bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accuracy of fractal analysis and PI-RADS assessment of prostate magnetic resonance imaging for prediction of cancer grade groups: a clinical validation study
Authors: Michallek Florian , Huisman Henkjan , Hamm Bernd , Elezkurtaj Sefer , Maxeiner Andreas , Dewey Marc 
Published: December 2021

Abstract

Objectives
Multiparametric MRI with Prostate Imaging Reporting and Data System (PI-RADS) assessment is sensitive but not specific for detecting clinically significant prostate cancer. This study validates the diagnostic accuracy of the recently suggested fractal dimension (FD) of perfusion for detecting clinically significant cancer.

Materials and methods
Routine clinical MR imaging data, acquired at 3 T without an endorectal coil including dynamic contrast-enhanced sequences, of 72 prostate cancer foci in 64 patients were analyzed. In-bore MRI-guided biopsy with International Society of Urological Pathology (ISUP) grading served as reference standard. Previously established FD cutoffs for predicting tumor grade were compared to measurements of the apparent diffusion coefficient (25th percentile, ADC25) and PI-RADS assessment with and without inclusion of the FD as separate criterion.

Results
Fractal analysis allowed prediction of ISUP grade groups 1 to 4 but not 5, with high agreement to the reference standard (κFD = 0.88 [CI: 0.79–0.98]). Integrating fractal analysis into PI-RADS allowed a strong improvement in specificity and overall accuracy while maintaining high sensitivity for significant cancer detection (ISUP > 1; PI-RADS alone: sensitivity = 96%, specificity = 20%, area under the receiver operating curve [AUC] = 0.65; versus PI-RADS with fractal analysis: sensitivity = 95%, specificity = 88%, AUC = 0.92, p < 0.001). ADC25 only differentiated low-grade group 1 from pooled higher-grade groups 2–5 (κADC = 0.36 [CI: 0.12–0.59]). Importantly, fractal analysis was significantly more reliable than ADC25 in predicting non-significant and clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75, p < 0.001). Diagnostic accuracy was not significantly affected by zone location.

Conclusions
Fractal analysis is accurate in noninvasively predicting tumor grades in prostate cancer and adds independent information when implemented into PI-RADS assessment. This opens the opportunity to individually adjust biopsy priority and method in individual patients.

Key Points
• Fractal analysis of perfusion is accurate in noninvasively predicting tumor grades in prostate cancer using dynamic contrast-enhanced sequences (κFD = 0.88).
• Including the fractal dimension into PI-RADS as a separate criterion improved specificity (from 20 to 88%) and overall accuracy (AUC from 0.86 to 0.96) while maintaining high sensitivity (96% versus 95%) for predicting clinically significant cancer.
• Fractal analysis was significantly more reliable than ADC25 in predicting clinically significant cancer (AUCFD = 0.96 versus AUCADC = 0.75).
Feasibility of artificial intelligence–supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein — a retrospective observational study
Authors: Fervers Philipp , Fervers Florian , Kottlors Jonathan , Lohneis Philipp , Pollman-Schweckhorst Philip , Zaytoun Hasan , Rinneburger Miriam , Maintz David , Große Hokamp Nils 
Published: December 2021

Abstract
Objectives
To demonstrate the feasibility of an automated, non-invasive approach to estimate bone marrow (BM) infiltration of multiple myeloma (MM) by dual-energy computed tomography (DECT) after virtual non-calcium (VNCa) post-processing.

Methods
Individuals with MM and monoclonal gammopathy of unknown significance (MGUS) with concurrent DECT and BM biopsy between May 2018 and July 2020 were included in this retrospective observational study. Two pathologists and three radiologists reported BM infiltration and presence of osteolytic bone lesions, respectively. Bone mineral density (BMD) was quantified CT-based by a CE-certified software. Automated spine segmentation was implemented by a pre-trained convolutional neural network. The non-fatty portion of BM was defined as voxels > 0 HU in VNCa. For statistical assessment, multivariate regression and receiver operating characteristic (ROC) were conducted.

