bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computational Image Analysis Techniques, Programming Languages and Software Platforms Used in Cancer Research: A Scoping Review
Authors: Arafat Youssef , Reyes-Aldasoro Constantino Carlos 
Published: April 2022

AbstractBackgroundCancer-related research, as indicated by the number of entries in Medline, the National Library of Medicine of the USA, has dominated the medical literature. An important component of this research is based on the use of computational techniques to analyse the data produced by the many acquisition modalities. This paper presents a review of the computational image analysis techniques that have been applied to cancer. The review was performed through automated mining of Medline/PubMed entries with a combination of keywords. In addition, the programming languages and software platforms through which these techniques are applied were also reviewed.MethodsAutomatic mining of Medline/PubMed was performed with a series of specific keywords that identified different computational techniques. These keywords focused on traditional image processing and computer vision techniques, machine learning techniques, deep learning techniques, programming languages and software platforms.ResultsThe entries related to traditional image processing and computer vision techniques have decreased at the same time that machine learning and deep learning have increased significantly. Within deep learning, the keyword that returned the highest number of entries was convolutional neural network. Within the programming languages and software environments, Fiji and ImageJ were the most popular, followed by Matlab, R, and Python. Within the more specialised softwares, QuPath has had a sharp growth overtaking other platforms like ICY and CellProfiler.ConclusionsThe techniques of artificial intelligence techniques and deep learning have grown to overtake most other image analysis techniques and the trend at which they grow is still rising. The most used technique has been convolutional neural networks, commonly used to analyse and classify images. All the code related to this work is available through GitHub: https://github.com/youssefarafat/Scoping-Review.
Identifiability of parameters in mathematical models of SARS-CoV-2 infections in humans
Authors: Ciupe Stanca M. , Tuncer Necibe 
Published: April 2022

AbstractDetermining accurate estimates for the characteristics of the severe acute respiratory syndrome coronavirus 2 in the upper and lower respiratory tracts, by fitting mathematical models to data, is made difficult by the lack of measurements early in the infection. To determine the sensitivity of viral predictions to the noise in the data, we developed a novel two-patch within-host mathematical model and investigated its ability to match population level data. We proposed several approaches that can improve practical identifiability of parameters, including an optimal experimental approach, and found that availability of viral data early in the infection is of essence for improving the accuracy of the estimates. Our findings can be useful for designing interventions.
Observed strong pervasive positive selection in the N-terminal domain, receptor-binding domain and furin-cleavage sites of SARS-CoV-2 Spike protein sampled from Zimbabwean COVID-19 patients
Authors: Kambarami Milton S , Justen Manasa , Tawanda Mushiri 
Published: April 2022

AbstractMutations primarily in the Spike (S) gene resulted in the emergence of many SARS-CoV-2 variants like Alpha, Beta, Delta and Omicron variants. This has also caused a number of COVID-19 pandemic waves which have impacted human lives in different ways due to restriction measures put in place to curb the spread of the virus. In this study, evolutionary patterns found in SARS-CoV-2 sequences of samples collected from Zimbabwean COVID-19 patients were investigated. High coverage SARS-CoV-2 whole genome sequences were downloaded from the GISAID database along with the GISAID S gene reference sequence. Biopython, NumPy and Pandas Data Science packages were used to load, slice and clean whole genome sequences outputting a fasta file with approximate Spike (S) gene sequences. Alignment of sliced dataset with GISAID reference sequence was done using Jalview 2.11.1.3 to find exact sequences of SARS-CoV-2 S gene. Evidence of recombination signals was investigated using RDP 4.1 and pervasive selection in the S gene was investigated using FUBAR algorithm hosted on the Datamonkey webserver. Matplotlib and Seaborn Python packages were used for Data Visualisation. A plot of Bayes factor hypothesizing non-synonymous substitution being greater than synonymous substitution (β > α) in the S protein sites showed 3 peaks with evidence of strong divergence. These 3 diverging S protein sites were found to be D142G, D614G and P681R. No evidence of recombination was detected by 9 methods of RDP which use different approaches to detect recombination signals. This study is useful in guiding drug, vaccine and diagnostic innovations toward better control of the pandemic. Additionally, this study can guide other non-biological interventions as we better understand the changes in various viral characteristics driven by the observed evolutionary patterns.
High-Dimensional Multinomial Multiclass Severity Scoring of COVID-19 Pneumonia Using CT Radiomics Features and Machine Learning Algorithms
Authors: Shiri Isaac , Mostafaei Shayan , Avval Atlas Haddadi , Salimi Yazdan , Sanaat Amirhossein , Akhavanallaf Azadeh , Arabi Hossein , Rahmim Arman , Zaidi Habib 
Published: April 2022

