bluuebunny/crossref_metadata_2025_split huggingface dataset:
Correlations Between Visual Acuity, Presenting Symptoms and Histopathological Findings in Enucleated Eyes with Uveal Melanoma
Authors: Asplund Elin , Pansell Tony , Brautaset Rune , Nilsson Maria , Stålhammar Gustav 
Published: October 2021

Abstract
Visual outcomes after treatment of uveal melanoma have been investigated repeatedly. In this paper, we evaluate the correlation between visual acuity (VA) prior to enucleation, presenting symptoms and findings upon histopathological examination of eyes and tumors. Sixty nine patients were included. Their mean best corrected visual acuity (BCVA) prior to enucleation was LogMAR 0.80 (SD 0.70). Patients that reported low VA or blurry vision did not have lower BCVA upon refraction (p=0.34). Patients with low BCVA had tumors with greater apical tumor thickness (p=0.027), basal diameter (p=0.012) and stage (p=0.014). The experience of a shadow in the visual field correlated with presence of vasculogenic mimicry in the tumor (p<0.001). In multivariate Cox regressions with tumor thickness and diameter as covariates, tumor cell type and infiltration of the optic nerve head were associated with increased hazard for melanoma-related mortality (3.2 and 3.6, respectively). We conclude that patients that report low VA do not necessarily have worse BCVA. The latter do however have tumors with larger dimensions, at more advanced stage. A shadow in the VF correlates with presence of vasculogenic mimicry, which is an independent predictor for poor prognosis along with tumor infiltration of the optic disc and tumor cell type.
Knowledge of Alzheimer's disease among healthcare providers and medical students in Sudan
Authors: Babiker Ismat , Elnaeim Awab , Elnaeim Mohamed , Saad Awab 
Published: October 2021

Abstract
Objective: The purpose of this study was to assess knowledge of Alzheimer's disease among medical students and healthcare providers in Sudan. Methods: In this cross-sectional study conducted in Sudan from July to De­cember 2020, we used Google forms to collect data from 212 medical students and healthcare provid­ers using Alzheimer's Disease Knowledge Scale (ADKS). ADKS is a widely used validated instrument that measures what people know about Alzheimer's disease using a 30-item true/false questionnaire across seven critical knowledge domains: risk factors, symptoms, assessment and diagnosis, disease trajectory, life impact, treatment, and management, and caregiving. Results: Our studied population consisted of doctors (n=106), medical students (n=23), Nursing staff (n=09), and other hospital staff (n=74). 76.9% were females. When rating their knowledge from 0-10, our participants' mean value was 5.3 ± 2.1. Regarding ADKS, the mean knowledge score (out of 30) is 20.6 ± 2.8. Regarding ADKS domains, the best average scores were treatment and management 86.8%, followed by life impact 78.3%, assessment, and diagnosis 72.4%, course 71.5%, symptoms 62.7%, caregiving 59.2%, and risk factors 59%. We did not detect any significant association between the average overall score, occupation, source of infor­mation, or having a relative with Alzheimer's disease. Conclusion: Specific knowledge deficits were observed in domains focusing on risk factors, symptoms, and caregiving for Alzheimer's disease.
An Immune-Related lncRNA Model for Predicting Prognosis, Immune Landscape and Chemotherapeutic Response in Bladder Cancer
Authors: Hou Jian , Liang Songwu , Xie Zhimin , Qu Genyi , Xu Yong , Yang Guang , Tang Cheng 
Published: October 2021

