bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation of Book selection planning based on deep learning and its application
Authors: Bian Kun 
Published: April 2023

Abstract
Due to the complexity of network information, it presents an unbalanced feature. For the current book publishers, the publishing theme selection and planning methods of the newsroom can no longer meet the development speed of the Internet background. As the publishing house can not accurately analyze the needs of users, it is difficult to obtain the specific standards of the book publishing market. Therefore, the demand of consumers for books has decreased, and further generated practical problems such as inventory accumulation and revenue impairment. Based on the demand of extracting and analyzing target information for books, it can be realized by using deep learning methods. Therefore, this study establishes a book selection planning system. If the information of the book itself and the corresponding evaluation information are required, the system first uses Anaconda to crawl the website to obtain the book information data, and then uses the proposed KIEM algorithm to supplement the information of the data crawled by Anaconda. After completing this step, use the first layer of CRF model to separate the evaluation sentence from the opinion sentence, and split the separated target sentence to get the attribute and emotion words. Finally, ARIA algorithm is combined with the improved recommendation algorithm to improve the recommendation performance and achieve the accuracy and personalization of the system. It can be seen from the actual measurement that the MAE value calculated by the application of the system proposed in this paper is relatively small, which shows that the system has certain practicability in prediction accuracy. This paper designs an effective system application by introducing deep learning technology into the field of book topic selection planning.
3D sculpture technology innovation based on image segmentation algorithm
Authors: Zhang Xincheng , Ge Chunyu 
Published: April 2023

Abstract
At present, 3D modeling technology is simplified due to the improvement of image segmentation algorithm and feature point detection and other related technologies, so it is widely used in major museums. For example, science and technology museums can display emerging scientific and technological models by using 3D modeling, while museums and art galleries can display works of art and antiques by using 3D modeling. By using 3D modeling to display, tourists can effectively enhance the sense of substitution. 3D sculpture is a brand new product under this technical background, which can enhance the vividness of the exhibition of works in the Expo Park, thus bringing tourists a better tour experience. Based on this background, this study introduces the image segmentation algorithm to adjust and optimize 3d sculpture works, and introduces the main optimization process. Then, modeling, modification and model reconstruction are carried out through the collection and calculation of multi angle pictures. In this process, file content import, model data import and 3d object image import are required. The simulation results show that the optimized 3d model can effectively improve the frame rate performance of the model, but also reduce the volume, number of faces and vertex points of the model to a certain extent. It is an effective and innovative research work to optimize 3d sculpture by using image segmentation algorithm. This paper introduces the image segmentation algorithm into the field of 3d sculpture to achieve technological innovation.
Cloud computing English teaching application platform based on machine learning algorithm
Authors: Zhang Peili 
Published: April 2023

Abstract
Due to the progress of computer technology at this stage, English teaching has gradually achieved reforms with the help of the Internet. This paper builds an English teaching application platform based on machine learning algorithms, and realizes personalized online teaching with the help of cloud computing technology. In view of the complex characteristics of learners' online learning behavior data, this paper uses Pearson's correlation coefficient to quantify the correlation between various learning behavior variables. In order to further improve the accuracy of system data analysis, regression analysis is used to detect abnormal data points, and data with a data prediction deviation greater than 1 is determined as abnormal data. In order to reduce the loss of a large amount of data loss caused by the regression analysis to judge some anomalies, this paper uses the local outlier detection method again to detect local outliers, and compares the data repeatedly detected in the two identification algorithms, and judges it as abnormal data. special handling. In order to further verify the practicability of the system, network simulation is used to increase the number of concurrent operators in the system. The experiment proves that the click delay time of the English teaching video of the system is maintained below 1.8 seconds under 5000 concurrent operations, and the running speed of the system is significantly improved when the dynamic scheduling function is enabled, which proves that the system can meet the needs of multiple users for concurrent operations. Finally, a survey is conducted on the personalized education of the English teaching platform, and the relevant experimental and survey data are analyzed in detail, and a series of constructive strategies for the development of personalized English teaching in the cloud education environment are proposed.
Pathologic complete response of ductal carcinoma in situ to neoadjuvant systemic therapy in HER2-positive invasive breast cancer patients: a nationwide analysis
Authors: Ploumen Roxanne , Claassens Eva , Kooreman Loes , Keymeulen Kristien , Kats Maartje van , Gommers Suzanne , Siesling Sabine , Nijnatten Thiemo van , Smidt Marjolein 
Published: April 2023

