bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm
Authors: Caruso Damiano , De Santis Domenico , Del Gaudio Antonella , Guido Gisella , Zerunian Marta , Polici Michela , Valanzuolo Daniela , Pugliese Dominga , Persechino Raffaello , Cremona Antonio , Barbato Luca , Caloisi Andrea , Iannicelli Elsa , Laghi Andrea 
Published: September 2023

Abstract
Objectives
To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm.

Materials and methods
Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale.

Results
Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4–5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%.

Conclusions
DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm.

Clinical relevance statement
Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm.

Key Points
• Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities.
• Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality.
• Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.
Ketone body 3-hydroxybutyrate elevates cardiac output through peripheral vasorelaxation and enhanced cardiac contractility
Authors: Homilius Casper , Seefeldt Jacob Marthinsen , Axelsen Julie Sørensen , Pedersen Tina Myhre , Sørensen Trine Monberg , Nielsen Roni , Wiggers Henrik , Hansen Jakob , Matchkov Vladimir V. , Bøtker Hans Erik , Boedtkjer Ebbe 
Published: September 2023

AbstractThe ketone body 3-hydroxybutyrate (3-OHB) increases cardiac output and myocardial perfusion without affecting blood pressure in humans, but the cardiovascular sites of action remain obscure. Here, we test the hypothesis in rats that 3-OHB acts directly on the heart to increase cardiac contractility and directly on blood vessels to lower systemic vascular resistance. We investigate effects of 3-OHB on (a) in vivo hemodynamics using echocardiography and invasive blood pressure measurements, (b) isolated perfused hearts in Langendorff systems, and (c) isolated arteries and veins in isometric myographs. We compare Na-3-OHB to equimolar NaCl added to physiological buffers or injection solutions. At plasma concentrations of 2–4 mM in vivo, 3-OHB increases cardiac output (by 28.3±7.8%), stroke volume (by 22.4±6.0%), left ventricular ejection fraction (by 13.3±4.6%), and arterial dP/dtmax (by 31.9±11.2%) and lowers systemic vascular resistance (by 30.6±11.2%) without substantially affecting heart rate or blood pressure. Applied to isolated perfused hearts at 3–10 mM, 3-OHB increases left ventricular developed pressure by up to 26.3±7.4 mmHg and coronary perfusion by up to 20.2±9.5%. Beginning at 1–3 mM, 3-OHB relaxes isolated coronary (EC50=12.4 mM), cerebral, femoral, mesenteric, and renal arteries as well as brachial, femoral, and mesenteric veins by up to 60% of pre-contraction within the pathophysiological concentration range. Of the two enantiomers that constitute racemic 3-OHB, D-3-OHB dominates endogenously; but tested separately, the enantiomers induce similar vasorelaxation. We conclude that increased cardiac contractility and generalized systemic vasorelaxation can explain the elevated cardiac output during 3-OHB administration. These actions strengthen the therapeutic rationale for 3-OHB in heart failure management.
Association between maternal pre-pregnancy body mass index and offspring’s outcomes at 9 to 15 years of age
Authors: Lichtwald Alexander , Weiss Cathérine , Lange Anja , Ittermann Till , Allenberg Heike , Grabe Hans J. , Heckmann Matthias 
Published: September 2023

Abstract
Objective
Maternal pre-pregnancy underweight, overweight and obesity might increase the risk for worse short- and long-term outcome in the offspring. There is a need for further study into the relationship between maternal pre-pregnancy body mass index (BMI) and the combined outcome of physical development, state of health and social behavior in children. Question: Is maternal pre-pregnancy BMI associated with the child outcome in terms of physical development, state of health and social behavior (school and leisure time behavior) at the age of 9 to 15 years?

Methods
In the population-based birth cohort study Survey of Neonates in Pomerania (SNIP) children at the age 9–15 years and their families were re-examined by questionnaire-based follow-up. 5725 mother–child pairs were invited to SNiP-follow-up. This analysis is based on the recall fraction of 24.1% (n = 1379). Based on the maternal pre-pregnancy BMI (ppBMI), 4 groups were formed: underweight (ppBMI < 19 kg/m2, n = 117), normal weight (ppBMI 19–24.99 kg/m2, n = 913, reference), overweight (ppBMI 25–30 kg). /m2, n = 237) and obesity (ppBMI > 30 kg/m2, n = 109).

Results
In the multiple regression model, the BMI-z-score for children of mothers in the underweight group was −0.50 lower, and 0.50/1.07 higher in the overweight/obese group (p < 0.001) compared to reference at median age of 12 years. No differences were found in children of underweight mothers with regard to social behavior (interaction with friends and family), school and sports performance (coded from “very good” to “poor”), other leisure activities (watching television, using mobile phones, gaming), and health (occurrence of illnesses) compared to children of normal weight mothers. In contrast, maternal pre-pregnancy overweight and obesity were associated with lower school and sports performance, and higher screen time (smart phone, gaming, television) compared to children of normal weight mothers.

