bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of long axial field-of-view (LAFOV) PET/CT for post-treatment dosimetry in Yttrium-90 radioembolization of liver tumors: a comparative study with conventional SPECT imaging
Authors: Zeimpekis Konstantinos G. , Sari Hasan , Gözlügöl Nasir , Achangwa Ngwe Rawlings , Shi Kuangyu , Schindewolf Marc , Afshar-Oromieh Ali , Rominger Axel , Seifert Robert 
Published: December 2024

Abstract

Purpose
Long axial field-of-view (LAFOV) positron emission tomography/computed tomography (PET/CT) scanners enable high sensitivity and wide anatomical coverage. Therefore, they seem ideal to perform post-selective internal radiation therapy (SIRT) 90Y scans, which are needed, to confirm that the dose is delivered to the tumors and that healthy organs are spared. However, it is unclear to what extent the use of LAFOV PET is feasible and which dosimetry approaches results in accurate measurements.

Methods
In this retrospective analysis, a total number of 32 patients was included (median age 71, IQR 14), which had hepatocellular carcinoma, cholangiocarcinoma, or liver metastases. All patients underwent SIRT, and the post-therapy scan was acquired on a single photon emission computed tomography/computed tomography (SPECT/CT) and a LAFOV Biograph Quadra PET/CT with a 20-minute acquisition time. Post-treatment dosimetry, regarding the tumor, whole-liver and lung (LMD) absorbed dose was done using an organ-wise (Simplicit90Y) and a voxel-wise approach (HERMIA Dosimetry) which used a semi-Monte Carlo algorithm. The lung shunt fraction (LSF) was also measured using the voxel-wise approach and compared to the planned.

Results
The planning, post-treatment SPECT and PET (SPECTpre, SPECTpost, PETpost) median tumor doses based on the organ-wise dosimetry were 276.0 Gy (200.0–330.0 Gy), 232.0 Gy (158.5–303.5 Gy) and 267.5 Gy (182.5–370.8 Gy). In contrast, the median voxel-wise PETpost dose was significantly smaller than the planned SPECTpre (152.5 Gy (94.8–223.8 Gy); p < 0.00001). Moreover, the median tumor absorbed dose at 90% (D90) of the tumor volume was significantly higher in SPECTpost compared with PETpost (123.5 Gy; 81.5–180.0 vs. 30.5 Gy; 11.3-106.3; p < 0.00001). The PETpost measured LSF was significantly lower compared to the planned SPECTpre (0.89%; 0.4–1.3% vs. 2.3%; 1.5–3.6%; p < 0.0001). Similarly, the measured PETpost median LMD was considerably lower to the planned SPECTpre (1.2 Gy; 0.6–2.3 vs. 2.5 Gy; 1.4–4.7; p < 0.0001).

Conclusion
LAFOV PET enabled the direct measurement of post therapy lung dose and tumor doses that correlated well with the planned treatment doses. However, current voxel-wise-based tumor dosimetry seems to be inaccurate for LAFOV PET. In addition, dose volume histogram-based metrics also significantly underestimate the delivered dose. Therefore, improved dosimetry tools are needed for reliable voxel-wise 90Y dosimetry to leverage the sensitivity and spatial resolution of LAFOV PET scanners.
Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
Authors: Horat Nina , Klerings Sina , Lerch Sebastian 
Published: December 2024

Abstract
Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting, where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production. Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather, and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions. However, ensemble prediction systems are known to exhibit systematic errors, and thus require post-processing to obtain accurate and reliable probabilistic forecasts. The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy: not applying any post-processing at all; post-processing only the irradiance predictions before the conversion; post-processing only the solar power predictions obtained from the model chain; or applying post-processing in both steps. In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S., we develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance (GHI) and solar power generation. Further, we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain. Our results indicate that postprocessing substantially improves the solar power generation forecasts, in particular when post-processing is applied to the power predictions. The machine learning methods for post-processing slightly outperform the statistical methods, and the direct forecasting approach performs comparably to the post-processing strategies.
DSTSPYN: a dynamic spatial-temporal similarity pyramid network for traffic flow prediction
Authors: Wang Xing , Chen Feifei , Jin Biao , Lin Mingwei , Zou Fumin , Zeng Ruihao 
Published: December 2024

