bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expression of toll-like receptors in cartilage endplates cells: a role of toll-like receptor 2 in pro-inflammatory and -catabolic gene expression
Authors: Mengis Tamara , Bernhard Laura , Herger Nick , Heggli Irina , Devan Jan , Marcus Roy , Laux Christoph , Brunner Florian , Farshad Mazda , Distler Oliver , Dudli Stefan 
Published: May 2024

Abstract

Introduction
The vertebral cartilage endplate (CEP), essential for intervertebral disc health, is susceptible to degeneration, which can be associated with chronic low back pain, disc degeneration, and Modic changes. Although it has been established that intervertebral disc cells express toll-like receptors (TLRs), which can recognize diverse pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs), triggering an immune response, it is unknown whether CEP cells (CEPC) share this trait. The CEP exhibits a significantly higher cell density compared to the disc, potentially heightening the relevance of CEPC in this context. This study aimed to identify TLRs on CEPC and explore their role in activating pro-inflammatory and catabolic gene expression.
Methods
Gene expression of TLR1-10 was measured with quantitative real-time polymerase chain reaction in human CEPs and expanded CEPC. Additionally, CEPC were stimulated with tumor necrosis factor alpha and interleukin 1 beta, specific TLR2/6, TLR2/1 and TLR4 agonist (Pam2csk4, Pam3csk4 and lipopolysaccharide) and with the 30 kDa N-terminal fibronectin fragment, a representative DAMP. TLR2 signaling was inhibited with TL2-C29. TLR2 protein expression was measured with flow cytometry.
Results
An ex-vivo analysis of CEP tissue found all 10 TLRs expressed while cultured CEPC lost TLR7 and TLR8 expression. TLR2 was the only TLR whose expression significantly increased after pro-inflammatory stimulation. Stimulation of the TLR2/6 heterodimer with Pam2csk4 upregulated TLR2 protein expression. After 48 hours of stimulation, all applied ligands upregulated expression of pro-inflammatory genes and the matrix metalloproteases 1 (MMP1), MMP3, and MMP13. TLR2 inhibition was able to specifically inhibit the upregulated genes.
Conclusion
The expression of TLR1-10 in CEPC indicates that the CEP is susceptible to PAMP and DAMP stimulation. TLR2 expression in CEPC is enhanced under inflammatory conditions and its stimulation has pro-inflammatory and pro-catabolic consequences. Therefore, TLR2 signaling in CEPC might play an important role in disc degeneration and Modic changes.
Satellites reveal recent increases in global land surface albedo that moderates global warming
Authors: Zhang Liqiang , Hou Zhengyang , Peng Jingjing , Forzieri Giovanni , Jia Aolin , Xiao Zhiqiang , Qu Ying , Lin Jintai , Ji Duoying , Zhu Zidong , Yao Xin , Peng Shuwen , Zhao Lanpu , Fan Wenjie , Wu Zhaocong , Geng Hao , Wang Qihao , Liu Suhong , Zhang Liangpei 
Published: May 2024

Abstract

Surface albedo greatly impacts how much energy the Earth absorbs. Intense human activities and rapid climate change have caused notable changes in surface albedo. Yet, how land use/land cover (LULC) and snow dynamics drive the global land surface mean albedo (GLMA) change is lack of comprehensive assessments to date. Thus, the GLMA dynamics over the last decades and their corresponding radiative forcing to the climate system remains poorly understood. Here, we construct a satellite-based, seamless monthly dataset of global land surface albedo and LULC look-up maps from 2001 to 2020. We then quantify the individual and combined effects of snow cover dynamics, LULC conversions and LULC non-conversion regions on albedo variations and their induced radiative forcing. From 2001 to 2020, GLMA increased by 0.6%, and the induced global radiative forcing was -0.0768±0.0253 W/m2. During the recent two decades, albedo over snow-free regions significantly increased by 2.28% with radiative forcing of -0.1257±0.0025 W/m2. This forcing was 2.5 times more than that induced by snow dynamics, and was equivalent in magnitude to 45.89% of that caused by CO2 emissions and 37.41% of that caused by the total greenhouse gas emissions from 2011 to 2019 estimated in the Intergovernmental Panel on Climate Change Sixth Assessment Report. The global radiative forcing owing to LULC conversions was less than one-sixth of the forcing arising from the LULC non-conversion regions. The radiative forcing induced by albedo change highlights the important role of land surface dynamics in modulating global warming.
Controls of low injectivity caused by interaction of reservoir and clogging processes in a sedimentary geothermal aquifer (Mezőberény, Hungary)
Authors: Markó Ábel Szabolcs , Brehme Maren , Pedretti Daniele , Zimmermann Günter , Huenges Ernst 
Published: May 2024