Abstract
Purpose
Ductal carcinoma in situ (DCIS) is present in more than half of HER2-positive invasive breast cancer (IBC). Recent studies show that DCIS accompanying HER2-positive IBC can be completely eradicated by neoadjuvant systemic therapy (NST). Our aim was to determine the percentage of pathologic complete response of the DCIS component in a nationwide cohort and to assess associated clinicopathologic variables. Furthermore, the impact on surgical treatment after NST was investigated.
Methods
Women diagnosed with HER2-positive IBC, treated with NST and surgery, between 2010-2020, were selected from the Netherlands Cancer Registry. Pre-NST biopsy and postoperative specimen pathology reports were obtained from the Dutch Nationwide Pathology Databank, and assessed for presence of DCIS. Clinicopathologic factors associated with DCIS response were assessed using logistic regression analyses.
Results
A DCIS component was present in the pre-NST biopsy in 1443 of the 5834 included patients (24.7%). Pathologic complete response of the DCIS component was achieved in 743 (51.5%) of these patients. Complete response of DCIS occurred more frequently in case of complete response of IBC (63.4% versus 33.8%, p<0.001). ER-negative IBC (OR 1.79; 95%CI 1.33-2.42) and treatment with HER2-targeted therapy (OR 5.97; 95%CI 1.82-19.55) were associated with complete response of DCIS. Mastectomy rates were higher in IBC+DCIS compared to IBC (53.6% versus 41.0%, p<0.001).
Conclusion
Pathologic complete response of DCIS occurred in 51.5% of HER2-positive IBC patients and was associated with ER-negative IBC and complete response of IBC. Future studies should investigate imaging evaluation of DCIS response to improve surgical decision making.
Investigating the effects of Brachiaria (Syn. Urochloa) varieties on soil properties and microbiome
Authors: Merloti Luis Fernando , Bossolani João William Bossolani William , Mendes Lucas William , Rocha Gabriel Silvestre , Rodriguez Mayara , Asselta Fernanda Ometto , Crusciol Carlos Alexandre Costa , Tsai Siu Mui 
Published: April 2023

Abstract
Background and Aims

The Brachiaria sp. (synonymous with Urochloa) is one of Brazil’s main grass species used in livestock production and has become the focus of breeding genetic programs to enhance its resistance to drought, flooding, and pests, as well as improving its palatability to animals. However, there is a limited understanding of how genetic breeding can affect the soil microbiome and its potential functions. Thus, this study aimed to investigate the impact of four different Brachiaria varieties on the soil prokaryotic and fungal communities, particularly emphasizing their potential functions related to the N-cycle.

Methods

We combined molecular techniques, such as quantitative PCR and amplicon sequencing, to target prokaryotic and fungi communities and traditional soil and plant chemical analyses.

Results

Our findings revealed that all varieties improved soil porosity, P content, and organic carbon. Soil acidity, nutrient availability, and porosity were the main drivers of the microbial communities. The Nitrososphaeraceae, Gaiellales, Conexibacter, Sphingomonas, Hydnophlebia meloi, Conocybe, and Cladosporium were the main taxa associated with the dissimilarities between the Brachiaria varieties and the Control. In addition, the presence of the plants increased potential microbial functions such as Chemoheterotroph, Aerobic-Chemoheterotroph, and Pathotroph-Saprotroph groups. The study also identified the ability of each variety to recruit nitrogen-fixing and bacterial and archaeal ammonia-oxidizing communities.

Conclusion

Our findings suggest that selecting an efficient Brachiaria variety could positively impact soil quality, improving agricultural systems and increasing food production.
Identification of anterior cruciate ligament fibroblasts and their contribution for knee osteoarthritis progression by single-cell analyses
Authors: Zhang Ziji , Li Zhiwen , Zhang Shiyong , Xu Yiyang , Mao Guping , Kang Yunze , Zheng Linli , Long Dianbo , Chen Weishen , Gu Minghui , Zhang Zhiqi , Kang Yan , Sheng Puyi 
Published: April 2023

Abstract
Objective
A better Understanding of the key regulatory cells in the anterior cruciate ligament (ACL), and their role and regulatory mechanisms in knee osteoarthritis (KOA) progression can facilitate the development of targeted treatment strategies for KOA.
Methods
The relationship between ACL degeneration and KOA was first explored using human ACL specimens and mouse models. Next, single-cell RNA sequencing (scRNA-seq) and single-cell detection of transposase accessible and chromatin sequencing (scATAC-seq) data were integrated to reveal the transcriptional and epigenomic landscape of ACL in normal and osteoarthritis (OA) states.
Results
Six cell populations were identified in the human ACL, among which were inflammation-associated fibroblasts (IAFs). Degeneration of the ACL during OA mechanically alters the knee joint homeostasis and influences the microenvironment by regulating inflammatory- and osteogenic-related factors, thereby contributing to the progression of KOA. Specifically, a IAF subpopulation identified in OA ACL was found to enhance the transcription and secretion of EGER via SOX5 upregulation, with consequent activation of the EGER–EGFR signaling pathway. These molecular events led to the upregulation of downstream inflammatory and osteogenic factors, and the downregulation of the extracellular matrix-associated factor, thereby leading to knee osteoid formation, cartilage degeneration, and OA progression.
Conclusions
In summary, this study identifies a novel subpopulation of fibroblasts in the ACL, which confirms the importance of the ACL in knee joint homeostasis and disease. Additionally, the specific mechanism by which these IAFs regulate KOA progression was uncovered, which provides new foundation for the development of targeted treatments for KOA.
Aging gene signature of IL-7 receptor alpha low effector memory CD8+ T cells is associated with neurocognitive functioning in Alzheimer’s disease
Authors: Young Juan , Park Hong-Jai , Kim Minhyung , Par-Young Jennefer , Bartlett Hugh , Kim Hye Sun , Unlu Serhan , Osmani Lais , Shin Min Sun , Bucala Richard , Dyck Christopher van , Allore Heather , Mecca Adam , You Sungyong , Kang Insoo 
Published: April 2023