Abstract
Traffic flow prediction plays a crucial role in intelligent transportation systems as it enables effective control and management of urban traffic. However, existing methods that based on Graph Convolutional Networks (GCNs) primarily utilize local neighborhood information for message passing, resulting in limited perception of global structures. Additionally, it is also a challenge to extract spatial-temporal similarity features due to the constraints of graph structures. To address these issues, we propose a novel traffic flow prediction model based on Dynamic Spatial-Temporal Similarity Pyramid Network (DSTSPYN). Our model employs a spatial-temporal pyramid architecture, which dynamically adjusts the weights of central, edge, and global spatial-temporal features using an enhanced attention mechanism. Furthermore, it captures dynamic temporal dependencies at different scales through pyramid gated convolution. Meanwhile, the spatial similarity features of different time steps can be extracted through the spatial-temporal global similarity (STGS) module. We evaluate our model on four public transportation datasets and demonstrate that the DSTSPYN model outperforms several baseline methods in terms of prediction accuracy. It effectively captures the dynamic spatial-temporal correlations of the road network and edge node features, making it well-suited for long-term traffic flow prediction.
Perceived Riskiness and Problem Gambling Across Different Forms of Gambling: A Focus on 'Soft' Gambling
Authors: Fiedor David , Charvát Miroslav , Kovařík Filip , Frajer Jindřich , Aigelová Eva , Šerý Miloslav 
Published: December 2024

AbstractGambling encompasses a wide variety of activities, and the structural characteristics of each form contribute to its potential risk. However, the literature does not fully agree on the risk levels of certain gambling forms. In this study, we classify less risky gambling forms (soft forms) based on public perceptions of their riskiness. We examine the link between gambling experience and problem gambling prevalence. A survey was conducted in a model region of the Czech Republic, a post-socialist country with high gambling availability, with N = 2,498 respondents. A typology of gambling forms (lotteries, betting, and casino games) was created based on perceived risk similarities. Lotteries are the most frequently played gambling form, with 86.3% reporting lifetime participation. Among those who exclusively engage in lottery-type forms, 15 percentage points more women than men participated in the last year, and the gap widens to 31 points over a lifetime. Forms of gambling perceived as more risky show a lower proportion of non-problem gamblers, both for recent and lifetime participation. Furthermore, individuals who gambled within the past month or year are at higher risk of developing gambling problems compared to those whose gambling experiences were less recent.
Modelling of a high-speed railway pantograph with non-linear head-suspension mechanism and parameter optimisation
Authors: Gil J. , Tur M. , Gregori S. , Pedrosa A. M. , Fuenmayor F. J. 
Published: December 2024

AbstractAccurate pantograph models are crucial to obtain reliable simulations of the pantograph-catenary dynamic interaction. Non-linearities can make it a challenge to find a model design that fits the dynamic behaviour of the real pantograph. In this work, we propose a novel pantograph model including a particular non-linear mechanism, whose dynamic behaviour significantly affects the pantograph response. This mechanism is composed of two rigid bars and an oblique pre-stressed spring with dry friction and exhibits a complex non-linear behaviour. The objective is to design a model capable of reproducing the behaviour of the real pantograph, which is represented by the dataset of dynamic measurements in the pantograph tests. From a proper parametric representation of the model, the optimisation of the parameters by a genetic algorithm has achieved a good agreement with the experimental data. The resulting optimised model behaves similarly to the real pantograph for a wide range of excitations that vary in type, amplitude and position. Since the higher number of simulations performed in the optimisation makes it necessary to use an efficient simulation tool, we propose an efficient strategy to integrate systems with Coulomb’s friction.
Silicon Nanoparticles (SiO2 NPs) Boost Drought Tolerance in Grapevines by Enhancing Some Morphological, Physiological, and Biochemical Traits
Authors: Daler Selda , Kaya Ozkan , Canturk Sevil , Korkmaz Nesrin , Kılıç Tuğba , Karadağ Ahmet , Hatterman‑Valenti Harlene 
Published: December 2024

