bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance of Microsoft Azure Kinect DK as a tool for estimating human body segment lengths
Authors: Wang Shiou-An , Lu Ming-Hua , Lee Ai-Teng , Chen Chao-Yu , Lee Li-Wen 
Published: July 2024

AbstractThe Microsoft Kinect depth sensor, with its built-in software that automatically captures joint coordinates without markers, could be a potential tool for ergonomic studies. This study investigates the performance of Kinect in limb segment lengths using dual-energy X-ray absorptiometry (DXA) as a reference. Healthy children and adults (n = 76) were recruited for limb length measurements by Kinect and DXA. The results showed consistent ratios of arm, forearm, thigh, and leg lengths to height, which were 0.16, 0.14, 0.23, and 0.22 respectively, for both age groups and methods. Kinect exhibited perfect correlation among all limb lengths, indicating fixed proportions assumed by its algorithm. Comparing the two methods, there was a strong correlation (R = 0.850–0.985) and good to excellent agreement (ICC = 0.829–0.977), except for the right leg in adults, where agreement was slightly lower but still moderate (ICC = 0.712). The measurement bias between the methods ranged from − 1.455 to 0.536 cm. In conclusion, Kinect yields outcomes similar to DXA, indicating its potential utility as a tool for ergonomic studies. However, the built-in algorithm of Kinect assumes fixed limb proportions for individuals, which may not be ideal for studies focusing on investigating limb discrepancies or anatomical differences.
Pan-cancer analysis and the oncogenic role of Glypican 1 in hepatocellular carcinoma
Authors: Cao Li , Li Fang , Cai Shuang , Zhang Jinyuan , Guo Chen , Ali Sadiq , Zhou Jing , Jing Xintao , Wang Xiaofei , Qin Yannan , Wu Fei 
Published: July 2024

AbstractRecent studies indicate that Glypican 1 (GPC-1) is aberrantly expressed and plays a key role in certain cancers, but little is known in the hepatocellular carcinoma. Raw data from TCGA, GTEx and TIMER databases were utilized to comprehensively analyze GPC-1 expression landscape in pan-cancer, and the biological function of GPC-1 was investigated in liver cancer cells. The results revealed that GPC-1 is highly expressed in HCC, negatively correlated with survival, and also positively correlated with immune infiltration and clinical stage. Furthermore, GPC-1 promoted cell proliferation and inhibited apoptosis in the HCC cell lines. WGCNA analysis and HCCDB database revealed that Akt acted as a key molecule related to GPC-1, influencing biological functions and regulating cell malignant behaviors via the AKT signaling pathway. In conclusion, our findings provide a relatively comprehensive understanding of the oncogenic role of GPC-1 in HCC, implying that GPC-1 could serve as an innovative therapeutic target.
Night target detection algorithm based on improved YOLOv7
Authors: Bowen Zheng , Huacai Lu , Shengbo Zhu , Xinqiang Chen , Hongwei Xing 
Published: July 2024

AbstractAiming at the problems of error detection and missing detection in night target detection, this paper proposes a night target detection algorithm based on YOLOv7(You Only Look Once v7). The algorithm proposed in this paper preprocesses images by means of square equalization and Gamma transform. The GSConv(Group Separable Convolution) module is introduced to reduce the number of parameters and the amount of calculation to improve the detection effect. ShuffleNetv2_×1.5 is introduced as the feature extraction Network to reduce the number of Network parameters while maintaining high tracking accuracy. The hard-swish activation function is adopted to greatly reduce the delay cost. At last, Scylla Intersection over Union function is used instead of Efficient Intersection over Union function to optimize the loss function and improve the robustness. Experimental results demonstrate that the average detection accuracy of the proposed improved YOLOv7 model is 88.1%. It can effectively improve the detection accuracy and accuracy of night target detection.
Biliary atresia and cholestasis plasma non-targeted metabolomics unravels perturbed metabolic pathways and unveils a diagnostic model for biliary atresia
Authors: Du Bang , Mu Kai , Sun Meng , Yu Zhidan , Li Lifeng , Hou Ligong , Wang Qionglin , Sun Jushan , Chen Jinhua , Zhang Xianwei , Zhang Wancun 
Published: July 2024