Abstract

Low injectivity is often experienced in geothermal doublets installed in sandstone reservoirs. This even led to a shut-down of the Mezőberény (Hungary) geothermal site. An on-site campaign was carried out in January 2021 to prepare a stimulation aiming to enhance the transmissivity of the sedimentary reservoir and the near well-bore zone of this site. Previous studies have concluded that insufficient injectivity may be linked to a high skin effect in the near well-bore zone and pore clogging in combination with the low net sandstone content of the fluvio-deltaic reservoir. A chemical soft stimulation based on the injection of hydrochloric acid (HCl) was successfully used to unclog and recover the well injectivity. Despite of such empirical evidence, the geochemical mechanisms leading to both, detrimental formation of clogging and the HCl-driven transmissivity restoration, have not been elucidated yet. This work presents the results of a novel analysis aiming at (a) predicting the dominant type of clogging forming in the near well bore zone, (b) quantifying the drop in hydraulic conductivity as clogging occurs and (c) support the optimization of the HCl dosage during the chemical soft stimulation. The study is supported by new experimental datasets never presented before from the Mezőberény site and a geochemical model simulating the main mechanisms involved in the clogging and unclogging processes. It is concluded that the biofilm formation was the dominant, while the precipitation of calcite and amorphous ferrihydrite – later reduced to magnetite by microbes – was the secondary clogging mechanism: In the long-term (yearly scale) simulating the hydraulic conductivity showed a decline with forming scales, therefore biofilm was presumably responsible for the experienced rapid (one month) clogging. When modelling the chemical stimulation, the estimated amount of precipitated minerals (Case 1) was dissolved already with 2.5 moles of HCl per liter water (~ 10 m/m%). Therefore, the chosen 20 m/m% of HCl might had a beneficial effect dissolving the potentially higher amount of scaling (Case 2) and/or the carbonate minerals of the matrix near the well-bore. Overall, it is concluded that the chemical and the microbial analysis together with the geochemical model was critical to tailor the remediation attempts and to propose further development or reconstruction of the surface system before going into operation to prevent recurrent impairments.
A novel hybrid 8-node plate element for capturing the nonlocal effect based on the Hellinger-Reissner variational principle
Authors: Li Haonan , Yao Linquan , Li Cheng , Guo Limin 
Published: May 2024

Abstract

The Hellinger-Reissner variational principle based hybrid finite element method (FEM) is developed and applied to study the nonlocal mechanics of plates and beams at a micro/nano-scale. For this purpose, a plane 8-node plate element termed as MHAS-24β with 24 independent internal force parameters is proposed to modelling the mechanical behaviors including static bending, free vibration and buckling. The Mindlin plate theory allows the use of generalized displacement to satisfy \({\text{C}}^{\text{0}}\) continuity requirements, making it applicable to plates with different thicknesses. To overcome shear locking, the assumed strain method (ASM) is adopted to modify original strains. The polynomials for internal forces are assumed to be related to the highest-order derivatives of variables, making them complete and capable of avoiding the zero-energy mode. The posterior error estimation indicates that the convergence order is not affected by the nonlocal parameter, plate thickness or element shape. The MHAS-24β element effectively captures the nonlocal effect and outperforms the displacement-type FEM and the low-order hybrid FEM described in previous literature.
Exploring sustainable construction through experimental analysis and AI predictive modelling of ceramic waste powder concrete
Authors: Kashyap Rishabh , Saxena Mukul , Gautam Arstu , Kushwaha Anuj , Priyanka Km. , Patel Anubhav , Maurya Rajneesh Kumar 
Published: May 2024

