bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short- and long-term outcomes of adrenalectomy for Primary Aldosteronism in a single UK centre: Rear Mirror View.
Authors: Abdel-Aziz Tarek , Abdelsalam Alaa , Chung Teng-Teng , Srirangalin Umasuthan , Hurel Steven , Conway Gerard , Baldeweg Stephanie E. , Kurzawinski Tom R 
Published: August 2024

Abstract

Purpose: Primary Aldosteronism (PA) is the commonest cause of secondary hypertension and can be cured by unilateral adrenalectomy. We report the short and long-term outcomes after adrenalectomy performed at a single UK centre over a period of 24 years.
Methods: Retrospective analysis of biochemical(potassium, aldosterone, renin and ARR) radiological (CT/MRI, AVS, nuclear scans) and clinical (surgical complications, blood pressure, number of antihypertensive medications) short and long terms outcomes in patients who underwent adrenalectomy for PA between 1998 and 2021. Standardised PASO and Clavien-Dindo criteria to assess biochemical, clinical and surgical outcomes were used.
Results: 82 patients treated with adrenalectomy for PA over a 24 year period, short term follow up data (within 3 months from surgery) was available for 82 patients (M45, F37, mean age 51.7 years), 24 of them were followed at least 60 months( range 60 to 72 months) 77 (93.9%) patients had laparoscopic surgery (1 conversion). 7 patients had postoperative complications classified as Clavien-Dindo II(4), IIIa(1) and IVa(2). Median LOS was 2.5 days (1-12).
Complete and partial clinical success was achieved in 29% and 58.3% and 41.7% and 45.8% of patients in short and long term respectively. Clinical benefit was observed in 88% of patients. Complete biochemical success was achieved in 95.8% of patients in short and long term.
Conclusion: Unilateral adrenalectomy in patients with PA achieved clinical benefit in 88% and biochemical cure in almost all of them. Our data suggest that these benefits persisted for at least 5 years.
Experimental Study on Laser Cutting Process of Simulated Fast Reactor Fuel Rods
Authors: Li Tianchi , Mo Zengliang , Chen Qi , Zhou Jia , Cao Zhi , Guo Jianhua , Yang Zhongyuan , Tang Chunwei , Zhang Hongmei , Xiao Tao , Li Wensi , Ming Yuzhou , Liu Fang , Yan Taihong , Mi Gaoyang , Zheng Weifang 
Published: August 2024

Abstract

The cladding of fast reactor fuel rod is fabricated using stainless steel, which, owing to its ductility, presents challenges such as increased tool wear and diminished cut quality when mechanically sheared. In light of technological advancements, laser cutting has emerged as a superior method due to its flexibility and high efficiency. The quality of laser-cut fuel rod significantly influences subsequent processing of short segments. This study systematically analyzes the impact of various laser cutting parameters on the cutting quality of simulated fast reactor fuel rod. Optimization of cutting speed, focal position, power, and gas pressure has substantially improved cutting effectiveness. Experimental findings indicate that a combination of 1 m/min cutting speed, focal position ranging from − 20 mm to -25 mm, power between 7200 W and 9600 W, and 10 MPa gas pressure achieves the optimal balance between efficiency and quality, providing crucial insights for the efficient cutting of fast reactor fuel rod.
Visual short-term memory in action and non-action video game players: A focus on short and long delay intervals
Authors: Pavan Andrea , Yilmaz Seyma Koc , Kafaligonul Hulusi , Föcker Julia , Greenlee Mark W. 
Published: August 2024

