bluuebunny/crossref_metadata_2025 huggingface dataset:
Hazard Function Analysis of Recurrence in Patients with Curatively Resected Lung Cancer: Results from the Japanese Lung Cancer Registry in 2010
Authors: Yamauchi Yoshikane , Kawamura Masafumi , Okami Jiro , Shintani Yasushi , Ito Hiroyuki , Ohtsuka Takashi , Toyooka Shinichi , Mori Takeshi , Watanabe Shun-ichi , Asamura Hisao , Chida Masayuki , Endo Shunsuke , Kadokura Mitsutaka , Nakanishi Ryoichi , Miyaoka Etsuo , Suzuki Hidemi , Yoshino Ichiro , Date Hiroshi
Published: October 2022

To optimize postoperative surveillance of lung cancer patients, we investigated the hazard function of tumor recurrence in patients with completely resected lung cancer. We analyzed the records of 12,897 patients in the 2010 Japanese Joint Committee of Lung Cancer Registry who underwent lobectomy to completely resect pathological stage I–III lung cancer. The risk of postoperative recurrence was determined using a cause-specific hazard function. The hazard function for recurrence exhibited a peak at approximately 9 months after surgery, followed by a tapered plateau-like tail extending to 60 months. The peak risk for intrathoracic recurrence was approximately two-fold higher compared with that of extrathoracic recurrence. Subgroup analysis showed that patients with stage IIIA adenocarcinoma had a continuously higher risk of recurrence compared with patients with earlier-stage disease. However, the risk of recurrence in patients with squamous cell carcinoma was not significantly different compared with that more than 24 months after surgery, regardless of pathological stage. In conclusion, the characteristics of postoperative tumor recurrence hazard in a large cohort of lung cancer patients may be useful for determining the time after surgery at which patients are at the highest risk of tumor recurrence. This information may improve stage-related management of postoperative surveillance.
Improved Long-Term Survival of Patients with Recurrent Medulloblastoma Treated with a “MEMMAT-like” Metronomic Antiangiogenic Approach
Authors: Slavc Irene , Mayr Lisa , Stepien Natalia , Gojo Johannes , Aliotti Lippolis Maria , Azizi Amedeo A. , Chocholous Monika , Baumgartner Alicia , Hedrich Cora S. , Holm Stefan , Sehested Astrid , Leblond Pierre , Dieckmann Karin , Haberler Christine , Czech Thomas , Kool Marcel , Peyrl Andreas
Published: October 2022

Medulloblastoma (MB) recurrence is usually incurable despite intensive therapy including high-dose chemotherapy. An evolving alternative approach to conventional chemotherapy aims at interfering with tumor angiogenesis at different levels. We report on a novel combinatorial metronomic antiangiogenic approach. The study is a retrospective observational study of 29 consecutive patients with first or multiple recurrences prospectively treated according to the MEMMAT strategy (“MEMMAT-like”) before the formal protocol (MEMMAT; ClinicalTrials.gov Identifier: NCT01356290) started. The study period was 11/2006 to 06/2016. Treatment consisted of daily oral thalidomide, fenofibrate, celecoxib, and alternating 21-day cycles of low-dose oral etoposide and cyclophosphamide supplemented by IV bevacizumab and intraventricular therapy consisting of alternating etoposide and liposomal cytarabine. Median overall survival (OS) after recurrence for the whole group was 29.5 months, OS was 48.3 ± 9.3% at three years and 34.5 ± 8.8% at five years, and progression-free survival was 42.0 ± 9.5% at three years and 29.4 ± 9% at five years. As of 07/2022, 9/29 patients are alive 86 to 164 months after the recurrence that prompted the “MEMMAT-like” therapy. Treatment was primarily out-patient and generally well-tolerated. Toxicities did occur but were manageable. In conclusion, antiangiogenic therapy according to the MEMMAT strategy increased median OS of patients with recurrent MB and may lead to long-term survival. Adherence to the protocol, including intraventricular therapy, appears important.
Two-Dimensional Fe-N-C Nanosheets for Efficient Oxygen Reduction Reaction
Authors: Wu Xin , Xie Wenke , Liu Xuanhe , Liu Xiaoming , Zhao Qinglan
Published: October 2022