AbstractThe clinical diagnosis of biliary atresia (BA) poses challenges, particularly in distinguishing it from cholestasis (CS). Moreover, the prognosis for BA is unfavorable and there is a dearth of effective non-invasive diagnostic models for detection. Therefore, the aim of this study is to elucidate the metabolic disparities among children with BA, CS, and normal controls (NC) without any hepatic abnormalities through comprehensive metabolomics analysis. Additionally, our objective is to develop an advanced diagnostic model that enables identification of BA. The plasma samples from 90 children with BA, 48 children with CS, and 47 NC without any liver abnormalities children were subjected to metabolomics analysis, revealing significant differences in metabolite profiles among the 3 groups, particularly between BA and CS. A total of 238 differential metabolites were identified in the positive mode, while 89 differential metabolites were detected in the negative mode. Enrichment analysis revealed 10 distinct metabolic pathways that differed, such as lysine degradation, bile acid biosynthesis. A total of 18 biomarkers were identified through biomarker analysis, and in combination with the exploration of 3 additional biomarkers (LysoPC(18:2(9Z,12Z)), PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0), and Biliverdin-IX-α), a diagnostic model for BA was constructed using logistic regression analysis. The resulting ROC area under the curve was determined to be 0.968. This study presents an innovative and pioneering approach that utilizes metabolomics analysis to develop a diagnostic model for BA, thereby reducing the need for unnecessary invasive examinations and contributing to advancements in diagnosis and prognosis for patients with BA.
Association between tobacco smoke exposure and serum parathyroid hormone levels among US adults (NHANES 2003–2006)
Authors: Hu Longqing , Qian Bei , Bing Kaijian , Mei Li , Ruan Shengnan , Qu Xincai 
Published: July 2024

AbstractTobacco smoke exposure has been demonstrated to impede bone remodeling and diminish bone density, yet research regarding its correlation with parathyroid hormone (PTH) remains limited. This study aims to investigate the relationship between tobacco smoke exposure and serum PTH levels in adults aged 20 years and older. This study included 7,641 participants from two cycles of the National Health and Nutrition Examination Survey (NHANES, United States, 2003- 2006). Reflect tobacco smoke exposure through serum cotinine levels, and use an adjusted weighted multivariate linear regression model to test the independent linear relationship between serum cotinine and PTH. Stratified analysis was conducted to validate the sensitivity of the conclusions. Smooth curve fitting and threshold effect analysis were performed to assess the non-linear relationship. After comprehensive adjustment using weighted multivariate regression analysis, a negative correlation was found between serum cotinine and PTH levels. The interaction p-values in subgroup analyses were all greater than 0.05. Moreover, smooth curve fitting indicated a non-linear relationship between serum cotinine and PTH, with a turning point observed. Our research indicates that tobacco smoke exposure is negatively correlated and independent of serum parathyroid hormone levels, indicating that long-term tobacco smoke exposure may lead to parathyroid dysfunction in adults.
A foundational large language model for edible plant genomes
Authors: Mendoza-Revilla Javier , Trop Evan , Gonzalez Liam , Roller Maša , Dalla-Torre Hugo , de Almeida Bernardo P. , Richard Guillaume , Caton Jonathan , Lopez Carranza Nicolas , Skwark Marcin , Laterre Alex , Beguir Karim , Pierrot Thomas , Lopez Marie 
Published: July 2024

AbstractSignificant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b for future research purposes.
Modeling and analysis of a two-stage tri-linear pendulum ultra-low frequency horizontal vibration isolator with experimental investigations
Authors: Li Qiang , Chen Zhiming , Xu Yan , Zhang Shengguo , He Zhaoyun , Li Yaqiong 
Published: July 2024