Abstract
CD45RA+ effector memory (EM) CD8+ T cell expansion was reported in Alzheimer’s disease (AD). Such cells are IL-7 receptor alpha (IL-7Rα)low EM CD8+ T cells, which expand with age and have a unique aging gene signature (i.e., IL-7Rαlow aging genes). Here we investigated whether IL-7Rαlow aging genes and previously reported AD and memory (ADM) genes overlapped with clinical significance in AD patients. RT-qPCR analysis of 40 genes, including 29 ADM, 9 top IL-7Ralow aging and 2 control genes, showed 8 differentially expressed genes between AD and cognitively normal groups; five (62.5%) of which were top IL-7Rαlow aging genes. Over-representation analysis revealed that these genes were highly present in molecular and biological pathways associated with AD. Distinct expression levels of these genes were associated with neuropsychological testing performance in 3 subgroups of dementia participants. Our findings support the possible implication of the IL-7Rαlow aging gene signature with AD.
Graphene Nanoplatelets Reinforced Al-Cu-Mg Composite Fabricated using Laser Powder Bed Fusion: Microstructure, Mechanical Properties, and Wear Behaviour
Authors: Pekok Mulla Ahmet , Setchi Rossitza , Ryan Michael , Brousseau Emmanuel , Han Quanquan , Gu Dongdong 
Published: April 2023

Abstract
Aluminium-based metal matrix composites reinforced with Graphene (Gr) and its derivatives have been reported as promising composites due to their superior properties such as strength, damage tolerance, fatigue resistance, and density. However, the crack and porosity susceptibility of Aluminium 2024 Alloy (AA2024) with added Gr when fabricated using additive manufacturing techniques is not sufficiently well understood. The present work addresses this knowledge gap by focusing on the effect of Graphene Nanoplatelets (GNPs) and scanning speed on the AA2024 composites’ wear performance, microstructural and mechanical properties of fabricated specimens using Laser Powder Bed Fusion (LPBF). The experimental findings demonstrate that the Gr presence in the composite up to 0.5% improves its crystallite size and microhardness by up to 37.6% and 45%, respectively; however, it increases the porosity and crack formation due to the high laser power requirement. Moreover, the composites’ macroscale scratch and nanoscale wear performances showed improvements by up to 50% and 56% with higher Gr concentration (0.5%), suggesting that Gr is distributed uniformly in the structure. The improved understanding of the relationship between microstructure and mechanical characteristics of the GNPs/Al2024 composites fabricated using LPBF in terms of cracking and porosity formation is another significant contribution of this work.
Early prediction of wheat yield using seasonal weather forecasts and the static stress drought index
Authors: García-Gamero Vanesa , Peñuela Andrés , Peña Adolfo , Vanwalleghem Tom 
Published: April 2023

Abstract
Crop yield prediction considering soil moisture as a proxy for water supply remains crucial for global food security. This study evaluates the potential of using seasonal weather forecasts combined with a drought index, Static Stress, based on both precipitation and soil moisture conditions to predict winter wheat yield 7 to 1 month in advance in Córdoba (South Spain). First, using observed climate and crop yield data we evaluate the use of Static Stress, as a potential crop yield predictor and compare it to a more traditionally used index, the SPEI, which is only based on precipitation conditions. Then we evaluate the performance of simple linear regression models to predict crop yields from forecasted Static Stress values calculated using weather forecast data from the ECMWF seasonal forecasting system (SEAS5). We find that Static Stress is better correlated to crop yield than SPEI and that Static Stress derived from seasonal forecasts has a good performance (R2 > 0.5; p-value < 0.05) for crop yield predictions of 4 or fewer months before harvest, i.e., from March to July. In this case study, these results indicate that drought indicators that consider soil moisture conditions are better predictors of crop yields than indicators that only consider precipitation. Furthermore, this study demonstrates the potential of using simple regression models together with mid-term forecasts of the Static Stress index to maximize cereal yields and mitigate drought impacts.
Induction of anti-tumor immunity by human telomerase reverse transcriptase (hTERT) and viral-derived peptides in breast cancer mice models
Authors: Kardar Gholam Ali , Songwe Fanuel , Colagar Abasalt Hosseinzadeh , Ghorbani Mehdi , Mohammadi Amir 
Published: April 2023

