bluuebunny/crossref_metadata_2025 huggingface dataset:
RhoGEF17—An Essential Regulator of Endothelial Cell Death and Growth
Authors: Weber Pamina , Baltus Doris , Jatho Aline , Drews Oliver , Zelarayan Laura C. , Wieland Thomas , Lutz Susanne
Published: March 2021

The Rho guanine nucleotide exchange factor RhoGEF17 was described to reside in adherens junctions (AJ) in endothelial cells (EC) and to play a critical role in the regulation of cell adhesion and barrier function. The purpose of this study was to analyze signal cascades and processes occurring subsequent to AJ disruption induced by RhoGEF17 knockdown. Primary human and immortalized rat EC were used to demonstrate that an adenoviral-mediated knockdown of RhoGEF17 resulted in cell rounding and an impairment in spheroid formation due to an enhanced proteasomal degradation of AJ components. In contrast, β-catenin degradation was impaired, which resulted in an induction of the β-catenin-target genes cyclin D1 and survivin. RhoGEF17 depletion additionally inhibited cell adhesion and sheet migration. The RhoGEF17 knockdown prevented the cells with impeded cell–cell and cell–matrix contacts from apoptosis, which was in line with a reduction in pro-caspase 3 expression and an increase in Akt phosphorylation. Nevertheless, the cells were not able to proliferate as a cell cycle block occurred. In summary, we demonstrate that a loss of RhoGEF17 disturbs cell–cell and cell–substrate interaction in EC. Moreover, it prevents the EC from cell death and blocks cell proliferation. Non-canonical β-catenin signaling and Akt activation could be identified as a potential mechanism.
Novel Technique for Retinal Nerve Cell Regeneration with Electrophysiological Functions Using Human Iris-Derived iPS Cells
Authors: Yamamoto Naoki , Hiramatsu Noriko , Ohkuma Mahito , Hatsusaka Natsuko , Takeda Shun , Nagai Noriaki , Miyachi Ei-ichi , Kondo Masashi , Imaizumi Kazuyoshi , Horiguchi Masayuki , Kubo Eri , Sasaki Hiroshi
Published: March 2021

Regenerative medicine in ophthalmology that uses induced pluripotent stem cells (iPS) cells has been described, but those studies used iPS cells derived from fibroblasts. Here, we generated iPS cells derived from iris cells that develop from the same inner layer of the optic cup as the retina, to regenerate retinal nerves. We first identified cells positive for p75NTR, a marker of retinal tissue stem and progenitor cells, in human iris tissue. We then reprogrammed the cultured p75NTR-positive iris tissue stem/progenitor (H-iris stem/progenitor) cells to create iris-derived iPS (H-iris iPS) cells for the first time. These cells were positive for iPS cell markers and showed pluripotency to differentiate into three germ layers. When H-iris iPS cells were pre-differentiated into neural stem/progenitor cells, not all cells became positive for neural stem/progenitor and nerve cell markers. When these cells were pre-differentiated into neural stem/progenitor cells, sorted with p75NTR, and used as a medium for differentiating into retinal nerve cells, the cells differentiated into Recoverin-positive cells with electrophysiological functions. In a different medium, H-iris iPS cells differentiated into retinal ganglion cell marker-positive cells with electrophysiological functions. This is the first demonstration of H-iris iPS cells differentiating into retinal neurons that function physiologically as neurons.
Progressive and Coordinated Mobilization of the Skeletal Muscle Niche throughout Tissue Repair Revealed by Single-Cell Proteomic Analysis
Authors: Borok Matthew , Didier Nathalie , Gattazzo Francesca , Ozturk Teoman , Corneau Aurelien , Rouard Helene , Relaix Frederic
Published: March 2021