Abstract

Previous research suggests that individuals who play action video games tend to outperform non-action video game players in various cognitive functions. In the current study, we compare the precision of visual short-term memory for the direction of coherent motion among action video game players (AVGPs) and age and gender-matched non-action video game players (NAVGPs). Participants were tasked with remembering the direction of up to four random dot kinematograms (RDKs) presented in a sequence, with the number of RDKs varying randomly between one and four in each trial. After the sequence, a probe number appeared on the screen, indicating which RDK the participant needed to reproduce. Following either a brief (0.5 s) or a longer interval (3 s), participants reproduced the direction of a designated RDK. To reach a specific performance level with a single RDK stimulus, participants underwent initial training. The training results revealed that AVGPs required slightly fewer training blocks than NAVGPs to reach the desired performance level. While the two groups did not differ in precision for the short delay, AVGPs performed the task more precisely than NAVGPs when tasked with reproducing the motion direction of a single RDK after a long interval. Using a complementary model-fitting methodology, we investigated the distribution of memory resources within the two groups. Our analysis revealed that, in contrast to the AVGPs, NAVGPs exhibited higher neural costs relative to the behavioral advantages linked to memory encoding. This was particularly evident during the long retention interval, resulting in NAVGPs allocating a reduced amount of memory-related resources in this condition. Future training studies could explore the integration of models that account for the allocation of memory resources, balancing the behavioral benefits of encoding precision against the neural costs involved.
Study on mechanical properties of particle-reinforced polymer composites
Authors: Qin Chen , Gao Wei , Guo Zhangxin , Niu Weijing , Chai Gin Boay , Zheng Jiajun 
Published: August 2024

Abstract

Acrylic resin is widely used in fields such as aerospace, military, coatings, and electronic equipment due to its excellent physical and chemical properties. The mechanical properties of its polymer composite materials are influenced by various factors such as component volume fractions, types, and treatment methods. In this paper, the mechanical properties of particle-reinforced acrylic composites and the factors affecting them are investigated. The effect patterns of the contents of glass beads, quartz sand, and heavy calcium carbonate on the tensile, bending, and compressive properties of the polymer composites were experimentally investigated. Combined with the digital image correlation (DIC) to observe the dynamic damage images of the specimens, the full strain field information was analyzed and the local strain distribution was studied. A finite element model of polymer composite materials was established, and the numerical simulation results were consistent with the experimental results. The progressive damage and failure process of the composite materials was analyzed. This research result can guide the experimental design and application of particle reinforced acrylic composite materials.
Radius solution of plastic zone in soft rock tunnel surroundings considering joint damage
Authors: Hu Taotao , Tu Peng , Zhao Yulong 
Published: August 2024

Abstract

Based on the Hohai model, a creep constitutive model of viscoelastic-plastic soft rock with joint fracture damage is proposed to solve the rheological failure problem of soft rock tunnel surrounding rock. Based on the proposed creep model of rock with joint damage and the current analytical analysis method of circular tunnel, the analytical formulas of viscoelastic-viscoplastic stress and displacement of circular tunnel with joint damage surrounding rock are derived. ABAQUS finite element software is used to simulate the excavation of surrounding rock with different joint angles. The numerical solution is compared with the analytical solution to verify the accuracy of the theoretical formula. The results show that when the joint inclination is 45°, the displacement values of the two are 16.36mm and 16.87mm respectively, and the error of the two is 3.02%. The theoretical calculation is in good agreement with the numerical simulation results, thus verifying the rationality of the derived analytical formula of viscoelastic-viscoplastic stress and displacement of the soft rock tunnel with joint damage.
Unpacking the Complexity of Corporate Sustainability: Green Innovation's Mediating Role in Risk Management and Performance
Authors: Al-Nimer Munnther 
Published: August 2024

Abstract

This study investigates the relationships among Corporate Sustainability Development (CSD), Enterprise Risk Management Performance (ERMP), and Green Innovation (GI) in the Jordanian manufacturing sector. Using data from 97 companies listed on the Amman Stock Exchange, we employed structural equation modeling to examine these complex dynamics. Our findings reveal that CSD negatively impacts both ERMP and enterprise sustainable performance in the short term, challenging conventional wisdom. However, CSD strongly promotes GI, which in turn positively influences ERMP while negatively affecting short-term performance. GI acts as a significant mediator, positively mediating the CSD-ERMP relationship and negatively mediating the CSD-performance link. These results extend the sustainability paradox concept to emerging economies and highlight the critical role of green innovation in balancing sustainability initiatives with risk management and performance outcomes. The study suggests that firms may experience initial disruptions when implementing sustainability practices, but these initiatives can drive innovation within organizations. Based on these findings, we recommend that managers in emerging economies adopt a long-term perspective when implementing sustainability initiatives and develop more flexible risk management systems. Policymakers should consider supportive frameworks to help firms navigate the tensions between sustainability, innovation, and short-term performance. Future research should employ longitudinal designs to capture the dynamic nature of these relationships and explore potential moderating factors such as firm size or industry-specific characteristics.
Socio-demographic and clinical predictors of post-acute, mid-and long-term psychological sequelae of COVID-19: A two-year cross-sectional investigation on 1317 patients at the University Hospital of Verona
Authors: Perlini Cinzia , Marcanti Maddalena , Zonta Marco Pattaro , Mazzi Maria Angela , Mason Anna , Apollonio Massimo , Calì Debora , Fasoli Michela , Brocco Chiara , Nesto Silvia Tulla , Humphris Gerald , Maccarrone Gaia , Gentilotti Elisa , Tacconelli Evelina , Piccolo Lidia Del 
Published: August 2024

