bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thoracic low grade glial neoplasm with concurrent H3 K27M and PTPN11 mutations
Authors: Argenziano Michael G. , Furnari Julia L. , Miller Michael L. , Sun Yu , Banu Matei A. , Neira Justin A. , Snuderl Matija , Bruce Jeffrey N. , Welch Mary , McCormick Paul , Canoll Peter 
Published: April 2022

AbstractWe present the case of a 41-year-old man who developed worsening mid-thoracic back pain and imaging revealed a well-circumscribed intramedullary tumor in the thoracic spinal cord. Subtotal resection was performed, and histopathological analysis showed a cytologically bland, minimally proliferative glial neoplasm. Sequencing revealed H3 K27M and an activating PTPN11 mutation. Serial imaging revealed slow tumor regrowth over a three year period which prompted a second resection. The recurrent tumor displayed a similar low grade-appearing histology and harbored the same H3 K27M and PTPN11 mutations as the primary. While the prognostic importance of isolated H3 K27M in spinal gliomas is well-known, the combination of these two mutations in spinal low grade glioma has not been previously reported. Importantly, PTPN11 is a component of the MAPK signaling pathway. Thus, as building evidence shows that low grade-appearing gliomas harboring H3 K27M mutations along with BRAF or FGFR1 mutations have a relatively more favorable course compared to isolated H3 K27M-mutant midline gliomas, the present case provides new evidence for the prognostic importance of activating mutations in other components of the MAPK signaling pathway. This case further highlights the importance of clinico-radio-pathologic correlation when incorporating evolving genetic data into the integrated diagnosis of rare neuroepithelial tumors.
Fibroblast growth factor receptor 4 promotes glioblastoma progression: a central role of integrin-mediated cell invasiveness
Authors: Gabler Lisa , Jaunecker Carola Nadine , Katz Sonja , van Schoonhoven Sushilla , Englinger Bernhard , Pirker Christine , Mohr Thomas , Vician Petra , Stojanovic Mirjana , Woitzuck Valentin , Laemmerer Anna , Kirchhofer Dominik , Mayr Lisa , LaFranca Mery , Erhart Friedrich , Grissenberger Sarah , Wenninger-Weinzierl Andrea , Sturtzel Caterina , Kiesel Barbara , Lang Alexandra , Marian Brigitte , Grasl-Kraupp Bettina , Distel Martin , Schüler Julia , Gojo Johannes , Grusch Michael , Spiegl-Kreinecker Sabine , Donoghue Daniel J. , Lötsch Daniela , Berger Walter 
Published: April 2022

AbstractGlioblastoma (GBM) is characterized by a particularly invasive phenotype, supported by oncogenic signals from the fibroblast growth factor (FGF)/ FGF receptor (FGFR) network. However, a possible role of FGFR4 remained elusive so far. Several transcriptomic glioma datasets were analyzed. An extended panel of primary surgical specimen-derived and immortalized GBM (stem)cell models and original tumor tissues were screened for FGFR4 expression. GBM models engineered for wild-type and dominant-negative FGFR4 overexpression were investigated regarding aggressiveness and xenograft formation. Gene set enrichment analyses of FGFR4-modulated GBM models were compared to patient-derived datasets. Despite widely absent in adult brain, FGFR4 mRNA was distinctly expressed in embryonic neural stem cells and significantly upregulated in glioblastoma. Pronounced FGFR4 overexpression defined a distinct GBM patient subgroup with dismal prognosis. Expression levels of FGFR4 and its specific ligands FGF19/FGF23 correlated both in vitro and in vivo and were progressively upregulated in the vast majority of recurrent tumors. Based on overexpression/blockade experiments in respective GBM models, a central pro-oncogenic function of FGFR4 concerning viability, adhesion, migration, and clonogenicity was identified. Expression of dominant-negative FGFR4 resulted in diminished (subcutaneous) or blocked (orthotopic) GBM xenograft formation in the mouse and reduced invasiveness in zebrafish xenotransplantation models. In vitro and in vivo data consistently revealed distinct FGFR4 and integrin/extracellular matrix interactions. Accordingly, FGFR4 blockade profoundly sensitized FGFR4-overexpressing GBM models towards integrin/focal adhesion kinase inhibitors. Collectively, FGFR4 overexpression contributes to the malignant phenotype of a highly aggressive GBM subgroup and is associated with integrin-related therapeutic vulnerabilities.
Deep learning from multiple experts improves identification of amyloid neuropathologies
Authors: Wong Daniel R. , Tang Ziqi , Mew Nicholas C. , Das Sakshi , Athey Justin , McAleese Kirsty E. , Kofler Julia K. , Flanagan Margaret E. , Borys Ewa , White Charles L. , Butte Atul J. , Dugger Brittany N. , Keiser Michael J. 
Published: April 2022

AbstractPathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address this problem, we present a deep learning (DL) approach that draws on a cohort of experts, weighs each contribution, and is robust to noisy labels. We collected 100,495 annotations on 20,099 candidate amyloid beta neuropathologies (cerebral amyloid angiopathy (CAA), and cored and diffuse plaques) from three institutions, independently annotated by five experts. DL methods trained on a consensus-of-two strategy yielded 12.6–26% improvements by area under the precision recall curve (AUPRC) when compared to those that learned individualized annotations. This strategy surpassed individual-expert models, even when unfairly assessed on benchmarks favoring them. Moreover, ensembling over individual models was robust to hidden random annotators. In blind prospective tests of 52,555 subsequent expert-annotated images, the models labeled pathologies like their human counterparts (consensus model AUPRC = 0.74 cored; 0.69 CAA). This study demonstrates a means to combine multiple ground truths into a common-ground DL model that yields consistent diagnoses informed by multiple and potentially variable expert opinions.
Functionality of early medieval roasters studied using archaeobotany and chromatography: preliminary studies
Authors: Lisowska Ewa , Rosiak Angelina , Sady-Bugajska Agata , Kałużna-Czaplińska Joanna 
Published: April 2022

Abstract
Roasters are clay, quadrilateral vessels that are found in areas of whole Slavic region. For years, there has been a discussion on their functionality, in which various concepts of their use keep appearing. In this paper we present the results of botanical and gas chromatographic studies carried out on several fragments of roasters that originated in the Sudetes Mountain area and Sudetic Foreland. The analyzed artefacts in total of 26 pieces come from three early medieval sites: Gilów, Jelenia Góra–Grabary and Kamieniec Ząbkowicki. The obtained results allowed to concluding that these were the vessels used for thermal processing of cereal grains in both processes of food processing and food preservation. The results made it possible to move the beginnings of agrarian activity in the Sudetes Mountains by 400 years, earlier than it was thought, which has a key meaning for observation of the economy development in mountainous areas in the early Middle Ages.
Comparing traditional news and social media with stock price movements; which comes first, the news or the price change?
Authors: Smith Stephen , O’Hare Anthony 
Published: April 2022

AbstractTwitter has been responsible for some major stock market news in the recent past, from rogue CEOs damaging their company to very active world leaders asking for brand boycotts, but despite its impact Twitter has still not been as impactful on markets as traditional news sources. In this paper we examine whether daily news sentiment of several companies and Twitter sentiment from their CEOs have an impact on their market performance and whether traditional news sources and Twitter activity of heads of government impact the benchmark indexes of major world economies over a period spanning the outbreak of the SAR-COV-2 pandemic. Our results indicate that there is very limited correlation between Twitter sentiment and price movements and that this does not change much when returns are taken relative to the market or when the market is calm or turbulent. There is almost no correlation under any circumstances between non-financial news sources and price movements, however there is some correlation between financial news sentiment and stock price movements. We also find this correlation gets stronger when returns are taken relative to the market. There are fewer companies correlated in both turbulent and calm economic times. There is no clear pattern to the direction and strength of the correlation, with some being strongly negatively correlated and others being strongly positively correlated, but in general the size of the correlation tends to indicate that price movement is driving sentiment, except in the turbulent economic times of the SARS-COV-2 pandemic in 2020.
Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches
Authors: Hansun Seng , Wicaksana Arya , Khaliq Abdul Q. M. 
Published: April 2022

