bluuebunny/crossref_metadata_2025_split huggingface dataset:
An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus
Authors: Chen Qingquan , Hu Haiping , She Yuanyu , He Qing , Huang Xinfeng , Shi Huanhuan , Cao Xiangyu , Zhang Xiaoyang , Xu Youqiong 
Published: January 2024

AbstractType 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.
Toxicity of nitrophenolic pollutant 4-nitroguaiacol to terrestrial plants and comparison with its non-nitro analogue guaiacol (2-methoxyphenol)
Authors: Adamek Maksimiljan , Kavčič Anja , Debeljak Marta , Šala Martin , Grdadolnik Jože , Vogel-Mikuš Katarina , Kroflič Ana 
Published: January 2024

AbstractPhenols, and especially their nitrated analogues, are ubiquitous pollutants and known carcinogens which have already been linked to forest decline. Although nitrophenols have been widely recognized as harmful to different aquatic and terrestrial organisms, we could not find any literature assessing their toxicity to terrestrial plants. Maize (monocot) and sunflower (dicot) were exposed to phenolic pollutants, guaiacol (GUA) and 4-nitroguaiacol (4NG), through a hydroponics system under controlled conditions in a growth chamber. Their acute physiological response was studied during a two-week root exposure to different concentrations of xenobiotics (0.1, 1.0, and 10 mM). The exposure visibly affected plant growth and the effect increased with increasing xenobiotic concentration. In general, 4NG affected plants more than GUA. Moreover, sunflower exhibited an adaptive response, especially to low and moderate GUA concentrations. The integrity of both plant species deteriorated during the exposure: biomass and photochemical pigment content were significantly reduced, which reflected in the poorer photochemical efficiency of photosystem II. Our results imply that 4NG is taken up by sunflower plants, where it could enter a lignin biosynthesis pathway.
Development and preliminary validation of a PROS scale for Chinese bladder cancer patients with abdominal stoma
Authors: Lu Jingya , Hong Hui , Xiong Zhufeng , Zhang Yonghui , Zeng Fanyan , Xie Zhiqin , Yu Mengjia , Liu Xiaohan , Li Huiting , Xian Daming , Shen Junjie 
Published: January 2024

AbstractBladder cancer is a common malignant tumor, and patients who have undergone radical cystectomy and urinary diversion require a lifelong abdominal stoma. This greatly affects their physiological, psychological, and social well-being. However, there is currently a lack of a self-assessment outcome scale specifically designed for bladder cancer patients with abdominal stomas. Therefore, we developed and validated a self-assessment outcome scale (PROS-BCAS) for Chinese bladder cancer patients with abdominal stomas. The scale was initially developed through literature research and expert consultation, and it comprised four dimensions: physiological, psychological, social, and treatment, with a total of 66 items. After item analysis, 44 items were retained. We collected scale data from 382 patients to examine its validity and reliability. The results showed that the PROS-BCAS scale had good content validity (S-CVI/Ave = 0.992), construct validity (KMO > 0.6), and discriminant validity (correlation coefficient 0.404–0.870). The Cronbach's alpha coefficients (0.801–0.954), test–retest reliability (0.778–0.956), and split-half reliability (0.896–0.977) all demonstrated good internal consistency for each dimension and the overall scale. The study demonstrated that the PROS-BCAS scale is a reliable and valid tool for accurately assessing the health-related quality of life of bladder cancer patients with abdominal stomas, providing reference for developing individualized clinical care plans.
Mechanical properties of rock under uniaxial compression tests of different control modes and loading rates
Authors: He Zhilei , Wu Guoli , Zhu Jun 
Published: January 2024

AbstractTo study the influence of control mode and loading rate on mechanical property of rock, uniaxial compression tests of four types of rocks (gray sandstone, red sandstone, mudstone and granite) are carried out under axial strain control mode and lateral strain control mode respectively. The characteristics of complete stress and strain curves, strength, brittleness and failure modes are analyzed. The results show that control mode has little influence on the pre-peak deformation, stress thresholds, while it has a greater impact on post-peak stress and strain curve, which makes the post-peak deformation stable and controllable, and shows the feature of Class II behavior. With lateral loading rates decrease, post-peak stress and strain curves appear more and more obvious fluctuations in the post-peak stage, and the time required for rock failure increases sharply, but the lateral control rate has little effect on the brittleness of rock. The failure mode of rock samples under axial strain control mode is mainly splitting failure, while that under lateral strain control is gradually changed to shear failure. The smaller the lateral loading control rate is, the more obvious the characteristics of shear failure is. Compared with uniaxial compression tests, under high confining pressure, the lateral dilation deformation is restricted, so peak strength is larger and stress redistribution can be better adjusted and stress fluctuation reduced accordingly in post-peak stage. The research results are an effective supplement to rate-dependent property of rocks and can provide some reference for deformation and strength characteristics research of brittle rock under lateral control mode.
An appraisal of lung computer tomography in very early anti-inflammatory treatment of two different ovine ARDS phenotypes
Authors: Wildi Karin , Colombo Sebastiano Maria , McGuire Daniel , Ainola Carmen , Heinsar Silver , Sato Noriko , Sato Kei , Liu Keibun , Bouquet Mahé , Wilson Emily , Passmore Margaret , Hyslop Kieran , Livingstone Samantha , Di Feliciantonio Marianna , Strugnell Wendy , Palmieri Chiara , Suen Jacky , Li Bassi Gianluigi , Fraser John 
Published: January 2024