Abstract
Objective: Long noncoding RNAs (lncRNAs) participate in cancer immunity. Herein, we characterized the clinical significance of immune-related lncRNA model and its associations with immune infiltrations and chemosensitivity in bladder cancer.Methods: Transcriptome data of bladder cancer specimens were employed from The Cancer Genome Atlas. Dysregulated immune-related lncRNAs were screened via Pearson correlation and differential expression analyses, followed by recognition of lncRNA pairs. Then, a LASSO regression model was constructed. Receiver operator characteristic curves of one-, three- and five-year survival were plotted. Akaike information criterion (AIC) value of one-year survival was determined as the cutoff of high- and low-risk subgroups. The differences in survival, clinical features, immune cell infiltrations and chemosensitivity were compared between subgroups.Results: Totally, 90 immune-related lncRNA pairs were selected, 15 of which were put into the prognostic model. The area under the curves of one-, three- and five-year survival were 0.806, 0.825 and 0.828, confirming the favorable predictive performance of this model. According to the AIC value, we clustered subjects into high- and low-risk subgroups. High-risk score indicated unfavorable outcomes. This risk model was in relation to survival status, age, stage and TNM. In comparison to conventional clinicopathological characteristics, the risk model displayed higher predictive efficacy and was an independent predictor. Also, it could well characterize immune cell infiltration landscape and predict immune checkpoint expression and sensitivity to cisplatin and methotrexate.Conclusion: This model conducted by paring immune-related lncRNAs regardless of expressions exhibited a favorable efficacy in predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.
Convolutional Neural Networks for High Spatial Resolution Remote Sensing Image Classification
Authors: P Rajagopal T K , G Sakthi , J Prakash 
Published: October 2021

Abstract
Hyperspectral remote sensing based image classification is found to be a very widely used method employed for scene analysis that is from a remote sensing data which is of a high spatial resolution. Classification is a critical task in the processing of remote sensing. On the basis of the fact that there are different materials with reflections in a particular spectral band, all the traditional pixel-wise classifiers both identify and also classify all materials on the basis of their spectral curves (or pixels). Owing to the dimensionality of the remote sensing data of high spatial resolution along with a limited number of labelled samples, a remote sensing image of a high spatial resolution tends to suffer from something known as the Hughes phenomenon which can pose a serious problem. In order to overcome such a small-sample problem, there are several methods of learning like the Support Vector Machine (SVM) along with the other methods that are kernel based and these were introduced recently for a remote sensing classification of the image and this has shown a good performance. For the purpose of this work, an SVM along with Radial Basis Function (RBF) method was proposed. But, a feature learning approach for the classification of the hyperspectral image is based on the Convolutional Neural Networks (CNNs). The results of the experiment that were based on various image datasets that were hyperspectral which implies that the method proposed will be able to achieve a better performance of classification compared to other traditional methods like the SVM and the RBF kernel and also all conventional methods based on deep learning (CNN).
Cross-Sectional Geometry Predicts Failure Location in Maize Stalks
Authors: Stubbs Christopher J , McMahan Christopher , Tabaracci Kaitlin , Kunduru Bharath , Sekhon Rajandeep S. , Robertson Daniel J 
Published: October 2021

Abstract
Background: Stalk lodging (breaking of agricultural plant stalks prior to harvest) is a multi-billion dollar a year problem. Stalk lodging occurs when high winds induce bending moments in the stalk which exceed the bending strength of the plant. Previous biomechanical models of plant stalks have investigated the effect of cross-sectional morphology on stalk lodging resistance (e.g., diameter and rind thickness). However, it is unclear if the location of stalk failure along the length of stem is determined by morphological or compositional factors. It is also unclear if the crops are structurally optimized, i.e., if the plants allocate structural biomass to create uniform and minimal bending stresses in the plant tissues. The purpose of this paper is twofold: (1) to investigate the relationship between bending stress and failure location of maize stalks, and (2) to investigate the potential of phenotyping for internode-level bending stresses to assess lodging resistance. Results: 868 maize specimens representing 16 maize hybrids were successfully tested in bending to failure. Internode morphology was measured, and bending stresses were calculated. It was found that bending stress is highly and positively associated with failure location. A user-friendly computational tool is presented to help plant breeders in phenotyping for internode-level bending stress. Phenotyping for internode-level bending stresses could potentially be used to breed for more biomechanically optimal stalks that are resistant to stalk lodging. Conclusions: Internode-level bending stress plays a potentially critical role in the structural integrity of plant stems. Equations and tools provided herein enable researchers to account for this phenotype, which has the potential to increase the bending strength of plants without increasing overall structural biomass.
Kinematics of the viscous filament during the droplet breakup in air
Authors: Broboana Diana , Bratu Ana-Maria , Magos Istvan , Patrascu Claudiu , Balan Corneliu 
Published: October 2021

