bluuebunny/crossref_metadata_2025_split huggingface dataset:
Environmental Sustainability at the Nexus of Energy Consumption Preferences and Economic Growth
Authors: ÖZPARLAK GERÇEK 
Published: November 2022

Abstract
The energy demand of national economies is increasing, with a substantial portion driven by non-renewable energy (NRE) sources. Increased consumption of NRE causes an increase in carbon dioxide (CO2) emission rates. Therefore, climate change is one of the serious issues worldwide. On the other hand, renewable energy (RE) sources are the best alternative clean energy sources to NRE sources. After leaving NRE, is it possible to deal with global warming by only using RE? To find an answer to the question, we investigated the long and short-term impact on the CE of the energy preferences of the analyzed. Therefore, we analysed the causality relationship between the rates of CO2 emissions (CE), use of RE and NRE sources, along with economic growth (EG) rates of various countries. The G-20 countries consume the most FF worldwide, and 19 of these nations were responsible for an average of 73.8% of the increase in global CE from 1966 to 2020. The G-20 countries were considered during 1966–2020 using the fully modified ordinary least square and dynamic ordinary least square methods. Additionally, the existence of cointegration between variables was examined by Pedroni, Johansen Multivariate Co-integration and Kao tests. According to the cointegration test results, in the long term, a 1% increase in the use of fossil fuels (FF) caused an increase in CE rates between 0.40% and 0.57%, whereas a 1% increase in the use of RE sources contributed to a 0.03% decrease in CE. Moreover, a 1% increase in EG caused an increase in CE rates ranging from 0.10 to 0.14%. According to the panel causality test, it is clear that RE sources alone are not sufficient to deal with climate change in the short term. Therefore, in addition to reducing the use of FF to zero, the protection and development of natural carbon sinks such as forests, oceans, and soils are mandatory to control climate change in the short and long term.
A conditional machine learning classification approach for spatio-temporal risk assessment of crime data
Authors: Rodrigues Alexandre , González Jonatan A. , Mateu Jorge 
Published: November 2022

Abstract
Crime data analysis is an essential source of information to aid social and political decisions makers regarding the allocation of public security resources. Computer-aided dispatch systems and technological advances in geographic information systems have made analysing and visualising historical spatial and temporal records of crimes a vital part of police operations and strategy. We look at our motivating crime problem as a spatio-temporal point pattern. Using a conditional approach based on properties of Poisson point processes, we transform the spatio-temporal point process prediction problem into a classification problem. We create spatio-temporal handcrafted features to link future and past events and use machine learning algorithms to learn behavioural patterns from the data. The fitted model is then used to carry out the reverse transformation, i.e. to perform spatio-temporal risk predictions based on the outcomes of the classification problem. Our procedure has theoretical formalism from point process theory and gains flexibility and computational efficiency inherited from the machine learning field. We show its performance under some simulated scenarios and a real application to spatio-temporal prediction and risk assessment of homicides in Bogota, Colombia.
Association of High Fat Diet with Bone Mineral Density in the General American Population
Authors: Li Na , Cheng Yuan , Jin Tao , Zhu Xiong , He Qing 
Published: November 2022