In engineering practice, the external horizontal oscillations always influence the working performance of precise instruments, advanced manufacture equipment, and gravitational wave detection. In order to ensure the normal operation of these instruments, it is necessary to attenuate these vibrations adequately. The pendulum mechanism horizontal vibration isolator is an efficient method. Hence, this paper presents a type of two-stage tri-linear pendulum horizontal vibration isolator (TPHVI). The first-stage TPHVI is connected in series with the second-stage one. The dynamic equations of the two-stage TPHVI are subsequently established so that the vibration isolation performance of the two-stage TPHVI is acquired. The analysis result of the natural frequency of the two-stage TPHVI reveals that it can obtain a low frequency vibration isolation performance when the first-stage TPHVI swings in a small value. As a case study, an experimental rig is constructed. The measured transmissibility keeps in good agreement with the calculated one. The natural frequency of the second-stage TPHVI is 0.25 Hz. The initial vibration isolation frequency is 0.3 Hz. When the external frequency is 0.8 Hz, the transmissibility of the second-stage TPHVI reaches −20 dB. Meanwhile, when the external frequency is 3 Hz, the transmissibility of the second-stage TPHVI is −40 dB. These measured data demonstrate that the proposed two-stage TPHVI can realize low frequency vibration isolation horizontally, which will have broad application prospects in the field of ultra-precision in engineering practice.
“We have a damn duty”
Authors: Pöllabauer Sonja , Bartłomiejczyk Magdalena , Zwischenberger Maria Bernadette , Straczek-Helios Viktoria 
Published: July 2024

Abstract
This article presents a case study of Ciocia Wienia, a collective of activists who facilitate abortion travel between Poland and Austria and provide support and interpretation in abortion clinics in Vienna. Drawing on a framework based on activist translation and motivational psychology, we investigate the factors that compel activist interpreters to join the collective and motivate their commitment to this activist work. Our study is based on a corpus of thirteen qualitative interviews with members and associates of the collective. The activists’ commitment is grounded in a range of purpose- and task-centred motivators, including their desire to respond to perceived gender-based injustice; to actively participate in society and be a member of a community of practice sharing the same pro-choice beliefs; to empower both oneself and individuals who need help; and to do good and feel satisfaction resulting from one’s work.
Finite element based homogenization of polypropylene/silica micro-composites: experimental work and numerical modeling
Authors: Tasneem Mohamad Hasan Bin , Al Jahwari Farooq , Al-Kindi Mahmoud , Al Mufarraji Said , Al-Lawati Emad 
Published: July 2024

Abstract
Polypropylene (PP) is one of the leading polymers in the polymer industry finding applications from automotive industry to medical equipment and adding silica can make it more desirable. A lot of experimental analysis has been done to study the effect of particle size among other parameters on PP/silica composites. However, very few literatures numerically analyze PP/silica composites. This study focuses on developing a 3D Finite Element based homogenization model that can accurately predict the elastic mechanical properties of PP/micro-silica composites. A Repeating Unit Cell is deployed during this study and periodic boundary conditions are imposed. The contribution of the model was demonstrated by comparing experimental results to the numerical analysis. The maximum error between the two for the Young’s modulus was less than 4% for 3% micro-silica composite. Similarly, to predict the compressive modulus, the maximum error was less than 7%. Parametric study was conducted to demonstrate how the proposed model behaves better than the analytical micro-mechanic models for soft materials like polymer composites and where contact failure occurs between the matrix and particle.
Real-space calculation of thermal Hall effects in strained and disordered kagome ferromagnets
Authors: Zhang Ning , Xu Hengyi 
Published: July 2024

Abstract
Ferromagnetic insulators may exhibit magnon Hall effects when subjected to a temperature gradient due to the Dzyaloshinsky–Moriya interaction. In this study, we investigate the magnon thermal Hall conductivity

κ

x
y

of kagome ferromagnets in real space using the kernel polynomial method. We first establish the formalism in real space within the framework of linear response theory, which enables efficient numerical calculations of thermal transport properties under various imperfections. The validity and accuracy of the real-space approach are confirmed by comparing the calculations with those obtained in momentum space. This approach is particularly advantageous for computing the thermal transport coefficients of disordered lattices. We consider two types of disorder in kagome ferromagnets and observe that both types significantly influence

κ

x
y

across the entire temperature range. This is in contrast to the effects of strains, where strains primarily impact the maximum values of

κ

x
y

.
Transparency-changing elastomers by controlling of the refractive index of liquid inclusions
Authors: Roh Sangchul , Yeo Seonju , Bang Rachel S , Han Koohee , Velikov Krassimir P , Velev Orlin D 
Published: July 2024