Abstract
The dripping regime in the vicinity of droplet breakup is analyzed concerning the evolution of the filament’s neck and its corresponding thinning velocity. Three flow regimes are observed as the relative time decreases: (i) a monotonous increase of the neck’s thinning velocity, where inertia and capillarity are balanced, (ii) a transition region characterized by the equilibrium between inertia, capillarity, and viscous forces, where the thinning velocity varies non-monotonically with the relative time and (iii) the final pinch-off regime, where velocity decreases or oscillates around a constant value. Based on the correlation between experimental data and numerics, the distribution of the zeta - coefficient (defined as the non-dimensional second invariant of the velocity gradient) on the droplet’s profile is used to quantify the ratio between elongation and rotation of the fluid at the interface. The regions dominated by extension, where pure elongation is located at zeta = 1 , are determined. One main result of this study is the confirmation that distribution of the zeta - coefficient is a relevant parameter to analyze and to quantify the breakup process. This result has the potential of developing novel techniques and more precise procedures in determining the interfacial rheology of viscous and complex fluids.
Development and External Validation of the 3D U-Net Algorithm for Segmentation of Pelvic Lymph Nodes On Diffusion-Weighted Images
Authors: Liu Xiang , Sun Zhaonan , Han Chao , Cui Yingpu , Huang Jiahao , Wang Xiangpeng , Zhang Xiaodong , Wang Xiaoying 
Published: October 2021

Abstract
Background: The 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the feasibility of the 3D U-Net algorithm for the automated detection and segmentation of lymph nodes (LNs) on pelvic diffusion-weighted imaging (DWI) images.Methods: A total of 393 DWI images of patients suspected of having prostate cancer (PCa) between January 2019 and December 2020 were collected for model development. Seventy-seven DWI images from another group of PCa patients imaged between January 2021 and April 2021 were collected for external validation. Segmentation performance was assessed using the Dice score, positive predictive value (PPV), true positive rate (TPR), and volumetric similarity (VS) with manual annotation of pelvic LNs as the reference. The accuracy with which the suspicious metastatic LNs (short diameter > 0.8 cm) were detected was evaluated using the area under the curve (AUC) at the patient level, and the precision, recall, and F1-score were determined at the lesion level. The consistency of LN staging on an external dataset between the model and radiologist was assessed using Cohen’s kappa coefficient.Results: In the testing set used for model development, the Dice score, TPR, PPV, and VS for the segmentation of suspicious LNs were 0.85, 0.82, 0.80 and 0.86, respectively. The precision, recall, and F1-score for the detection of suspicious LNs were 0.97, 0.98 and 0.97, respectively. In the external validation dataset, the AUC of the model for identifying PCa patients with suspicious LNs was 0.963 (95% CI: 0.892-0.993). High consistency of LN staging (Kappa = 0.922) was achieved between the model and expert radiologist.Conclusion: The 3D U-Net algorithm can accurately detect and segment pelvic LNs based on DWI images.
Prognostic Parameters on Baseline and Interim F-18-FDG-PET/CT in Diffuse Large B-cell Lymphoma Patients
Authors: Czibor Sándor , Carr Robert , Redondo Francisca , Gorospe Charity , Auewarakul Chirayu , Cerci Juliano , Paez Diana , Fanti Stefano , Györke Tamás 
Published: October 2021

Abstract
Purpose FDG-PET/CT is a widely used imaging method in the management of diffuse large B-cell lymphomas (DLBCL). Our aim was to investigate the prognostic performance of different PET-biomarkers in a multicentre setting. Methods We investigated baseline volumetric values (MTV and TLG, also normalized for body weight) segmented with three different methods (>SUV4 [glob4]; 41% isocontour [41pc], and a gradiant-based lesion growing algorithm [grad]) and interim parameters (Deauville-score, ΔSUVmax, modified qPET, and rPET) alongside clinical parameters (stage, R-IPI), using 24-month progression-free survival as the clinical endpoint. Receiver operating characteristics analyses were performed to define optimal cut-off points for the continous PET-parameters.Results 107 DLBCL patients were included (54 women; mean age: 53.7 years). MTV and TLG calculations showed good correlation among glob4, 41pc and grad methods, however, optimal cut-off points were markedly different. Significantly different PFS was observed between low- and high-risk groups according to baseline MTV, bwaMTV, TLG, bwaTLG, as well as interim parameters Deauville-score, ΔSUVmax, mqPET, and rPET. Univariate Cox-regression analyses showed hazard ratios lowest for bwaMTVglob4 (HR=2.3) and highest for rPET (HR=9.09). In a multivariate Cox-regression model, rPET was shown to be an independent predictor of PFS (p=0.041; HR=9.15). A combined analysis showed that ΔSUVmax positive patients with high MTV formed a group with distinctly poor PFS (35.3%).Conclusion Baseline MTV and TLG values and optimal cut-off points achieved with different segmentation methods varied markedly and showed limited prognostic impact. Interim PET/CT parameters provided more accurate prognostic information with semiquantitative „Deauville-like” parameters performing best in the present study.
Correlation analysis between language gene polymorphism and geography/society parameter from twenty-six countries
Authors: Liu Zongxuan , Xia Wei , Sun Bo , Guo Changlu , Zhang Zhizhou 
Published: October 2021