Abstract
Background
The effect of a high-fat diet on bone mineral density is still controversial. The present study is to evaluate the relationship between a high-fat diet and bone mineral density in the American adults.
Methods
We performed a cross-sectional analysis of the US population aged 50 years or older based on data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018. The primary outcome measure was the femur bone mineral density by dual energy x-ray absorptiometry (DXA) of osteoporosis or osteopenia. Multivariable logistic regression analysis was used to evaluate the effect of high-fat diet on the presence of osteopenia and osteoporosis.
Results
The prevalence of total femur, femoral neck, trochanter and intertrochanter osteopenia or osteoporosis respectively was 22.8%, 51.3%, 22.6% and 19.4% in men and 43.5%, 66.9%, 39% and 38% in women. Multivariable logistic regression analysis after adjustment for confounders showed that a high-fat diet was significantly associated with higher odds of total femur and trochanter osteopenia or osteoporosis in men and intertrochanteric osteopenia or osteoporosis in women. However, a high-fat diet was negatively associated with the occurrence of osteopenia or osteoporosis in male femoral neck and total femur and femoral neck in female. There were no significant associations between high-fat diet and osteopenia or osteoporosis in male intertrochanteric and in female trochanter. The subgroup analyses found that compared with normal fat intake, the highest fat intake levels were only significantly and negatively associated with femoral neck osteopenia or osteoporosis in men and total femur, trochanter and intertrochanteric in women.
Conclusions
The effects of a high-fat diet as a separate contribution to the incidence of osteopenia or osteoporosis were biphasic with both positive and negative effects that varied by femoral sites and were not gender-related. In addition, higher levels of dietary fat intake did not increase subject's the odds of osteopenia or osteoporosis.
Follicular thyroid carcinoma with intramedullary spinal cord metastasis: case-report
Authors: Shafiekhani Paria , Shahmohammadi Mohammadreza , Moradi Afshin , Zandpazandi Sara 
Published: November 2022

Abstract
Metastatic carcinomas are more prevalent in the vertebrae and epidural spaces than in the intradural and intramedullary spinal cord. Although thyroid carcinomas seldom induce intramedullary spinal cord metastases, no evidence has been presented to prove that pure follicular thyroid carcinomas can generate these metastases. In this case, a man in his late 30s who had undergone lymphadenectomy, thyroidectomy, and radioactive iodine therapy developed spastic paraparesis and urine retention. Owing to the patient's aggressive clinical presentation with intramedullary metastasis, an initial tumor biopsy was performed to determine the histology of the tumor. Following an unexpectedly negative first biopsy, the patient underwent thorough tumor excision, the histopathology of which revealed a metastatic pure thyroid follicular carcinoma. Hence, it is imperative that all patients with pure follicular thyroid cancer who have developed acute neurological symptoms, regardless of how long they have been in remission, be immediately evaluated for intramedullary spinal cord metastases.
Safety, reactogenicity, immunogenicity, and effectiveness of COVID-19 vaccines for pregnant persons: A protocol for a living systematic review and meta-analysis
Authors: Ciapponi Agustín , Berrueta Mabel , Ballivian Jamile , Bardach Ariel , Mazzoni Agustina , Anderson Steven , Argento Fernando J. , Bok Karin , Comandé Daniel , Goucher Erin , Kampmann Beate , Parker Edward P. K. , Rodriguez-Cairoli Federico , Maria Victoria Santa , Stergachis Andy , Voss Gerald , Xiong Xu , Zaraa Sabra , Munoz Flor M. , Karron Ruth A. , Gottlieb Sami L. , Buekens Pierre M. 
Published: November 2022

Abstract

Background
Numerous vaccines have been evaluated and approved for coronavirus disease 2019 (COVID-19). Since pregnant persons have been excluded from most clinical trials of COVID-19 vaccines, sufficient data regarding safety of these vaccines for the pregnant persons and their fetus have rarely been available at the time of product licensure. However, as COVID-19 vaccines have been deployed, data on the safety, efficacy or effectiveness, reactogenicity, and immunogenicity of COVID-19 vaccines for pregnant persons and neonates are becoming increasingly available. A living systematic review (LSR) of the safety and effectiveness of COVID-19 vaccines for pregnant persons and newborns could provide information necessary to help guide vaccine policy decisions.
Methods
We aim to conduct a LSR based on biweekly searches of medical databases (e.g., MEDLINE, EMBASE, CENTRAL) and clinical trial registries to systematically identify relevant studies of COVID-19 vaccines for pregnant persons. Pairs of reviewers will independently select, extract data, and conduct risk of bias assessments. We will include randomized clinical trials, quasi-experimental studies, cohort, case-control, cross-sectional studies, and case reports. Primary outcomes will be the safety (i.e., impacts on obstetric and neonatal outcomes), efficacy or effectiveness of COVID-19 vaccines in pregnant persons. Secondary outcomes will be immunogenicity and reactogenicity. We will conduct paired meta-analyses, including pre-specified subgroup and sensitivity analyses, and will use the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to evaluate the certainty of evidence.
Conclusion
This will be the first living systematic review (LSR) and meta-analysis assessing the safety, reactogenicity, immunogenicity, and effectiveness of COVID-19 vaccines for pregnant persons. An online interactive dashboard for data visualization using Microsoft Power BI will be developed to regularly update and disseminate the latest findings (an in-progress version is available at https://safeinpregnancy.org/lsr/). In addition, the findings will be disseminated through publications and presentations.
Prognostic value and mechanism of EPHB2 overexpression in cervical cancer
Authors: Zhao Mengli , Cong Hui , Li Zhen , Gao Zhongrong , Wang Shanshan , Wang Yudong , Fu Jie 
Published: November 2022