Abstract

This research tackles the one of the major environmental issues raised by the ceramic industry’s production of ceramic waste powder (CWP) during the cutting and polishing stages, Which requires a different strategy to reduce pollution and cut landfill usage.The main goal of this study is to make use of AI model for CWP concrete by applying Machine Learning (ML) techniques to effectively estimate the mechanical property of the concrete. A thorough investigation is carried out on 54 different concrete mixes, using CWP in place of cement in percentages of 10%, 20%, 30%, 40%, and 50%. The baseline is the compressive strength of plain concrete. A supervised machine learning (ML) techniques such as gradient boosting, Random forest and Regeration analysis are used to predict the compressive strength of CWP concrete (CWPC). Important metrics including R2, Mean Absolute Error (MAE), and Mean Square Error (MSE) are used to assess the performance of the model. The results show that Random forest performs better than the other models (R2 = 0.95, MSE = 5.25 KN/mm2, MAE = 1.05 KN/mm2). The study emphasizes how using CWPC as a building material might help reduce water pollution and land degradation. It also emphasizes the efficiency advantages that may be attained by using ML approaches for concrete characteristic estimate, which will ultimately result in time and resource savings for researchers in the construction industry.
WITHDRAWN: Stock price prediction based on generative adversarial network
Authors: Zhou Yan , Lin Yuhao , Hu Zongliang , Song Bo , Tang Jianliang 
Published: May 2024

Abstract

Investors can reduce the investment risk of stock investors and improve the investment return by accurately predicting stock price trends. It is difficult to predict the trend of stock prices due to high noise, nonlinearity and high volatility of stock data. In this paper, we propose a novel Wasserstein generative adversarial network with the temporal convolution module and the self-attention mechanism (TA-WGAN), which is based on the generative adversarial network (GAN), to predict the stock closing price of the next day. The key components of TA-WGAN are the generator and the discriminator, where the generator predicts the closing price of the stock and the discriminator evaluates the performance of the generator. The generator includes a temporal convolutional network and self-attention mechanism, and the discriminator is composed of a bidirectional long short-term memory network and temporal convolutional network. In the training process, TA-WGAN combines adversarial loss and square loss to improve its prediction performance by adversarial training of the generator and the discriminator. To demonstrate the effectiveness of TA-WGAN, we compare it with existing methods for two datasets: the CSI 300 Index and the Ping An Bank stock. The results demonstrate that TA-WGAN outperforms single-stage models, such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Temporal Convolutional Network (TCN).
Short-term outcomes of delta-shaped anastomosis versus functional end-to-end anastomosis using linear staplers for colon cancer
Authors: Ono Rika , Tominaga Tetsuro , Ishii Mitsutoshi , Hisanaga Makoto , Araki Masato , Sumida Yorihisa , Nonaka Takashi , Hashimoto Shintaro , Shiraishi Toshio , Noda Keisuke , Takeshita Hiroaki , Fukuoka Hidetoshi , Oyama Shosaburo , Ishimaru Kazuhide , Sawai Terumitsu , Matsumoto Keitaro 
Published: May 2024

Abstract

Background
Several methods are used for reconstruction in colon cancer surgery, including hand-sewn or stapled anastomosis. However, few reports have compared short-term outcomes among reconstruction methods. This study compared short-term outcomes between delta-shaped anastomosis (Delta) and functional end-to-end anastomosis (FEEA).
Methods
We retrospectively reviewed 1314 consecutive patients who underwent colorectal surgery with FEEA or Delta reconstruction between January 2016 and December 2023. Patients were divided into two groups according to reconstruction by FEEA (F group; n = 1242) or Delta (D group; n = 72). Propensity score matching was applied to minimize the possibility of selection bias and to balance covariates that could affect postoperative complications. Short-term outcomes were compared between groups.
Results
Postoperative complications occurred in 215 patients (17.3%) in F group and 8 patients (11.1%) in D group. Before matching, transverse colon cancer was more frequent (p = 0.002), clinical N-positive status was less frequent (44.1% vs 16.7%, p < 0.001), distant metastasis was less frequent (11.7% vs 1.4%, p = 0.003), and laparoscopic approach was more frequent (87.8% vs 100%, p < 0.001) in D group. After matching, no differences in any clinical factor were evident between groups. Blood loss was lower (28 mL vs 10 mL, p = 0.002) in D group. However, operation time and postoperative complication rates were similar between groups.
Conclusions
Delta and FEEA were both considered safe as reconstruction methods. Delta anastomosis may be more useful for patients with transverse colon cancer for whom transfusion should be minimized.
Factors Predicting Completion of Four or More Antenatal Care Visits in Sarlahi District, Nepal
Authors: Yue Yiwei , Hazel Elizabeth A. , Subedi Seema , Zeger Scott , Mohan Diwakar , Mullany Luke C , Tielsch James M , Khatry Subarna K , LeClerq Steven C. , Katz Joanne 
Published: May 2024