Background: Skeletal muscle is one of the only mammalian tissues capable of rapid and efficient regeneration after trauma or in pathological conditions. Skeletal muscle regeneration is driven by the muscle satellite cells, the stem cell population in interaction with their niche. Upon injury, muscle fibers undergo necrosis and muscle stem cells activate, proliferate and fuse to form new myofibers. In addition to myogenic cell populations, interaction with other cell types such as inflammatory cells, mesenchymal (fibroadipogenic progenitors—FAPs, pericytes) and vascular (endothelial) lineages are important for efficient muscle repair. While the role of the distinct populations involved in skeletal muscle regeneration is well characterized, the quantitative changes in the muscle stem cell and niche during the regeneration process remain poorly characterized. Methods: We have used mass cytometry to follow the main muscle cell types (muscle stem cells, vascular, mesenchymal and immune cell lineages) during early activation and over the course of muscle regeneration at D0, D2, D5 and D7 compared with uninjured muscles. Results: Early activation induces a number of rapid changes in the proteome of multiple cell types. Following the induction of damage, we observe a drastic loss of myogenic, vascular and mesenchymal cell lineages while immune cells invade the damaged tissue to clear debris and promote muscle repair. Immune cells constitute up to 80% of the mononuclear cells 5 days post-injury. We show that muscle stem cells are quickly activated in order to form new myofibers and reconstitute the quiescent muscle stem cell pool. In addition, our study provides a quantitative analysis of the various myogenic populations during muscle repair. Conclusions: We have developed a mass cytometry panel to investigate the dynamic nature of muscle regeneration at a single-cell level. Using our panel, we have identified early changes in the proteome of stressed satellite and niche cells. We have also quantified changes in the major cell types of skeletal muscle during regeneration and analyzed myogenic transcription factor expression in satellite cells throughout this process. Our results highlight the progressive dynamic shifts in cell populations and the distinct states of muscle stem cells adopted during skeletal muscle regeneration. Our findings give a deeper understanding of the cellular and molecular aspects of muscle regeneration.
The Game of Tubulins
Authors: Kristensson Maria Alvarado
Published: March 2021

Members of the tubulin superfamily are GTPases; the activities of GTPases are necessary for life. The members of the tubulin superfamily are the constituents of the microtubules and the γ-tubulin meshwork. Mutations in members of the tubulin superfamily are involved in developmental brain disorders, and tubulin activities are the target for various chemotherapies. The intricate functions (game) of tubulins depend on the activities of the GTP-binding domain of α-, β-, and γ-tubulin. This review compares the GTP-binding domains of γ-tubulin, α-tubulin, and β-tubulin and, based on their similarities, recapitulates the known functions and the impact of the γ-tubulin GTP-binding domain in the regulation of the γ-tubulin meshwork and cellular homeostasis.
Regulation of Transcription Factor NF-κB in Its Natural Habitat: The Nucleus
Authors: Bacher Susanne , Meier-Soelch Johanna , Kracht Michael , Schmitz M. Lienhard
Published: March 2021

Activation of the transcription factor NF-κB elicits an individually tailored transcriptional response in order to meet the particular requirements of specific cell types, tissues, or organs. Control of the induction kinetics, amplitude, and termination of gene expression involves multiple layers of NF-κB regulation in the nucleus. Here we discuss some recent advances in our understanding of the mutual relations between NF-κB and chromatin regulators also in the context of different levels of genome organization. Changes in the 3D folding of the genome, as they occur during senescence or in cancer cells, can causally contribute to sustained increases in NF-κB activity. We also highlight the participation of NF-κB in the formation of hierarchically organized super enhancers, which enable the coordinated expression of co-regulated sets of NF-κB target genes. The identification of mechanisms allowing the specific regulation of NF-κB target gene clusters could potentially enable targeted therapeutic interventions, allowing selective interference with subsets of the NF-κB response without a complete inactivation of this key signaling system.
Esophagitis Dissecans Superficialis (EDS) Secondary to Hair Dye Ingestion: Case Report and Literature Review
Authors: Then Eric Omar , Grantham Tyler , Lopez Michell , Reddy Madhavi , Gaduputi Vinaya
Published: March 2021

Esophagitis dissecans superficialis (EDS) is a rare and underdiagnosed esophageal lesion characterized by sloughing of the esophageal mucosa that has been associated with medications, various autoimmune disorders, and exposure to some chemical irritants. Anatomically, EDS is most commonly seen in the middle and distal thirds of the esophagus. When present, EDS is best treated by discontinuing the offending agent and initiating pharmacologic therapy with proton pump inhibitors. Steroids may also be effective if the etiology is autoimmune in nature. Our case highlights a 65-year-old female diagnosed with EDS after incidental ingestion of hair dye containing resorcinol and para-phenylenediamine (PPD).
Study of the Annealing Effect on the γ-Phase Aluminum Oxide Films Prepared by the High-Vacuum MOCVD System
Authors: Li Zhao , Xin Yangmei , Liu Yunyan , Liu Huiqiang , Yu Dan , Xiu Junshan
Published: March 2021

γ-phase aluminum oxide (γ-Al2O3) films are grown on MgO (100) wafers by metal organic chemical vapor deposition (MOCVD). Post-annealing process is conducted to study the influence of annealing temperature on the properties of the films. Structural analyses indicate that all the deposited and annealed films present a preferred growth orientation of γ-Al2O3 (220) along the MgO (200) direction. And the film annealed at 1100 °C exhibits the best film quality compared with those of the films grown and annealed at other temperatures. Scanning electron microscopy measurements also imply the best surface morphology for the γ-Al2O3 film annealed at 1100 °C, which is in good accordance with the structural analyses. Optical transmittance spectra show good transparency for all the deposited and annealed films in the visible wavelength region with an average transmittance value of 83.5%. The optical bandgaps are estimated to be in the range of 5.56–5.79 eV for the deposited and annealed films. Semiconductor films with high optical transmittance in the visible region as well as wide bandgaps are appropriate for the manufacture of transparent optoelectronic devices and ultraviolet optoelectronic devices.
Extrusion-Based 3D Printing Applications of PLA Composites: A Review
Authors: Tümer Eda Hazal , Erbil Husnu Yildirim
Published: March 2021