Conclusion
Maternal pre-pregnancy overweight and obesity but not underweight was negatively associated with school performance and leisure time behavior in the offspring at 9–15 years of age.
Intrinsic neural network dynamics underlying the ability to down-regulate emotions in male perpetrators of intimate partner violence against women
Authors: Amaoui Sofia , Marín-Morales Agar , Martín-Pérez Cristina , Pérez-García Miguel , Verdejo-Román Juan , Morawetz Carmen 
Published: September 2023

AbstractResearch has pointed to difficulties in emotion regulation as a risk factor for perpetrating intimate partner violence against women (IPVAW). While efforts have been made to understand the brain mechanisms underlying emotion regulation strategies such as reappraisal, little is known about the intrinsic neural dynamics supporting this strategy in male perpetrators. Resting-state functional magnetic resonance imaging was used to characterise the network dynamics underlying reappraisal. Spectral dynamic causal modelling was performed to examine the effective connectivity (EC) within a predefined reappraisal-related brain network. 26 men convicted for an IPVAW crime [male perpetrators] were compared to 29 men convicted of other crimes [other offenders] and 29 men with no criminal records [non-offenders]. The ability to down-regulate emotions in response to IPVAW stimuli was used as a covariate to explore its association with male perpetrators' EC. The analysis revealed that (1) compared to non-offenders, both convicted groups exhibited increased EC within prefrontal areas, enhanced EC from prefrontal to temporoparietal regions and decreased EC in the opposite direction; (2) male perpetrators compared to other offenders showed increased EC from temporoparietal to prefrontal regions and, increased EC from the supplementary motor area to frontal areas; (3) connections involving dorsolateral prefrontal cortex were found to be potential predictors of the ability to down-regulate emotions. The study provides a deeper characterisation of the brain architecture of the processes that underlie IPVAW. This knowledge could inform the work of adaptive emotion regulation strategies in intervention programmes for male perpetrators in order to reduce the high recidivism rates.
Maximum Matchings in Geometric Intersection Graphs
Authors: Bonnet Édouard , Cabello Sergio , Mulzer Wolfgang 
Published: September 2023

AbstractLetGbe an intersection graph ofngeometric objects in the plane. We show that a maximum matching inGcan be found in$$O\hspace{0.33325pt}(\rho ^{3\omega /2}n^{\omega /2})$$O(ρ3ω/2nω/2)time with high probability, where$$\rho $$ρis the density of the geometric objects and$$\omega >2$$ω>2is a constant such that$$n\times n$$n×nmatrices can be multiplied in$$O(n^\omega )$$O(nω)time. The same result holds for any subgraph of G, as long as a geometric representation is at hand. For this, we combine algebraic methods, namely computing the rank of a matrix via Gaussian elimination, with the fact that geometric intersection graphs have small separators. We also show that in many interesting cases, the maximum matching problem in a general geometric intersection graph can be reduced to the case of bounded density. In particular, a maximum matching in the intersection graph of any family of translates of a convex object in the plane can be found in$$O(n^{\omega /2})$$O(nω/2)time with high probability, and a maximum matching in the intersection graph of a family of planar disks with radii in$$[1, \Psi ]$$[1,Ψ]can be found in$$O\hspace{0.33325pt}(\Psi ^6\log ^{11}\hspace{-0.55542pt}n + \Psi ^{12 \omega } n^{\omega /2})$$O(Ψ6log11n+Ψ12ωnω/2)time with high probability.
Analyzing cultural relationships visual cues through deep learning models in a cross-dataset setting
Authors: Stacchio Lorenzo , Angeli Alessia , Lisanti Giuseppe , Marfia Gustavo 
Published: September 2023

AbstractTo study the evolution of specific cultures and times different kinds of pictures could be adopted. Family album photos may reveal socio-historical insights regarding those specific cultures and times. Along this path, this work addresses the problem of automatically dating an image by resorting to the analysis of an analog family album photo dataset. In particular, the IMAGO collection, which contains Italian photos shot in the 20th century, was considered. Thanks to the IMAGO dataset, it was possible to apply different deep learning-based architectures to date images belonging to photo albums without needing any other sources of information. In addition, we carried out cross-dataset experiments, which also involved models trained on American datasets, observing temporal shifts which may be due to known intercultural influences. We further explore such a possibility by qualitatively analyzing the cross-dataset interpretation of the trained deep-learning models with the Uniform Manifold Approximation and Projection (UMAP) algorithm. In conclusion, deep learning models revealed their potential in terms of possible applications to intercultural research, from different points of view.
IoT-based intelligent waste management system
Authors: Ahmed Mohammed M. , Hassanien Ehab , Hassanien Aboul Ella 
Published: September 2023

