, no. 5 (2022): 1752. http://dx.doi.org/10.3390/s22051752.

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Recognition using ear images has been an active field of research in recent years. Besides faces and fingerprints, ears have a unique structure to identify people and can be captured from a distance, contactless, and without the subject’s cooperation. Therefore, it represents an appealing choice for building surveillance, forensic, and security applications. However, many techniques used in those applications—e.g., convolutional neural networks (CNN)—usually demand large-scale datasets for training. This research work introduces a new dataset of ear images taken under uncontrolled conditions t

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Mukushev, Medet, Aidyn Ubingazhibov, Aigerim Kydyrbekova, Alfarabi Imashev, Vadim Kimmelman, and Anara Sandygulova. "FluentSigners-50: A signer independent benchmark dataset for sign language processing." PLOS ONE 17, no. 9 (2022): e0273649. http://dx.doi.org/10.1371/journal.pone.0273649.

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This paper presents a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) for the purposes of Sign Language Processing. We envision it to serve as a new benchmark dataset for performance evaluations of Continuous Sign Language Recognition (CSLR) and Translation (CSLT) tasks. The proposed FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers resulting in 43,250 video samples. Dataset contributors recorded videos in real-life settings on a wide variety of backgrounds using various devices such as smartphones and web cameras. Therefore, dis

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Vinnik, Ilana, and Tatjana Miljkovic. "Modeling the Inter-Arrival Time Between Severe Storms in the United States Using Finite Mixtures." Risks 13, no. 2 (2025): 19. https://doi.org/10.3390/risks13020019.

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When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling sev

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Bodrogi, P., Ch Schiller, and TQ Khanh. "Testing the CIE system for mesopic photometry in a threshold detection experiment." Lighting Research & Technology 48, no. 8 (2016): 992–1004. http://dx.doi.org/10.1177/1477153515595672.

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Results from a mesopic threshold detection experiment using static, quasi-monochromatic, 2° incremental detection targets were used to test the International Commission on Illumination (CIE) recommended system for mesopic photometry based on visual performance. An experimental apparatus was built to produce different mesopic backgrounds (four different spectral compositions at two different luminances, 0.1 cd/m2 and 1.0 cd/m2) on which the targets were projected at different eccentricities (2.65° and 10°). The CIE system was tested by two methods, variability of mesopic threshold contrast for

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Ji, Fei, Acacia S. Pepler, Stuart Browning, Jason P. Evans, and Alejandro Di Luca. "Trends and low frequency variability of East Coast Lows in the twentieth century." Journal of Southern Hemisphere Earth Systems Science 68, no. 1 (2018): 1. http://dx.doi.org/10.1071/es18001.

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East Coast Lows (ECLs) are important weather systems that affect the eastern seaboard of Australia. They have attracted research interest for both their destructive nature and water supplying capability. In this paper, three objective ECL tracking methods are applied to the twentieth century reanalysis ensemble (20CRV2C) for the period of 1851–2014 to identify historical trends and variability in ECLs. While the ensemble mean is unsuitable for tracking ECLs, when all methods are applied to the full 56-member ensemble there is large agreement between tracking methods as to the low-frequency var

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Courault, Romain, Alexiane Franclet, Kévin Bourrand, Clélia Bilodeau, Sonia Saïd, and Marianne Cohen. "Recent vegetation phenology variability and wild reindeer migration in Hardangervidda plateau (Norway)." Proceedings of the ICA 1 (May 16, 2018): 1–7. http://dx.doi.org/10.5194/ica-proc-1-24-2018.

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More than others, arctic ecosystems are affected by consequences of global climate changes. The herbivorous plays numerous roles both in Scandinavian natural and cultural landscapes (Forbes et al., 2007). Wild reindeer (<i>Rangifer tarandus</i> L.) herds in Hardangervidda plateau (Norway) constitute one of the isolated populations along Fennoscandia mountain range. The study aims to understand temporal and spatial variability of intra- and inter-annual home ranges extent and geophysical properties. We then characterize phenological variability with Corine Land Cover ecological habi

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Bedre, Jyoti S., and P. Lakshmi Prasanna. "Novel SR-RNN Classifier for Accurate Emotion Detection in Facial Analysis." Statistics, Optimization & Information Computing 13, no. 4 (2024): 1557–77. https://doi.org/10.19139/soic-2310-5070-2142.