Polylactic acid (PLA) is the most widely used raw material in extrusion-based three-dimensional (3D) printing (fused deposition modeling, FDM approach) in many areas since it is biodegradable and environmentally friendly, however its utilization is limited due to some of its disadvantages such as mechanical weakness, water solubility rate, etc. FDM is a simple and more cost-effective fabrication process compared to other 3D printing techniques. Unfortunately, there are deficiencies of the FDM approach, such as mechanical weakness of the FDM parts compared to the parts produced by the conventional injection and compression molding methods. Preparation of PLA composites with suitable additives is the most useful technique to improve the properties of the 3D-printed PLA parts obtained by the FDM method. In the last decade, newly developed PLA composites find large usage areas both in academic and industrial circles. This review focuses on the chemistry and properties of pure PLA and also the preparation methods of the PLA composites which will be used as a raw material in 3D printers. The main drawbacks of the pure PLA filaments and the necessity for the preparation of PLA composites which will be employed in the FDM-based 3D printing applications is also discussed in the first part. The current methods to obtain PLA composites as raw materials to be used as filaments in the extrusion-based 3D printing are given in the second part. The applications of the novel PLA composites by utilizing the FDM-based 3D printing technology in the fields of biomedical, tissue engineering, human bone repair, antibacterial, bioprinting, electrical conductivity, electromagnetic, sensor, battery, automotive, aviation, four-dimensional (4D) printing, smart textile, environmental, and luminescence applications are presented and critically discussed in the third part of this review.
Erratum: Correia, S.D., et al. Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization. Computers 2020, 9, 87
Authors: Correia Sérgio D. , Fé João , Tomic Slavisa , Beko Marko
Published: March 2021

The third affiliation of the paper should have been completed in our original article [...]
Entrapped Transient Chloroform Solvates of Bilastine
Authors: Puigjaner Cristina , Portell Anna , Blasco Arturo , Font-Bardia Mercè , Vallcorba Oriol
Published: March 2021

The knowledge about the solid forms landscape of Bilastine (BL) has been extended. The crystal structures of two anhydrous forms have been determined, and the relative thermodynamic stability among the three known anhydrous polymorphs has been established. Moreover, three chloroform solvates with variable stoichiometry have been identified and characterized, showing that S3CHCl3-H2O and SCHCl3 can be classified as transient solvates which transform into the new chloroform solvate SCHCl3-H2O when removed from the mother liquor. The determination of their crystal structures from combined single crystal/synchrotron X-ray powder diffraction data has allowed the complete characterization of these solvates, being two of them heterosolvates (S3CHCl3-H2O and SCHCl3-H2O) and SCHCl3 a monosolvate. Moreover, the temperature dependent stability and interrelation pathways among the chloroform solvates and the anhydrous forms of BL have been studied.
Green Synthesis and Biomedical Applications of ZnO Nanoparticles: Role of PEGylated-ZnO Nanoparticles as Doxorubicin Drug Carrier against MDA-MB-231(TNBC) Cells Line
Authors: Batool Madiha , Khurshid Shazia , Daoush Walid M. , Siddique Sabir Ali , Nadeem Tariq
Published: March 2021

