bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antioxidant and anti-Alzheimer's potential of Tetragonisca angustula (Jataí) stingless bee pollen
Authors: dos Santos Natalia Carine Lima , Malta Serena Mares , Franco Rodrigo Rodrigues , Silva Heitor Cappato Guerra , Silva Matheus Henrique , Rodrigues Tamiris Sabrina , de Oliveira Rafael Martins , Araújo Thayane Nogueira , Augusto Solange Cristina , Espindola Foued Salmen , Ueira-Vieira Carlos 
Published: January 2024

AbstractAlzheimer's disease (AD) is considered the leading cause of dementia in the elderly worldwide. It results in progressive memory loss and impairment of cognitive and motor skills, leading to a high degree of disability and dependence. The development of AD is associated with the accumulation of senile plaques in the brain, caused by the amyloidogenic pathway of the disease. Several genetic and biochemical events are linked to AD development, with oxidative stress being one of them. Due to the scarcity of drugs aimed at treating AD, antioxidant compounds are increasingly studied as therapeutic targets for the disease. In this study, we investigate the antioxidant and anti-Alzheimer potential of the Tetragonisca angustula (Jataí) pollen extract in a Drosophila melanogaster Alzheimer's model. For this purpose, we utilized a D. melanogaster AD-like model, which expresses genes related to the amyloidogenic pathway of Alzheimer's disease. We explored the floral origin of the collected pollen, conducted phytochemical prospecting, and evaluated its antioxidant capacity in vitro. In vivo experiments involved assessing the survival and climbing ability of the D. melanogaster AD-like model with various concentrations of the pollen extract. Our findings revealed that the pollen extract of Tetragonisca angustula exhibits a significant antioxidant response and high concentrations of important phytochemicals, such as flavonoids and polyphenols. Furthermore, it enhanced the survival rate of D. melanogaster, and across all concentrations tested, it improved the climbing ability of the flies after 15 days of treatment with methanolic pollen extract. Additionally, the pollen extract reduced the neurodegeneration index in histopathological analysis. Thus, our study demonstrates the potential of Tetragonisca angustula pollen as an important subject for further investigation, aiming to isolate molecules that could potentially serve as therapeutic targets for Alzheimer's disease.
Nearly complete genome sequences of the first two identified Colorado potato beetle viruses
Authors: Antonets Maria , Bodnev Sergei , Rotskaya Ulyana , Kosman Elena , Tregubchak Tatyana , Bauer Tatyana , Azaev Mamedyar , Kryukov Vadim , Antonets Denis 
Published: January 2024

AbstractThe Colorado potato beetle is one of the most devastating potato pests in the world. However, its viral pathogens, which might have potential in pest control, have remained unexplored. With high-throughput sequencing of Colorado potato beetle samples derived from prepupal larvae which died from an unknown infection, we have identified two previously unknown RNA viruses and assembled their nearly complete genome sequences. The subsequent genetic and phylogenetic analysis demonstrated that the viruses, tentatively named Leptinotarsa iflavirus 1 and Leptinotarsa solinvi-like virus 1, are the novel representatives of the Iflaviridae and Solinviviridae families, respectively. To the best of our knowledge, these are the first sequencing-confirmed insect viruses derived from Colorado potato beetle samples. We propose that Leptinotarsa iflavirus 1 may be associated with a lethal disease in the Colorado potato beetle.
A new indicator for estimating the degree of mining-induced land subsidence: the overburden’s average GSI value
Authors: Gong Yaqiang , Zha Jianfeng , Guo Qingbiao , Guo Guangli 
Published: January 2024