AbstractMortality and morbidity of Acute Respiratory Distress Syndrome (ARDS) are largely unaltered. A possible new approach to treatment of ARDS is offered by the discovery of inflammatory subphenotypes. In an ovine model of ARDS phenotypes, matching key features of the human subphenotypes, we provide an imaging characterization using computer tomography (CT). Nine animals were randomized into (a) OA (oleic acid, hypoinflammatory; n = 5) and (b) OA-LPS (oleic acid and lipopolysaccharides, hyperinflammatory; n = 4). 48 h after ARDS induction and anti-inflammatory treatment, CT scans were performed at high (H) and then low (L) airway pressure. After CT, the animals were euthanized and lung tissue was collected. OA-LPS showed a higher air fraction and OA a higher tissue fraction, resulting in more normally aerated lungs in OA-LPS in contrast to more non-aerated lung in OA. The change in lung and air volume between H and L was more accentuated in OA-LPS, indicating a higher recruitment potential. Strain was higher in OA, indicating a higher level of lung damage, while the amount of lung edema and histological lung injury were largely comparable. Anti-inflammatory treatment might be beneficial in terms of overall ventilated lung portion and recruitment potential, especially in the OA-LPS group.
A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images
Authors: Sampath Kanimozhi , Rajagopal Sivakumar , Chintanpalli Ananthakrishna 
Published: January 2024

AbstractBone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages. Prior detection of these lumps or masses can reduce the risk of death and treat bone cancer early. The goal of this current study is to utilize image processing techniques and deep learning-based Convolution neural network (CNN) to classify normal and cancerous bone images. Medical image processing techniques, like pre-processing (e.g., median filter), K-means clustering segmentation, and, canny edge detection were used to detect the cancer region in Computer Tomography (CT) images for parosteal osteosarcoma, enchondroma and osteochondroma types of bone cancer. After segmentation, the normal and cancerous affected images were classified using various existing CNN-based models. The results revealed that AlexNet model showed a better performance with a training accuracy of 98%, validation accuracy of 98%, and testing accuracy of 100%.
Knowledge and attitudes toward the use of anabolic–androgenic steroids among physical education university students: a cross-sectional study from Palestine
Authors: Hattab Suhaib , Saleh Bashar , Qasarweh Laith , Draidi Mahmoud , Zyoud Sa’ed H. 
Published: January 2024

AbstractThe use of androgenic–anabolic steroids (AASs) has increased in recent years, especially among athletes, due to their effect on body shape and performance. These agents could have serious side effects on this highly susceptible population, which tends to use these substances frequently to promote muscle growth and physical performance. Therefore, this study aimed to evaluate the knowledge and attitudes toward the use of anabolic androgenic steroids among physical education university students in Palestine. A cross-sectional study of physical education students from November 2020 to January 2021 was conducted using an electronic questionnaire. The main outcome was to measure the level of knowledge and use of the AAS. A total of 380 students were included. The mean age of the students was 21 years (SD = 4.2), and the study participants were distributed almost equally according to sex. Approximately a quarter of the students were smokers. Eighty percent (80%) of the study participants were from the West Bank, while the remaining 20% were from Jerusalem and 48 territories. Furthermore, most of the students lived with their families; half lived in cities, approximately 152 (40%) lived in villages, and only 29 (8%) lived in camps. The average level of knowledge of the AAS was 2.95 out of 8 (37/100), with a median of three. Furthermore, only 36 (10%) of the participants had satisfactory knowledge, scoring 80% or more. Regarding the use of AAS, 11 (2.9%) participants, all males, confirmed that they were currently using AAS. Additionally, approximately 28 (7%) had previously used them, while 30 (8%) planned to try them in the future. Overall, 221 (58%) patients were confirmed to use vitamins and minerals. Our study showed that most of the participants had a substantial lack of information on the potential side effects of AAS, while the level of use was comparable with that of other populations.
Real-time motion force-feedback system with predictive-vision for improving motor accuracy
Authors: Matsui Ryo , Aoyama Tadayoshi , Kato Kenji , Hasegawa Yasuhisa 
Published: January 2024