Fe-N-doped carbon (Fe-N-C)-based electrocatalysts are considered to be promising alternatives to replace Pt-based catalysts for oxygen reduction reactions (ORR). Here, we reported a simple and effective approach to prepare Fe-N-C-based electrocatalysts with the shape of two-dimensional nanosheets (termed Fe/NCNSs) to enhance the ORR performance. Fe/NCNSs were prepared by the calcination of Fe/Zn dual-metal ZIFs nanosheets as precursors. Benefiting from its higher specific surface area, electrochemically active surface area, and proportion of pyridinic N and Fe-N, the optimized Fe/NCNS showed excellent ORR performance both in acidic (E1/2 = 0.725 V vs. RHE) and alkaline (E1/2 = 0.865 vs. RHE) media, being 23 mV more negative and 24 mV more positive than that of a commercial Pt/C. The optimized Fe/NCNS also exhibited long durability. In addition, the Zn-air battery with Fe/NCNS-1 and RuO2 as the air catalyst exhibited high power density (1590 mW cm−2 at a current density of 2250 mA cm−2) and superior charging/discharging durability.
Catalytic Oxidative Decomposition of Dimethyl Methyl Phosphonate over CuO/CeO2 Catalysts Prepared Using a Secondary Alkaline Hydrothermal Method
Authors: Kong Weimin , Zhou Shuyuan , Wang Xuwei , He Qingrong , Yang Piaoping , Yuan Ye , Dong Yanchun
Published: October 2022

Bimetallic synergism plays an important role in lattice-doped catalysts. Therefore, lattice-doped bimetallic CuO/CeO2 catalysts were prepared by secondary alkaline hydrothermal reaction. During this process, the CeO2 nanomaterials were partially dissolved and recrystallized; thus, Cu ions were doped into the CeO2 lattice. The physical and chemical properties of CeO2, CuO/CeO2, and CuO were investigated. H2 temperature-programmed reduction characterization showed that the oxidation activity of CuO/CeO2 was significantly improved. X-ray photoelectron spectroscopy results showed that electron transfer occurred between Ce and Cu in the CuO/CeO2 catalyst. Additionally, Raman characterization confirmed the strong interaction between Cu and Ce. After CuO was loaded, the thermal catalytic decomposition performance of the catalyst was significantly improved with respect to the sarin simulant dimethyl methyl phosphonate (DMMP); with an increase in the Cu/Ce ratio, the performance first strengthened and then weakened. Additionally, the reaction tail gas and catalyst surface products were analyzed using mass spectrometry and ion chromatography, and the changes in the surface products during the thermal catalytic decomposition of DMMP were characterized at different temperatures using in situ diffuse reflectance infrared Fourier transform spectroscopy. Finally, the catalytic reaction pathways of DMMP on CeO2, CuO/CeO2, and CuO were inferred. The study results not only demonstrate an effective catalyst for the removal of nerve agent but also a feasible preparation method for lattice-doped bimetallic catalysts in the field of environmental protection.
Polyubiquitination and SUMOylation Sites Regulate the Stability of ZO-2 Protein and the Sealing of Tight Junctions
Authors: Cano-Cortina Misael , Alarcón Lourdes , Miranda Jael , Huber Otmar , González-Mariscal Lorenza
Published: October 2022

Tight junctions (TJs) regulate the transit of ions and molecules through the paracellular pathway in epithelial cells. Zonula occludens 2 (ZO-2) is a cytoplasmic TJ protein. Here, we studied the ubiquitination of hZO-2 employing mutants of SUMOylation site K730 present in the GuK domain and the putative ubiquitination residues K759 and K992 located at the GuK domain and proline-rich region, respectively. In immunoprecipitation experiments done with MDCK cells transfected with wild-type (WT) hZO-2 or the ubiquitination-site mutants hZO-2-K759R or -K992R, we observed diminished ubiquitination of the mutants, indicating that residues K759 and K992 in hZO-2 are acceptors for ubiquitination. Moreover, using TUBES, we found that residues K759 and K992 of hZO-2 are targets of K48 polyubiquitination, a signal for proteasomal degradation. Accordingly, compared to WT hZO-2, the half-life of hZO-2 mutants K759R and K992R augmented from 19.9 to 37.3 and 23.3 h, respectively. Instead, the ubiquitination of hZO-2 mutant K730R increased, and its half-life diminished to 6.7 h. The lack of these lysine residues in hZO-2 affects TJ sealing as the peak of TER decreased in monolayers of MDCK cells transfected with any of these mutants. These results highlight the importance of ZO-2 ubiquitination and SUMOylation to maintain a healthy and stable pool of ZO-2 molecules at the TJ.
Compressibility and Rarefaction Effects on Particle Dynamics and Heat Transfer in Aerosol Deposition Process
Authors: Farahani Bahareh , Jadidi Mehdi , Moghtadernejad Sara
Published: October 2022