AbstractUnderground coal mining leads to land subsidence, which, in turn, results in damage to buildings and infrastructure, disturbs the original ecological environment, and hinders the sustainable development of coal mining cities. A reasonable estimation of land subsidence, on the other hand, is the foundation for building protection, land reclamation, and ecological environment reconstruction. However, when we applied the existing land subsidence estimation theory to the deep mining areas of the Ordos coalfield in western China, there was a significant deviation between the estimations and the measurements. To explain such unusual case, we propose using the overburden’s average GSI (Geological Strength Index) value instead of the compressive strength (UCS) of rock specimens for a better representation of the overburden’s overall properties. By using on-site subsidence monitoring results and historical data, we provided evidence which supports that the overburden’s average GSI value has a much greater impact on subsidence rates than the UCS. Subsequently, we investigated the relationship between three typical overburden’s GSI values and the subsidence rates via a calibrated numerical model, revealing the variation patterns of maximum surface subsidence when the overburden’s average GSI value is set at 30, 50, and 75, respectively. Finally, on the basis of the measured and simulated results, we discussed a non-conventional strip mining method for mining subsidence control in the deep mining areas of the Ordos coalfield in western China, and explained why it is possible and what are the significant advantages behind. The proposed methods, findings, and suggestions in this paper are therefore quite helpful for researchers and engineers who wish to estimate and control the mining-induced land subsidence, as well as for those who are particularly interested in the study of environment science related to land subsidence.
A comprehensive dataset of luminescence chronologies and environmental proxy indices of loess-paleosol deposits across Asia
Authors: Li Guoqiang , Yan Zhongfeng , Song Yougui , Fitzsimmons Kathryn E. , Yi Shuangwen , Kang Shugang , E Chongyi , Stevens Thomas , Lai Zhongping , Dave Aditi K. , Chen Chunzhu , Deng Yanqing , Yang He , Wang Leibin , Zhang Xiaojian , Qin Caixin , Zhao Qiuyue , Buylaert Jan-Pieter , Lu Tao , Wang Yixuan , Liu Xiangjun , Ling Zhiyong , Chang Qiufang , Wei Haitao , Wang Xiaoyan , Chen Fahu 
Published: January 2024

AbstractLoess-paleosol sequences have been used in Asia to study climate and environmental changes during the Quaternary. The scarcity of age control datasets and proxy indices analysis data for Asian loess has limited our understanding of loess depositional processes and the reconstruction of paleoclimatic changes from loess-paleosol records. In this study, we present a dataset that includes 1785 quartz optically stimulated luminescence ages and 1038 K-feldspar post-infrared infrared stimulated luminescence ages from 128 loess-paleosol sequences located in different regions of Asia. We generate 38 high-resolution age-depth models of loess records based on the provided datasets. We provide data on 12,365 grain size records, 14,964 magnetic susceptibility records, 2204 CaCO3 content records, and 3326 color reflection records. This dataset contains the most detailed and accurate chronologies and proxy index data for loess records in Asia yet published. It provides fundamental data for understanding the spatial-temporal variations in loess depositional processes and climatic changes across the continent during the mid-late Quaternary.
Narrow-band acceleration of gold ions to GeV energies from ultra-thin foils
Authors: Martin Philip , Ahmed Hamad , Doria Domenico , Cerchez Mirela , Hanton Fiona , Gwynne Deborah , Alejo Aaron , Fernández-Tobías Javier , Green James , Macchi Andrea , Maclellan David , McKenna Paul , Ruiz Jesús Álvarez , Swantusch Marco , Willi Oswald , Zhai Shuhua , Borghesi Marco , Kar Satyabrata 
Published: January 2024

AbstractInteraction of intense lasers with nm thick targets provides an attractive regime for the acceleration of ions of all types. Acceleration of heavy ions however is undermined in the presence of low charge contaminant species due to their higher charge-to-mass ratio. Here we show narrow-band acceleration of very heavy Au ions from ~15 nm Au foils driven by a sub-Petawatt laser, with spectral peaks of 1.5 ± 0.5 GeV at fluxes on the order of 1012 particles per steradian. 3D and 2D particle-in-cell simulations show a complex interplay between different acceleration mechanisms at different stages of the interaction, suggesting the spectrally peaked Au ion bunches stem from strong radiation pressure acceleration on a heavy-ion dominant plasma in the moments just before transparency, followed by an efficient acceleration due to transparency-enhanced mechanisms.
Realistic tropical cyclone wind and pressure fields can be reconstructed from sparse data using deep learning
Authors: Eusebi Ryan , Vecchi Gabriel A. , Lai Ching-Yao , Tong Mingjing 
Published: January 2024

