bluuebunny/crossref_metadata_2025_split huggingface dataset:
E-Cadherin expression in human tumors: a tissue microarray study on 10,851 tumors
Authors: Burandt Eike , Lübbersmeyer Felix , Gorbokon Natalia , Büscheck Franziska , Luebke Andreas M. , Menz Anne , Kluth Martina , Hube-Magg Claudia , Hinsch Andrea , Höflmayer Doris , Weidemann Sören , Fraune Christoph , Möller Katharina , Jacobsen Frank , Lebok Patrick , Clauditz Till Sebastian , Sauter Guido , Simon Ronald , Uhlig Ria , Wilczak Waldemar , Steurer Stefan , Minner Sarah , Krech Rainer , Dum David , Krech Till , Marx Andreas Holger , Bernreuther Christian 
Published: June 2021

Abstract
Background
The E-Cadherin gene (CDH1, Cadherin 1), located at 16q22.1 encodes for a calcium-dependent membranous glycoprotein with an important role in cellular adhesion and polarity maintenance.

Methods
To systematically determine E-Cadherin protein expression in normal and cancerous tissues, 14,637 tumor samples from 112 different tumor types and subtypes as well as 608 samples of 76 different normal tissue types were analyzed by immunohistochemistry in a tissue microarray format.

Results
E-Cadherin was strongly expressed in normal epithelial cells of most organs. From 77 tumor entities derived from cell types normally positive for E-Cadherin, 35 (45.5%) retained at least a weak E-Cadherin immunostaining in ≥99% of cases and 61 (79.2%) in ≥90% of cases. Tumors with the highest rates of E-Cadherin loss included Merkel cell carcinoma, anaplastic thyroid carcinoma, lobular carcinoma of the breast, and sarcomatoid and small cell neuroendocrine carcinomas of the urinary bladder. Reduced E-Cadherin expression was linked to higher grade (p = 0.0009), triple negative receptor status (p = 0.0336), and poor prognosis (p = 0.0466) in invasive breast carcinoma of no special type, triple negative receptor status in lobular carcinoma of the breast (p = 0.0454), advanced pT stage (p = 0.0047) and lymph node metastasis in colorectal cancer (p < 0.0001), and was more common in recurrent than in primary prostate cancer (p < 0.0001). Of 29 tumor entities derived from E-Cadherin negative normal tissues, a weak to strong E-Cadherin staining could be detected in at least 10% of cases in 15 different tumor entities (51.7%). Tumors with the highest frequency of E-Cadherin upregulation included various subtypes of testicular germ cell tumors and renal cell carcinomas (RCC). E-Cadherin upregulation was more commonly seen in malignant than in benign soft tissue tumors (p = 0.0104) and was associated with advanced tumor stage (p = 0.0276) and higher grade (p = 0.0035) in clear cell RCC, and linked to advanced tumor stage (p = 0.0424) and poor prognosis in papillary RCC (p ≤ 0.05).

Conclusion
E-Cadherin is consistently expressed in various epithelial cancers. Down-regulation or loss of E-Cadherin expression in cancers arising from E-Cadherin positive tissues as well as E-Cadherin neo-expression in cancers arising from E-Cadherin negative tissues is linked to cancer progression and may reflect tumor dedifferentiation.
LED light sources improved the essential oil components and antioxidant activity of two genotypes of lemon balm (Melissa officinalis L.)
Authors: Ahmadi Tayebeh , Shabani Leila , Sabzalian Mohammad R. 
Published: June 2021

Abstract
Background
Nowadays, light-emitting diodes (LEDs) as a new lighting technology, have been emerged as an alternative source of light for plants due to their wavelength specificity, the narrow width of their bands, small size, solid structure, long lifetime, and low heat generation. Here we investigated the effect of different LED light sources on the essential oil components and antioxidant activity of Melissa officinalis. Two genotypes of lemon balm (Ilam and Isfahan) were subjected to four artificial light treatments, including white, red, blue, red + blue LEDs, and greenhouse light as natural lighting.