AbstractMany haptic guidance systems have been studied over the years; however, most of them have been limited to predefined guidance methods. Calculating guidance according to the operator’s motion is important for efficient human motor adaptation and learning. In this study, we developed a system that haptically provides guidance trajectory by sequential weighting between the operator’s trajectory and the ideal trajectory calculated from a predictive-vision system. We investigated whether motion completion with a predictive-vision system affects human motor accuracy and adaptation in time-constrained goal-directed reaching and ball-hitting tasks through subject experiments. The experiment was conducted with 12 healthy participants, and all participants performed ball-hitting tasks. Half of the participants get forceful guidance from the proposed system in the middle of the experiment. We found that the use of the proposed system improved the operator’s motor performance. Furthermore, we observed a trend in which the improvement in motor performance using this system correlated with that after the washout of this system. These results suggest that the predictive-vision system effectively enhances motor accuracy to the target error in dynamic and time-constrained reaching and hitting tasks and may contribute to facilitating motor learning.
Sex modulation of faces prediction error in the autistic brain
Authors: Lacroix Adeline , Harquel Sylvain , Mermillod Martial , Garrido Marta , Barbosa Leonardo , Vercueil Laurent , Aleysson David , Dutheil Frédéric , Kovarski Klara , Gomot Marie 
Published: January 2024

AbstractRecent research suggests that autistic females may have superior socio-cognitive abilities compared to autistic males, potentially contributing to underdiagnosis in females. However, it remains unclear whether these differences arise from distinct neurophysiological functioning in autistic males and females. This study addresses this question by presenting 41 autistic and 48 non-autistic adults with a spatially filtered faces oddball paradigm. Analysis of event-related potentials from scalp electroencephalography reveal a neurophysiological profile in autistic females that fell between those of autistic males and non-autistic females, highlighting sex differences in autism from the initial stages of face processing. This finding underscores the urgent need to explore neurophysiological sex differences in autism and encourages efforts toward a better comprehension of compensation mechanism and a clearer definition of what is meant by camouflaging.
Hubble Asteroid Hunter
Authors: García-Martín Pablo , Kruk Sandor , Popescu Marcel , Merín Bruno , Stapelfeldt Karl R. , Evans Robin W. , Carry Benoit , Thomson Ross 
Published: January 2024

Context. Determining the size distribution of asteroids is key to understanding the collisional history and evolution of the inner Solar System.
Aims. We aim to improve our knowledge of the size distribution of small asteroids in the main belt by determining the parallaxes of newly detected asteroids in the Hubble Space Telescope (HST) archive and subsequently their absolute magnitudes and sizes.
Methods. Asteroids appear as curved trails in HST images because of the parallax induced by the fast orbital motion of the spacecraft. Taking into account the trajectory of this latter, the parallax effect can be computed to obtain the distance to the asteroids by fitting simulated trajectories to the observed trails. Using distance, we can obtain the absolute magnitude of an object and an estimation of its size assuming an albedo value, along with some boundaries for its orbital parameters.
Results. In this work, we analyse a set of 632 serendipitously imaged asteroids found in the ESA HST archive. Images were captured with the ACS/WFC and WFC3/UVIS instruments. A machine learning algorithm (trained with the results of a citizen science project) was used to detect objects in these images as part of a previous study. Our raw data consist of 1031 asteroid trails from unknown objects, not matching any entries in the Minor Planet Center (MPC) database using their coordinates and imaging time. We also found 670 trails from known objects (objects featuring matching entries in the MPC). After an accuracy assessment and filtering process, our analysed HST asteroid set consists of 454 unknown objects and 178 known objects. We obtain a sample dominated by potential main belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution log N(H > H0) ∝ α log(H0) confirms the previously reported slope change for 15 < H < 18, from α ≈ 0.56 to α ≈ 0.26, maintained in our case down to absolute magnitudes of around H ≈ 20, and therefore expanding the previous result by approximately two magnitudes.
Conclusions. HST archival observations can be used as an asteroid survey because the telescope pointings are statistically randomly oriented in the sky and cover long periods of time. They allow us to expand the current best samples of astronomical objects at no extra cost in regard to telescope time.
A new method of measuring Forbush decreases
Authors: Dumbović M. , Kramarić L. , Benko I. , Heber B. , Vršnak B. 
Published: January 2024