Results
Thirty-five patients (mean age 65 ± 12 years; 18 female) were evaluated. The non-fatty portion of BM significantly predicted BM infiltration after adjusting for the covariable BMD (p = 0.007, r = 0.46). A non-fatty portion of BM > 0.93% could anticipate osteolytic lesions and the clinical diagnosis of MM with an area under the ROC curve of 0.70 [0.49–0.90] and 0.71 [0.54–0.89], respectively. Our approach identified MM-patients without osteolytic lesions on conventional CT with a sensitivity and specificity of 0.63 and 0.71, respectively.

Conclusions
Automated, AI-supported attenuation assessment of the spine in DECT VNCa is feasible to predict BM infiltration in MM. Further, the proposed method might allow for pre-selecting patients with higher pre-test probability of osteolytic bone lesions and support the clinical diagnosis of MM without pathognomonic lesions on conventional CT.

Key Points
• The retrospective study provides an automated approach for quantification of the non-fatty portion of bone marrow, based on AI-supported spine segmentation and virtual non-calcium dual-energy CT data.
• An increasing non-fatty portion of bone marrow is associated with a higher infiltration determined by invasive biopsy after adjusting for bone mineral density as a control variable (p = 0.007, r = 0.46).
• The non-fatty portion of bone marrow might support the clinical diagnosis of multiple myeloma when conventional CT images are negative (sensitivity 0.63, specificity 0.71).
Bi-exponential modelling of $$W^{^{\prime}}$$ reconstitution kinetics in trained cyclists
Authors: Chorley Alan , Bott Richard P. , Marwood Simon , Lamb Kevin L. 
Published: December 2021

Abstract
Purpose
The aim of this study was to investigate the individual $$W^{^{\prime}}$$

W

′

reconstitution kinetics of trained cyclists following repeated bouts of incremental ramp exercise, and to determine an optimal mathematical model to describe $$W^{^{\prime}}$$

W

′

reconstitution.

Methods
Ten trained cyclists (age 41 ± 10 years; mass 73.4 ± 9.9 kg; $$\dot{V}{\text{O}}_{2\max }$$

V
˙

O

2
max

58.6 ± 7.1 mL kg min−1) completed three incremental ramps (20 W min−1) to the limit of tolerance with varying recovery durations (15–360 s) on 5–9 occasions. $$W^{^{\prime}}$$

W

′

reconstitution was measured following the first and second recovery periods against which mono-exponential and bi-exponential models were compared with adjusted R2 and bias-corrected Akaike information criterion (AICc).

Results
A bi-exponential model outperformed the mono-exponential model of $$W^{^{\prime}}$$

W

′

reconstitution (AICc 30.2 versus 72.2), fitting group mean data well (adjR2 = 0.999) for the first recovery when optimised with parameters of fast component (FC) amplitude = 50.67%; slow component (SC) amplitude = 49.33%; time constant (τ)FC = 21.5 s; τSC = 388 s. Following the second recovery, W′ reconstitution reduced by 9.1 ± 7.3%, at 180 s and 8.2 ± 9.8% at 240 s resulting in an increase in the modelled τSC to 716 s with τFC unchanged. Individual bi-exponential models also fit well (adjR2 = 0.978 ± 0.017) with large individual parameter variations (FC amplitude 47.7 ± 17.8%; first recovery: (τ)FC = 22.0 ± 11.8 s; (τ)SC = 377 ± 100 s; second recovery: (τ)FC = 16.3.0 ± 6.6 s; (τ)SC = 549 ± 226 s).