Abstract

Background
The present paper focuses on socio-demographics, clinical variables, and the distance from the infection in predicting the long-term psycho-social consequences of COVID-19.
Methods
Patients were screened with a cross-sectional design at the Psychological Service of the University Hospital of Verona (Italy) at 3, 6, 12, and 18 months after their SARS-CoV-2 infection. The assessment was part of the Horizon 2020-funded ORCHESTRA Project and included the Hospital Anxiety and Depression Scale (HADS), the Short Form Health Survey 36 (SF-36), the Impact of Event Scale-Revised (IES-R), and ad-hoc questions measuring pre-post COVID-19 changes on psycho-social dimensions (sleep quality, nutrition, level of autonomy, work, social relationships, emotional wellbeing).
Results
Between June 2021 and June 2023, we evaluated 1317 patients (mean age 56.6 ± 14.8 years; 48% male): 35% at three months, 40% at 6, 20% at 12, and 5% at 18 months after the infection. Thirty-five percent were hospitalized due to COVID-19. Overall, 16% reported some form of clinically significant mental distress following the infection (HADS-TOT), with 13% and 6%, respectively, experiencing anxiety (HADS-Anxiety) and depressive symptoms (HADS-Depression). Four percent testified post-traumatic symptoms. The SF-36 scale revealed that 16% and 17% of subjects had physical or psychological deterioration in quality of life, respectively. The regression analyses showed that females experienced higher levels of anxiety and depression compared to males, along with worse mental and physical quality of life and pre-post infection changes in nearly all the investigated psycho-social dimensions. Younger people felt more anxiety and had a reduced mental quality of life than their older counterparts, who, in turn, had poorer scores in terms of autonomy and physical functioning. Hospitalized patients had lower levels of self-sufficiency, social relationships, and work than non-hospitalized people. The latter were more anxious and reported a lower physical quality of life. Finally, patients evaluated for the first time at 12- and 18 months showed a more significant impairment in mental and physical quality of life than those assessed at three months.
Conclusions
our data show that COVID-19 psychological sequelae tend to persist over time, still needing clinical attention and intervention planning, especially for females.
The Effect of Topical Curcumin Nanoparticles Encapsulated with β- Cyclodextrin into Hyaluronic Acid-Based Hydrogel Complex on Experimental Ulcerative Keratitis in Comparison with N-Acetylcysteine in Rabbit Model
Authors: Sohani Zahra , Jamshidi Shahram , Koohi Mohammad Kazem , Malakootikhah Javad , Abarkar Mohammad , Golchin Diba , Roshani Sarina , Liyeh Haniyeh Naghdi , Gazafroudi Negin Aghajanpour Moghaddam , Amjadi Nava , Niaki Reihaneh Izadi 
Published: August 2024