AbstractDrought is a significant abiotic stress that adversely affects plant growth, development, and metabolic processes, posing a global threat to food security. In recent years, nanotechnology has emerged as a promising strategy to mitigate environmental challenges and has been tested on various plant species; however, its application to grapevines remains largely unexplored. This study aims to investigate the potential of silicon nanoparticles (SiO2 NPs) to modulate morphological, physiological, and biochemical parameters in grapevine saplings (5 BB/Crimson Seedless, 41 B/Crimson Seedless, and 1103 P/Crimson Seedless) under drought stress conditions. Saplings were treated with foliar applications of different concentrations of SiO2 NP solutions (0 (control), 1, 10, and 100 ppm) under well-irrigated (90–100% field capacity) and drought stress (40–50% field capacity) conditions. The results indicated that the application of SiO2 NPs at 10 ppm concentration enhanced growth parameters (such as leaf area, leaf number, shoot fresh and dry weight, root fresh and dry weight) and chlorophyll content under both well-irrigated and drought conditions. Additionally, there was an improvement in leaf relative water content (RWC) and stomatal conductance, correlating with increased protein content. Under drought conditions, oxidative stress indicators, including drought index, leaf temperature, membrane injury index, hydrogen peroxide (H2O2) content, and malondialdehyde (MDA) levels, showed a significant decrease. Concurrently, proline content, total phenolic compounds, and activities of antioxidant enzymes (SOD, CAT, and APX), which increased under drought stress, were reduced in the presence of 10 ppm SiO2 NPs, suggesting a mitigation of oxidative stress. To comprehensively evaluate the effects of SiO2 NP concentrations, irrigation regimes, and rootstock/variety combinations, correlation analysis, principal component analysis (PCA), and hierarchical clustering heatmap analysis were performed. Collectively, the findings suggest that the observed drought-induced oxidative stress tolerance in grapevine saplings in the presence of 10 ppm SiO2 NPs may result from the influence of antioxidant systems leading to a balanced redox potential. These results support the argument that SiO2 NPs play a crucial role in enhancing drought tolerance, with implications for managing drought stress in other agricultural crops as well.
Riesz Energy with a Radial External Field: When is the Equilibrium Support a Sphere?
Authors: Chafaï Djalil , Matzke Ryan W. , Saff Edward B. , Vu Minh Quan H. , Womersley Robert S. 
Published: December 2024

AbstractWe consider Riesz energy problems with radial external fields. We study the question of whether or not the equilibrium measure is the uniform distribution on a sphere. We develop general necessary and general sufficient conditions on the external field that apply to powers of the Euclidean norm as well as certain Lennard – Jones type fields. Additionally, in the former case, we completely characterize the values of the power for which a certain dimension reduction phenomenon occurs: the support of the equilibrium measure becomes a sphere. We also briefly discuss the relationship between these problems and certain constrained optimization problems. Our approach involves the Frostman characterization, the Funk–Hecke formula, and the calculus of hypergeometric functions.
NPGPT: natural product-like compound generation with GPT-based chemical language models
Authors: Sakano Koh , Furui Kairi , Ohue Masahito 
Published: December 2024

AbstractNatural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the intricate structures of these compounds present challenges in terms of structure determination and synthesis, particularly compared to the efficiency of high-throughput screening of synthetic compounds. In recent years, deep learning-based methods have been applied to the generation of molecules. In this study, we trained chemical language models on a natural product dataset and generated natural product-like compounds and verified the performance of the generated compounds as a drug candidate library. The results showed that the distribution of the compounds generated was similar to that of natural products. We also evaluated the effectiveness of the generated compounds as drug candidates. Our method can be used to explore the vast chemical space and reduce the time and cost of drug discovery of natural products.
Updated imaging markers in cerebral amyloid angiopathy: What radiologists need to know
Authors: Tanaka Fumine , Maeda Masayuki , Kishi Seiya , Kogue Ryota , Umino Maki , Ishikawa Hidehiro , Ii Yuichiro , Shindo Akihiro , Sakuma Hajime 
Published: December 2024