Context. Forbush decreases (FDs) are short-term depressions in the galactic cosmic ray (GCR) flux and one of the common signatures of coronal mass ejections (CMEs) in the heliosphere. They often show a two-step profile, the second one associated with the CME’s magnetic structure. This second step can be described by the recently developed analytical FD model for flux ropes (FRs) – ForbMod.
Aims. The aim of this study is to utilise ForbMod to develop a best-fit procedure to be applied on FR-related FDs as a convenient measurement tool. Our motivation is to develop a best-fit procedure that can be applied to a data series from an arbitrary detector. Thus, the basic procedure would facilitate measurement estimation of the magnitude of the FR-related FD, with the possibility of being adapted for the energy response of a specific detector for a more advanced analysis.
Methods. The non-linear fitting was performed by calculating all possible ForbMod curves constrained within the FR borders to the designated dataset and minimising the mean square error (MSE). In order to evaluate the performance of the ForbMod best-fit procedure, we used synthetic measurements produced by calculating the theoretical ForbMod curve for a specific example CME and then applying various effects to the data to mimic the imperfection of the real measurements. We also tested the ForbMod best-fit function on the real data, measured by detector F of the SOHO/EPHIN instrument on a sample containing 30 events, all of which have a distinct FD corresponding to the magnetic obstacle. The extraction of FD profiles (from the onset to the end) was performed manually by an observer, whereby we applied two different versions of border selection and assigned a quality index to each event.
Results. We do not find notable differences between events marked by a different quality index. For events with a selection of two different borders, we find that the best fit applied on extended interplanetary coronal mass ejection (ICME) structure borders results in a slightly larger MSE and differences compared to the traditional method due to a larger scatter of the data points. We find that the best-fit results can visually be categorised into six different FD profile types. Although some profiles do not show a visually pleasing FD, the ForbMod best-fit function still manages to find a solution with a relatively small MSE.
Conclusions. Overall, we find that the ForbMod best-fit procedure performs similar to the traditional algorithm-based observational method, but with slightly smaller values for the FD amplitude, as it’s taking into account the noise in the data. Furthermore, we find that the best-fit procedure has an advantage compared to the traditional method as it can estimate the FD amplitude even when there is a data gap at the onset of the FD.
Unveiling hidden companions in post-common-envelope binaries: A robust strategy and uncertainty exploration
Authors: Giuppone Cristian A. , Gramajo Luciana V. , Gianuzzi Emmanuel , Ramos Matías N. , Cuello Nicolás , Hinse Tobias C. 
Published: January 2024

Context. Some post-common-envelope binaries (PCEBs) are binary stars with short periods that exhibit significant period variations over long observational time spans. These eclipse timing variations (ETVs) are most likely to be accounted for by the presence of an unseen massive companion, potentially of planetary or substellar nature, and the light-travel time (LTT) effect. The existence of such companions challenges our current understanding of planetary formation and stellar evolution.
Aims. In this study, our main objective is to describe the diversity of compatible nontransit companions around PCEBs and explore the robustness of the solutions by employing tools for uncertainty estimation. We select the controversial data of the QS Vir binary star, which previous studies have suggested hosts a planet.
Methods. We employ a minimizing strategy, using genetic algorithms to explore the global parameter space followed by refinement of the solution using the simplex method. We evaluate errors through the classical Markov chain Monte Carlo (MCMC) approach and discuss the error range for parameters, considering the 1σ values obtained from the minimization.
Results. Our results highlight the strong dependence of ETV models for close binaries on the dataset used, which leads to relatively loose constraints on the parameters of the unseen companion. We find that the shape of the O – C curve is influenced by the dataset employed. We propose an alternative method to evaluate errors on the orbital fits based on a grid search surrounding the best-fit values, obtaining a wider range of plausible solutions that are compatible with goodness-of-fit statistics. We also analyze how the parameter solutions are affected by the choice of the dataset, and find that this system continuously changes the compatible solutions as new data are obtained from eclipses.
Conclusions. The best-fit parameters for QS Vir correspond to a low-mass stellar companion (57.71 Mjup ranging from ~40 to ~64 Mjup) on an eccentric orbit (e = 0.91−0.17+0.07) with a variety of potential periods (P = 16.69−0.42+0.47 yr.). Most solutions within 1σ exhibit regular orbits, despite their high eccentricity. Additional observations are required to accurately determine the period and other parameters of the unseen companion. In this context, we propose that a fourth body should not be modeled to fit the data, unless new observations considerably modify the computed orbital parameters. This methodology can be applied to other evolved binary stars suspected of hosting companions.