AbstractTropical cyclones are responsible for large-scale loss of life and property1–4, motivating accurate risk assessment and forecasting. These objectives require accurate reconstructions of storms’ wind and pressure fields which assimilate real-time observations5–9, but current methods used for these reconstructions remain computationally expensive and limited10. Here, we show that a physics-informed neural network11,12 can be a promising and computationally efficient algorithm for tropical cyclone data assimilation. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a physics-informed neural network is able to reconstruct full realistic 2- and 3-dimensional wind and pressure fields which capture key features of the cyclone. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results highlight how recent advances in deep learning can augment data assimilation schemes. The methods are also general and can be applied to other flow problems.
Substantial but spatially heterogeneous progress in male circumcision for HIV prevention in South Africa
Authors: Thomas Matthew L. , Zuma Khangelani , Loykissoonlal Dayanund , Dube Ziphozonke Bridget , Vranken Peter , Porter Sarah E. , Kripke Katharine , Seatlhodi Thapelo , Meyer-Rath Gesine , Johnson Leigh F. , Imai-Eaton Jeffrey W. 
Published: January 2024

Abstract
Background
Voluntary medical male circumcision (VMMC) reduces the risk of male HIV acquisition by 60%. Programmes to provide VMMCs for HIV prevention have been introduced in sub-Saharan African countries with high HIV burden. Traditional circumcision is also a long-standing male coming-of-age ritual, but practices vary considerably across populations. Accurate estimates of circumcision coverage by age, type, and time at subnational levels are required for planning and delivering VMMCs to meet targets and evaluating their impacts on HIV incidence.

Methods
We developed a Bayesian competing risks time-to-event model to produce region-age-time-type specific probabilities and coverage of male circumcision with probabilistic uncertainty. The model jointly synthesises data from household surveys and health system data on the number of VMMCs conducted. We demonstrated the model using data from five household surveys and VMMC programme data to produce estimates of circumcision coverage for 52 districts in South Africa between 2008 and 2019.

Results
Nationally, in 2008, 24.1% (95% CI: 23.4–24.8%) of men aged 15–49 were traditionally circumcised and 19.4% (18.9–20.0%) were medically circumcised. Between 2010 and 2019, 4.25 million VMMCs were conducted. Circumcision coverage among men aged 15–49 increased to 64.0% (63.2–64.9%) and medical circumcision coverage to 42% (41.3–43.0%). Circumcision coverage varied widely across districts, ranging from 13.4 to 86.3%. The average age of traditional circumcision ranged between 13 and 19 years, depending on local cultural practices.

Conclusion
South Africa has made substantial, but heterogeneous, progress towards increasing medical circumcision coverage. Detailed subnational information on coverage and practices can guide programmes to identify unmet need to achieve national and international targets.
Computational pathology identifies immune-mediated collagen disruption to predict clinical outcomes in gynecologic malignancies
Authors: Aggarwal Arpit , Khalighi Sirvan , Babu Deepak , Li Haojia , Azarianpour-Esfahani Sepideh , Corredor Germán , Fu Pingfu , Mokhtari Mojgan , Pathak Tilak , Thayer Elizabeth , Modesitt Susan , Mahdi Haider , Avril Stefanie , Madabhushi Anant 
Published: January 2024

AbstractBackgroundThe role of immune cells in collagen degradation within the tumor microenvironment (TME) is unclear. Immune cells, particularly tumor-infiltrating lymphocytes (TILs), are known to alter the extracellular matrix, affecting cancer progression and patient survival. However, the quantitative evaluation of the immune modulatory impact on collagen architecture within the TME remains limited.MethodsWe introduce CollaTIL, a computational pathology method that quantitatively characterizes the immune-collagen relationship within the TME of gynecologic cancers, including high-grade serous ovarian (HGSOC), cervical squamous cell carcinoma (CSCC), and endometrial carcinomas. CollaTIL aims to investigate immune modulatory impact on collagen architecture within the TME, aiming to uncover the interplay between the immune system and tumor progression.ResultsWe observe that an increased immune infiltrate is associated with chaotic collagen architecture and higher entropy, while immune sparse TME exhibits ordered collagen and lower entropy. Importantly, CollaTIL-associated features that stratify disease risk are linked with gene signatures corresponding to TCA-Cycle in CSCC, and amino acid metabolism, and macrophages in HGSOC.ConclusionsCollaTIL uncovers a relationship between immune infiltration and collagen structure in the TME of gynecologic cancers. Integrating CollaTIL with genomic analysis offers promising opportunities for future therapeutic strategies and enhanced prognostic assessments in gynecologic oncology.
Scaling models of visual working memory to natural images
Authors: Bates Christopher J. , Alvarez George A. , Gershman Samuel J. 
Published: January 2024