Abstract
Human language diversity, as a biological phenotype, shall be genetically linked with language gene polymorphism. Meanwhile, this phenotype is historically shaped by local geographical/social factors. But how many language gene polymorphisms have direct correlations with some geography/society characteristics during the long-run evolution of human languages is an interesting question and largely remains uninvestigated. This study selected a series of geography/society factors (including 13 geographical factors and 21 social factors) from 26 countries and 111 single nucleotide polymorphisms (SNPs) randomly selected from 13 language genes. Principal component analysis (PCA) was performed to explore their potential correlations. Preliminary but interesting results were obtained as follow. (1) Most geographical parameters are concentrated into one cluster in the PCA diagram. The cluster contains 12 parameters that are positively correlated with each other; (2) PCA diagrams divide social parameters into four clusters, among which exist positive and negative correlations; (3) The strongest positive correlations were observed at one of ATP2C2 gene SNPs (ATP-1: rs78371901); the strongest negative correlations were found at one of NFXL1 gene SNPs (NFX-6: rs1440228); and the least correlations with language gene SNPs were observed at four geography/society factors: aash (Annual average rainfall), fore (Forest coverage), pden (Population density of the country) and rway (Runway traffic mode).
Systematic Review and Meta-analysis of Real-time Polymerase Chain Reaction assay for the detection of COVID-19 from clinical samples in low-and middle-income countries: Protocol
Authors: Babafemi Emmanuel Oladipo 
Published: October 2021

Abstract
Background: COVID-19 has spread globally since its discovery in Hubei province, China in December 2019 and became pandemic in 2020. COVID-19 is a new betacoronavirus and a variant of severe acute respiratory syndrome coronavirus 2 (SARA- CoV-2). Rapid, accurate and reliable diagnosis of COVID-19 will prevent the spread and allow for appropriate management. The main objective of this systematic review is to identify, appraise and summarise the published evidence on the diagnostic performance and effectiveness of SARS-CoV-2 virus in the diagnosis of current or previous COVID-19 using real-time polymerase chain reaction (RT-PCR) assay in low-and middle-income countries (LMICs). Methods: We will search MEDLINE/PubMed, EMBASE, BIOSIS, LILACS, Cochrane Infectious Diseases Group Specialised Register (CIDG SR), Global Health, and CINAHL for published studies for the diagnosis of COVID-19 using real-time polymerase chain reaction assay in LMICs There will be no restriction regarding the language, date of publication, and publication status. We will include retrospective, cross-sectional and cohort observational studies will be included in the review. Selection of studies, data extraction and management, assessment of risk of bias, and quality of evidence will be performed by two independent reviewers (EB and BC). A third researcher (GM) will be consulted in case of discrepancies. Depending on the availability and quality of the data, a meta-analysis will be performed. Otherwise, findings will be qualitatively reported. Discussion: To our knowledge, this is the first systematic review and meta-analysis to assess the uptake of RT-PCR assay for SARS-CoV-2 detection from clinical samples in human in LMICs. This review will make available evidence on the uptake, accuracy, approach, and interpretation of results of this assay in the context of COVID-19 diagnosis which will meet an urgent need, considering the diagnostic challenges of RT-PCR assay for COVID-19 diagnosis in humans. Systematic review registration: PROSPERO CRD42021271894
The prognostic value and immune microenvironment association of androgen receptor in HER2 positive early breast invasive ductal carcinoma
Authors: Zhou Danyang 
Published: October 2021