AbstractWe aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). CT scans were preprocessed with bin discretization and resized, followed by segmentation of the entire lung and extraction of radiomics features. We utilized two feature selection algorithms, namely Bagging Random Forest (BRF) and Multivariate Adaptive Regression Splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. Subsequently, 10-fold cross-validation with bootstrapping (n=1000) was performed to validate the classification results. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4×4 confusion matrices. In addition, the areas under the receiver operating characteristic (ROC) curve (AUCs) for multi-class classifications were calculated and compared for both models using “multiROC” and “pROC” R packages. Using BRF, 19 radiomics features were selected, 9 from first-order, 6 from GLCM, 1 from GLDM, 1 from shape, 1 from NGTDM, and 1 from GLSZM radiomics features. Ten features were selected using the MARS algorithm, namely 2 from first-order, 1 from GLDM, 2 from GLRLM, 2 from GLSZM, and 3 from GLCM features. The Mean Absolute Deviation and Median from first-order, Small Area Emphasis from GLSZM, and Correlation from GLCM features were selected by both BRF and MARS algorithms. Except for the Inverse Variance feature from GLCM, all selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p-values<0.05). BRF+MLR and MARS+MLR resulted in pseudo-R2 prediction performances of 0.295 and 0.256, respectively. Meanwhile, there were no significant differences between the feature selection models when using a likelihood ratio test (p-value =0.319). Based on confusion matrices for BRF+MLR and MARS+MLR algorithms, the precision was 0.861 and 0.825, the recall was 0.844 and 0.793, whereas the accuracy was 0.933 and 0.922, respectively. AUCs (95% CI)) for multi-class classification were 0.823 (0.795-0.852) and 0.816 (0.788-0.844) for BRF+MLR and MARS+MLR algorithms, respectively. Our models based on the utilization of radiomics features, coupled with machine learning, were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.
Insulin-like growth factor 2 hypermethylation in peripheral blood leukocytes and colorectal cancer risk and prognosis: a propensity score analysis of multiple-centre populations
Authors: Sun HongRu , Liu YanLong , Zhang YuXue , Wang Yibaina , Cui BinBin , Zhao YaShuang , Liu YuPeng 
Published: April 2022