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Facial Expression Recognition (FER) is crucial for understanding human emotions in fields like human-computer interaction and psychology. Despite advances in deep learning (DL), existing FER methods often struggle with noise, lighting variations, and inter-subject variability, leading to inaccurate emotion classification. This paper addresses these challenges by proposing a novel SwikyRelu Recurrent Neural Network (SR-RNN) classifier. The aim is to enhance FER accuracy while reducing computational complexity. The methodology involves a multi-step process starting with image pre-processing usin

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Adamson, George C. D., David J. Nash, and Stefan W. Grab. "Quantifying and reducing researcher subjectivity in the generation of climate indices from documentary sources." Climate of the Past 18, no. 5 (2022): 1071–81. http://dx.doi.org/10.5194/cp-18-1071-2022.

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Abstract. The generation of index-based series of meteorological phenomena, derived from narrative descriptions of weather and climate in historical documentary sources, is a common method to reconstruct past climatic variability and effectively extend the instrumental record. This study is the first to explicitly examine the degree of inter-rater variability in producing such series, a potential source of bias in index-based analyses. Two teams of raters were asked to produce a five-category annual rainfall index series for the same dataset, consisting of transcribed narrative descriptions of

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Hérent, Paul, Hélène Philippe, Elodie Ferreres, et al. "Promptable foundation model for automatic whole body RECIST measurement." Journal of Clinical Oncology 42, no. 16_suppl (2024): e13643-e13643. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e13643.

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e13643 Background: RECIST 1.1 is the gold standard for tumor evaluation in clinical trials and patient care. However, it faces challenges due to the subjective selection and measurement of lesions, notably because of inter-observer variability in the 20-30% range (1), which can potentially result in inaccuracies in therapeutic response classification (2). The main objective of this study is to assess the added value of a radiological foundation model to assist the radiologist to measure RECIST across different lesion topographies, using visual prompting. Methods: We use a promptable segmentati

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Kainz, Philipp, Michael Mayrhofer-Reinhartshuber, Roland Sedivy, and Helmut Ahammer. "Learning discriminative classification models for grading anal intraepithelial neoplasia." Current Directions in Biomedical Engineering 2, no. 1 (2016): 419–22. http://dx.doi.org/10.1515/cdbme-2016-0093.

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AbstractGrading intraepithelial neoplasia is crucial to derive an accurate estimate of pre-cancerous stages and is currently performed by pathologists assessing histopathological images. Inter- and intra-observer variability can significantly be reduced, when reliable, quantitative image analysis is introduced into diagnostic processes. On a challenging dataset, we evaluated the potential of learning a classifier to grade anal intraepitelial neoplasia. Support vector machines were trained on images represented by fractal and statistical features. We show that pursuing a learning-based grading

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Chen, Miaoju, Charitha B. Pattiaratchi, Anas Ghadouani, and Christine Hanson. "Seasonal and inter-annual variability of water column properties along the Rottnest continental shelf, south-west Australia." Ocean Science 15, no. 2 (2019): 333–48. http://dx.doi.org/10.5194/os-15-333-2019.

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Abstract. A multi-year ocean glider dataset, obtained along a representative cross-shelf transect along the Rottnest continental shelf, south-west Australia, was used to characterise the seasonal and inter-annual variability of water column properties (temperature, salinity, and chlorophyll fluorescence distribution). All three variables showed distinct seasonal and inter-annual variations that were related to local and basin-scale ocean atmosphere processes. Controlling influences for the variability were attributed to forcing from two spatial scales: (1) the local scale (due to Leeuwin Curre

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Kalpathy-Cramer, J., S. You, K. Keck, D. Erdogmus, M. F. Chiang, and E. Ataer-Cansizoglu. "Analysis of Underlying Causes of Inter-expert Disagreement in Retinopathy of Prematurity Diagnosis." Methods of Information in Medicine 54, no. 01 (2015): 93–102. http://dx.doi.org/10.3414/me13-01-0081.

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SummaryObjective: Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support sy

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Matthes, J. H., S. H. Knox, C. Sturtevant, O. Sonnentag, J. Verfaillie, and D. Baldocchi. "Predicting landscape-scale CO<sub>2</sub> flux at a pasture and rice paddy with long-term hyperspectral canopy reflectance measurements." Biogeosciences Discussions 12, no. 6 (2015): 5079–122. http://dx.doi.org/10.5194/bgd-12-5079-2015.

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Abstract. Measurements of hyperspectral canopy reflectance provide a detailed snapshot of information regarding canopy biochemistry, structure and physiology. In this study, we collected five years of repeated canopy hyperspectral reflectance measurements for a total of over 100 site visits within the flux footprints of two eddy covariance towers at a pasture and rice paddy in Northern California. The vegetation at both sites exhibited dynamic phenology, with significant inter-annual variability in the timing of seasonal patterns that propagated into inter-annual variability in measured hypers

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Kwon, Ye Jin, Cristina E. Stanciu, M. Katherine Philpott, and Christopher J. Ehrhardt. "Flow cytometry dataset for cells collected from touched surfaces." F1000Research 5 (October 7, 2016): 390. http://dx.doi.org/10.12688/f1000research.8338.2.

