bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sacroiliitis diagnosis based on interpretable features and multi-task learning
Authors: Liu Lei , Zhang Haoyu , Zhang Weifeng , Mei Wei , Huang Ruibin 
Published: January 2024

Abstract

Objective. Sacroiliitis is an early pathological manifestation of ankylosing spondylitis (AS), and a positive sacroiliitis test on imaging may help clinical practitioners diagnose AS early. Deep learning based automatic diagnosis algorithms can deliver grading findings for sacroiliitis, however, it requires a large amount of data with precise labels to train the model and lacks grading features visualization. In this paper, we aimed to propose a radiomics and deep learning based deep feature visualization positive diagnosis algorithm for sacroiliitis on CT scans. Visualization of grading features can enhance clinical interpretability with visual grading features, which assist doctors in diagnosis and treatment more effectively. Approach. The region of interest (ROI) is identified by segmenting the sacroiliac joint (SIJ) 3D CT images using a combination of the U-net model and certain statistical approaches. Then, in addition to extracting spatial and frequency domain features from ROI according to the radiographic manifestations of sacroiliitis, the radiomics features have also been integrated into the proposed encoder module to obtain a powerful encoder and extract features effectively. Finally, a multi-task learning technique and five-class labels are utilized to help with performing positive tests to reduce discrepancies in the evaluation of several radiologists. Main results. On our private dataset, proposed methods have obtained an accuracy rate of 87.3%, which is 9.8% higher than the baseline and consistent with assessments made by qualified medical professionals. Significance. The results of the ablation experiment and interpreting analysis demonstrated that the proposed methods are applied in automatic CT scan sacroiliitis diagnosis due to their excellently interpretable and portable advantages.
Material decomposition maps based calibration of dual energy CT scanners for proton therapy planning: a phantom study
Authors: Viar-Hernández David , Vera-Sánchez Juan Antonio , Schmidt-Santiago Lucia , Rodriguez-Vila Borja , Lorenzo-Villanueva Isabel , Canals-de-las-Casas Elisabet , Castro-Novais Juan , Maria Perez-Moreno Juan , Cerrón-Campoo Fernando , Malpica Norberto , Torrado-Carvajal Angel , Mazal Alejandro 
Published: January 2024

AbstractWe introduce a new calibration method for dual energy CT (DECT) based on material decomposition (MD) maps, specifically iodine and water MD maps. The aim of this method is to provide the first DECT calibration based on MD maps. The experiments were carried out using a general electric (GE) revolution CT scanner with ultra-fast kV switching and used a density phantom by GAMMEX for calibration and evaluation. The calibration process involves several steps. First, we tested the ability of MD values to reproduce Hounsfield unit (HU) values of single energy CT (SECT) acquisitions and it was found that the errors were below 1%, validating their use for HU reproduction. Next, the different definitions of computedZvalues were compared and the robustness of the approach based on the materials’ composition was confirmed. Finally, the calibration method was compared with a previous method by Bourqueet al, providing a similar level of accuracy and superior performance in terms of precision. Overall, this novel DECT calibration method offers improved accuracy and reliability in determining tissue-specific physical properties. The resulting maps can be valuable for proton therapy treatments, where precise dose calculations and accurate tissue differentiation are crucial for optimal treatment planning and delivery.
Structure of the space of folding protein sequences defined by large language models
Authors: Zambon A , Zecchina R , Tiana G 
Published: January 2024