AbstractRecently, the population density in cities has increased at a higher pace, so waste generation is on the rise in most societies due to population growth. Given this concern, it would be highly important to manage waste generation. Intelligent city planning is necessary to improve the quality of city life and make cities more livable. This paper presents an intelligent waste management system (IWMS) in smart cities based on Internet of Things components like sensors, detectors, and actuators. IWMS contains three main phases. The first phase of the system is to adapt the low energy adaptive clustering hierarchy approach as an optimization process to better balance the energy consumption of smart waste bins (SBs), thus leading to extending the life of the smart waste network. The second phase is handling the missing values which are retrieved from SBs using an improved version of the k-nearest neighbor algorithm based on artificial hummingbird optimization (AHA), while the third phase presents an optimal energy-efficient route process for the routing of waste trucks that improves fuel efficiency and reduces the time to get an appropriate SB. According to the experimental results, the proposed system has achieved energy savings of 34% for the smart waste bin network. Moreover, compared to other systems, it has a lower mean error rate when generating missing values, and the results related to convergence and running time validate its superiority compared with other metaheuristic algorithms.
Approaching the Ultimate Limit in Measurement Precision with RASER NMR
Authors: Fleischer Simon , Lehmkuhl Sören , Lohmann Lars , Appelt Stephan 
Published: September 2023

AbstractRadio-frequency Amplification by Stimulated Emission of Radiation (RASER) is a promising tool to study nonlinear phenomena or measure NMR parameters with unprecedented precision. Magnetic fields, J-couplings, and chemical shifts can be recorded over long periods of time without the need for radiofrequency excitation and signal averaging. One key feature of RASER NMR spectroscopy is the improvement in precision, which grows with the measurement time $$T_{{\text{m}}}^{3/2}$$

T

m

3
/
2

, unlike conventional NMR spectroscopy, where the precision increases with $$T_{{\text{m}}}^{1/2}$$

T

m

1
/
2

. However, when detecting NMR signals over minutes to hours, using available NMR magnets (ppb homogeneity), the achieved frequency resolution will eventually be limited by magnetic field fluctuations. Here, we demonstrate that full compensation is possible even for open low-field electromagnets, where magnetic field fluctuations are intrinsically present (in the ppm regime). A prerequisite for compensation is that the spectrum contains at least one isolated RASER line to be used as a reference, and the sample experiences exclusively common magnetic field fluctuations, that is, ones that are equal over the entire sample volume. We discuss the current limits of precision for RASER NMR measurements for two different cases: The single-compartment RASER involving J-coupled modes, and the two-compartment RASER involving chemically shifted species. In the first case, the limit of measurable difference approaches the Cramér-Rao lower bound (CRLB), achieving a measurement precision $${\sigma }_{f}<{10}^{-4}$$

σ
f

<

10

-
4

Hz. In the second case, the measured chemical shift separation is plagued by independently fluctuating distant dipolar fields (DDF). The measured independent field fluctuation between the two chambers is in the order of tens of mHz. In both cases, new limits of precision are achieved, which paves the way for sub-mHz detection of NMR parameters, rotational rates, and non-linear phenomena such as chaos and synchrony.
Assessing the potential of low-transmissivity aquifers for aquifer thermal energy storage systems: a case study in Flanders (Belgium)
Authors: Tas Luka , Simpson David , Hermans Thomas 
Published: September 2023

AbstractThe Member States of the European Union pledged to reduce greenhouse gas emissions by 80–95% by 2050. Shallow geothermal systems might substantially contribute by providing heating and cooling in a sustainable way through seasonally storing heat and cold in the shallow ground (<200 m). When the minimum yield associated with the installation of a cost-effective aquifer thermal energy storage (ATES) system cannot be met, borehole thermal energy storage, relying mostly on the thermal conductivity of the ground, is proposed. However, for large-scale applications, this requires the installation of hundreds of boreholes, which entails a large cost and high disturbance of the underground. In such cases, ATES systems can nevertheless become interesting. This paper presents a case study performed on a Ghent University campus (Belgium), where the feasibility of ATES in an area with a low transmissivity was determined. The maximum yield of the aquifer was estimated at 5 m3/h through pumping tests. Although this low yield was attributed to the fine grain size of the aquifer, membrane filtering index tests and long-term injection tests revealed that the clogging risk was limited. A groundwater model was used to optimize the well placement. It was shown that a well arrangement in a checkerboard pattern was most effective to optimize the hydraulic efficiency while maintaining the thermal recovery efficiency of the ATES system. Hence, for large-scale projects, efficient thermal energy storage can also be achieved using a (more cost-effective) ATES system even in low-permeability sediments.
Physiological responses to the virtual reality-based Trier social stress test in patients with psychosis
Authors: Lee Bo Mi , Kang Chae Yeong , Li Ling , Rami Fatima Zahra , Chung Young-Chul 
Published: September 2023

Abstract
Objectives
The Trier social stress test (TSST) is one of the most reliable and widely used laboratory tests for evaluating the physiological stress response. We developed a virtual reality-based TSST (VR-TSST) and investigated the physiological responses to this test in patients with psychosis and healthy controls (HCs).