AbstractCerebral amyloid angiopathy (CAA) is an age-related small vessel disease pathologically characterized by the progressive accumulation of amyloid-beta (Aβ) peptide in cerebrovascular walls, affecting both cortical and leptomeningeal vessels. Amyloid deposition results in fragile vessels, which may lead to lobar intracerebral hemorrhage (ICH) and cognitive impairment. To evaluate the probability and severity of CAA, the imaging markers depicted on CT and MRI techniques are crucial, as brain pathological examination is highly invasive. Although the Boston criteria have established diagnostic value and have been updated to version 2.0, due to an aging population, the patients with CAA should also be assessed for their risk of future ICH or cognitive impairment. Furthermore, an increased awareness of CAA is essential when introducing anticoagulants for infarct in elderly patients or anti-amyloid antibodies for Alzheimer’s disease, as these may worsen CAA-related hemorrhagic lesions. However, the radiological literature on CAA has not been comprehensively updated. Here, we review the imaging markers of CAA and clinical significance. We also discuss the clinical and imaging characteristics of CAA-related inflammation, amyloid-related imaging abnormalities, and iatrogenic-CAA.
Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [18F]-FDG-PET-radiomic features
Authors: Nakajo Masatoyo , Hirahara Daisuke , Jinguji Megumi , Idichi Tetsuya , Hirahara Mitsuho , Tani Atsushi , Takumi Koji , Kamimura Kiyohisa , Ohtsuka Takao , Yoshiura Takashi 
Published: December 2024

Abstract
Objectives
This study evaluates the effectiveness of machine learning (ML) models that incorporate clinical and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-radiomic features for predicting outcomes in gallbladder cancer patients.

Materials and methods
The study analyzed 52 gallbladder cancer patients who underwent pre-treatment [18F]-FDG-PET/CT scans between January 2011 and December 2021. Twenty-seven patients were assigned to the training cohort between January 2011 and January 2018, and the data randomly split into training (70%) and validation (30%) sets. The independent test cohort consisted of 25 patients between February 2018 and December 2021. Eight clinical features (T stage, N stage, M stage, Union for International Cancer Control [UICC] stage, histology, tumor size, carcinoembryonic antigen level, and carbohydrate antigen 19-9 level) and 49 radiomic features were used to forecast progression-free survival (PFS). Three feature selection methods were applied including the univariate statistical feature selection test method, least absolute shrinkage and selection operator Cox regression method and recursive feature elimination method, and two ML algorithms (Cox proportional hazard and random survival forest [RSF]) were employed. Predictive performance was assessed using the concordance index (C-index).

Results
Two clinical variables (UICC stage, N stage) and three radiomic features (total lesion glycolysis, grey-level size-zone matrix_grey level non-uniformity and grey-level run-length matrix_run-length non-uniformity) were identified by the statistical feature selection method as significant for PFS prediction. The RSF model incorporating these features demonstrated strong predictive performance, with C-indices above 0.80 in both training and testing sets (training 0.81, testing 0.89). This model almost closely matched the actual and predicted progression timelines with a low mean absolute error of 1.435, a median absolute error of 0.082, and a root mean square error of 2.359.