The aerosol deposition (AD) method is an emerging coating technique to create a dense ceramic or metal layer on a substrate through the kinetic impaction and cumulative deposition of ultrafine solid particles under near-vacuum conditions. Prediction of the particles’ impact velocity and temperature during the AD process is crucial in enhancing the coating quality. In the present work, a two-way coupled Eulerian-Lagrangian model is developed for an AD system equipped with a converging-barrel nozzle to simulate the supersonic gas flow, particle in-flight behavior, as well as particle conditions upon impact on a flat substrate. The focus of the current study is to understand the effects of compressibility and rarefaction on particle velocity and temperature during the AD process. The effects of compressibility and rarefaction can be assessed using the Mach and Knudsen numbers. Therefore, different models for the drag coefficient and the heat transfer coefficient that take into account the Knudsen, Mach, and Reynolds number effects are implemented into the computational fluid dynamics (CFD) models. The results show that compressibility and rarefaction have significant influence on the particle temperature and velocity. As the particle size reduces, the effects of compressibility and rarefaction become more important.
Gum Acacia- and Gum Tragacanth-Coated Silver Nanoparticles: Synthesis, Physiological Stability, In-Vitro, Ex-Vivo and In-Vivo Activity Evaluations
Authors: Ansari Mohammad , Rehman Najeeb , Ibnouf Elmoatasim , Alalaiwe Ahmed , Ganaie Majid , Zafar Ameeduzzafar
Published: October 2022

The current research article presents development, characterization, stability, antimicrobial activity, antispasmodic activity and antidiarrheal activity of silver nanoparticles synthesized and stabilized by polymeric coating of gum tragacanth solution. The nanoparticles were developed by a chemical reduction of silver nitrate. The reducing sugars and polysaccharides-based natural polymers such as gum acacia, gum tragacanth, alginates and cellulose derivatives were investigated as both reducing agents and stabilizers of silver nanoparticles. Influence of the molar concentration of silver nitrate, type and concentration of reducing agent on the formation and stability of silver nanoparticles have been investigated in detail. The stability or aggregation behavior of silver nanoparticles when diluted with simulated gastric fluid, simulated intestinal fluid and phosphate buffer saline were investigated to understand the influence of biological fluids on the stability of silver nanoparticles. SNPs in basic buffers were found to be more stable compared to those in acidic buffers. Silver nanoparticles were characterized by UV absorption spectrometry, particle size and zeta potential analyzer, FTIR spectroscopy, differential scanning calorimetry, X-ray diffraction and atomic force microscopy. SNPs were found spherical within 2.5–4 nm as per atomic force microscopic studies. The silver nanoparticles developed from gum tragacanth were better and more stable than those produced by gum acacia. The smaller particle size, low polydispersity index and high zeta potential resulted in silver nanosuspensions stable over a period of six months. The silver nanoparticles were found to exhibit significant antimicrobial, antispasmodic and antidiarrheal activities.
Transparent Conductive Indium Zinc Oxide Films: Temperature and Oxygen Dependences of the Electrical and Optical Properties
Authors: Akhmedov Akhmed K. , Murliev Eldar K. , Asvarov Abil S. , Muslimov Arsen E. , Kanevsky Vladimir M.
Published: October 2022