Abstract
Proteins populate a manifold in the high-dimensional sequence space whose geometrical structure guides their natural evolution. Leveraging recently-developed structure prediction tools based on transformer models, we first examine the protein sequence landscape as defined by an effective energy that is a proxy of sequence foldability. This landscape shares characteristics with optimization challenges encountered in machine learning and constraint satisfaction problems. Our analysis reveals that natural proteins predominantly reside in wide, flat minima within this energy landscape. To investigate further, we employ statistical mechanics algorithms specifically designed to explore regions with high local entropy in relatively flat landscapes. Our findings indicate that these specialized algorithms can identify valleys with higher entropy compared to those found using traditional methods such as Monte Carlo Markov Chains. In a proof-of-concept case, we find that these highly entropic minima exhibit significant similarities to natural sequences, especially in critical key sites and local entropy. Additionally, evaluations through Molecular Dynamics suggests that the stability of these sequences closely resembles that of natural proteins. Our tool combines advancements in machine learning and statistical physics, providing new insights into the exploration of sequence landscapes where wide, flat minima coexist alongside a majority of narrower minima.
Monte Carlo programs for small-angle Bhabha scattering*
Authors: Arbuzov A.B. , Bondarenko S.G. , Boyko I.R. , Kalinovskaya L.V. , Kampf A.A. , Sadykov R.R. , Yermolchyk V.L. 
Published: January 2024

Abstract
Luminosity monitoring at

colliders was investigated using

Monte Carlo event generator

and integrator

for simulation of Bhabha scattering at low angles. Results are presented for center-of-mass energies of the Z boson resonance and at 240 GeV for the conditions of typical luminosity detectors. It is shown that bremsstrahlung events with extremely low electron scattering angles are relevant to match the precision tags of future electron-positron colliders.
Data augmentation for invasive brain–computer interfaces based on stereo-electroencephalography (SEEG)
Authors: Wu Xiaolong , Zhang Dingguo , Li Guangye , Gao Xin , Metcalfe Benjamin , Chen Liang 
Published: January 2024

Abstract

Objective. Deep learning is increasingly used for brain–computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness. However, all the existing studies on brain signals were based on convolutional neural networks and ignored the temporal dependence. This paper attempted to enhance generative models by capturing the temporal relationship from a time-series perspective. Approach. A conditional generative network (conditional transformer-based generative adversarial network (cTGAN)) based on the transformer model was proposed. The proposed method was tested using a stereo-electroencephalography (SEEG) dataset which was recorded from eight epileptic patients performing five different movements. Three other commonly used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE), and conditional Wasserstein generative adversarial network with gradient penalty (cWGANGP). Using the proposed method, the artificial SEEG data was generated, and several metrics were used to compare the data quality, including visual inspection, cosine similarity (CS), Jensen–Shannon distance (JSD), and the effect on the performance of a deep learning-based classifier. Main results. Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE outputted inferior samples when visualized as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of CS and JSD and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively). Significance. This is the first time that DA methods have been applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.
Clinical outcomes of peripheral nerve interfaces for rehabilitation in paralysis and amputation: a literature review
Authors: Taghlabi Khaled M , Cruz-Garza Jesus G , Hassan Taimur , Potnis Ojas , Bhenderu Lokeshwar S , Guerrero Jaime R , Whitehead Rachael E , Wu Yu , Luan Lan , Xie Chong , Robinson Jacob T , Faraji Amir H 
Published: January 2024

Abstract
Peripheral nerve interfaces (PNIs) are electrical systems designed to integrate with peripheral nerves in patients, such as following central nervous system (CNS) injuries to augment or replace CNS control and restore function. We review the literature for clinical trials and studies containing clinical outcome measures to explore the utility of human applications of PNIs. We discuss the various types of electrodes currently used for PNI systems and their functionalities and limitations. We discuss important design characteristics of PNI systems, including biocompatibility, resolution and specificity, efficacy, and longevity, to highlight their importance in the current and future development of PNIs. The clinical outcomes of PNI systems are also discussed. Finally, we review relevant PNI clinical trials that were conducted, up to the present date, to restore the sensory and motor function of upper or lower limbs in amputees, spinal cord injury patients, or intact individuals and describe their significant findings. This review highlights the current progress in the field of PNIs and serves as a foundation for future development and application of PNI systems.
Fabricating large scale titanium alloy thin-walled double-sided part by hot-wire arc additive manufacturing
Authors: Yu Zeqing , Li Changyuan , Liu Changmeng 
Published: January 2024