AbstractOver the last few decades, psychologists have developed precise quantitative models of human recall performance in visual working memory (VWM) tasks. However, these models are tailored to a particular class of artificial stimulus displays and simple feature reports from participants (e.g., the color or orientation of a simple object). Our work has two aims. The first is to build models that explain people’s memory errors in continuous report tasks with natural images. Here, we use image generation algorithms to generate continuously varying response alternatives that differ from the stimulus image in natural and complex ways, in order to capture the richness of people’s stored representations. The second aim is to determine whether models that do a good job of explaining memory errors with natural images also explain errors in the more heavily studied domain of artificial displays with simple items. We find that: (i) features taken from state-of-the-art deep encoders predict trial-level difficulty in natural images better than several reasonable baselines; and (ii) the same visual encoders can reproduce set-size effects and response bias curves in the artificial stimulus domains of orientation and color. Moving forward, our approach offers a scalable way to build a more generalized understanding of VWM representations by combining recent advances in both AI and cognitive modeling.
Whale optimization‐based PTS scheme for PAPR reduction in UFMC systems
Authors: Tuli Esmot Ara , Akter Rubina , Lee Jae Min , Kim Dong‐Seong 
Published: January 2024

AbstractThis paper proposes a whale optimization algorithm (WOA)‐based partial transmit sequence (PTS) scheme called WOA‐PTS to reduce the high peak‐to‐average power ratio (PAPR) for universal filtered multi‐carrier (UFMC) systems. High PAPR is a prevalent challenge encountered in multi‐carrier systems. In the conventional PTS technique, the optimization of phase rotation factors is achieved through multiplication with the sub‐blocks. In this undertaking, WOA optimization is integrated as a phase optimizer in the PTS‐based PAPR reduction scheme. The experimental results show that, when compared with PTS‐based UFMC signal, the UFMC signal with WOA‐PTS can achieve 4.15 dB PAPR reduction at the complementary cumulative distribution function value of 10−3, additionally power spectral density performance and bit error rate also improved.
A deep learning framework for multi‐object tracking in team sports videos
Authors: Cao Wei , Wang Xiaoyong , Liu Xianxiang , Xu Yishuai 
Published: January 2024

AbstractIn response to the challenges of Multi‐Object Tracking (MOT) in sports scenes, such as severe occlusions, similar appearances, drastic pose changes, and complex motion patterns, a deep‐learning framework CTGMOT (CNN‐Transformer‐GNN‐based MOT) specifically for multiple athlete tracking in sports videos that performs joint modelling of detection, appearance and motion features is proposed. Firstly, a detection network that combines Convolutional Neural Networks (CNN) and Transformers is constructed to extract both local and global features from images. The fusion of appearance and motion features is achieved through a design of parallel dual‐branch decoders. Secondly, graph models are built using Graph Neural Networks (GNN) to accurately capture the spatio‐temporal correlations between object and trajectory features from inter‐frame and intra‐frame associations. Experimental results on the public sports tracking dataset SportsMOT show that the proposed framework outperforms other state‐of‐the‐art methods for MOT in complex sport scenes. In addition, the proposed framework shows excellent generality on benchmark datasets MOT17 and MOT20.
A new air gap flux‐based technique to detect loss of field in synchronous generators
Authors: Haseli Mohammad Reza , Haghjoo Farhad , Hasani Abbas 
Published: January 2024