Abstract
Human telomerase reverse transcriptase (hTERT) is the most common tumor-associated antigen (TAA) and its activity is upregulated in more than 90% of cancers. Therefore, its targeting could be used in cancer immunotherapy. In continuing with our previous study, we evaluated hTERT derived peptides conjugated to KLH in mice breast cancer models. To induce tumors in Balb/C mice, about 5 × 105 mice breast cancer 4T1 cell were washed and suspended in 100 μl sterile PBS and subcutaneously (s.c) injected to 4th mammary fat pat of 6-8 weeks old mice. Thereafter, tumor size measurements were taken two times a week. About 100 ug KLH conjugated peptides accompanied by Incomplete Freund′s adjuvant (IFA) were injected s.c on days 10th and 20th after cancer cells inoculation. In spite of their significantly lower immunogenicity and affinities on mice HLA compared to human (approximately 5-30 times), these peptides inhibited tumor growth as well as being able to increase the overall survival of vaccinated Balb/C mice compare untreated group.
Prediction and examination of the impact of the raster angle on the orthotropic elastic response of 3D-printed objects using a novel homogenization strategy based on the real clustering of RVEs
Authors: BENAISSA Hamza AIT , Zaghar Hamid , Moujibi Nabil , Sossey-Alaoui Ismail 
Published: April 2023

Abstract
The distinct process of layer-by-layer 3D printing and the differential cooling of the 3D-printed component generates a low adhesive force between the layers, defects, and gaps interlayers, which results in an anisotropy distribution of mechanical and physical properties. Computational models, such as homogenization via green functions or homogenization based on ideal geometry-material models, are required as a first approximation of the elastic response of 3D-printed components, but have received less attention than experimental methods. This research establishes a novel multi-scale method for accurately predicting the mechanical behavior of 3D-printed components. At the micro and mesoscale, the micro-mechanical analysis of a representative volume element (RVE) corresponding to the real morphologies is acquired by clustering the microscopic observations of the internal structure of the 3D-printed samples using K-means algorithms, then used to generate micro-mechanical models in a function of the raster angle to compute the effective orthotropic constants, and these outputs are used to generate macro-scale numerical models that simulate the mechanical behavior under a tensile stress of the 3D-printed samples Compared to the idealized antecedent models and confirmed by experimental data, this methodology yields results that are consistent with reality. In conclusion, the incorporation of clustering approaches applying K-means algorithms into the homogenization procedure yields accurate prediction results that match the experimental elastic response of 3D-printed components. This study presents reliable prediction laws that enable the designer to conduct a faster iterative analysis and choose the optimal printing process parameters based on FE analysis to produce high-quality 3D FFF-printed components.
Periodic dynamics of predator-prey system with Beddington-DeAngelis functional response and discontinuous harvesting
Authors: Wang Yingying , Xia Zhinan 
Published: April 2023

Abstract
In this paper, a delayed predator-prey model with discontinuous harvesting and Beddington-DeAngelis functional response is investigated. Using the theory of differential inclusion theory, the existence of positive solutions in the sense of Filippov is discussed. Under reasonable assumptions and periodic disturbances, the existence of positive periodic solutions of the model is studied based on the theory of Mawhin coincidence degree. Finally, through numerical simulation, the correctness and feasibility of the conclusions are verified.
Increasing Image Classification Speed in Python Environments Using Convolutional Neural Networks Supported by High-Performance Computing
Authors: ASHRAF MD SADI , HOSSIAN NOBI , FAIZA FAHMEE 
Published: April 2023

Abstract
Convolutional Neural Networks (CNNs) have been found to be extremely effec- tive at categorising photos. However, because CNN models are so large and image processing requires so many calculations, classification time can be significantly decreased. We look at how to leverage High-Performance Computing (HPC) to speed up how CNNs identify images in Python in this work. We create and test alternative CNN architectures on an HPC-enabled cluster by using paral- lel processing and GPU acceleration to speed up image categorization. We also investigate how different data pretreatment methods and hardware configurations influence how effectively categorization works. Our research shows that employing Python environments that are optimised for high-performance computing (HPC) can significantly enhance image classification speed while maintaining excellent accuracy rates. This research is critical for developing better and easier-to-use image classification systems, particularly in computer vision, remote sensing, and biomedical imaging.