Abstract
Background
Cervical cancer (CC) still represents an important challenge for female malignant tumors. In recent years, increasing evidence has indicated that EPHB2 plays a significant part in the origin and progression of many types of cancer in humans. Nevertheless, the mechanism of EPHB2 in CC remains inexplicable. The purpose of this study is to determine the prognostic value and related regulatory mechanism of EPHB2 in CC.
Methods
The basic clinical information and transcriptome data related to CC patients were downloaded from TCGA database and conducted standardization processing. TIMER2.0 was used for analyzing the EPHB2 expression differences of between 38 cancer types and corresponding normal tissues. The expression and prognostic value (including OS and RFS) of EPHB2 in different cancer types were determined by GEPIA2. And the starbase database was used for exploring the upstream miRNAs and lncRNAs of EPHB2. Then, The correlation analysis of EPHB2, miRNA and lncRNA and the exploration of prognostic value and expression of miRNAs in CESC was conducted using the R language packages. We also analyzed the prognostic value and expression difference of 187 lncRNAs based on the UALCAN database. Finally, we performed the correlation analysis between EPHB2 and multiple immune cells using the R language package.
Results
In our study, the AC073957.3/hsa-miR-150-5p axis was found to be the most potential upstream ncRNA-related regulator of EPHB2 in CC. And the results of immune correlation infiltration analysis suggest that as EPHB2 copy numbers fluctuate in CESC there is an obvious difference in immune cell infiltration levels, and EPHB2 was markedly positively correlated with Th2 cell, NK cell, Eosinophils, Tgd, Mast cell, NK CD56bright cells and Tem while negatively correlated with Tcm and B cell in CC.
Conclusion
Our study shows that EPHB2 is a potential prognostic marker, and its overexpression is related to poor prognosis of CC, which is expected to be a new therapeutic target for CC.
Universal Values as Predictors for Career Identity Indicators and Academic Major Satisfaction in Hong Kong Tertiary Students
Authors: Guo Yizhe , AU Winton Wing-Tung , HO Jessie Ching-sze , TAI Andy Chi-lok , Chan Sarah 
Published: November 2022

Abstract
The formation of career identity is an important coming-of-age issue in both developmental and vocational psychology. This paper introduces universal values (under Schwartz’s theory of basic human values) in addition to the popularly used career interest congruence as predictors of three career identity indicators (commitment making, identification with commitment and self-doubt). Meanwhile, satisfaction with academic major was investigated both as direct influenced by values and as a secondary outcome of having a more established career identity. As a set of factors that have received less attention in career development research, universal values are important internalized beliefs and criteria that deeply influence one’s goal setting and decision making. In the current study, they are hypothesized to influence one’s career identity indicators and academic major satisfaction through their career decision making and self-evaluation processes. We surveyed 562 students from a community college and a public university in Hong Kong and formulated linear regression and structural models that showed significant associations among values, career identity indicators and academic major satisfaction. Openness to change and self-transcendence values were significant positive predictors of identification with career commitment, and self-enhancement values positively predicted career self-doubt. Academic major satisfaction was found to be positively influenced by commitment making and negatively influenced by career self-doubt. Career interest congruence, however, only showed a significant negative slope coefficient when used to predict career self-doubt in linear regression. Implications for career counselling practice were discussed.
The value of a group of serum miRNAs in screening prostate cancer
Authors: Sun Chen , Li Xinji , Li Rongkang , Lu Chong , Wen Zhenyu , Ge Zhenjian , Chen Wenkang , Li Yingqi , Sun Suolei , Yang Qingshan , Tao Lingzhi , Li Hang , lai yongqing 
Published: November 2022