ABSTRACTBackgroundTo comprehensively assess and validate the associations between insulin-like growth factor 2 (IGF2) gene methylation in peripheral blood leukocytes (PBLs) and colorectal cancer (CRC) risk and prognosis.MethodsThe association between IGF2 methylation in PBLs and CRC risk was initially evaluated in a case-control study and then validated in a nested case-control study and a twins’ case-control study, respectively. Meanwhile, an initial CRC patient cohort was used to assess the effect of IGF2 methylation on CRC prognosis and then the finding was validated in the EPIC-Italy CRC cohort and TCGA datasets. A propensity score (PS) analysis was performed to control for confounders, and extensive sensitivity analyses were performed to assess the robustness of our findings.ResultsPBL IGF2 hypermethylation was associated with an increased risk of CRC in the initial study (ORPS-adjusted, 2.57, 95% CI: 1.65 to 4.03, P<0.0001), and this association was validated using two independent external datasets (ORPS-adjusted, 2.21, 95% CI: 1.28 to 3.81, P=0.0042 and ORPS-adjusted, 10.65, 95% CI: 1.26 to 89.71, P=0.0295, respectively). CRC patients with IGF2 hypermethylation in PBLs had significantly improved overall survival compared to those patients with IGF2 hypomethylation (HRPS-adjusted, 0.47, 95% CI: 0.29 to 0.76, P=0.0019). The prognostic signature was also observed in the EPIC-Italy CRC cohort, although the HR did not reach statistical significance (HRPS-adjusted, 0.69, 95% CI: 0.37 to 1.27, P=0.2359).ConclusionsIGF2 hypermethylation may serve as a potential blood-based predictive biomarker for the identification of individuals at high risk of developing CRC and for CRC prognosis.FundingThis work was supported by the China Postdoctoral Science Foundation (grant number 2018M641875 to YPL); the Natural Science Foundation of Heilongjiang Province (grant number YQ2019H021 to YPL); the National Natural Science Foundation of China (grant number 81473055 to YSZ), and by grant from the SCORE Foundation (Y-MX2016-045 to YLL).Clinical trial numberNot applicable.
Racial Disparities in Post-Transplant Stroke and Mortality Following Stroke in Adult Cardiac Transplant Recipients in the United States
Authors: Liou Lathan , Mostofsky Elizabeth , Lehman Laura , Salia Soziema , Gupta Suruchi , Barrera Francisco J. , Mittleman Murray A. 
Published: April 2022

ABSTRACTBackgroundBlack heart transplant recipients have a higher mortality rate than white recipients 6-12 months after transplant. Whether there are racial disparities in post-transplant stroke incidence and all-cause mortality following post-transplant stroke among cardiac transplant recipients is unknown.Methods and FindingsUsing a nationwide transplant registry, we assessed the association between race and incident post-transplant stroke using logistic regression and the association between race and mortality among adults who survived a post-transplant stroke using Cox proportional hazards regression. We found no evidence of an association between race and the odds of post-transplant stroke (OR = 1.00, 95% CI: 0.83 – 1.21). The median survival time of those with a post-transplant stroke in this cohort was 4.1 years (95% CI: 3.0, 5.4). There were 726 deaths among the 1139 patients with post-transplant stroke, including 127 deaths among 203 Black patients and 599 deaths among 936 white patients. Among post-transplant stroke survivors, Black transplant recipients experienced a 27% higher rate of mortality compared to white recipients (HR = 1.27, 95% CI: 1.03 – 1.57). This disparity is strongest in the period beyond the first 6 months and appears to be mediated by differences in the post-transplant setting of care between Black and white patients. The racial disparity in mortality outcomes was not evident in the past decade.ConclusionsThe improved survival of Black patients in the recent decade may reflect overall protocol improvements for heart transplant recipients irrespective of race, such as advancements in surgical techniques and immediate postoperative care as well as increased awareness about reducing racial disparities.
Downregulating PTBP1 fails to convert astrocytes into hippocampal neurons and to alleviate symptoms in Alzheimer’s mouse models
Authors: Guo Tiantian , Pan Xinjia , Jiang Guangtong , Zhang Denghong , Qi Jinghui , Shao Lin , Wang Zhanxiang , Xu Huaxi , Zhao Yingjun 
Published: April 2022