Abstract

Background: A significant number of women die from pregnancy and childbirth complications globally, particularly in low- and middle-income countries (LMICs). Receiving at least four antenatal care (ANC) visits may be important in reducing maternal and perinatal deaths. This study investigates factors associated with attending ≥ 4 ANC visits in Sarlahi district of southern Nepal.
Methods: A secondary analysis was conducted on data from the Nepal Oil Massage Study (NOMS), a cluster-randomized, community-based longitudinal pregnancy cohort study encompassing 34 Village Development Committees. We quantified the association between receipt/attendance of ≥ 4 ANC visits and socioeconomic, demographic, morbidity, and pregnancy history factors using logistic regression; Generalized Estimating Equations were used to account for multiple pregnancies per woman.
Results: All pregnancies resulting in a live birth (n=31,867) were included in the model and 31.4% of those pregnancies received 4+ ANC visits. Significant positive associations include socioeconomic factors such as participation in non-farming occupations for women (OR=1.52, 95% CI: 1.19, 1.93), higher education (OR=1.79, 95% CI: 1.66, 1.93) and wealth quintile OR=1.44, 95% CI: 1.31, 1.59), nutritional status such as non-short stature (OR=1.17, 95% CI: 1.07, 1.27), obstetric history such as adequate interpregnancy interval (OR=1.31, 95% CI: 1.19, 1.45) and prior pregnancy but no live birth (OR=2.14, 95% CI: 1.57, 2.92), symptoms such as vaginal bleeding (OR=1.35, 95% CI:1.11, 1.65) and awareness of the government's conditional cash transfer ANC program (OR=2.26, 95% CI: 2.01, 2.54). Conversely, belonging to the lower Shudra caste (OR=0.56, 95% CI: 0.47, 0.67), maternal age below 18 or above 35 (OR=0.81, 95% CI:0.74, 0.88; OR=0.77, 95% CI: 0.62, 0.96)), preterm birth (OR=0.41, 95% CI: 0.35, 0.49), parity ≥ 1 (OR=0.66, 95% CI: 0.61, 0.72), and the presence of hypertension during pregnancy (OR=0.79, 95% CI: 0.69, 0.90) were associated with decreased likelihood of attending ≥ 4 ANC visits.
Conclusions: These findings underscore the importance of continuing and promoting the government's program and increasing awareness among women. Moreover, understanding these factors can guide interventions aimed at encouraging ANC uptake in the most vulnerable groups, subsequently reducing maternal-related adverse outcomes in LMICs.
Trial registration: The clinicaltrial.gov trial registration number for NOMS was #NCT01177111. Registration date was August 6th, 2010.
ECG-Based Cardiac Disease Detection in Time-Frequency Domain using Grid Search Optimized Wavelet Transforms
Authors: Diwakar  , Deepa Prof. 
Published: May 2024

Abstract

Cardiovascular diseases (CVDs), including abnormal arrhythmias and congestive heart failure, are a leading cause of mortality worldwide, with electrocardiogram (ECG) signals serving as a critical diagnostic tool. This study introduces a novel approach for classifying diseases in ECG signals into three categories: normal sinus rhythm (NSR), abnormal arrhythmia (ARR), and congestive heart failure (CHF). The classification is based on a combination of features extracted from both the time domain (mean and standard deviation) and the frequency domain (power spectral density and spectral centroid) of the ECG signals. Additionally, energy values from selected frequency bands are utilized. To enhance the model's robustness, incorporate data augmentation techniques, including time-shifting and flipping of the signals. These augmented datasets are then employed with various classifiers, and an optimization process using grid search is applied to enhance the classification performance. This methodology presents a promising framework for automated ECG signal analysis, The results of the proposed work have been exceptionally promising, showcasing a remarkable specificity rate of 99.7% and achieving an accuracy level of 99.58%. These findings hold significant promise for advancing early detection methods and enhancing patient outcomes in the realm of CVDs.
Path-MGCN: a pathway activity based multi-view graph convolutional network for determining spatial domains with attention mechanism
Authors: Zhou Qirui , Li Chaowen , Chen Chao , Li Mingyue , Liu Jiabei , Sun Weijun , Zhang Zongmeng , Gu Songqing , Cai Yishan , Huang Yonghui , Liu Hongtao , Yang Chao , Chen Xin 
Published: May 2024