Abstract
Background
Prostate cancer remains a worldwide public health problem that poses a serious threat to the health of men worldwide. Many studies have found that miRNA in serum has the possible to be a biomarker for cancer. Our study was conducted to investigate the valuation of serum miRNAs in prostate cancer screening.
Methods
We selected 12 miRNAs from past studies associated with prostate cancer. We checked the expression levels of these miRNAs in the serum of 112 prostate cancer patients and 112 healthy controls in a two-stage experiment. We plotted the receiver operating characteristic curve of miRNAs in the validation stage and constructed a four-miRNA panel with the highest diagnostic value using stepwise logistic regression. We also predicted the target genes for these four miRNAs through online databases and performed Gene Ontology functional annotation and pathway analysis.
Results
The results showed that 6 miRNAs (miR-429, miR-10a-5p, miR-183-5p, miR-181a-5p, miR-1231, miR-129-5p) were abnormally expressed in the serum of prostate cancer patients. We used four of these miRNAs including miR-1231, miR-10a-5p, miR-429 and miR-129-5p to construct a combination of miRNAs with high specificity and sensitivity in screening prostate cancer (area under the curve (AUC) = 0.878). Bioinformatic analysis has shown that the genes targeted by these miRNAs can be linked to the development of cancer.
Conclusions
Our study detected and identified a set of miRNAs that serve as screening markers for prostate cancer, which may assist in early diagnosis and treatment of prostate cancer.
Increased D-Neopterin biosynthesis in gut microbiome promoted colonic carcinogenesis after ampicillin or amoxicillin treatment
Authors: zhang yuhang , Dong Yuan , Wang Wen-Yu , Zhou Hang , Cui Yi-Min 
Published: November 2022

AbstractBackground:Emerging data have manifested that antibiotic-induced perturbations can potentially increase susceptibility to colorectal cancer (CRC), while the underlying mechanism is still unclear.Results:Herein we established the humanized microbiome mice transplanted with feces and hematopoietic stem cells from CRC patients to explore how ampicillin or amoxicillin contributed to colorectal cancer progression. Metagenomics and untargeted metabolomics analyses have suggested the effect of ampicillin or amoxicillin on colonic tumorigenesis is correlated with an alteration of gut microbiota and increased levels of the microbiota-derived metabolite D-Neopterin (P< 0.05). Then D-Neopterin is evidenced to activate the expressions of IGF2BP1 and ARG1 in myeloid-derived suppressor cells (MDSCs) by scRNA-seq. Moreover, IGF2BP1 was determined to maintain the stability of ARG1 transcript by binding to three m6A sites of ARG1‐3’UTR in the sorted MDSCs.Conclusion:In essence, this study has identified the important role of ampicillin or amoxicillin-induced increase of D-Neopterin in enhancing colorectal cancer proliferation. It thus implicates that therapeutic regimen of CRC patients should eliminate the application of ampicillin or amoxicillin.
CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset
Authors: Kizilirmak Firat , Yanikoglu Berrin 
Published: November 2022

Abstract
We present a CNN-BiLSTM system for the problem of offline English handwriting recognition, with extensive evaluations on the public IAM dataset, including the effects of model size, data augmentation and the lexicon. Our best model achieves 3.59% CER and 9.44% WER using CNN-BiLSTM network with CTC layer.Test time augmentation with rotation and shear transformations applied to the input image, is proposed to increase recognition of difficult cases and found to reduce the word error rate by 2.5% points. We also conduct an error analysis of our proposed method on IAM dataset, show hard cases of handwriting images and explore samples with erroneous labels. We provide our source code as public-domain, to foster further research to encourage scientific reproducibility.
PopNode-PopCom: Identifying The Popular Nodes to Predict the Popular Communities in Dynamic Social Networks
Authors: Khafaei Taleb , Taraghi Alireza Tavakoli , Patil Nitin P. 
Published: November 2022