The present study aimed to develop the synthesis of zinc oxide nanoparticles (ZnO-NPs) using the green method, with Aloe barbadensis leaf extract as a stabilizing and capping agent. In vitro antitumor cytotoxic activity, as well as the surface-functionalization of ZnO-NPs and their drug loading capacity against doxorubicin (DOX) and gemcitabine (GEM) drugs, were also studied. Morphological and structural properties of the produced ZnO-NPs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersion X-ray diffraction (EDX), UV-Vis spectrophotometry, Fourier-transform infrared analysis (FTIR), and X-ray diffraction (XRD). The prepared ZnO-NPs had a hexagonal shape and average particle size of 20–40 nm, with an absorption peak at 325 nm. The weight and atomic percentages of zinc (50.58% and 28.13%) and oxygen (26.71% and 60.71%) were also determined by EDAX (energy dispersive x-ray analysis) compositional analysis. The appearance of the FTIR peak at 3420 m–1 confirmed the synthesis of ZnO-NPs. The drug loading efficiency (LE) and loading capacity (LC) of unstabilized and PEGylated ZnO-NPs were determined by doxorubicin (DOX) and gemcitabine (GEM) drugs. DOX had superior LE 65% (650 mg/g) and higher LC 32% (320 mg/g) than GEM LE 30.5% (30 mg/g) and LC 16.25% (162 mg/g) on ZnO-NPs. Similar observation was observed in the case of PEG-ZnO-NPs, where DOX had enhanced LE 68% (680 mg/g) and LC 35% (350) mg/g in contrast to GEM, which had LE and LC values of 35% (350 mg/g) and 19% (190 mg/g), respectively. Therefore, DOX was chosen to encapsulate nanoparticles, along with the untreated nanoparticles, to check their in vitro antiproliferative potential against the triple-negative breast cancer (TNBC) cell line (MDA-MB-231) through the MTT (3-(4,5-Dimethylthiazol-2-Yl)-2,5-Diphenyltetrazolium Bromide) assay. This drug delivery strategy implies that the PEGylated biogenically synthesized ZnO-NPs occupy an important position in chemotherapeutic drug loading efficiency and can improve the therapeutic techniques of triple breast cancer.
Mineral Characterization in Human Body: A Dual Energy Approach
Authors: Martini Niki , Koukou Vaia , Michail Christos , Fountos George
Published: March 2021

Kidney and uteric stones are a common cause of pain and disturbance in numerous people worldwide, as they tend to reappear. There are several studies investigating the association of urolithiasis and nephrolithiasis with atherosclerosis, as patients suffering from the former diseases were found to have been diagnosed with atherosclerotic plaques. Hydroxyapatite and calcium oxalate are the most common mineral crystals found in both kidney/uteric stones and atherosclerotic plaques’ calcifications. Even though for stones smaller than 5 mm surgery is not recommended, the knowledge of the stone composition is an important tool for the physician in order to provide better treatment for the patient. The mineral crystal characterization of atherosclerotic plaques’ calcifications smaller than 3 mm (spotty calcifications) will assist the physician to limit the possibility of myocardial infraction and stroke, as the presence of hydroxyapatite indicates possible plaque rapture. To this aim, a dual energy (DE) X-ray method was developed in this work. The calcium/phosphorus mass ratio (mCa/mP) was determined through analytical simulations and the results were verified experimentally. Both monoenergetic and polyenergetic simulation studies were implemented for hydroxyapatite, calcium carbonate and calcium oxalate with thicknesses ranging from 0.50 to 3.00 mm, at 100 μm increments, to obtain the optimized irradiation conditions. The experimental verification of the proposed method was performed using an X-ray tube combined with a high resolution complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) detector. The Mann–Whitney U test indicated that statistically significant differences were found between the different types of minerals examined for thicknesses of 0.70 mm or higher.
Evaluation of Glass Coatings with Various Silica Content Corrosion in a 0.5 M HCl Water Solution
Authors: Wang Hang , Zhang Chuan , Jiang Chengyang , Zhu Lijuan , Cui Jiakai , Han Lihong , Chen Minghui , Geng Shujiang , Wang Fuhui
Published: March 2021

Two enamel coatings with a high and a low silica content were prepared on 35CrMo steels via vacuum firing. Their corrosion behavior in a 0.5 M HCl solution including corrosion kinetics, microstructures and electrochemistry performance were studied in comparison with uncoated steels. The results show that catastrophic corrosion occurred for uncoated steels while enamel coatings significantly decreased the corrosion rate. Enamel coatings with a high silica content exhibited the best corrosion resistance against hydrochloric acid due to their highly connected silicate network, which inhibited the leaching process of alkali metals in the acid solution. The corrosion inhibition efficiency for enamel containing a high silica content reached a maximum of 94.3%.
Three-Dimensional Printing of Hydroxyapatite Composites for Biomedical Application
Authors: Han Yanting , Wei Qianqian , Chang Pengbo , Hu Kehui , Okoro Oseweuba Valentine , Shavandi Amin , Nie Lei
Published: March 2021

Hydroxyapatite (HA) and HA-based nanocomposites have been recognized as ideal biomaterials in hard tissue engineering because of their compositional similarity to bioapatite. However, the traditional HA-based nanocomposites fabrication techniques still limit the utilization of HA in bone, cartilage, dental, applications, and other fields. In recent years, three-dimensional (3D) printing has been shown to provide a fast, precise, controllable, and scalable fabrication approach for the synthesis of HA-based scaffolds. This review therefore explores available 3D printing technologies for the preparation of porous HA-based nanocomposites. In the present review, different 3D printed HA-based scaffolds composited with natural polymers and/or synthetic polymers are discussed. Furthermore, the desired properties of HA-based composites via 3D printing such as porosity, mechanical properties, biodegradability, and antibacterial properties are extensively explored. Lastly, the applications and the next generation of HA-based nanocomposites for tissue engineering are discussed.
A Review on MoS2 Properties, Synthesis, Sensing Applications and Challenges
Authors: Samy Omnia , Zeng Shuwen , Birowosuto Muhammad Danang , El Moutaouakil Amine
Published: March 2021

