bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robot-assisted surgery for rectal cancer in patients after neoadjuvant chemoradiotherapy Immediate results
Authors: Moiseev M. E. , Kotiv V. N. , Gladyshev D. V. , Vetoshkin V. A. , Kovalenko S. A. , Murashko R. A. , Khondziya S. Z. , Svitich V. Yu. , Gnedash S. S. , Butorina A. S. , Skobaro A. S. 
Published: September 2024

Introduction. The benefits of robotic surgery for rectal cancer remain controversial. Only a few studies have evaluated the safety and feasibility of robot-assisted surgery after neoadjuvant chemoradiotherapy.Aim. Our study aimed to compare the short-term outcomes of robotic and traditional laparoscopic surgery after neoadjuvant chemoradiotherapy for rectal cancer.Materials and methods. This retrospective study was based on the analysis of 210 medical records of patients with rectal tumors who underwent robot-assisted and laparoscopic-assisted surgeries after neoadjuvant chemoradiotherapy from 2015 to 2021.Results. There was no significant difference in short-term outcomes such as operation time, conversion rate, anastomotic leakage, and quality of circular resection margin. However, a significant difference was noted in the quality of total mesorectal excision. Grade 3 quality in the R group was 93.9 %, while in the L group it was 74.07 % (Odds Ratio (OR) 2.335; 95 % Confidence Interval (CI) 1.107–4.925). A statistically significant difference was also found in the rate of performing sphincter-preserving procedures, with 12.2 % in the R group and 50 % in the L group (OR 0.139; 95 % CI 0.066–0.293).Conclusion. The advantages of the robotic surgical system in the surgical treatment of rectal cancer in patients after neoadjuvant chemoradiotherapy improve clinical outcomes, primarily reflected in the rate of sphincter-preserving operations. Additionally, it provides a trend towards achieving more oncologically adequate tumor removal compared to laparoscopic procedures.
Review of Power-to-Liquid (PtL) Technology for Renewable Methanol (e-MeOH): Recent Developments, Emerging Trends and Prospects for the Cement Plant Industry
Authors: Marques Luisa , Vieira Maria , Condeço José , Sousa Henrique , Henriques Carlos , Mateus Margarida 
Published: September 2024

The cement industry is a significant contributor (around 8 %) to CO2 global emissions. About 60 % of emissions come from limestone calcination, essential for clinker production, while 40 % result from fuel combustion. Reducing these emissions is challenging due to limestone’s role as the primary raw material for cement. Cement plants are required to achieve carbon neutrality by 2050, as outlined in the 13th United Nations Sustainable Goals. One strategy to achieve this goal, involves Carbon Capture and Utilisation (CCU). Among the options for CO2 utilisation, the Power-to-Liquid (PtL) strategy offers a means to mitigate CO2 emissions. In PtL, CO2 captured from cement industrial flue gas is combined with hydrogen generated by renewable electrolysis (green hydrogen) and catalytically converted into renewable methanol (e-MeOH). In this sense, this review provides a comprehensive overview of the worldwide existing pilot and demonstration units, and projects funded by EU across several industries. It specifically focuses on PtL technology worldwide within cement plants. This work covers 18 locations worldwide, detailing technology existent at plants of different capacities, location, and project partners. Finally, the review analyses techno-economic assessments related to e-MeOH production processes, highlighting the potential impact on achieving carbon neutrality in the cement industry.
Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures
Authors: Yang Yuedong , Song Yidong , Yuan Qianmu , Chen Sheng , Zeng Yuansong , Zhao Huiying 
Published: September 2024

Abstract

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.
Wavelet-Based Dual-Stream Network for Inpainting of Dunhuang Murals
Authors: Li Jiacheng , Shi Yuqing , Lian Jing , Du Shiqiang , Zhang Yang 
Published: September 2024

Abstract

The preservation and restoration of the Dunhuang murals, a paramount element of China's ancient artistic heritage, are confronted with significant challenges due to their extensive degradation over centuries. Traditional inpainting methods, which are mainly focused on the spatial domain, frequently fall short in authentically restoring these murals, especially considering their distinct artistic style. In response to these challenges, our study introduces a novel wavelet-based dual-stream network (WDSN). This innovative approach bifurcates the restoration process into distinct structure and texture pathways, employing wavelet decomposition to dissect the damaged image into low-frequency (structural) and high-frequency (textural) subbands. A key feature of our method is the incorporation of an Efficient Transformer Block (ETB) within the structure branch, alongside a Residual in Residual Dense Block (RRDB) in the texture branch. These elements synergistically enhance the fidelity of global structural reconstruction and local texture refinement. Furthermore, the integration of the CIEDE2000 color difference formula plays a pivotal role in ensuring the color authenticity of the restored murals, aligning more closely with human visual perception. Our comprehensive tests indicate that this methodology substantially outperforms existing algorithms in mural inpainting, offering a groundbreaking solution for the restoration and preservation of these invaluable cultural artifacts, thereby contributing significantly to the conservation of global art heritage.
TMEM100 acts as a TAK1 receptor that prevents pathological cardiac hypertrophy progression
Authors: Zhang Bin-Bin , Zhao Yi-Lin , Shen Ji-Hong , Li Hui-Yong , Zhang Han-Xue , Yu Xiao-Yue , Zhang Wen-Cai , Li Gang , Han Zhan-Ying , Guo Sen , Zhang Xu-Tao , Lu Yan-Yu 
Published: September 2024