Abstract
Social networks play an important role in human life, and the study of communities within them is of particular importance. In this research, sustainable communities called popular communities have been introduced and studied. In this regard, the life of social networks was considered in time snapshots. Then, the communities that which at least 45% of their members were present the next time snapshot were considered popular communities. In the next step, the structure of popular communities and their distinguishing features from other communities as well as some of their structural features along with the other centrality features were introduced. Finally, using the theory of rough set, the importance of the structural features was examined to predict popular communities using the rules derived from this theory. Also, the importance of popular nodes introduced in this study in terms of, Integrated Value of Influence (IVI), Collective Influence (CF) and Spreading Score was compared with the maximum nodes of these values in each of the popular communities. In this study, we tested the performance of our proposed model on the actual Facebook database. We used rough set theory to generate the rules and model learning. Leader-Node, Popular-Node, Closeness, Eigen Centralization, Betweenness and Community Density were used as features and Popular Community was used as a label. In the experiment, 10-Fold Cross-Validation was used for model learning and Standard Voting was used as a classifier in the ROSETTA toolkit environment. The experiment has shown that if the average of an entire community is low on closeness, betweenness, and eigenvector centrality but with a high number of popular nodes, the chances of the community remaining popular increase. In addition, the high density of the community is not a good criterion for the community to remain popular at all. It was also shown that with the help of these features, the popular communities were predicted with almost 84% accuracy. On the other hand, the nodes with the maximum values of IVI, CF and Spreading Score were a subset of popular nodes. This shows the importance of popular nodes in social networks which introduced in this study.
Printed Ottoman Text Recognition Using Synthetic Data and Data Augmentation
Authors: Tasdemir Esma F. Bilgin 
Published: November 2022

Abstract
Ottoman script is an Arabic alphabet-basedscript as well. It was a writing system of theTurkish language for several centuries until it was replaced with the modern Turkish script,which is based on the Latin alphabet, in 1928. With the ever increasing digitization campaigns, millions of Ottoman documents are coming to light. But, their contents are not directly accessible, nor they are digitally editable and searchable. OCR and text recognition technologies can bea solution to this problem in the form of auto-mated and semi-automated conversion systems. This study presents a DL-based characterrecognition system for the printed Ottoman script. We first generate a synthetic text image dataset from a text corpus, and then augment it using some image processing methods. We develop a hybrid Convolutional Neural Network-Bidirectional Long Short Term Memory recognizer and train it with the original and the augmented datasets. Finally we apply a Transfer Learning procedure for adapting the system to real image data. The proposed system obtains 0.16 CER on a test set containing line images from a historical printed Ottoman book.
Brain Tumor Detection in Magnetic Resonance Imaging using Deep Learning Approach
Authors: Hussain Altaf 
Published: November 2022

Abstract
In this paper, the design and implementation of a tumor detection system using two CNN models is considered. Digital image processing and Deep Learning technologies enable us to develop an automatic system for the diagnosis/detection of various kind of diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data augmentation, features extraction and classification; all these steps are discussed in details in the above sections. To work on CNNs, powerful GPU based system are required to speed up the process, lot of processing is carried out and also lot of RAM is required to process the images for testing. CNNs have also some options such as optimization technique selection, Number of Epoch, Batch size, iteration and learning rate. These options are tuned to get the optimal results from the CNN model. Learning rate is used to update the weights and bias in training phase, learning rate changes the weights. One Epoch is when the model see all images in training, as the training data maybe of very big sizes, the data in each Epoch is divided into batch sizes. Every epoch has a training and test session, after each Epoch the weights are updated according to the learning rate, optimization algorithms are used to update the learning of a CNN adaptively. When the best weights for training are computed, the model is said to be trained. All the experimental work is carried out in MATLAB simulation tool.