AbstractConversion of astroglia into functional neurons has been considered as a promising therapeutic strategy for neurodegenerative diseases. Recent studies reported that downregulation of the RNA binding protein, PTBP1, converts astrocytes into neurons in situ in multiple mouse brain regions, consequently improving pathological phenotypes associated with Parkinson’s disease, RGC loss, and aging. Here, we demonstrate that PTBP1 downregulation using antisense oligonucleotides or an astrocyte specific AAV-mediated shRNA system fails to convert hippocampal astrocytes into neurons in WT, and β- amyloid (5×FAD) and tau (PS19) Alzheimer’s disease (AD) mouse models, and fails to reverse synaptic/cognitive deficits and AD-associated pathology. Similarly, PTBP1 downregulation cannot convert astrocytes into neurons in the striatum and substantia nigra. Together, our study suggests that cell fate conversion strategy for neurodegenerative disease therapy through manipulating one single gene, such as PTBP1, warrants more rigorous scrutiny.
Whole genome analysis in APOE4 homozygotes identifies the DAB1-RELN pathway in Alzheimer’s disease pathogenesis
Authors: Bracher-Smith Matthew , Leonenko Ganna , Baker Emily , Crawford Karen , Graham Andrew C. , Salih Dervis A. , Howell Brian W. , Hardy John , Escott-Price Valentina 
Published: April 2022

AbstractThe APOE-ε4 allele is known to predispose to amyloid deposition and consequently is strongly associated with Alzheimer’s disease (AD) risk. There is debate as to whether the APOE gene accounts for all genetic variation of the APOE locus. Another question which remains is whether APOE-ε4 carriers have other genetic factors influencing the progression of amyloid positive individuals to AD. We conducted a genome-wide association study in a sample of 5,390 APOE-ε4 homozygous (ε4ε4) individuals (288 cases and 5,102 controls) aged 65 or over in the UK Biobank. We found no significant associations of SNPs in the APOE locus with AD in the sample of ε4ε4 individuals. However, we identified a novel genome-wide significant locus associated to AD, mapping to DAB1 (rs112437613, OR=2.28, CI=1.73-3.01, p=5.4×10−9). This identification of DAB1 led us to investigate other components of the DAB1-RELN pathway for association. Analysis of the DAB1-RELN pathway indicated that the pathway itself was associated with AD, therefore suggesting an epistatic interaction between the APOE locus and the DAB1-RELN pathway.
Towards a liberatory pedagogy of disaster risk reduction among built environment educators
Authors: Chmutina Ksenia , von Meding Jason 
Published: April 2022

PurposeThis paper aims to enhance the understanding of what is being taught – and how – to future built environment (BE) professionals in higher education (HE) BE curricular in the context of disaster risk reduction (DRR).Design/methodology/approachReflecting on the results of an extensive survey carried out among 21 BE educators representing 14 countries, the pedagogies used to educate tomorrow’s BE professionals about DRR-related subjects are explored.FindingsThe vast majority of HE training for the future BE professionals focuses on hazards as a “problem” posed by nature – something that can be “solved” through a technical solution. Little reflection is required as to the social implications of DRR “solutions”, and knowledge too often remains analytical and distant from any sort of lived experience. Whilst many DRR-related subjects introduce the ideas of human-centric DRR, there is still a disconnection between technical engineering subjects and broader social science subjects. This is a missed opportunity for students acquiring technical knowledge to reflect on and engage with a wider societal context.Originality/valueThe paper draws on the liberative pedagogies of Paulo Freire, bell hooks and others to engage BE educators in collectively drawing on philosophies and practices that emphasise holistic ways of knowing and learning and encourage the broader consideration of non-technical ideas. This kind of DRR pedagogy is required if the society is to collectively strive for a BE that enhances equity and well-being, while avoiding the creation of risk through development and redevelopment.
The heterogeneous effect of forest tenure security on forestry management efficiency of farmers for different forest management types
Authors: Zou Yaqin , Jiang Xuemei , Wen Caiyun , Li Yang 
Published: April 2022