Abstract

Pathological cardiac hypertrophy is the primary cause of heart failure, yet its underlying mechanisms remain incompletely understood. Transmembrane protein 100 (TMEM100) plays a role in various disorders, such as nervous system disease, pain and tumorigenesis, but its function in pathological cardiac hypertrophy is still unknown. In this study, we observed that TMEM100 is upregulated in cardiac hypertrophy. Functional investigations have shown that adeno-associated virus 9 (AAV9) mediated-TMEM100 overexpression mice attenuates transverse aortic constriction (TAC)-induced cardiac hypertrophy, including cardiomyocyte enlargement, cardiac fibrosis, and impaired heart structure and function. We subsequently demonstrated that adenoviral TMEM100 (AdTMEM100) mitigates phenylephrine (PE)-induced cardiomyocyte hypertrophy and downregulates the expression of cardiac hypertrophic markers in vitro, whereas TMEM100 knockdown exacerbates cardiomyocyte hypertrophy. The RNA sequences of the AdTMEM100 group and control group revealed that TMEM100 was involved in oxidative stress and the MAPK signaling pathway after PE stimulation. Mechanistically, we revealed that the transmembrane domain of TMEM100 (amino acids 53–75 and 85–107) directly interacts with the C-terminal region of TAK1 (amino acids 1-300) and inhibits the phosphorylation of TAK1 and its downstream molecules JNK and p38. TAK1-binding-defective TMEM100 failed to inhibit the activation of the TAK1-JNK/p38 pathway. Finally, the application of a TAK1 inhibitor (iTAK1) revealed that TAK1 is necessary for TMEM100-mediated cardiac hypertrophy. In summary, TMEM100 protects against pathological cardiac hypertrophy through the TAK1-JNK/p38 pathway and may serve as a promising target for the treatment of cardiac hypertrophy.
Developing An Automatic Road Safety Model for Accident Identification, Detection, And Prevention Using Deep Learning Algorithms
Authors: Devi Chokkakula , S Gowri 
Published: September 2024

Abstract

Road traffic monitoring systems are one of the leading real-time applications that use the Internet of Things to monitor and identify traffic scenarios on the road. IoT devices are integrated with sensors to sense and capture data within a specific distance range and transmit it to other sensors within a coverage range. Communication is possible, and data will be passed from one device to another only if they are located within the sensing and coverage region. Thus, multiple IoT devices are interconnected logically and communicate with one another within a coverage region. This paper focused on creating an efficient IoT network to monitor and broadcast accidental information immediately to the other vehicles on the road at a defined distance. Some exciting works included installing CCTV cameras, IoT devices, and other sensors only on road junctions and signals, where they can monitor only at particular locations, and they are not efficient in accident detection over urban city roads. This paper has focused on deploying more IoT devices within an urban city and creating an IoT network for accident detection and prevention. The IoT data are analyzed using a robust and efficient deep learning model, Convolution Neural Network work, that can quickly predict accidents from the IoT data analytics and intimate to the admin to broadcast the message to all the vehicles and the users on the road to take prevention actions. The IoT data is analyzed using the CNN algorithm implemented in Python, and the results are verified. The performance of the proposed CNN model is evaluated by comparing its output with the other state-of-the-art methods and proving that CNN outperforms the others.
The context-dependent epigenetic and organogenesis programs determine 3D vs. 2D cellular fitness of MYC-driven murine liver cancer cells
Authors: Yang Jun , Fang Jie , Singh Shivendra , Wells Brennan , Wu Qiong , Jin Hongjian , Janke Laura , Wan Shibiao , Steele Jacob , Connelly Jon , Murphy Andrew , Wang Ruoning , Davidoff Andrew , Ashcroft Margaret , Pruett-Miller Shondra 
Published: September 2024