Results
The LED lights significantly increased shoot fresh and dry weights and leaf number in the two genotypes as compared to greenhouse condition. The results showed that the content and composition of essential oil in the two genotypes were variable under different light treatments and the total amount of compounds in the Ilam genotype was higher than the other genotype. The results of analysis of the essential oil by GC/MS indicated that the highest amount of monoterpenes in the genotypes was related to citronellal under red + blue LED lamps (15.3 and 17.2% in Ilam and Isfahan genotypes, respectively) but blue, white, and greenhouse condition had the most effect on sesquiterpenes content in both genotypes. The results showed that the observed variation between the two genotypes in the essentials oil composition was related to the relative percentage of the constituents and not to the appearance or lack of a specific component. Red + blue lighting also provided the highest radical scavenging activity in both genotypes (80.77 and 82.09% for Ilam and Isfahan genotypes, respectively). Based on principal component analyses (PCA), three main groups were identified regarding genotypes and all light treatments.

Conclusions
Overall, results indicated that the essentials oil composition of two genotypes of lemon balm was affected both qualitatively and quantitatively by different LED light sources; hence, LED lights might be used to improve monoterpenes, sesquiterpenes, and antioxidant activity in the selected genotypes.
A data value metric for quantifying information content and utility
Authors: Noshad Morteza , Choi Jerome , Sun Yuming , Hero Alfred , Dinov Ivo D. 
Published: June 2021

AbstractData-driven innovation is propelled by recent scientific advances, rapid technological progress, substantial reductions of manufacturing costs, and significant demands for effective decision support systems. This has led to efforts to collect massive amounts of heterogeneous and multisource data, however, not all data is of equal quality or equally informative. Previous methods to capture and quantify the utility of data include value of information (VoI), quality of information (QoI), and mutual information (MI). This manuscript introduces a new measure to quantify whether larger volumes of increasingly more complex data enhance, degrade, or alter their information content and utility with respect to specific tasks. We present a new information-theoretic measure, called Data Value Metric (DVM), that quantifies the useful information content (energy) of large and heterogeneous datasets. The DVM formulation is based on a regularized model balancing data analytical value (utility) and model complexity. DVM can be used to determine if appending, expanding, or augmenting a dataset may be beneficial in specific application domains. Subject to the choices of data analytic, inferential, or forecasting techniques employed to interrogate the data, DVM quantifies the information boost, or degradation, associated with increasing the data size or expanding the richness of its features. DVM is defined as a mixture of a fidelity and a regularization terms. The fidelity captures the usefulness of the sample data specifically in the context of the inferential task. The regularization term represents the computational complexity of the corresponding inferential method. Inspired by the concept of information bottleneck in deep learning, the fidelity term depends on the performance of the corresponding supervised or unsupervised model. We tested the DVM method for several alternative supervised and unsupervised regression, classification, clustering, and dimensionality reduction tasks. Both real and simulated datasets with weak and strong signal information are used in the experimental validation. Our findings suggest that DVM captures effectively the balance between analytical-value and algorithmic-complexity. Changes in the DVM expose the tradeoffs between algorithmic complexity and data analytical value in terms of the sample-size and the feature-richness of a dataset. DVM values may be used to determine the size and characteristics of the data to optimize the relative utility of various supervised or unsupervised algorithms.
Analysis of Bayesian optimization algorithms for big data classification based on Map Reduce framework
Authors: Banchhor Chitrakant , Srinivasu N. 
Published: June 2021