Molybdenum disulfide (MoS2) is one of the compounds discussed nowadays due to its outstanding properties that allowed its usage in different applications. Its band gap and its distinctive structure make it a promising material to substitute graphene and other semiconductor devices. It has different applications in electronics especially sensors like optical sensors, biosensors, electrochemical biosensors that play an important role in the detection of various diseases’ like cancer and Alzheimer. It has a wide range of energy applications in batteries, solar cells, microwave, and Terahertz applications. It is a promising material on a nanoscale level, with favorable characteristics in spintronics and magnetoresistance. In this review, we will discuss MoS2 properties, structure and synthesis techniques with a focus on its applications and future challenges.
Final Remarks of Special Issue “Biodiversity of Macroalgae”
Authors: García Marcos Rubal
Published: March 2021

Macroalgae are a diverse and pervasive element of rocky shores [...]
Genetic Factors Associated with COPD Depend on the Ancestral Caucasian/Amerindian Component in the Mexican Population
Authors: Pérez-Rubio Gloria , Falfán-Valencia Ramcés , Fernández-López Juan Carlos , Ramírez-Venegas Alejandra , Hernández-Zenteno Rafael de Jesús , Flores-Trujillo Fernando , Silva-Zolezzi Irma
Published: March 2021

Genetic variability influences the susceptibility to and severity of complex diseases; there is a lower risk of COPD in Hispanics than in non-Hispanic Caucasians. In this study, we included 830 Mexican-Mestizo subjects; 299 were patients with COPD secondary to tobacco smoking, and 531 were smokers without COPD. We employed a customized genotyping array of single nucleotide polymorphisms (SNPs). The population structure was evaluated by principal component analysis and allele association through a logistic regression model and haplotype identification. In this study, 118 individuals were identified with a high Caucasian component and 712 with a high Amerindian component. Independent of the ancestral contribution, two SNPs were associated with a reduced risk (p ≤ 0.01) of developing COPD in the CYP2A6 (rs4105144) and CYP2B6 (rs10426235) genes; however, a haplotype was associated with an increased risk of COPD (p = 0.007, OR = 2.47) in the CHRNA5-CHRNA3 loci among smokers with a high Caucasian component. In Mexican-Mestizo smokers, there are SNPs in genes that encode proteins responsible for the metabolism of nicotine associated with a lower risk of COPD; individuals with a high Caucasian component harboring a haplotype in the CHRNA5-CHRNA3 loci have a higher risk of suffering from COPD.
Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges
Authors: El-Rashidy Nora , El-Sappagh Shaker , Islam S. , M. El-Bakry Hazem , Abdelrazek Samir
Published: March 2021

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.
Ultrasound Imaging of Abdominal Wall Endometriosis: A Pictorial Review
Authors: Cocco Giulio , Delli Pizzi Andrea , Scioscia Marco , Ricci Vincenzo , Boccatonda Andrea , Candeloro Matteo , Tana Marco , Balconi Giuseppe , Romano Marcello , Schiavone Cosima
Published: March 2021

Endometriosis is a debilitating disease characterized by endometrial glands and stroma outside the endometrial cavity. Abdominal wall endometriosis (AWE) indicates the presence of ectopic endometrium between the peritoneum and the skin, including subcutaneous adipose tissue and muscle layers, often following obstetric and gynecological surgical procedures. AWE is a not infrequent gynecological surgical complication, due to the increasing number of cesarean sections worldwide. In this pictorial review, we discuss the importance of medical history and physical examination, including the main ultrasound features in the diagnosis of AWE.
A High-Accuracy Model Based on Plasma miRNAs Diagnoses Intrahepatic Cholangiocarcinoma: A Single Center with 1001 Samples
Authors: Hu Jie , Wang Yi-Ning , Song Dan-Jun , Tan Jin-Peng , Cao Ya , Fan Jia , Wang Zheng , Zhou Jian
Published: March 2021