AbstractAs a new type of currency introduced in the new millennium, cryptocurrency has established its ecosystems and attracts many people to use and invest in it. However, cryptocurrencies are highly dynamic and volatile, making it challenging to predict their future values. In this research, we use a multivariate prediction approach and three different recurrent neural networks (RNNs), namely the long short-term memory (LSTM), the bidirectional LSTM (Bi-LSTM), and the gated recurrent unit (GRU). We also propose simple three layers deep networks architecture for the regression task in this study. From the experimental results on five major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Tether (USDT), and Binance Coin (BNB), we find that both Bi-LSTM and GRU have similar performance results in terms of accuracy. However, in terms of the execution time, both LSTM and GRU have similar results, where GRU is slightly better and has lower variation results on average.
The impact of ensemble learning on surgical tools classification during laparoscopic cholecystectomy
Authors: Jaafari Jaafar , Douzi Samira , Douzi Khadija , Hssina Badr 
Published: April 2022

AbstractLaparoscopic surgery also know as minimally invasive surgery (MIS), is a type of surgical procedure that allows a surgeon to examine the organs inside of the abdomen without having to make large incisions in the skin. It unifies the competence and skills of highly trained surgeons with the power and precision of machines. Furthermore, surgical instruments are inserted through the abdomen with the help of a laparoscope, which is a tube with a high-intensity light and a high-resolution camera at the end. In addition, recorded videos from this type of surgery have become a steadily more important information source. However, MIS videos are often very long, thereby, navigating through these videos is time and effort consuming. The automatic identification of tool presence in laparoscopic videos leads to detecting what tools are used at each time in surgery and helps in the automatic recognition of surgical workflow. The aim of this paper is to predict surgical tools from laparoscopic videos using three states of the arts CNNs, namely: VGG19, Inception v-4, and NASNet-A. In addition, an ensemble learning method is proposed, combining the three CNNs, to solve the tool presence detection problem as a multi-label classification problem. The proposed methods are evaluated on a dataset of 80 cholecystectomy videos (Cholec80 dataset). The results present an improvement of approximately 6.19% and a mean average precision of 97.84% when the ensemble learning method is applied.
NLP-based platform as a service: a brief review
Authors: Pais Sebastião , Cordeiro João , Jamil M. Luqman 
Published: April 2022

AbstractNatural language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It has recently gained much attention for analyzing human language computationally and has spread its applications for various tasks such as machine translation, information extraction, summarization, question answering, and others. With the rapid growth of cloud computing services, merging NLP in the cloud is a significant benefit. It allows researchers to conduct NLP-related experiments on large amounts of data handled by big data techniques while harnessing the cloud’s vast, on-demand computing power. However, it has not sufficiently spread its tools and applications as a service in the cloud and there is little literature available that discusses the scope of interdisciplinary work. NLP, cloud Computing, and big data are vast domains and contain their challenges and potentials. By overcoming those challenges and integrating these fields, great potential for NLP and its applications can be unleashed. This paper presents a survey of NLP in cloud computing with a key focus on the comparison of cloud-based NLP services, challenges of NLP and big data while emphasizing the necessity of viable cloud-based NLP services. In the first part of this paper, an overview of NLP is presented by discussing different levels of NLP and components of natural language generation (NLG), followed by the applications of NLP. In the second part, the concept of cloud computing is discussed that highlights the architectural layers and deployment models of cloud computing and cloud-hosted NLP services. In the third part, the field of big data in the cloud is discussed with an emphasis on NLP. Furthermore, information extraction via NLP techniques within big data is introduced.
An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain
Authors: Sabharwal Renu , Miah Shah J. 
Published: April 2022

AbstractBig data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.
Potential for the use of large unstructured data resources by public innovation support institutions
Authors: Cetera Wiesław , Gogołek Włodzimierz , Żołnierski Aleksander , Jaruga Dariusz 
Published: April 2022