AbstractThe process of big data handling refers to the efficient management of storage and processing of a very large volume of data. The data in a structured and unstructured format require a specific approach for overall handling. The classifiers analyzed in this paper are correlative naïve Bayes classifier (CNB), Cuckoo Grey wolf CNB (CGCNB), Fuzzy CNB (FCNB), and Holoentropy CNB (HCNB). These classifiers are based on the Bayesian principle and work accordingly. The CNB is developed by extending the standard naïve Bayes classifier with applied correlation among the attributes to become a dependent hypothesis. The cuckoo search and grey wolf optimization algorithms are integrated with the CNB classifier, and significant performance improvement is achieved. The resulting classifier is called a cuckoo grey wolf correlative naïve Bayes classifier (CGCNB). Also, the performance of the FCNB and HCNB classifiers are analyzed with CNB and CGCNB by considering accuracy, sensitivity, specificity, memory, and execution time.
Automatic analysis of social media images to identify disaster type and infer appropriate emergency response
Authors: Asif Amna , Khatoon Shaheen , Hasan Md Maruf , Alshamari Majed A. , Abdou Sherif , Elsayed Khaled Mostafa , Rashwan Mohsen 
Published: June 2021

AbstractSocial media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important role in identifying appropriate emergency responses to a particular disaster. In this paper, we proposed a disaster taxonomy of emergency response and used the same taxonomy with an emergency response pipeline together with deep-learning-based image classification and object identification algorithms to automate the emergency response decision-making process. We used the card sorting method to validate the completeness and correctness of the disaster taxonomy. We also used VGG-16 and You Only Look Once (YOLO) algorithms to analyze disaster-related images and identify disaster types and relevant cues (such as objects that appeared in those images). Furthermore, using decision tables and applied analytic hierarchy processes (AHP), we aligned the intermediate outputs to map a disaster-related image into the disaster taxonomy and determine an appropriate type of emergency response for a given disaster. The proposed approach has been validated using Earthquake, Hurricane, and Typhoon as use cases. The results show that 96% of images were categorized correctly on disaster taxonomy using YOLOv4. The accuracy can be further improved using an incremental training approach. Due to the use of cloud-based deep learning algorithms in image analysis, our approach can potentially be useful to real-time crisis management. The algorithms along with the proposed emergency response pipeline can be further enhanced with other spatiotemporal features extracted from multimedia information posted on social media.
Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest
Authors: Prasetiyowati Maria Irmina , Maulidevi Nur Ulfa , Surendro Kridanto 
Published: June 2021

AbstractFeature selection is a pre-processing technique used to remove unnecessary characteristics, and speed up the algorithm's work process. A part of the technique is carried out by calculating the information gain value of each dataset characteristic. Also, the determined threshold rate from the information gain value is used in feature selection. However, the threshold value is used freely or through a rate of 0.05. Therefore this study proposed the threshold rate determination using the information gain value’s standard deviation generated by each feature in the dataset. The threshold value determination was tested on 10 original datasets transformed by FFT and IFFT and classified using Random Forest. On processing the transformed dataset with the proposed threshold this study resulted in lower accuracy and longer execution time compared to the same process with Correlation-Base Feature Selection (CBF) and a standard 0.05 threshold method. Similarly, the required accuracy value is lower when using transformed features. The study showed that by processing the original dataset with a standard deviation threshold resulted in better feature selection accuracy of Random Forest classification. Furthermore, by using the transformed feature with the proposed threshold excluding the imaginary numbers leads to a faster average time than the three methods compared.
Smart learning environments: a basic research towards the definition of a practical model
Authors: García-Tudela Pedro Antonio , Prendes-Espinosa Paz , Solano-Fernández Isabel María 
Published: June 2021

AbstractThis paper is basic research focused on the analysis of scientific advances related to Smart Learning Environments (SLE). Our main objective is to single out the common aspects to propose a new definition which will constitute the starting point to design an innovative model which we can apply to the analysis of real cases and good practices. For this, we have proposed a qualitative methodology that has been implemented in two phases: on the one hand, a documentary analysis of the existing definitions for SLE using the NVIVO program (frequency of words, coding and cross-references) and, on the other, an expert judgement by means of the Delphi method in order to validate the proposed model. The main results are reflected in the coalescence of a new definition of SLE and the proposal of the model entitled SLE-5. With the present research, we have been able to provide a model, defined in five dimensions and other key elements in SLE such as ergonomics and learning analytics, which transcends the technological-pedagogical gap of the SLE and offers a framework for the design and analysis of didactic proposals based on this model.
Citizen advisory groups for the creation and improvement of decision aids: experience from two Swiss centers for primary care
Authors: Selby Kevin , Cardinaux Regula , Metry Beatrice , de Rougemont Simone , Chabloz Janine , Meier-Herrmann Verena , Stoller Jürg , Durand Marie-Anne , Auer Reto 
Published: June 2021