Conclusions
W′ reconstitution kinetics were best described by a bi-exponential model consisting of distinct fast and slow phases. The amplitudes of the FC and SC remained unchanged with repeated bouts, with a slowing of W′ reconstitution confined to an increase in the time constant of the slow component.
On the reliability of behavioral measures of cognitive control: retest reliability of task-inhibition effect, task-preparation effect, Stroop-like interference, and conflict adaptation effect
Authors: Schuch Stefanie , Philipp Andrea M. , Maulitz Luisa , Koch Iring 
Published: December 2021

AbstractThis study examined the reliability (retest and split-half) of four common behavioral measures of cognitive control. In Experiment 1 (N = 96), we examined N – 2 task repetition costs as a marker of task-level inhibition, and the cue-stimulus interval (CSI) effect as a marker of time-based task preparation. In Experiment 2 (N = 48), we examined a Stroop-like face-name interference effect as a measure of distractor interference control, and the sequential congruency effect (“conflict adaptation effect”) as a measure of conflict-triggered adaptation of cognitive control. In both experiments, the measures were assessed in two sessions on the same day, separated by a 10 min-long unrelated filler task. We observed substantial experimental effects with medium to large effect sizes. At the same time, split-half reliabilities were moderate, and retest reliabilities were poor, for most measures, except for the CSI effect. Retest reliability of the Stroop-like effect was improved when considering only trials preceded by congruent trials. Together, the data suggest that these cognitive control measures are well suited for assessing group-level effects of cognitive control. Yet, except for the CSI effect, these measures do not seem suitable for reliably assessing interindividual differences in the strength of cognitive control, and therefore are not suited for correlational approaches. We discuss possible reasons for the discrepancy between robustness at the group level and reliability at the level of interindividual differences.
Profiles of competence development in mathematics and reading in early secondary education
Authors: Freund Micha-Josia , Gnambs Timo , Lockl Kathrin , Wolter Ilka 
Published: December 2021

AbstractThis article examines the development of reading and mathematical competence in early secondary education and aims at identifying distinct profiles of competence development. Since reading and mathematical competences are highly correlated both cross-sectionally and longitudinally, we expected to find a generalized profile of competence development with students developing parallel in reading and mathematical competences. Moreover, previous research confirmed individuals’ specific focus on one of the two domains, for example, in their interest, self-concept, or motivation. Also, differences in competence levels between both domains were found in cross-sectional studies. Therefore, we hypothesized that additional to the generalized profile, there are specialized profiles of competence development with students developing distinctively faster in one of the two domains. To identify both types of profiles, latent growth mixture modeling was used on a sample of 5,301 students entering secondary education from the German National Educational Panel Study. To demonstrate the robustness of the results, these analyses were repeated using different model specifications and subgroups with higher homogeneity (with students belonging to the highest track, i.e., “Gymnasium”). The results indicate only small to non-existent specialized profiles of competence development in all conditions. This finding of roughly parallel development of reading and mathematical competences throughout early secondary education indicates that potential specializations are less important at this point in students’ educational careers.
Event-driven temporal models for explanations - ETeMoX: explaining reinforcement learning
Authors: Parra-Ullauri Juan Marcelo , García-Domínguez Antonio , Bencomo Nelly , Zheng Changgang , Zhen Chen , Boubeta-Puig Juan , Ortiz Guadalupe , Yang Shufan 
Published: December 2021

AbstractModern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI)-based systems. This is the case of Reinforcement Learning (RL), where autonomous agents learn through trial-and-error how to find good solutions to a problem. Thus, the underlying decision-making criteria may become opaque to users that interact with the system and who may require explanations about the system’s reasoning. Available work for eXplainable Reinforcement Learning (XRL) offers different trade-offs: e.g. for runtime explanations, the approaches are model-specific or can only analyse results after-the-fact. Different from these approaches, this paper aims to provide an online model-agnostic approach for XRL towards trustworthy and understandable AI. We present ETeMoX, an architecture based on temporal models to keep track of the decision-making processes of RL systems. In cases where the resources are limited (e.g. storage capacity or time to response), the architecture also integrates complex event processing, an event-driven approach, for detecting matches to event patterns that need to be stored, instead of keeping the entire history. The approach is applied to a mobile communications case study that uses RL for its decision-making. In order to test the generalisability of our approach, three variants of the underlying RL algorithms are used: Q-Learning, SARSA and DQN. The encouraging results show that using the proposed configurable architecture, RL developers are able to obtain explanations about the evolution of a metric, relationships between metrics, and were able to track situations of interest happening over time windows.
Changing depositional environments in the semi-restricted Late Jurassic Lemeš Basin (Outer Dinarides; Croatia)
Authors: Vitzthum Michael A. J. , Gawlick Hans-Jürgen , Sachsenhofer Reinhard F. , Neumeister Stefan 
Published: December 2021