Abstract

Corneal ulcers, whether melting or indolent, are common in humans and companion animals. Treatment involves local administration of antibiotic eye drops and corneal healing drugs. Compared to traditional treatments for ulcerative keratitis, herbal medicines have unique advantages, such as potent anti-inflammatory effects and inhibition of proinflammatory cytokines. Curcumin, extracted from the Curcuma Longa plant, possesses extensive pharmacological properties, such as anti-inflammatory, anti-cancer, and antioxidant properties, and is used in various medicines. In this study, using a formulation of Curcumin NPs encapsulated with β-cyclodextrin and hyaluronic acid, an ophthalmic drop hydrogel was developed to accelerate corneal healing and improve the quality of healed structures. The formation of Curcumin NPs into Hyaluronic acid-based hydrogels was characterized by Fourier transform infrared (FTIR), zeta, and scanning electron microscope (SEM) analyses. A total of 25 healthy male New Zealand Albino rabbits were experimentally induced with ulcerative keratitis and treated individually with topical medication. Rabbits were divided into five groups. Fluorescein dye staining, corneal clarity score, Schirmer tear test, proinflammatory cytokine measurement, and pathologic factors assessments were used to evaluate the optimised Curcumin NPs with β-cyclodextrin in Hyaluronic acid hydrogel. Intriguingly, the frequency of administering medication is reduced compared to conventional therapies, which enhances the quality of healed structures and harnesses ulcerative keratitis. (p<0.05) All findings in this study provide new insight into designing and fabricating novel ophthalmic medicine for ulcerative keratitis for topical usage.
Predicting substrates for orphan Solute Carrier Proteins using multi- omics datasets
Authors: Zhang Y. , Newstead S. , Sarkies P. 
Published: August 2024

Abstract

Solute carriers (SLC) are integral membrane proteins responsible for transporting a wide variety of metabolites, signaling molecules and drugs across cellular membranes. Despite key roles in metabolism, signaling and pharmacology, around one third of SLC proteins are ‘orphans’ whose substrates are unknown. Experimental determination of SLC substrates is technically challenging given the wide range of possible physiological candidates. Here, we develop a predictive algorithm to identify correlations between SLC expression levels and intracellular metabolite concentrations by leveraging existing cancer multi-omics datasets. Our predictions recovered known SLC-substrate pairs with high sensitivity and specificity compared to simulated random pairs. CRISPR loss-of-function screen data and metabolic pathway adjacency data further improved the performance of our algorithm. In parallel, we combined drug sensitivity data with SLC expression profiles to predict new SLC-drug interactions. Together, we provide a novel bioinformatic pipeline to predict new substrate predictions for SLCs, offering new opportunities to de-orphanise SLCs with important implications for understanding their roles in health and disease.
The IgLON family of cell adhesion molecules expressed in developing neural circuits ensure the proper functioning of the sensory system in mice
Authors: Singh Katyayani , Jayaram Mohan , Hanumantharaju Arpana , Tõnissoo Tambet , Jagomäe Toomas , Mikheim Kaie , Muthuraman Srirathi , Gilbert Scott F. , Plaas Mario , Schäfer Michael K.E. , Innos Jürgen , Lilleväli Kersti , Philips Mari-Anne , Vasar Eero 
Published: August 2024

Abstract

Deletions and malfunctions of the IgLON family of cell adhesion molecules are associated with anatomical, behavioral, and metabolic manifestations of neuropsychiatric disorders. We have previously shown that IgLON genes are expressed in sensory nuclei/pathways and that IgLON proteins modulate sensory processing. Here, we examined the expression of IgLON alternative promoter-specific isoforms during embryonic development and studied the sensory consequences of the anatomical changes when one of the IgLON genes, Negr1, is knocked out. At the embryonal age of E12.5 and E13.5, various IgLONs were distributed differentially and dynamically in the developing sensory areas within the central and peripheral nervous system, as well as in limbs and mammary glands. Sensory tests showed that Negr1 deficiency causes differences in vestibular function and temperature sensitivity in the knockout mice. Sex-specific differences were noted across olfaction, vestibular functioning, temperature regulation, and mechanical sensitivity. Our findings highlight the involvement of IgLON molecules during sensory circuit formation and suggest Negr1's critical role in somatosensory processing.
Low-Light Image Enhancement Model with Nonlinear Enhancement Probability
Authors: Chen Zefei , Lin Yongjie , Xu Jianmin , Lu Kai , Shou Yanfang 
Published: August 2024