Abstract
Complex materials that change their optical properties in response to changes in environmental conditions can find applications in displays, smart windows, and optical sensors. Here a class of biphasic composites with stimuli-adaptive optical transmittance is introduced. The biphasic composites comprise aqueous droplets (a mixture of water, glycerol, and surfactant) embedded in an elastomeric matrix. The biphasic composites are tuned to be optically transparent through a careful match of the refractive indices between the aqueous droplets and the elastomeric matrix. We demonstrate that stimuli (e.g., salinity and temperature change) can trigger variations in the optical transmittance of the biphasic composite. The introduction of such transparency-changing soft matter with liquid inclusions offers a novel approach to designing advanced optical devices, optical sensors, and metamaterials.
Fabric defect detection based on feature enhancement and complementary neighboring information
Authors: Liu Guohua , Guo Changrui , Lian Haiyang 
Published: July 2024

Abstract
Fabric defect detection is a crucial aspect of quality control in the textile industry. Given the complexities of fabric backgrounds, the high similarity between patterned backgrounds and defects, and the variety of defect scales, we propose a fabric defect detection method based on feature enhancement and complementary neighboring information. The core of this method lies in two main components: the feature enhancement module and the neighboring information complementation strategy. The feature enhancement module includes two sub-modules: similarity feature enhancement (SFE) and edge detail feature enhancement (EDFE). The SFE aims to capture the similarities between features to strengthen the distinction between defects and complex backgrounds, thereby highlighting the correlations among defects and the differences between defects and the background. The EDFE focuses on improving the network’s ability to capture the edge details of fabrics, preventing edge information from becoming blurred or lost due to deeper network layers. The neighboring information complementation strategy consists of shallow-level information complementation (SLIC) and top-down information fusion complementation (TDIFC). The SLIC integrates newly introduced shallow features with neighboring features that have a smaller semantic gap, injecting richer detail information into the network. The TDIFC adaptively guides the interaction of information between adjacent feature maps, effectively aggregating multi-scale features to ensure information complementarity between features of different scales. Additionally, to further optimize model performance, we introduced partial convolution (Pconv) in the backbone of the feature extraction network. Pconv reduces redundant computations and decreases the model’s parameter count. Experimental results show that our proposed method achieved an mAP@50 of 82.4%, which is a 6.6% improvement over the baseline model YOLOv8s. The average inference frame rate reached 61.8 FPS, meeting the real-time detection requirements for fabric defects. Moreover, the model demonstrated good generalization capabilities, effectively adapting to detecting defects in different types and colors of fabrics.
Research on multi-velocity measurement method of solid particles based on interdigital electrostatic sensor
Authors: Gao Heming , Liao Zhongyu , Niu Yuguang , Pan Haoyang 
Published: July 2024

Abstract
In gas-solid two-phase flow, the velocities of solid particles will show different distributions as the flow pattern changes, and it is a challenging task to achieve the measurement of different velocity parameters in the flow field. To obtain the velocity information of solid particles in the flow field, a novel method to achieve multi-velocity measurement of solid particles is proposed by using the spatial filtering characteristics of interdigital electrostatic sensor with variational mode decomposition (VMD). Firstly, a three-dimensional model of the interdigital electrostatic sensor is established using COMSOL simulation software. Based on its spatial dynamic sensitivity, a theoretical analysis of its spatial filtering characteristics is conducted to obtain a mathematical relationship for velocity measurement. Subsequently, the VMD decomposition method is applied to an experiment involving charged wires, with the experimental results verifying the effectiveness of VMD in achieving multi-scale decomposition of electrostatic signals. Finally, the proposed measurement method is validated on a gravity feeding device and a pneumatic conveying system. The experimental results demonstrate that the method exhibits good feasibility.
Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis
Authors: Touati Redha , Trung Le William , Kadoury Samuel 
Published: July 2024

Abstract

Objective. Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation. Dose calculation from imaging modalities such as MRI remains an unsolved problem since this imaging modality does not provide information about the density of electrons. Approach. We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network. Results. The proposed model achieves a mean absolute error (MAE) of

18.76
±
5.167

in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of

0.95
±
0.09

and a Frechet inception distance (FID) of

145.60
±
8.38

. The model yields a MAE of

26.83
±
8.27

to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of

0.73
±
0.06

and a FID distance equal to

122.58
±
7.55

. The improvement of our model over other state-of-the-art GAN approaches is of 3.8%, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio of

27.89
±
2.22

and

26.08
±
2.95

to synthesize MRI from CT input. Significance. The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.