Abstract

Gene functional relationships are always ignored in spatial-domain recognition based on spatial transcriptomics (ST). We develop Path-MGCN, a multi-view graph convolutional network (MGCN) with attention mechanism that embeds pathway information. We generate a pathway activity profile with spot-specific pathway enrichment. Unique and shared embeddings from pathway and spatial graphs are extracted by a MGCN encoder, dynamically optimized by attention mechanism, followed by a decoder to retain the original pathway information. Path-MGCN outperforms state-of-the-art spatial clustering methods. Moreover, Path-MGCN could identify spatial domain-specific pathways for further mechanism study in the context of microenvironment, enabling the precision medicine of complex diseases.
Competências na base nacional comum curricular e nos referenciais curriculares dos estados do Brasil: abordagem crítica do conceito
Authors: Vásquez Astudillo Mario , Ríos-Muñoz Daniel , Carneiro Sarturi Rosane 
Published: May 2024

Analisa o uso do questionado conceito de competência na educação básica, formação profissional e trabalho. A metodologia é a revisão narrativa de documentos oficiais. Conclui-se que não precisamos silenciar sua polissemia, mas bem discutir criticamente suas perspectivas e contextos de uso. A maioria dos estados brasileiros usa uma definição básica de competência, com um enfoque parcial ou reduzido sem discussão ou aprofundamentos teóricos epistemológicos, na perspectiva sistêmica e holística, para o desenvolvimento humano integral, na construção de uma sociedade justa, democrática e inclusiva.
A genetic algorithm's novel rainfall distribution method for optimized hydrological modeling at basin scales
Authors: Skoulikaris Charalampos , Nagkoulis Nikolaos 
Published: May 2024

ABSTRACT

Rainfall has a dominant role in rainfall-runoff models, with the rendering of these models depending on the data accuracy and on the way that rainfall is spatially allocated. The research proposes a methodological framework where a genetic algorithm (GA)-based method responsible for the spatial distribution of gauge observations at the basin scale is coupled with the HEC-HMS hydrological model to produce simulated discharges of high accuracy. The custom-developed GA is used to divide a 2D space, adhering to specific criteria, into polygonal geometries that represent gauge zones of influences, similar to the Thiessen polygon method concept. A collection of vectorial polygonal areas, equivalent in number to the employed monitoring stations, is produced with the areal weights to be used for distributing the rainfall across the case study basin and subsequently to force the hydrological simulations. The generated gauge weights are validated for a different temporal precipitation event. The final outputs expressed through a series of statistical measures, clearly demonstrate the effectiveness of the specific methodology (e.g. R2 and Nash–Sutcliffe are larger than 0.83 and 0.73). The methodology can foster accurate hydrological simulations, especially in cases where there is a limited number of rainfall stations and corresponding observations.
Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer
Authors: Al-Maktoumi Ali , Rajabi Mohammad Mahdi , Zekri Slim , Govindan Rajesh , Panjehfouladgaran Aref , Hajibagheri Zahra 
Published: May 2024

ABSTRACT
This study presents the ‘Dual Path CNN-MLP’, a novel hybrid deep neural network (DNN) architecture that merges the strengths of convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) for regional groundwater flow simulations. This model stands out from previous DNN approaches by managing mixed input types, including both imagery and numerical vectors. Such flexibility allows the diverse nature of groundwater data to be efficiently utilized without the need to convert it into a uniform format, which often leads to oversimplification or unnecessary expansion of the dataset. When applied to the northeast Qatar aquifer, the model demonstrates high accuracy in simulating transient groundwater flow fields, benchmarked against the well-established MODFLOW model. The model's efficacy is confirmed through k-fold cross-validation, showing an error margin of less than 12% across all examined locations. The study also examines the model's ability to perform uncertainty analysis using Monte Carlo simulations, finding that it achieves around 1% average absolute percentage error in estimating the mean hydraulic head. Errors are mostly found in areas with significant variations in the hydraulic head. Switching to this machine learning model from the conventional MODFLOW simulator boosts computational efficiency by about 99%, showcasing its advantage for tasks like uncertainty analysis in repetitive groundwater simulations.