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‘Touch’ or trace cell mixtures submitted as evidence are a significant problem for forensic laboratories as they can render resulting genetic profiles difficult or even impossible to interpret. Optical signatures that distinguish epidermal cell populations from different contributors could facilitate the physical separation of mixture components prior to genetic analysis, and potentially the downstream production of single source profiles and/or simplified mixtures. This dataset comprises the results from antibody hybridization surveys using Human Leukocyte Antigen (HLA) and Cytokeratin (CK) p

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Jansen, M., M. Vernaz-Gris, C. DesJardins, et al. "Population pharmacokinetics (PPK) of eribulin mesylate in patients with locally advanced or metastatic breast cancer (MBC)." Journal of Clinical Oncology 27, no. 15_suppl (2009): 2524. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.2524.

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2524 Background: Eribulin mesylate (E7389) is a non-taxane microtubule dynamics inhibitor with a novel mechanism of action. A study was conducted to evaluate efficacy, safety and pharmacokinetics of eribulin at a dose of 1.4 mg/m2 for locally advanced or MBC in patients previously treated with an anthracycline, a taxane, and capecitabine. Methods: Eribulin was administered intravenously over 2–5 minutes at a dose of 1.4 mg/m2 on days 1 and 8 of a 21- day cycle to 291 patients. Four plasma samples were collected between 5 min and 120 hours after the first dose. Plasma eribulin concentrations we

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Wilder-Smith, Adrian Jonathan, Shan Yang, Thomas Weikert, et al. "Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network." Diagnostics 12, no. 5 (2022): 1045. http://dx.doi.org/10.3390/diagnostics12051045.

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Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016–01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the rema

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Orfanoz-Cheuquelaf, Andrea, Carlo Arosio, Alexei Rozanov, et al. "Tropospheric ozone column dataset from OMPS-LP/OMPS-NM limb–nadir matching." Atmospheric Measurement Techniques 17, no. 6 (2024): 1791–809. http://dx.doi.org/10.5194/amt-17-1791-2024.

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Abstract. A tropospheric ozone column (TrOC) dataset from the Ozone Mapping and Profiler Suite (OMPS) observations was generated by combining the retrieved total ozone column from OMPS – Nadir Mapper (OMPS-NM) and limb profiles from OMPS – Limb Profiler (OMPS-LP) data. All datasets were generated at the University of Bremen, and the TrOC product was obtained by applying the limb–nadir matching technique (LNM). The retrieval algorithm and a comprehensive analysis of the uncertainty budget are presented here. The OMPS-LNM-TrOC dataset (2012–2018) is analysed and validated through comparison with

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Pellicer-Valero, Oscar J., Victor Gonzalez-Perez, Juan Luis Casanova Ramón-Borja, et al. "Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks." Applied Sciences 11, no. 2 (2021): 844. http://dx.doi.org/10.3390/app11020844.

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Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, check

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Chrysanthou, Andreas, Amanda C. Maycock, Martyn P. Chipperfield, et al. "The effect of atmospheric nudging on the stratospheric residual circulation in chemistry–climate models." Atmospheric Chemistry and Physics 19, no. 17 (2019): 11559–86. http://dx.doi.org/10.5194/acp-19-11559-2019.

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Abstract. We perform the first multi-model intercomparison of the impact of nudged meteorology on the stratospheric residual circulation using hindcast simulations from the Chemistry–Climate Model Initiative (CCMI). We examine simulations over the period 1980–2009 from seven models in which the meteorological fields are nudged towards a reanalysis dataset and compare these with their equivalent free-running simulations and the reanalyses themselves. We show that for the current implementations, nudging meteorology does not constrain the mean strength of the stratospheric residual circulation a

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Kwon, Ye Jin, Cristina E. Stanciu, M. Katherine Philpott, and Christopher J. Ehrhardt. "Flow cytometry analysis of epithelial cell populations from touch samples using the BD Influx flow cytometry platform." F1000Research 5 (March 23, 2016): 390. http://dx.doi.org/10.12688/f1000research.8338.1.

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‘Touch’ or trace cell mixtures submitted as evidence are a significant problem for forensic laboratories as they can render resulting genetic profiles difficult or even impossible to interpret. Optical signatures that distinguish epidermal cell populations from different contributors could facilitate the physical separation of mixture components prior to genetic analysis, and potentially the downstream production of single source profiles and/or simplified mixtures. For this dataset, optical properties including forwards scatter (FSC), side scatter (SSC), and fluorescence emissions in the Allo

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