Abstract
Using traditional process to fabricate large scale titanium alloy thin-walled part such as wings and grid rudder, has the problem of low material utilization. Wire arc additive manufacturing (WAAM) is a flexible manufacturing technology with high deposition efficiency and high material utilization. In addition, a hot-wire device is attached to the WAAM system as auxiliary heat source, and the WAAM system becomes hot-wire arc additive manufacturing (HWAAM) system. In this paper, a large scale titanium alloy drone frame was fabricated by HWAAM. A double-side alternating deposition method was proposed to reduce the thermal deformation of the substrate. Two additional sections of the drone frame was taken off for mechanical analysis. The shape of the whole drone frame is completed and the mechanical properties meet the requirements of the project.
An Approach to Measure Slips in Friction Contacts Using a Self-Powered Sensor
Authors: Ba Mingsen , Qin Wenjie , Zhao Zhenlong , Sun Yu 
Published: January 2024

Abstract
The accumulation of small tangential slip will occur on the contact surfaces of components in mechanical structures when subjected to cyclic loadings, which will lead to system failure. However, the small tangential slip is difficult to measure directly. In this paper, a method for measuring the contact slip by using a self-powered displacement sensor based on triboelectric nanogenerator (TENG) is proposed, and the accuracy is verified by the finite element analysis (FEA). The contact slips for a flat-on-flat contact configuration are measured by this method. When the test piece is subjected to a cyclic tangential load with a frequency of 10Hz, the average obtained slip displacement of the test piece under single load cycle is less than 10μm. Then the finite element simulation of the contact configuration is carried out using software Abaqus 6.14. The analysis results are close to the test results, which verifies the effectiveness of the measurement method. Moreover, the slips of the flat-on-flat contact configuration under different frequency and amplitude of cyclic tangential load are investigated.
Preparation of Adhesive Hydrogels Based on Phase Separation
Authors: Zhang Jiamei , Gui Yunfan , Zhou Sheng , Chen Jiachang , Chen Junyin , Huang Xia 
Published: January 2024

Abstract
As the focus on smart materials continues to grow, the development of self-healing hydrogels has advanced significantly in recent years. Traditional hydrogels suffer from poor mechanical properties and limited or no adhesion, hindering their usage in popular fields such as sensors. To expand the applications of hydrogels, it is crucial to enhance their adhesion properties. In this study, a non-adhesive PVA hydrogel was produced using the freeze-thaw method. The PVA hydrogel was then soaked in a 70% acetone solution, which caused the phase separation of the hydrogel. This phase separation technique transforms the original non-adhesive hydrogel into a strong adhesive with high adhesion, addressing the shortcomings of PVA hydrogels in practical engineering applications and providing a novel approach for the preparation of new adhesives.
Self-assembled metal-polyphenol nanomedicines for tumor chemotherapy/chemokinetic synergistic therapy
Authors: An Weiqin , Wu Qinghe , Hou Mengfei , Zhang Penli , Zhang Chunfu 
Published: January 2024

Abstract
Chemodynamic therapy (CDT) converts endogenous H2O2 into highly toxic reactive oxygen species through the Fenton reaction or Fenton-like reaction. With the “one-pot” approach, the nanodrug EGCG-Cu-cisplatin NPs were created. The nanodrug has a particle size of approximately 150 nm and a homogeneous spherical shape. In vitro, results showed that the nanodrug degraded in a weakly acidic environment and released Cu2+ and cisplatin. Cisplatin raises intracellular H2O2 through a cascade reaction, Cu2+ consumes GSH while generating Cu+. Cu+ catalyzes H2O2 to generate •OH, which breaks the intracellular redox balance, induces ferroptosis, reduces tumor resistance, and promotes chemotherapy. CDT uses the Fenton /Fenton-like reaction to transform endogenous H2O2 into extremely hazardous reactive oxygen species.
Study on preparation and heat transfer enhancement of expanded graphite/paraffin composites
Authors: Yu Rongsheng , Wang Xueqin , Tao Guilin , Cheng Wenyi , Li Aiju 
Published: January 2024