Methods
The participants comprised 60 patients with psychosis and 66 HCs. The VR-TSST consisted of three scenarios: a resting phase (baseline; 2 min), a job interview (5 min), and a mental arithmetic task (5 min). Blood cortisol levels were measured at baseline, during the test, and at 5–10 min and 30 min after the test. The skin conductance level, heart rate, and RR intervals were measured at baseline, during the job interview, and during the arithmetic task.

Results
The VR-TSST produced no discernible cortisol response in patients with psychosis compared to the HCs. However, a higher skin conductance level and heart rate and shorter RR intervals were found in the patients than in the HCs at baseline, during the job interview, and during the arithmetic task.

Conclusion
These findings suggest that the current version of the VR-TSST induces stronger autonomic and cardiovascular, but not endocrine, responses in patients with psychosis than in HCs. The VR-TSST could be a valuable tool to evaluate or train the stress response in patients with psychosis.
Impact of sustainable tax revenue and expenditure on the achievement of sustainable development goals in some selected African countries
Authors: Ajeigbe Kola Benson , Ganda Fortune , Enowkenwa Rawlings Obenembot 
Published: September 2023

AbstractThe study examined the impact of Sustainable Tax Revenue and Expenditure on the achievement of Sustainable Development Goals in African countries using secondary data. The dataset was extracted from the World Development Indicators database. The large gap between developed and developing countries when comparing the probability of them achieving the SDGs was the main motivation behind this study. Data retrieved from 45 countries comprised of both African and developed countries for the period 2010–2020 was analyzed using the Generalized Method of Moments technique. The results revealed that the coefficients of grants received, various forms of taxes, and other revenue have a positive effect on economic growth but a negative effect on poverty and unemployment for African and developed countries. This finding suggests that improvements in tax revenue generation, grants and other revenue accumulation across different sources boost economic performance and the welfare of individuals in the analyzed countries. The outcome is an indication that accumulating more grants from different sources will help to achieve sustainable development, improve financial stability, contributes to the economic growth and development in these countries. This study can guide policymakers, governments, international institutions, revenue bodies such as SARS and other stakeholders in their various planning and other decision-making endeavors. Governments and other policymakers must ensure the efficient generation and sustainable utilization of revenue generated from taxes and other revenues to spur the growth and development of their countries. They should have Growth-Sustainability-Oriented Fiscal Adjustment Programs and Sustainable Government Expenditure that can help push and redirect governments to achieve the SDGs in Africa.
A thermosensitive gel matrix for bioreactor-assisted in-cell NMR of nucleic acids and proteins
Authors: Dzurov Matej , Pospíšilová Šárka , Krafčíková Michaela , Trantírek Lukáš , Vojtová Lucy , Ryneš Jan 
Published: September 2023

AbstractIntroducing the flow through the bioreactor has revolutionized in-cell NMR spectroscopy by prolonging the measurement time available to acquire spectral information about biomacromolecules in metabolically active cells. Bioreactor technology relies on immobilizer matrices, which secure cells in the active volume of the NMR coil and enable uniform perfusion of the growth medium, supplying fresh nutrients to the cells while removing toxic byproducts of their metabolism. The main drawbacks of commonly used matrices include the inability to recover intact cells post-measurement for additional analyses and/or requirements for specific operating temperatures. Here, we report on the development and characterization of a set of thermosensitive and nontoxic triblock copolymers based on poly(D,L-lactide)-b-poly(ethylene glycol)-b-poly(D,L-lactide) (PLA-PEG-PLA). Here, we show for the first time that these copolymers are suitable as immobilizer matrices for the acquisition of in-cell NMR spectra of nucleic acids and proteins over a commonly used sample temperature range of 15–40 °C and, importantly, allow recovery of cells after completion of in-cell NMR spectra acquisition. We compared the performances of currently used matrices in terms of cell viability (dye exclusion assays), cellular metabolism (1D 31P NMR), and quality of in-cell NMR spectra of two model biomacromolecules (hybrid double-stranded/i-motif DNA and ubiquitin). Our results demonstrate the suitability and advantages of PLA-PEG-PLA copolymers for application in bioreactor-assisted in-cell NMR.