AbstractThe up to 450 m-thick Upper Jurassic Lemeš Formation includes organic-rich deep-water (max. ~ 300 m) sedimentary rocks deposited in the Lemeš Basin within the Adriatic Carbonate Platform (AdCP). The Lemeš Formation was investigated regarding (1) bio- and chemostratigraphy, (2) depositional environment, and (3) source rock potential. A multi-proxy approach—microfacies, Rock–Eval pyrolysis, maceral analysis, biomarkers, and stable isotope ratios—was used. Based on the results, the Lemeš Formation is subdivided from base to top into Lemeš Units 1–3. Deposition of deep-water sediments was related to a late Oxfordian deepening event causing open-marine conditions and accumulation of radiolarian-rich wackestones (Unit 1). Unit 2, which is about 50 m thick and Lower early Kimmeridgian (E. bimammatumtoS. platynota,ammonite zones) in age, was deposited in a restricted, strongly oxygen-depleted basin. It consists of radiolarian pack- and grainstones with high amounts of kerogen type II-S organic matter (avg. TOC 3.57 wt.%). Although the biomass is predominantly marine algal and bacterial in origin, minor terrestrial organic matter that was transported from nearby land areas is also present. The overlying Unit 3 records a shallowing of the basin and a return to oxygenated conditions. The evolution of the Lemeš Basin is explained by buckling of the AdCP due to ophiolite obduction and compressional tectonics in the Inner Dinarides. Lemeš Unit 2 contains prolific oil-prone source rocks. Though thermally immature at the study location, these rocks could generate about 1.3 t of hydrocarbon per m2surface area when mature.
Improved renal cancer prognosis among users of drugs targeting renin-angiotensin system
Authors: Eskelinen Tommi , Veitonmäki Thea , Kotsar Andres , Tammela Teuvo L. J. , Pöyhönen Antti , Murtola Teemu J. 
Published: December 2021

Abstract
Purpose
We explored renal cell cancer (RCC) survival among users of antihypertensive medication as hypertension is proposed to be a risk factor for RCC and ACE-inhibitors and angiotensin receptor blockers (ARBs) have been associated with improved prognosis of RCC.

Methods
Finnish cohort of 13,873 participants with RCC diagnosed between 1995–2012 was formed from three national databases. RCC cases were identified from Finnish Cancer Registry, medication usage from national prescription database and co-morbidities from Care Registry of Healthcare. Logistic regression was used to calculate odds ratios for metastatic tumor extent at the time of diagnosis. Risk of RCC specific death after diagnosis was analyzed using Cox regression adjusted for tumor clinical characteristics.

Results
A total of 5,179 participants died of RCC during the follow-up. No risk association was found for metastatic tumor extent for any drug group. ACE-inhibitors, but no other drug group were associated with decreased risk of RCC specific death overall (HR 0.88, 95% CI 0.82–0.95) compared to non-users. In time-dependent analysis high-dose use of ACE-inhibitors (392 Defined Daily Dose (DDD)/year), HR 0.54, 95% CI 0.45–0.66) and ARBs (786.1 DDD/year, HR 0.66, 95% CI 0.50–0.87) associated with improved RCC survival. No information of TNM-classification or tobacco smoking was available.