Abstract
Using paraffin as the phase change material, expanded graphite (EG) as the carrier, due to the good absorption and the high thermal conductivity of EG, the EG/paraffin composites were prepared. The EG/paraffin composites were characterized by scanning electron microscope (SEM), differential scanning calorimeter (DSC), thermal conductivity meter and temperature patrol instrument. The results show that with the decrease of paraffin content, the latent heat and temperature of phase change of EG/ paraffin composites decrease slightly, and the highest endothermic peak shifts to the left, however, in EG/ paraffin composites, with the increase of EG mass fraction, the thermal conductivity, thermal diffusivity and heat transfer enhancement ability are greatly improved.
Machine learning for precise hit position reconstruction in Resistive Silicon Detectors
Authors: Siviero F. , Arcidiacono R. , Cartiglia N. , Costa M. , Ferrero M. , Lanteri L. , Madrid C. , Menzio L. , Mulargia R. , Sola V. 
Published: January 2024

Abstract
RSDs are LGAD silicon sensors with 100% fill factor, based on the principle of AC-coupled resistive read-out. Signal sharing and internal charge multiplication are the RSD key features to achieve picosecond-level time resolution and micron-level spatial resolution, thus making these sensors promising candidates as 4D-trackers for future experiments. This paper describes the use of a neural network to reconstruct the hit position of ionizing particles, an approach that can boost the performance of the RSD with respect to analytical models. The neural network has been trained in the laboratory and then validated on test beam data. The device-under-test in this work is a 450 μm-pitch matrix from the FBK RSD2 production, which achieved a resolution of about 65 μm at the DESY Test Beam Facility, a 50% improvement compared to a simple analytical reconstruction method, and a factor two better than the resolution of a standard pixel sensor of equal pitch size with binary read-out. The test beam result is compatible with the laboratory ones obtained during the neural network training, confirming the ability of the machine learning model to provide accurate predictions even in environments very different from the training one. Prospects for future improvements are also discussed.
Experimental measurement of the quality factor of a Fabry-Pérot open-cavity axion haloscope
Authors: Hernández-Cabrera Juan F. , De Miguel Javier , Hernández-Suárez E. , Joven-Álvarez Enrique , Lorenzo-Hernández H. , Otani Chiko , Rapado-Tamarit Miguel A. , Rubiño-Martín J. Alberto ,   the DALI collaboration
Published: January 2024

Abstract
The axion is a hypothetical boson arising from the most
natural solution to the problem of charge and parity symmetry in the
strong nuclear force. Moreover, this pseudoscalar emerges as a dark
matter candidate in a parameter space extending several decades in
mass. The Dark-photons & Axion-Like particles Interferometer (DALI)
is a proposal to search for axion dark matter in a range that
remains under-examined. Currently in a design and prototyping phase,
this haloscope is a multilayer Fabry-Pérot interferometer. A
proof-of-principle experiment is performed to observe the resonance
in a prototype. The test unveils a quality factor per layer of a few
hundred over a bandwidth of the order of dozens of megahertz. The
result elucidates a physics potential to find the, so far elusive,
axion, in a sector which can simultaneously solve the symmetry
problem in the strong interaction and the enigma of dark matter.
First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning
Authors: Gao Ling , Zhang Han , Yang Fukun , Tan Wangshu , Wu Ronghua , Song Yi 
Published: January 2024