Conclusion
This study highlights the potential of using ML approaches with clinical and pre-treatment [18F]-FDG-PET radiomic data for predicting the prognosis of gallbladder cancer.
SWI brush sign of cerebral parenchymal veins in central nervous system diseases
Authors: kishi Seiya , Maeda Masayuki , Kogue Ryota , Tanaka Fumine , Umino Maki , Toma Naoki , Sakuma Hajime 
Published: December 2024

AbstractBrush sign (BS) was first reported as prominent hypointensity of deep medullary veins and subependymal veins on T2*-weighted images at 3 T MRI in patients with acute stroke in the territory of the middle cerebral artery. Subsequently, BS in central nervous system (CNS) diseases such as moyamoya disease, cerebral venous thrombosis, and Sturge–Weber syndrome was also described on susceptibility-weighted imaging (SWI), and the clinical implications of BS were discussed. The purpose of this review is to demonstrate BS on SWI in various CNS diseases and its mechanisms in the above-mentioned diseases. We also explain the clinical implications of this finding in each disease.
Prediction of grading of ovarian endometrioid carcinoma using conventional MRI features
Authors: Kawaguchi Masaya , Kato Hiroki , Furui Tatsuro , Isobe Masanori , Noda Yoshifumi , Hyodo Fuminori , Miyazaki Tatsuhiko , Matsuo Masayuki 
Published: December 2024

Abstract
Objective
The purpose of this study was to evaluate MRI findings of ovarian endometrioid carcinoma (OEC) as a predictor of histological grade.

Materials and methods
This study included 60 patients with histopathologically confirmed OEC (20, 30, and 10 with grades 1, 2, and 3, respectively). Clinical and MRI results were retrospectively reviewed. We compared the following parameters between the three grades: age, tumor markers, presence of uterine corpus cancer, bilaterality, configuration, peritoneal dissemination, abnormal ascites, signal intensities of cystic and solid components, tumor size, and apparent diffusion coefficient (ADC) values of solid components.

Results
T1-hyperintense cysts were more common in grade 1 than in grades 2–3 OEC (80% vs. 60%, vs. 40%, p < 0.05). The signal intensity ratio between the cystic components with the largest solid component and muscle (1.49 vs. 1.08 vs. 0.98, p < 0.05) was higher in grade 1 than in grades 2–3 OEC. Necrosis within solid components was less common in grade 1 than in grades 2–3 OEC (31% vs. 68% vs. 88%, p < 0.05), and the ADC values of solid components were higher in grade 1 than in grades 2–3 OEC (1.10 vs. 0.99 vs. 0.79 × 10−3 mm2/sec, p < 0.05). There were no significant differences in other factors.

Conclusion
On T1-weighted images, grade 1 OEC showed a higher signal intensity in the cystic components than grades 2–3 OEC. Necrosis and lower ADC values were more frequently observed in grades 2–3 than in grade 1 OEC.
The Matricial Szegő Mapping from the Perspective of Schur Analysis
Authors: Fritzsche Bernd , Kirstein Bernd , Mädler Conrad 
Published: December 2024

Abstract
We study the interrelations between non-negative Hermitian

$${q\times q}$$

q
×
q

measures on the unit circle and non-negative Hermitian

$${q\times q}$$

q
×
q

measures on a symmetric interval

$${[-2r,2r]}$$

[
-
2
r
,
2
r
]

where

$$r \in (0, \infty )$$

r
∈
(
0
,
∞
)

. Since both types of non-negative Hermitian

$${q\times q}$$

q
×
q

measures are uniquely determined by their sequences of Fourier coefficients and power moments, respectively, we also express these interrelations in terms of moment sequences. Our approach significantly utilizes previous investigations by the authors on the inner structure of these sequences. Specifically, previous work by the authors employed Schur analysis methods to describe the intrinsic structure of

$$\mathbb {T}$$

T

-non-negative definite sequences of complex

$${q\times q}$$

q
×
q

matrices and

$${[\alpha ,\beta ]}$$

[
α
,
β
]

-non-negative definite sequences of complex

$${q\times q}$$

q
×
q

matrices. The primary aim of this paper is to uncover the relationships between corresponding parameters of sequences connected by a matrix version of the Szegő mapping, which was introduced by Szegő in the context of orthogonal polynomials. In essence, a complete concordance between both objects is achieved in the symmetric case.