PurposeAfter the Collective Forest Tenure Reform (CFTR) in China, the enthusiasm of farmers for forestry management is stimulated. However, the forest tenure security varies among farmers, making the research conclusions of its impact on forestry management efficiency inconsistent. Based on the survey data of 1,627 households from the collective forest regions in 6 provinces of China in 2017, this paper not only discusses the differences of farmers' forestry management efficiency after the reform, but also further explores the heterogeneous impact of forest tenure security on forestry management efficiency in combination with different forest management types.Design/methodology/approachThis study employed the stochastic frontier production function model to measure the forestry management efficiency of farmers. Then, Tobit models were used to discuss the influencing factors of farmers' forestry management efficiency.FindingsThe results demonstrate that the improvement of farmers' forest tenure security can effectively improve forestry management efficiency, but the effect is affected by forest management types. For farmers who manage economic forests and non-timber forests, safe tenure promotes the forestry management efficiency; while for those who manage ecological public welfare forests, tenure security plays an opposite role.Originality/valueTherefore, satisfying farmers' differentiated demands for forest tenure according to forest management types to improve forest tenure security and further refining supporting policies of collective forestry reform is of great significance to improve the efficiency of farmers' forestry management in collective forest regions.
Pathfinder; alternative care pathways for older adults who phone the emergency medical services in North Dublin: a case study
Authors: O’Brien Claire , Hogan Laura , Ward Peter , Howard William , Mooney Rebecca , Bernard Paul , Corcoran Grace 
Published: April 2022

Purpose
Emergency Department (ED) presentations in older people are associated with a wide range of adverse events, which increase the risk of lengthy hospitalisation and poor outcomes. Pathfinder is an inter-organisational initiative delivered in partnership between Beaumont Hospital Occupational Therapy and Physiotherapy departments and the National Ambulance Service. Pathfinder responds to non-serious and non-life-threatening emergency medical service (EMS) calls. This study aims to demonstrate how Pathfinder can safely treat a proportion of older people at home by using alternative care pathways (ACPs), therefore avoiding unnecessary ED presentations. Once a decision has been reached to treat the person at home, the Pathfinder follow-up team delivers functional rehabilitation and case management in the persons’ home over the subsequent days.

Design/methodology/approach
This paper outlines the Pathfinder assessment, management and interventions in one clinical case example. Outcome measures include the level of patient satisfaction obtained via routine telephone feedback questionnaire and re-presentation to Beaumont Hospital within 30 days.

Findings
This paper illustrates through a case example the benefit of a collaborative multi-disciplinary rapid response team for non-serious and non-life-threatening EMS calls in older adults. The patient in this case example had no further EMS calls or ED presentations for 30 days after Pathfinder intervention and reported a high level of satisfaction with the service.

Research limitations/implications
ED presentation was avoided through comprehensive multi-disciplinary assessment, including immediate access to intensive follow-up support in the person’s own home.

Practical implications
The Pathfinder service is improving access to ACPs for older people in the Beaumont Hospital catchment area. Pathfinder will now be spread nationally, with local adaptation, so that older people in other parts of Ireland will also benefit from this integrated model of care.

Originality/value
Patient feedback surveys confirm older adults want access to alternative care pathways.
Where does internal governance mechanism matter in emerging markets? Insight from financial and non-financial firms
Authors: Agyemang Badu Ebenezer , Nyarko Assabil Ebenezer 
Published: April 2022

PurposeThe purpose of this paper is to explore the relationship between board attributes and firm value to identify board attributes that are “pleasant” to have from what is required to have in financial and non-financial firms.Design/methodology/approachThe paper uses five measures of firm value to estimate the relationship between internal governance mechanism for financial and non-financial firms using system generalized methods of moments.FindingsThe paper finds that board independence and board size is a “must” have and value-enhancing board attributes for financial firms. On the contrary, board independence may be considered as a “pleasant” board attribute for non-financial firms. Further, the paper finds that duality is not value-enhancing board attribute for both financial and non-financial firms.Practical implicationsThe findings imply that differences in requirements for strategic or resource and monitoring functions in financial and non-financial firms are responsible for the differences in board attributes that are value-relevant for these firms.Originality/valueThe findings suggest that the value relevance of board attributes differs in financial and non-financial firms.