Abstract

3D cellular-specific epigenetic and transcriptomic reprogramming is critical to organogenesis and tumorigenesis. Here we dissect the distinct cell fitness in 2D (normoxia vs. chronic hypoxia) vs 3D (normoxia) culture conditions for a MYC-driven murine liver cancer model. We identify over 600 shared essential genes and additional context-specific fitness genes and pathways. Knockout of the VHL-HIF1 pathway results in incompatible fitness defects under normoxia vs. 1% oxygen or 3D culture conditions. Moreover, deletion of each of the mitochondrial respiratory electron transport chain complex has distinct fitness outcomes. Notably, multicellular organogenesis signaling pathways including TGFb-SMAD specifically constrict the uncontrolled cell proliferation in 3D while inactivation of epigenetic modifiers (Bcor, Kmt2d, Mettl3 and Mettl14) has opposite outcomes in 2D vs. 3D. We further identify a 3D-dependent synthetic lethality with partial loss of Prmt5 due to a reduction of Mtap expression resulting from 3D-specific epigenetic reprogramming. Our study highlights unique epigenetic, metabolic and organogenesis signaling dependencies under different cellular settings.
Bulk adhesion of ice to concrete – strength
Authors: Barker Anne , Bruneau Stephen , Colbourne Bruce , Bugden Austin 
Published: September 2024

Abstract

This paper presents the results of a laboratory test program designed to investigate the adhesive effects of large-scale (bulk) ice on concrete. Medium-strength concrete cylinders were sawn into discs, and attached to a sample table. Freshwater ice samples, frozen using smaller, standard-sized concrete cylinders, were adhered to the concrete with both varying bond times and added weight during bonding. Shear strength tests were conducted at a set displacement rate, under a number of temperatures. The effect of these variables on the adhesive strength of ice to concrete was examined, as well as whether there was any noticeable removal of concrete cement paste or aggregate during testing. The tests indicate that the adhesive strength is negligible when the method of adhesion is “dry” (no liquid layer at the onset of adhesion). Tests with “wet” adhesion indicated a significantly higher strength. The nominal versus the apparent contact area had significant implications for the determination of the adhesive strength of the bond between the ice and the concrete. Removal of cement paste was evident in a number of tests, however the amount was not significant. The results have relevance for design of structures in a marine environment, such as revetement dams or rubblemound breakwaters, as well as for the standardization of adhesion tests with ice and concrete.
Effectiveness of long-short term memory network in financial fraud detection
Authors: Qiao YongSheng 
Published: September 2024

Abstract

To limit the impact of the financial frauds in capital market, it is essential to create a rigorous and effective financial fraud identification model. Our paper discusses the effectiveness of long-term and short-term memory (LSTM) network in detecting financial fraud. In total, 660 Shanghai and Shenzhen listed companies from 1994 to 2018 have been selected as our research samples. Among them, 165 entities were with records of false financial information disclosure. Different types of research approaches are adopted during two stages of the study. The first stage is data preprocessing, during which Artificial neural networks (ANN) are used to screen important variables. In order to accomplish a high-precision financial fraud detection model, both financial and non-financial variables are included. The second stage is the performance evaluation and classifiers comparison. Various classifiers are used to execute and compare the performance of the model: support vector machine, K-Nearest Neighbor (KNN), random forest, multilayer perceptron and LSTM neural network. The results show that the variables screened by ANN and processed by LSTM neural network have high accuracy in identifying financial statement fraud.
Production capacity prediction of fractured oil wells based on improved Convolutional Neural Network
Authors: Wang Jianbo , Gao Bingkun , Guo Zhengyang , Xue Yuxiang 
Published: September 2024

Abstract

The significance of predicting the productivity of fractured oil wells for optimizing production strategies and enhancing efficiency is widely acknowledged. However, traditional algorithms often fall short in accurately predicting productivity, especially when dealing with fractured oil wells with limited production data, significant data fluctuations, and missing values. An algorithm for predicting fractured oil well productivity based on improved convolutional neural networks(CNN) is proposed in this study. This algorithm not only enables adaptive feature extraction but also enhances prediction performance on small sample data. Experimental results demonstrate that compared to classic CNN and MLP models, the proposed algorithm achieves superior performance. The average absolute errors of initial oil production and incremental oil production are 0.023t and 15.04t, respectively, with corresponding root mean square errors of 0.0011t and 23.74t. The prediction accuracy rates stand at 95.62% and 86.68%, respectively. These performance indicators outperform those of classic models, showcasing promising predictive capabilities. This research holds practical significance in evaluating fractured oil well productivity, devising optimization strategies, and guiding oilfield development endeavors to improve resource utilization efficiency and overall production outcomes.
In-silico study of E169G and F242K double mutations in Leucine-Rich Repeats (LRR) PolyGalacturonase Inhibiting Protein (PGIP) of Gossypium barbadense and associated defense mechanism against plant pathogens
Authors: Murmu Sneha , Rashmi Mayank , Nagrale Dipak T , Kour Tejasman , Singh Mahender Kumar , Chaurasia Anurag , Behera Santosh Kumar , Shankar Raja , Ranjan Rajiv , Jha Girish Kumar , Gawande Shailesh P. , Hiremani Neelakanth S. , Prasad Y. G. , Kumar Sunil 
Published: September 2024