Objectives: Intrahepatic cholangiocarcinoma (iCCA) is a highly malignant cancer. More than 70% of patients are diagnosed at an advanced stage. The aim of this study was to evaluate the diagnostic value of plasma miR-21, miR-122, and CA19-9, hoping to establish a novel model to improve the accuracy for diagnosing iCCA. Materials and methods: Plasma miR-21 and miR-122 were detected in 359 iCCA patients and 642 controls (healthy, benign liver lesions, other malignant liver tumors). All 1001 samples were allocated to training cohort (n = 668) and validation cohort (n = 333) in a chronological order. A logistic regression model was applied to combine these markers. Area under the receiver operating characteristic curve (AUC) was used as an accuracy index to evaluate the diagnostic performance. Results: Plasma miR-21 and miR-122 were significantly higher in iCCA patients than those in controls. Higher plasma miR-21 level was significantly correlated with larger tumor size (p = 0.030). A three-marker model was constructed by using miR-21, miR-122 and CA19-9, which showed an AUC of 0.853 (95% CI: 0.824–0.879; sensitivity: 73.0%, specificity: 87.4%) to differentiate iCCA from controls. These results were subsequently confirmed in the validation cohort with an AUC of 0.866 (0.825–0.901). The results were similar for diagnosing early (stages 0–I) iCCA patients (AUC: 0.848) and CA19-9negative iCCA patients (AUC: 0.795). Conclusions: We established a novel three-marker model with a high accuracy based on a large number of participants to differentiate iCCA from controls. This model showed a great clinical value especially for the diagnosis of early iCCA and CA19-9negative iCCA.
A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm
Authors: Kang Hongwei , Bei Fengfan , Shen Yong , Sun Xingping , Chen Qingyi
Published: March 2021

The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.
Student Preferences Impact Outcome of Flipped Classroom in Dental Education: Students Favoring Flipped Classroom Benefited More
Authors: Xiao Nan , Thor Der , Zheng Meixun
Published: March 2021

Many reports in dental education showed that student learning improved with the flipped classroom method. However, there are few reports that describe how different subsets of students may benefit from the flipped classroom. In this study, we investigated how students’ preference for the flipped classroom impacted their learning outcome. We used a flipped classroom module on the physiology of the autonomic nervous system taught to year one Doctor of Dental Surgery students to test the hypothesis that students who favored the flipped classroom performed better on assessment quizzes. The module was composed of pre-class activity, out-of-class assignment, in-class discussion, and two in-class quizzes. Quiz 1 was given after students self-studied the foundational content online through the pre-class activity, and Quiz 2 was at the end of the module. Students filled out a survey to report learning experiences and preferences. Fewer students scored below 75% on Quiz 2 than on Quiz 1. Students’ self-evaluated understanding of content significantly improved after finishing the assignment and discussion compared to finishing the pre-class activity alone. Moreover, students who preferred to learn through the flipped classroom scored higher in Quiz 2. Students with higher overall grades in the course preferred the flipped classroom more than low performers. Our results indicated that students favoring the flipped classroom method spent more time on the assignment, understood the content better, and performed better on assessments than students who prefer traditional lectures.
Correlative Study between Personality Traits, Student Mental Skills and Educational Outcomes
Authors: Bouiri Oussama , Lotfi Said , Talbi Mohammed
Published: March 2021

The main purpose of this study is determining the correlations between personality traits, academic mental skills and educational outcomes using a quantitative methodology, based on a non-experimental, correlational study. In addition, the following variables are taken into consideration: gender, grade averages and school cycle. The sample is composed of 695 students: these are two institutions (middle and high school) under the provincial direction of Mediouna. In order to gather the information, participants were asked to complete the 16pf 5 questionnaire and scale measuring academic mental skills. Note that the results obtained are processed by the IBM SPSS 23 software. The results demonstrate that the 16 personality scales of the Cattell 16PF5 test and the 9 school grades have significant correlations: 77.77% of all correlations, with essentially the following factors: abstractedness, tension, emotional stability, dominance, social-boldness, vigilance and apprehension maintain (8/9) significant and positive relationships with 38.88% and low intensity (r = 0.031 to 0.0465). Additionally, mental skills (affective, cognitive and metacognitive strategies) and grades have significant correlations with 70.37% of all calculated correlations, with low, average and/or positive, negative intensities, according to each strategy with each grade of school subjects. In the end, it is necessary to make considerable efforts to better understanding the multidimensionality of school success and to ensure an effective and relevant pedagogical intervention.
On the Gap between Domestic Robotic Applications and Computational Intelligence
Authors: Zhong Junpei , Ling Chaofan , Cangelosi Angelo , Lotfi Ahmad , Liu Xiaofeng
Published: March 2021

Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.
A 2.1 GHz, 210 μW, —189 dBc/Hz DCO with Ultra Low Power DCC Scheme
Authors: Zuo Shi , Zhao Jianzhong , Zhou Yumei
Published: March 2021