AbstractEffective programming of research and development (R&D) support, adjusted to the actual potential of beneficiaries, requires the use of modern analytical tools. An efficient R&D support system requires up-to-date data on technological trends, ongoing (and planning) research, market needs and developing innovation. The most popular programming methods were based on the analysis of data with a 4 to 5-year time delay until recently. Having described the method of refining information from unstructured data, we explore how to make it possible not only to solve the issue of up-to-date data but to identify of the latest trends in R&D activities.The analytical tools we describe were already fully functional in 2018 and are constantly being improved. The article presents the potential of one tool that can be applied in public support institutions. Methods of identifying and diagnosing technology trends are presented within the case study of the electric car technology trend. The presented case study shows the effectiveness of the method we developed for identifying and diagnosing areas requiring support from public funds. Public institutions, including public institutions supporting R&D and innovation processes, can apply tools that allow an increase in the quality of public support programmes offered, but also beneficial for the quality of strategic resources management within the institution itself. The comparison of the predictions made by the described tools with the classifications made by experts, the former are more accurate and precise. Moreover, the results of the analyses performed by the presented model are not influenced by distorting factors—fads, trends, political pressures, or processes with an unidentified, non-substantive background. It should be emphasized that the accuracy of the whole model is 0.84. The described tools and methods are already directly applicable in many areas related to the support of R&D activity worldwide. The article presents a solution that effectively enables the management of more precise programmes supporting innovative activities used for the first time in Poland. It is also one of the first uses of these methods by public administration in the world. Our approach not only strengthens improved adjustment of the support offered for R&D activity, but also makes it possible to apply and improve management methods in public institutions.
Investigation into the combustion kinetics and spontaneous ignition of sweet sorghum as energy resource
Authors: Luthfi Numan , Ohkoshi Tappei , Tamaru Yutaka , Fukushima Takashi , Takisawa Kenji 
Published: April 2022

AbstractThis study investigated the combustion kinetics and spontaneous ignition of sweet sorghum using thermogravimetric analysis and the Frank-Kamenetskii theory. The aim was to determine the proper operating conditions for a direct combustion reactor and predict the safe ambient temperature limits for given silo designs. Oxidative heating rates of 2, 5, and 10 °C/min were set up. Graphical observation shows that combustion was composed of two different stages representing the overlapping processes of pyrolysis and char oxidation, at 131–336 °C and 336–475 °C, respectively. Samples were found to ignite at 215 °C and were extinguished at 433 °C. Different heating rates shifted combustion characteristics to higher temperatures and increased reactivity for ignition and combustion indices up to 12 and 10 times higher. The Friedman method determined the apparent activation energies representing the combustion reaction by 132.91 kJ/mol. Regarding spontaneous ignition, the temperature safe limits were predicted to be 83–84 °C and 84–87 °C for cylindrical and box silos with diameter and height of 15 and 10 m, respectively. Calculations of silos were designed within the limits of certain dimension ratios.
Graphical Abstract
Insights into the microbiome assembly during different growth stages and storage of strawberry plants
Authors: Olimi Expedito , Kusstatscher Peter , Wicaksono Wisnu Adi , Abdelfattah Ahmed , Cernava Tomislav , Berg Gabriele 
Published: April 2022

AbstractBackgroundMicrobiome assembly was identified as an important factor for plant growth and health, but this process is largely unknown, especially for the fruit microbiome. Therefore, we analyzed strawberry plants of two cultivars by focusing on microbiome tracking during the different growth stages and storage using amplicon sequencing, qPCR, and microscopic approaches.ResultsStrawberry plants carried a highly diverse microbiome, therein the bacterial familiesSphingomonadaceae(25%),Pseudomonadaceae(17%), andBurkholderiaceae(11%); and the fungal familyMycosphaerella(45%) were most abundant. All compartments were colonized by high number of bacteria and fungi (107–1010marker gene copies per g fresh weight), and were characterized by high microbial diversity (6049 and 1501 ASVs); both were higher for the belowground samples than in the phyllosphere. Compartment type was the main driver of microbial diversity, structure, and abundance (bacterial: 45%; fungal: 61%) when compared to the cultivar (1.6%; 2.2%). Microbiome assembly was strongly divided for belowground habitats and the phyllosphere; only a low proportion of the microbiome was transferred from soil via the rhizosphere to the phyllosphere. During fruit development, we observed the highest rates of microbial transfer from leaves and flowers to ripe fruits, where most of the bacteria occured inside the pulp. In postharvest fruits, microbial diversity decreased while the overall abundance increased. Developing postharvest decay caused byBotrytis cinereadecreased the diversity as well, and induced a reduction of potentially beneficial taxa.ConclusionOur findings provide insights into microbiome assembly in strawberry plants and highlight the importance of microbe transfer during fruit development and storage with potential implications for food health and safety.