Abstract
Ground-level ozone (O3), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O3 observations, this study proposes an innovative method for hourly full-coverage ground-level O3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O3 estimation, termed ML-derived-O3. Cross-validation results demonstrate the robustness of ML-derived-O3 yielding a coefficient of determination (R
2) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m−3. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O3 exhibits high prediction accuracy, with R
2 values surpassing 0.95 and RMSE remaining below 7.5 µg m−3. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O3 data across China. The diurnal variation of ML-derived-O3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.
The genetic polymorphism of c.-1221A>C and discovery of novel SNPs in the promoter region of Mitochondrial Transcription Factor A (TFAM) gene in Bali Cattle
Authors: Shidiq F , Anwar S , Rahmiyati S , Sutikno S , Khaerunnisa I , Furqon A , Irnidayanti Y 
Published: January 2024

Abstract
The expression of the mitochondrial transcription factor A (TFAM) gene has a marked impact on mitochondrial biogenesis. It is suggested to play a critical role in lipogenesis, including the deposition of marbling fat in cattle. Bali cattle (Bos javanicus) are known for their lean meat but exhibit poor marbling scores. This study aimed to investigate the polymorphism of c.-1221A>C and novel SNPs in the promoter region of the TFAM gene in Bali cattle. A total of 245 Bali cattle individuals were included in this study. Three Bali, three Wagyu, and one Limousin cattle were used for sequencing analysis. Genomic DNA was extracted either from blood or sperm for genotyping and variant analysis. The c.-1221A>C SNP was genotyped by PCR-RFLP, and the novel SNPs of the TFAM gene promoter region were identified by sequencing analysis. Our results revealed that the c.-1221A>C SNP was found to be monomorphic, with the presence of a fixed A allele, making it unsuitable for further association study in Bali cattle. However, the study successfully identified four novel SNPs and one novel DNP found in the promoter region of the TFAM gene. Notably, one double nucleotide polymorphism (DNP) of g.[45690945C>T;45690946A>G] was unique in Bali cattle compared to other breeds. In addition, only the c.-911T>A SNP was found heterozygous within the Bali cattle samples. These SNPs provide valuable insights into the genetic diversity of the TFAM gene in Bali cattle and serve as a basis for future investigations on the distinctiveness of Bali cattle, especially in the efforts of enhancing the quality of Bali beef.
Exterior characteristics of male Sakub sheep in the different ages and types
Authors: Hamdani M D I , Panjono  , Bintara S , Jannah Z N , Ibrahim A , Atmoko B A , Maharani D 
Published: January 2024

Abstract
This study aims to determine the exterior characteristics of male Sakub sheep with different ages and types. The study was conducted on Sakub sheep contest in the Pandansari village, Brebes. A total of 134 heads of male Sakub sheep with horns (SSWH: 95 heads) and Sakub sheep without horns (SSNH; 39 heads) were collected. Both consist of two categories of rams (aged 12 month-36 months) and young rams (aged 0-11 months). Four exterior characters (skin color-SC; horn shape-HS; tail shape-TS; face shape-FS) and 3 quantitative characteristics of the shape (body weight-BW; body length-BL; and height shoulder-HS) were scored. The descriptive analysis was used to examine the data. The result showed that ram SSWH has BW=67.23 ± 15.59 kg; BL= 75.75± 6.23 cm; HS= 75.22 ± 5.17cm. The Ram SSNH has BW =62.72 ± 14.50kg; BL = 76.11 ± 5.91cm; HS= 72.39 ± 4.54cm. Qualitative results such as CS, SH, ST and SF both of them have specifications for each other. Additionally, ram Sakub sheep with horns (SSWH) has higher score compare to SSNH for height shoulder. In young ram HS, BW and BL showing not different each other’s. The correlation value between body weight with the other measurements has a positive correlation. Qualitative results explained for us SSWH and SSNH have dominant trait. They are color of skin is white, big horn and back curved, shape of face is convex and shape of tail is long and hang straight. In conclusion, the exterior characteristics of Sakub Sheep with horn and without horn have different performances in both quantitative and qualitative traits.