This article presents a low power digital controlled oscillator (DCO) with an ultra low power duty cycle correction (DCC) scheme. The DCO with the complementary cross-coupled topology uses the controllable tail resistor to improve the tail current efficiency. A robust duty cycle correction (DCC) scheme is introduced to replace self-biased inverters to save power further. The proposed DCO is implemented in a Semiconductor Manufacturing International Corporation (SMIC) 40 nm CMOS process. The measured phase noise at room temperature is −115 dBc/Hz at 1 MHz offset with a dissipation of 210 μμW at an oscillating frequency of 2.12 GHz, and the resulin figure-of-merit is s −189 dBc/Hz.
Means of IoT and Fuzzy Cognitive Maps in Reactive Navigation of Ubiquitous Robots
Authors: Vaščák Ján , Pomšár Ladislav , Papcun Peter , Kajáti Erik , Zolotová Iveta
Published: March 2021

Development of accessible and cheap sensors as well as the possibility to transfer and process huge amounts of data offer new possibilities for many areas utilizing till now conventional approaches. Navigation of robots and autonomous vehicles is no exception in this aspect and Internet of Things (IoT), together with the means of computational intelligence, represents a new way for construction and use of robots. In this paper, the possibility to move sensors from robots to their surroundings with the help of IoT is presented and the modification of the IoT concept in the form of intelligent space as well as the concept of ubiquitous robot are shown in the paper. On an example of route tracking, we will clarify the potential of distributed networked sensors and processing their data with the use of fuzzy cognitive maps for robotic navigation. Besides, two modifications of adaptation approaches, namely particle swarm optimization and migration algorithm, are presented here. A series of simulations was performed, which are discussed and future research directions are proposed.
A Comparison of Deep Learning Methods for Timbre Analysis in Polyphonic Automatic Music Transcription
Authors: Hernandez-Olivan Carlos , Zay Pinilla Ignacio , Hernandez-Lopez Carlos , Beltran Jose R.
Published: March 2021

Automatic music transcription (AMT) is a critical problem in the field of music information retrieval (MIR). When AMT is faced with deep neural networks, the variety of timbres of different instruments can be an issue that has not been studied in depth yet. The goal of this work is to address AMT transcription by analyzing how timbre affect monophonic transcription in a first approach based on the CREPE neural network and then to improve the results by performing polyphonic music transcription with different timbres with a second approach based on the Deep Salience model that performs polyphonic transcription based on the Constant-Q Transform. The results of the first method show that the timbre and envelope of the onsets have a high impact on the AMT results and the second method shows that the developed model is less dependent on the strength of the onsets than other state-of-the-art models that deal with AMT on piano sounds such as Google Magenta Onset and Frames (OaF). Our polyphonic transcription model for non-piano instruments outperforms the state-of-the-art model, such as for bass instruments, which has an F-score of 0.9516 versus 0.7102. In our latest experiment we also show how adding an onset detector to our model can outperform the results given in this work.
PD-Type Iterative Learning Control with Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator
Authors: Dai Mingguang , Qi Rong , Zhao Yiyun , Li Yang
Published: March 2021

To realize the high-performance load torque tracking of an electric dynamic load simulator system with random measurement noises and strong position disturbances, a PD-type iterative learning control (ILC) algorithm with adaptive learning gains is proposed in this paper. With the principle of system analyzing, a nonlinear discrete state-space model is established. The adaptive learning gains is used to suppress the effects of periodic disturbances and random measurement noises on the load torque tracking performance. A traditional PD feedback controller in parallel with the proposed ILC is designed to stabilize the system and render the ILC converge quickly. The convergence analysis of the proposed control method ensures the stability of the system. Compared with the fixed learning gains, the experiment results show that the proposed control method has better load torque tracking performance and can effectively suppress the adverse effects of periodic and aperiodic disturbances on tracking accuracy.
Analysis of the Spatial Effect of Fiscal Decentralization and Environmental Decentralization on Carbon Emissions under the Pressure of Officials’ Promotion
Authors: Xia Sailian , You Daming , Tang Zhihua , Yang Bo
Published: March 2021