Abstract

PolyGalacturonase Inhibiting Proteins (PGIPs) play a pivotal role in plant defense against pathogens by inhibiting PolyGalacturonase (PG), an enzyme produced by pathogens to degrade plant cell wall pectin. PGIPs, also known as leucine-rich repeat pathogenesis-related (PR) proteins, activate the host's defense response upon interaction with PG, thereby fortifying the plant against pathogenic attacks. In cotton (Gossypium barbadense), the interaction between PGIP and PG is a crucial step in the defense mechanism against major pathogens such as Xanthomonas citri pv. malvacearum and Alternaria macrospora, responsible for bacterial leaf blight and leaf spot diseases, respectively. To unravel the molecular mechanisms underlying these pathogenesis-related proteins, we conducted a comprehensive study involving molecular modeling, protein-protein docking, site-specific double mutation (E169G and F242K), and molecular dynamics simulations. Both wild-type and mutated cotton PGIP were examined in interaction with the PG enzyme from pathogens. Our findings revealed that changes in conformations of double-mutated residues in the active site of PGIP lead to the inhibition of PG binding. The molecular dynamics simulations provide insights into the dynamic behavior and stability of the PGIP-PG complexes, shedding light on the intricate details of the inhibitory and exhibitory mechanism against the fungal and bacterial pathogens respectively. This study not only enhances our understanding of the molecular interactions between PG and PGIP but also presents a potential strategy for developing disease-resistant cotton varieties. By variations in the binding affinities of PG through specific mutations in PGIP, this research offers a promising avenue for the development of cotton plants with enhanced resistance to bacterial leaf blight and leaf spot diseases.
The Supplier Selection of Prefabricated Component Production Line through a Lean- based AHP-Improved VIKOR framework
Authors: Dang Pei , Geng Linna , Niu Zhanwen , Gao Hui , Sun Chao 
Published: September 2024

Abstract

Over the past decades, prefabrication has emerged as a sustainable construction practice, with its performance significantly affected by the efficiency of production lines for the prefabricated components (PCs). However, insights from the industry reveal that selecting suppliers for PCs production lines has become increasingly challenging due to evolving, uncertain demands and complexity. Such issue negatively impacts PCs production performance, which in turn, affects overall efficiency of prefabrication. Despite previous research focusing on various PCs supplier selection criteria, there is limited research concerning its improvement in aligning with current industrial demands. Hence, this study developed an integrated AHP-improved VIKOR decision making framework with the advanced Lean principles incorporated, to enhance the efficiency of selected PCs production line suppliers. To achieve this, a Lean-based AHP-Improved VIKOR evaluation framework, including general criteria and specific criteria, was designed via an extensive literature review and expert interviews. Lastly, the developed framework was validated via a real-world case with Yizhong Construction Co., Ltd. in China. It demonstrates that this developed evaluation framework can be a valuable tool for selecting PCs production line suppliers, enhancing its efficiency through the lens of lean, and advancing prefabrication in developing countries.
Spatial Association Network Structure of Agricultural Carbon Emission Efficiency in Chinese Cities and Its Driving Factors
Authors: Wei Xu , Chen Bohong 
Published: September 2024

Abstract

In light of the Chinese government's dual carbon goals, achieving cleaner production activities has become a central focus, with regional environmental collaborative governance, including the management of agricultural carbon reduction, emerging as a mainstream approach. This study examines 268 prefecture-level cities in China, measuring the carbon emission efficiency of city agriculture from 2000 to 2021. By integrating social network analysis and a modified gravity model, the study reveals the characteristics of the spatial association network of city agricultural carbon emission efficiency in China. Additionally, the quadratic assignment procedure is employed to investigate the driving factors. The findings indicate that: (1) The carbon emission efficiency of cities agriculture in China displays substantial spatiotemporal heterogeneity, characterized by marked regional clustering. Central cities generally exhibit higher efficiency levels, while the surrounding cities tend to have lower efficiency. (2) The carbon emission efficiency of city agriculture in China displays multidimensional, complex, and organic characteristics, with potential for enhanced network stability. (3) Agricultural regions in central and southeastern China dominate the spatial network, while regions with weaker agricultural sectors, like Beijing, Shanghai, and Ningxia, occupy peripheral positions. (4) Within the spatial association network of city agricultural carbon emission efficiency in China, Intra block spatial correlations are low, while interblock spatial correlations are strong, exhibiting significant spillover effects. (5)Variations in agricultural development levels and mechanization significantly enhance the formation of networks related to city agricultural carbon emission efficiency in China. Conversely, differences in industrial structure and fertilizer application levels exert a substantial negative influence on these networks.