Abstract
BackgroundThe prognosis of HER2+ early breast cancer is heterogeneous. AR, as an indicator of prognosis and treatment, is uncertain in HER2+ subtypes. We aimed to investigate the prognostic value of AR and the relationship between AR expression and immune microenvironment in HER2+ early breast invasive ductal carcinoma (IDC).MethodsHER2+ early breast IDC patients diagnosed by pathology who underwent surgery at Sun Yat-sen University Cancer Center from 2016 to 2017 were the main population. All patients included performed AR test and their clinicopathological data were collected. The disease-free survival (DFS) and overall survival (OS) were evaluated by the Kaplan–Meier method and Cox proportional hazards model. AR+ and AR- breast IDCs were matched 1:1 according to age, T stage and N stage for immune infiltration analysis.ResultsA total of 554 patients with HER2+ early breast cancer were included in this retrospective study, regardless of HR status. Taking 10% as the cutoff values of AR, 81.6% of patients were AR positive and 18.4% were AR negative. ER+ (P<0.001) and PR+ (P<0.001) had significant relations with the positive expression of AR. Kaplan-Meier survival curves analysis suggested that AR had close links with OS (P=0.001), not DFS (P=0.051). Eliminating the potential impact caused by HR, AR also predicted a longer OS (P=0.014) and AR was an independent impact factor for OS by multivariate analysis (P=0.036) in HER2+HR- early breast IDC patients. In AR+ and AR- matched HER2+HR- patients, TILs (P=0.043) and PD-L1 (P=0.027) is significantly low in AR+ patients. The strongest negative correlation was observed between AR and PD-L1 (Pearson’s r =-0.299, P=0.001). AR+ seemed to trend a favorable clinical survival in HER2+HR- IDCs with low TILs or positive PD-L1.ConclusionsAR+ were markedly related to the better OS in HER2+HR- early breast cancer, while the negative correlation was observed between AR and PD-L1/TILs. We provided new insights for the prognostic value and immune microenvironment association of AR to optimize treatment strategies in HER2+ early breast IDCs.
A Proposal to Facilitate Mandatory Bridge Load Tests with Artificial Neural Network Analyses Using a Digital Data Aggregation Platform
Authors: OWERKO Piotr , KAŁUŻA Jerzy , WAZOWSKI Marek 
Published: October 2021

Abstract
The paper presents an innovative concept of digital aggregation of data related to mandatory in-situ load tests of bridge structures. The proposed approach allows to manage various types of information regarding those experiments, in a way which is consistent with current good practises in BIM technology and digitalisation of construction industry. The proposed web platform will allow for vast improvements in decision-making process regarding admission of a given bridge for service, in proper analyses and even predictions of bridges mechanical response. Initial architecture of the system is introduced along with an appropriate literature review and the identification of key actors and their roles in the described information management process. To highlight the potential of the solution, two examples are shown. In both cases key advantages of digital aggregation are emphasised: the possibility to learn from previous analogical in-situ experiments, and the possibility to utilise modern machine learning algorithms and state-of-the-art open-source solutions.
Implementation to Industry and Municipal Sector the Compact Trickle Bed Bioreactors Technology to Odor and Vocs Removal
Authors: PARZENTNA-GABOR Anita , BARBUSIŃSKI Krzysztof , KASPERCZYK Damian 
Published: October 2021

Abstract
Biotrickling filters are one of the most effective methods of air bio-purification, this bioremediation process if of high efficiency in pollution reduction. It is an eco-friendly process and economically viable. The technology of biotrickling filters includes Compact Trickle Bed Bioreactors (CTBB), which are currently used in an increasingly wide range. The aim of this work will be an objective assessment of the implementation potential of the CTBB technology to various industries, including the municipal sector. The paper briefly discusses the characteristics and operating parameters of biotrickling filters, a review of their applications as an effective method of VOC and odor removal including sources of their emissions, as well as the characteristics of CTBB and implementation possibilities to various industries. It was concluded that CTBB are promising solution for the future, as it combines the high degradation efficiency of a wide range of pollutants with cost-effectiveness and ecology. According to the analyzed data and results, this technology can be successfully used to remove VOCs and odors from various industries.