Conclusion
ACE-inhibitors and ARBs in high dose associated with improved RCC specific survival. This may reflect overall benefit of treating hypertension with medication targeting renin-angiotensin system (RAS) system among RCC patients. Further studies are needed to explore the role of RAS in RCC.
Identifying and correcting biases in localized downscaling estimates of daily precipitation return values
Authors: Risser Mark D. , Feldman Daniel R. , Wehner Michael F. , Pierce David W. , Arnold Jeffrey R. 
Published: December 2021

AbstractExtreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.
An Experimental Analysis of the Effects of Imperfect Compliance on Technology Adoption
Authors: Vidal-Meliá Lidia , Arguedas Carmen , Camacho-Cuena Eva , Zofío José Luis 
Published: December 2021

AbstractWe present the results of an experimental investigation on incentives to adopt cleaner abatement technologies in the presence of imperfect compliance. We consider two emission control instruments—emission taxes and tradable permits—as well as different combinations of the inspection probability and fine for non-compliance, which can result in full or weak enforcement scenarios. We review and qualify existing theoretical predictions in several ways and find the main result is that allowing for weak enforcement causes tax evasion, reductions in permit prices and lower adoption rates of cleaner abatement technologies. As a result, there are increases in aggregate emissions. Finally, treatments with tradable permits under weak enforcement encounter insufficient trading.
Teacher noticing and its growth toward expertise: an expert–novice comparison with pre-service and in-service secondary mathematics teachers
Authors: Bastian Anton , Kaiser Gabriele , Meyer Dennis , Schwarz Björn , König Johannes 
Published: December 2021

AbstractAlthough strong references to expertise in different theoretical approaches to teacher noticing have been made in the last decades, empirical knowledge about the development of teacher noticing from novice to expert level is scarce. The present study aims to close this research gap by comparing three different groups of mathematics teachers with different degrees of professional teaching experience—pre-service teachers at the master’s level, early career teachers, and experienced teachers—using data sampled in the frame of the research program from the Teacher Education and Development Study in Mathematics (TEDS-M). Furthermore, the construct of teacher noticing is assessed in a differentiated way by analyzing different noticing facets. Findings confirm that three facets of teacher noticing can be empirically distinguished—perception of important classroom events, their interpretation, and decisions regarding further developments. The results reveal a considerable increase in professional noticing between master’s students and practicing teachers. However, in contrast to other studies, among examples from East Asia, a stagnation or decrease in professional noticing between early career teachers and experienced teachers could be observed. Overall, the study highlights the cultural dependency of expertise development regarding teachers’ noticing.
Application of Phytotests to Study of Environmental Safety of Biologicaly Synthetised Au and Au/ZnO Nanoparticles Using Tanacetum parthenium Extract
Authors: Szymanski Marcin , Dobrucka Renata 
Published: December 2021

AbstractDue to their small sizes and high reactivity, nanoparticles have a completely different toxicity profile than larger particles, and it is difficult to predict their potential ecological impact. There is a need for broad ecotoxicological studies of nanomaterials in order to specify their environmental impact and ensure safe application of nanotechnology products. In this work, we have assessed the toxicity of Au and Au/ZnO metal nanoparticles obtained with the use of Tanacetum parthenium (herba) extract. The obtained nanoparticles were characterized by UV–Vis spectrophotometry (UV–VIS), Transmission electron microscopy (TEM), Atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR). In order to assess the toxicity of biologically synthesized nanoparticles, we used seeds of various plants: Lepidium sativum, Linum flavum, Zea mays, Salvia hispanica-chia, Lupinus angustifolius, Petroselinum crispum subsp. Crispum, Beta vulgaris, Phaseolus vulgaris. The in vitro phytotests showed that gold nanoparticles at a specific range of concentrations for all plants stimulated their growth. The highest growth activity was exhibited by the solution at the concentration of 0.300 mg/ml towards corn (Aw ≈ − 135 ± 16) and flax (Aw ≈ − 44 ± 10). Only for parsley the IC50 was determined at 0.57 mg/ml, but solutions at the concentration of 0.030 to 0.150 mg/ml also stimulated plant growth. Au/ZnO had a toxic effect at all concentrations applied in the study.