Abstract
Background
Guidelines for patient decision aids (DA) recommend target population involvement throughout the development process, but developers may struggle because of limited resources. We sought to develop a feasible means of getting repeated feedback from users.

Methods
Between 2017 and 2020, two Swiss centers for primary care (Lausanne and Bern) created citizen advisory groups to contribute to multiple improvement cycles for colorectal, prostate and lung cancer screening DAs. Following Community Based Participatory Research principles, we collaborated with local organizations to recruit citizens aged 50 to 75 without previous cancer diagnoses. We remunerated incidental costs and participant time. One center supplemented in-person meetings by mailed paper questionnaires, while the other supplemented meetings using small-group workshops and analyses of meeting transcripts.

Results
In Lausanne, we received input from 49 participants for three DAs between 2017 and 2020. For each topic, participants gave feedback on the initial draft and 2 subsequent versions during in-person meetings with ~ 8 participants and one round of mailed questionnaires. In Bern, 10 participants were recruited among standardized patients from the university, all of whom attended in-person meetings every three months between 2017 and 2020. At both sites, numerous changes were made to the content, appearance, language, and tone of DAs and outreach materials. Participants reported high levels of satisfaction with the participative process.

Conclusions
Citizen advisory groups are a feasible means of repeatedly incorporating end-user feedback during the creation of multiple DAs. Methodological differences between the two centers underline the need for a flexible model adapted to local needs.
A novel approach to explore Safety-I and Safety-II perspectives in in situ simulations—the structured what if functional resonance analysis methodology
Authors: MacKinnon Ralph James , Pukk-Härenstam Karin , Kennedy Christopher , Hollnagel Erik , Slater David 
Published: June 2021

Abstract
Objectives
With ever increasingly complex healthcare settings, technology enhanced simulation (TES) is well positioned to explore all perspectives to enhance patient safety and patient outcomes. Analysis from a Safety-II stance requires identification of human adjustments in daily work that are key to maintaining safety. The aim of this paper is to describe an approach to explore the consequences of human variability from a Safety-II perspective and describe the added value of this to TES.

Methods
The reader is guided through a novel application of functional resonance analysis methodology (FRAM), a method to analyse how a system or activity is affected by human variability, to explore human adaptations observed in in situ simulations (ISS). The structured applicability of this novel approach to TES is described by application to empirical data from the standardised ISS management of paediatric time critical head injuries (TCHI).

Results
A case series is presented to illustrate the step-wise observation of key timings during ISSs, the construction of FRAM models and the visualisation of the propagation of human adaptations through the FRAM models. The key functions/actions that ensure the propagation are visible, as are the sequelae of the adaptations.

Conclusions
The approach as described in this paper is a first step to illuminating how to explore, analyse and observe the consequences of positive and negative human adaptations within simulated complex systems. This provides TES with a structured methodology to visualise and reflect upon both Safety-I and Safety-II perspectives to enhance patient safety and patient outcomes.
Embracing informed learner self-assessment during debriefing: the art of plus-delta
Authors: Cheng A. , Eppich W. , Epps C. , Kolbe M. , Meguerdichian M. , Grant V. 
Published: June 2021