AbstractA new technique is proposed for loss of field (LOF) detection in synchronous generators (SGs). The proposed scheme employs the variations of SG angular velocity ∆ωφ and load angle ∆δφ to reach this goal, while the mentioned parameters are estimated through air gap flux estimation. The proposed scheme performance is evaluated using realistic simulation case studies based on IEEE C. 37. 102 standard compared to the conventional impedance‐based one. The obtained results show that the presented scheme excels the conventional scheme from speed and security viewpoints. Moreover, the estimation procedure of ∆ωφ and ∆δφ is experimentally validated by using a 10 kVA SG and the results demonstrate that the proposed technique can be easily set and implemented.
Dynamic estimator selection for double‐bit‐range estimation in VVC CABAC entropy coding
Authors: Im Sio‐Kei , Chan Ka‐Hou 
Published: January 2024

AbstractCABAC is the only entropy coding used in Versatile Video Coding (VVC). This is achieved through multiple estimators approach that provide more accurate predictions by considering different estimated probability results, but CABAC coding requires higher complexity and bit‐range accuracy than other approaches. Therefore, there is more potential to refine the performance from the perspective of bit allocation and architecture design. In this paper, a selection method is proposed to determine which estimator is recommended to dynamically perform the current entropy coding. Taking advantage of the double‐bit‐range architecture, the bits contained in the different estimators are also rearranged based on Most Probable Symbol () determination and Least Probable Symbol () considerations. Experimental reports in the work report that the coding time can be reduced using the proposed method and there is a slight gain in Peak Signal‐to‐Noise Ratio (PSNR) while saving some Rate‐distortion (RD) performance in bitrate.
Cross‐modal fusion encoder via graph neural network for referring image segmentation
Authors: Zhang Yuqing , Zhang Yong , Piao Xinglin , Yuan Peng , Hu Yongli , Yin Baocai 
Published: January 2024

AbstractReferring image segmentation identifies the object masks from images with the guidance of input natural language expressions. Nowadays, many remarkable cross‐modal decoder are devoted to this task. But there are mainly two key challenges in these models. One is that these models usually lack to extract fine‐grained boundary information and gradient information of images. The other is that these models usually lack to explore language associations among image pixels. In this work, a Multi‐scale Gradient balanced Central Difference Convolution (MG‐CDC) and a Graph convolutional network‐based Language and Image Fusion (GLIF) for cross‐modal encoder, called Graph‐RefSeg, are designed. Specifically, in the shallow layer of the encoder, the MG‐CDC captures comprehensive fine‐grained image features. It could enhance the perception of target boundaries and provide effective guidance for deeper encoding layers. In each encoder layer, the GLIF is used for cross‐modal fusion. It could explore the correlation of every pixel and its corresponding language vectors by a graph neural network. Since the encoder achieves robust cross‐modal alignment and context mining, a light‐weight decoder could be used for segmentation prediction. Extensive experiments show that the proposed Graph‐RefSeg outperforms the state‐of‐the‐art methods on three public datasets. Code and models will be made publicly available at https://github.com/ZYQ111/Graph_refseg.
Robust image watermarking using ant colony optimization and fast generic radial harmonic Fourier moment calculation
Authors: Wang Wenbing , Feng Liu 
Published: January 2024

AbstractIn open networks, the geometric deformation and common image processing are common image manipulation modes, which pose a great challenge in robust watermarking. To improve the robustness of GRHFM‐based watermarking, a watermarking algorithm based on the fast GRHFM calculation method and ant colony optimization (ACO) based fractional parameter selection method is proposed. With the similarity between the discrete Fourier transform and GRHFM moment calculation, the algorithm utilizes fast Fourier transform to improve the GRHFM calculation accuracy and speed. To select the optimal GRHFM fractional parameter for watermarking algorithm, ACO is introduced to the fractional parameter adaptive selection method. Here, the fast Fourier transform‐based calculation method is combined with the adaptive parameter selection method to maximize the invisibility and robustness of watermarking. The experimental results indicate that the algorithm achieves higher robustness with the same payload and invisibility compared with other existing watermarking methods.