Abstract

Because low-light image enhancement is of great significance for improving the accuracy of object detection, it has gained the attention of many researchers. In the process of low-light image enhancement, darker pixels have a greater probability of being enhanced, while brighter pixels have a greater probability of being suppressed. Therefore, based on the Zero DCE, we propose a low-light image enhancement model with nonlinear enhancement probability, named TransNEP. On the one hand, an enhancement function with attention is proposed to simultaneously enhance over-dark pixels and suppress over-bright pixels with a greater probability than the existing methods. On the other hand, we split the low-light image into multiple patches and embed these patches as patch queries. Then, based on the transformer’s key-query mechanism, patch queries are decoded to obtain the enhancement probability corresponding to each patch. Finally, this study verify the improvement in texture and object detection’s accuracy of the image enhanced with TransNEP through comprehensive experiments.
Graph Neural Networks with Autoregressive Moving Average Graph Filter and Graph- Regularized Sparse Coding for Accurate Hyperspectral Image Classification on FPGA
Authors: Muralikrishnan P , M Maheswari 
Published: August 2024

Abstract

Classification techniques based on hyperspectral images (HSIs) have gained significant importance in target identification, mineral mapping, and environmental management due to rapid advancements in hyperspectral remote sensing technology. Graph Neural Networks (GNNs) have become a key technique, attracting considerable attention in HSI analysis. However, GNN-based techniques frequently depend on a graph filter to extract the intricate spectral-spatial characteristics inherent in HSI data, which limits the ability to fully exploit the diverse and rich information available. This can lead to less comprehensive feature representation. To overcome these limitations, we propose a novel method called AMAGC-GNN, Autoregressive Moving Average Graph Filter and Graph-Regularized Sparse Coding for the GNN. The ARMA graph filters play a crucial role in spectral filtering, effectively capturing and preserving complex spectral relationships while mitigating the common oversmoothing problem in traditional GNNs, ensuring node features retain their uniqueness and discriminative power. Concurrently, graph-regularized sparse coding transforms the input HSI data into sparse coefficients, representing the most relevant features efficiently. This combined approach leverages the strengths of both techniques, providing adaptive and precise spectral filtering and compact, discriminative feature representation. AMAGC-GNN outperforms other techniques, achieving significant improvements in Cohen's Kappa coefficient (6.58% and 6.15%), per-class accuracy (6.24% and 5.57%), and overall accuracy (6.24% and 6.30%) across Kennedy Space Centre and Indian pines datasets. Furthermore, our implementation of AMAGF-GNN on Virtex-7 field-programmable gate arrays (FPGAs) demonstrates promising results for real-world applications in HSI classification, particularly in achieving highly accurate target localization.
Risk-based Mapping of Pesticide Usage and Social Vulnerability in the Contiguous United States
Authors: Taiba Jabeen , Beseler Cheryl , Kolok Alan , Zahid Muhammad , Bartelt-Hunt Shannon , Rogan Eleanor 
Published: August 2024

Abstract

Background In the contiguous United States, environmental justice burdens and disparities in pesticide exposure are prevalent among racial and ethnic minorities and low socioeconomic groups. Identifying the counties with high pesticide exposure and social vulnerability is essential to mitigating risk.
Methods We created an index for pesticides commonly used in the contiguous US states from 1992 to 2019, as well as a social vulnerability index. We identified the US counties with elevated pesticide exposure and elevated social vulnerability. The USGS Pesticide National Synthesis Project quantified pesticide exposures at a county scale for frequently applied pesticides from 1992 to 2019 in 3069 contiguous US counties. We retrieved social vulnerability data from five-year estimates (2015–2019) of the American Community Survey (ACS) for selected variables: race, income, and educational attainment, and created a social vulnerability index. We implemented the pesticide index and social vulnerability index using a principal component analysis (PCA) approach. We used an Intergovernmental Panel on Climate Change ICCP risk-based approach to identify the counties with both high pesticide exposure and social vulnerability.
Results One hundred and forty-three US counties had high pesticide use and social vulnerability. Illinois, North Carolina, Michigan, California, Ohio, Indiana, Iowa, and Pennsylvania had significantly higher proportions of these high pesticide application and social vulnerability counties than any other state. In conclusion, disparities in pesticide exposure and associated health outcomes due to social vulnerability are widespread across the contiguous US counties in both rural and urban communities.
Conclusions Our study will inform regulatory bodies about areas with both high pesticide exposure and social vulnerability areas, as well as facilitate regulatory and public health decisions.