AbstractThe healthcare simulation field has no shortage of debriefing options. Some demand considerable skill which serves as a barrier to more widespread implementation. The plus-delta approach to debriefing offers the advantages of conceptual simplicity and ease of implementation. Importantly, plus-delta promotes learners’ capacity for a self-assessment, a skill vital for safe clinical practice and yet a notorious deficiency in professional practice. The plus-delta approach confers the benefits of promoting uptake of debriefing in time-limited settings by educators with both fundamental but also advanced skills, and enhancing essential capacity for critical self-assessment informed by objective performance feedback. In this paper, we describe the role of plus-delta in debriefing, provide guidance for incorporating informed learner self-assessment into debriefings, and highlight four opportunities for improving the art of the plus delta: (a) exploring the big picture vs. specific performance issues, (b) choosing between single vs. double-barreled questions, (c) unpacking positive performance, and (d) managing perception mismatches.
Effects of bicarbonate/lactate-buffered neutral peritoneal dialysis fluids on angiogenesis-related proteins in patients undergoing peritoneal dialysis
Authors: Ueno Hiromichi , Miyamoto Tetsu , Morimoto Hiroyuki , Sanada Kenya , Furuno Ikutaro , Nakazono Kazutoshi , Hasegawa Emi , Kuma Akihiro , Oginosawa Yasushi , Tsuda Yuki , Araki Masaru , Tamura Masahito , Ueta Yoichi , Otsuji Yutaka , Kataoka Masaharu 
Published: June 2021

Abstract
Background
In order to facilitate the safe and long-term delivery of peritoneal dialysis (PD), it is necessary to improve the biocompatibility of peritoneal dialysis fluids (PDFs). The novel bicarbonate/lactate-buffered neutral PDFs (B/L-PDFs) are expected to be improved biocompatible. This study evaluated the biocompatibility of B/L-PDFs by analysis on the profile of angiogenesis-related proteins in drained dialysate of patients undergoing PD.

Methods
Concentrations of 20 angiogenesis-related proteins in the dialysate were semi-quantitatively determined using a RayBio® Human Angiogenesis Antibody Array and were compared between B/L-PDFs and conventional lactate-buffered neutral PDFs (L-PDFs).

Results
The expression of growth-related oncogene (GRO α/β/γ), which belongs to the CXC chemokine family, decreased significantly after use of the B/L-PDFs compared to the L-PDFs (P = 0.03). The number of the proteins with lower level in the B/L-PDFs compared with L-PDFs was significantly negatively correlated with the PD duration (Spearman ρ = − 0.81, P = 0.004).

Conclusion
This study suggested that B/L-PDFs are more biocompatible than conventional PDFs.
Pretransplant dialysis treatment and vascular calcification of the iliac artery and abdominal aorta in kidney transplant patients
Authors: Nakai Kentaro , Yamamoto Shutaro , Inoue Megumi , Kohara Chiaki , Shukuri Tomoya , Motoyama Kentaro , Mitsuiki Koji 
Published: June 2021

Abstract
Introduction
Patients with chronic kidney disease often suffer from cardiovascular disease, and vascular calcification has been identified as one of the risk factors for cardiovascular disease. We aimed to examine the effect of dialysis treatment before kidney transplantation on graft survival, vascular calcification, and its progression after kidney transplantation.

Methods
Among the 102 patients who underwent kidney transplant between 2008 and 2017, two patients were excluded for moved and lost to follow-up and primary nonfunction. The clinical characteristics and laboratory data were assessed according to pretransplant treatment modality. Rapid progression of vascular calcification was defined when patients showed an increase in the highest tertile of progression of each iliac artery calcification thickness (IACT) and aortic calcification index (ACI).

Results
Cox proportional hazard models did not show any significant association between pretransplant treatment modality and graft survival to the doubling of creatinine from nadir creatinine during the first 3 months after kidney transplantation. At baseline, the IACT was significantly higher in hemodialysis patients than in preemptive kidney transplant patients, whereas the ACI was comparable among the pretransplant treatment modality groups. IACT was independently associated with dialysis vintage. There was no significant association between rapid progression of vascular calcification (IACT and ACI) and dialysis modality.

Conclusions
Dialysis modality was an independent factor related to IACT, whereas there was no legacy effect for the progression of vascular calcification after kidney transplantation.