Using panel data of 30 provinces and regions in Mainland China (excluding Tibet) from 2006 to 2016, the Spatial Durbin Model was employed for the empirical research, and the spatial impact of fiscal decentralization and environmental decentralization on regional carbon emissions were analyzed from the perspective of promotion pressure of officials. The empirical study concludes: ① Fiscal decentralization, both within the region and in its neighborhood, will contribute to carbon emissions in the region; ② Environmental decentralization will help reduce carbon emissions, while environmental decentralization in neighboring regions will increase carbon emissions in the region; ③ The promotion pressure of officials plays a positive role in moderating the impact of fiscal decentralization on carbon emissions, and at the same time weakens the suppression of carbon emissions by environmental decentralization; ④ From a regional point of view, there is a positive relationship between fiscal decentralization and carbon emissions in various regions; but environmental decentralization has obvious spatial heterogeneity. The research suggests that reducing the degree of local fiscal decentralization, investment in major infrastructure projects involving high carbon emissions should be relatively centralized; appropriately increase the environmental management authority of local environmental protection agencies, fully use the advantages of local environmental protection departments to protect the environment according to local conditions; gradually improve the assessment system for local officials, moderately reduce the proportion of fiscal revenue and GDP assessment in areas with fragile ecological environment, and increase incentives for ecological performance assessment, put the development of low-carbon economy into practice.
Digitization, Digital Twins, Blockchain, and Industry 4.0 as Elements of Management Process in Enterprises in the Energy Sector
Authors: Borowski Piotr
Published: March 2021

In the 21st century, it is becoming increasingly clear that human activities and the activities of enterprises affect the environment. Therefore, it is important to learn about the methods in which companies minimize the negative effects of their activities. The article presents the steps taken and innovative actions carried out by enterprises in the energy sector. The article analyzes innovative activities undertaken and implemented by enterprises from the energy sector. The relationships between innovative strategies, including, inter alia, digitization, and Industry 4.0 solutions, in the development of companies and the achieved results concerning sustainable development and environmental impact. Digitization has far exceeded traditional productivity improvement ranges of 3–5% per year, with a clear cost improvement potential of well above 25%. Enterprises on a large scale make attempts to increase energy efficiency by implementing the state-of-the-art innovative technical and technological solutions, which increase reliability and durability (material and mechanical engineering). Digitization of energy companies allows them to reduce operating costs and increases efficiency. With digital advances, the useful life of an energy plant can be increased up to 30%. Advanced technologies, blockchain, and the use of intelligent networks enables the activation of prosumers in the electricity market. Reducing energy consumption in industry and at the same time increasing energy efficiency for which the European Union is fighting in the clean air package for all Europeans have a positive impact on environmental protection, sustainable development, and the implementation of the decarbonization program.
Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning
Authors: Peng Xiaosheng , Cheng Kai , Lang Jianxun , Zhang Zuowei , Cai Tao , Duan Shanxu
Published: March 2021

Wind power prediction (WPP) of wind farm clusters is important to the safe operation and economic dispatch of the power system, but it faces two challenges: (1) The dimensions of the input parameters for WPP of wind farm clusters are very high so that the input parameters contain irrelevant or redundant features; (2) it is difficult to build a holistic WPP model with high-dimensional input parameters for wind farm clusters. To overcome these challenges, a novel short-term WPP model for wind farm clusters, based on sequential floating forward selection (SFFS) feature selection and bidirectional long short-term memory (BLSTM) deep learning, is proposed in this paper. First, more than 300,000 input features of the wind farm cluster are constructed. Second, the SFFS method is applied to sort the high-dimensional features and analyze the rule that the forecasting accuracy changes with the number of features to obtain the optimal number of features and feature sets. Finally, based on the results of feature selection, BLSTM is applied to build a WPP model for wind farm clusters with a combination of feature selection and deep learning. This case study shows that (1) SFFS is an effective method for selecting the core features for WPP of wind farm clusters; (2) BLSTM shows not only higher WPP accuracy than long short-term memory and backpropagation neural network but also outstanding performance in terms of reducing the phase errors of WPP.
Optimum Sizing of Photovoltaic and Energy Storage Systems for Powering Green Base Stations in Cellular Networks
Authors: Javidsharifi Mahshid , Pourroshanfekr Hamoun , Kerekes Tamas , Sera Dezso , Spataru Sergiu , Guerrero Josep M.
Published: March 2021

Satisfying the mobile traffic demand in next generation cellular networks increases the cost of energy supply. Renewable energy sources are a promising solution to power base stations in a self-sufficient and cost-effective manner. This paper presents an optimal method for designing a photovoltaic (PV)-battery system to supply base stations in cellular networks. A systematic approach is proposed for determining the power rating of the photovoltaic generator and battery capacity from a technical and economical point of view in order to minimize investment cost as well as operational expenditure, while the power autonomy of the PV-battery system is maximized in a multi-objective optimization framework. The proposed method is applied to optimally size a photovoltaic-battery system for three cases with different availability of solar power to investigate the effect of environmental conditions. Problem-solving using the proposed approach leads to a set of solutions at different costs versus different levels of power autonomy. According to the importance of each criterion and the preference of decision-makers, one of the achieved solutions can be selected for the implementation of the photovoltaic-battery system to supply base stations in cellular networks.
A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan
Authors: Merembayev Timur , Kurmangaliyev Darkhan , Bekbauov Bakhbergen , Amanbek Yerlan
Published: March 2021

Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.