Achieving high-efficiency optoelectronic devices often requires the development of high transparency in the extended range and high-conductivity materials, which can be ensured by the high mobility of charge carriers being used as the electrode. Among the candidate materials, transparent conductive indium zinc oxide (IZO) has attracted significant interest because of its superior electron mobility (5−60 cm2/V·s) and the thermal stability of its structure. In this study, the IZO films were deposited by the radio frequency magnetron sputtering of the IZO ceramic target (containing 10 wt.% ZnO) by varying the two variables of the substrate temperature and the oxygen content in the working gas. Here, the importance of the deposition of the IZO films at a low substrate temperature, not exceeding 100 °C, in order to get the minimum values of the film resistivity is revealed. At a substrate temperature of 100 °C, the film deposited in pure argon demonstrated a minimum resistance of 3.4 × 10−4 Ω·cm. Despite the fact that, with the addition of O2 in the working gas, an increase in resistivity was observed, the IZO film that deposited under 0.4% O2 content demonstrated the highest mobility (μ = 35 cm2/V·s at ρ = 6.0 × 10−4 Ω·cm) and enhanced transparency in the visible (VIS, 400−800 nm) and near-infrared (NIR, 800−1250 nm) ranges (TVIS ≥ 77% and TNIR ≥ 76%). At an oxygen content above 0.4%, a significant deterioration in electrical properties and a decrease in optical characteristics were observed. SEM and XRD studies of the microstructure of the IZO films allowed the clarification of the effect of both the substrate temperature and the oxygen content on the functional characteristics of the transparent conducting IZO films.
Effect of Al Content on the Microstructure and Tensile Properties of Zr-Co-Al Alloy Prepared by Rapid Solidification
Authors: Wu Zixiang , Lu Wenfei , Li Caiju , Gao Peng , You Xin , Tan Jun
Published: October 2022

The Zr50−x/2Co50−x/2Alx (x = 0, 4, 5, 6, 7, 8) alloy sheets were prepared by copper mold suction casting, and the effect of Al content on the mechanical properties and the strengthening mechanism was thoroughly investigated. With Al addition, a Zr5Co7Al3 intermetallic compound formed and precipitated at the grain boundaries or inside the grains, which produced strong synergistic effects of secondary phase strengthening, fine grain strengthening and solid solution strengthening. However, the precipitation of intermetallic compounds can lead to the formation of shrinkage cavities at grain boundaries, and the negative effect of adding too much Al is greater than the strengthening effect, resulting in a gradual decrease in strength. Therefore, the best synergistic strengthening effect is achieved when the Al content is 5 at.%, the grain size of the alloy is reduced from 60 μm to 4 μm, and the room temperature tensile strength is increased from 240 MPa to 464 MPa.
Hyaluronic Acid-Protein Conjugate Modified Iron-Based MOFs (MIL-101 (Fe)) for Efficient Therapy of Neuroblastoma: Molecular Simulation, Stability and Toxicity Studies
Authors: Nikam Ajinkya N. , Pandey Abhijeet , Nannuri Shivanand H. , Fernandes Gasper , Kulkarni Sanjay , Padya Bharath Singh , Birangal Sumit , Shenoy Gautham G. , George Sajan D. , Mutalik Srinivas
Published: October 2022

Iron-based metal-organic frameworks (MIL (101)) have recently gained attention in materials science for biomedical applications. In the present work, Iron-based MOF (MIL-101(Fe)) were coated with lactoferrin (Lf) conjugated with hyaluronic acid (HA) and investigated its potential for delivering 5-fluorouracil (5-FU), along with assessing the toxicity profile. The synthesised nanoparticles were extensively characterised using spectroscopic, X-Ray, thermal and electron microscopic techniques. 5-FU was loaded into MOFs, and the drug-loading efficiency and drug release pattern were studied, along with stability testing in pH and serum protein. The toxicity of MIL-101(Fe) was assessed using both in vitro and in vivo techniques such as the haemolysis assay, cell viability assay and acute and subacute toxicity studies in animals. In silico molecular simulation was done to assess the Lf and Tf interaction. The molecular interaction of Lf with Transferrin (Tf) showed strong molecular interaction and negligible fluctuation in the RMSD (root mean square deviation) values. The MOFs were stable and demonstrated sustained drug release patterns. The in vitro cell studies demonstrated biocompatibility and enhanced cellular internalisation of MOFs. The in vivo toxicity studies supported the in vitro results. The synthesised MOFs demonstrated potential as a targeted delivery platform for cancer targeting.
The Effect of Ne+ Ion Implantation on the Crystal, Magnetic, and Domain Structures of Yttrium Iron Garnet Films
Authors: Fodchuk Igor , Kotsyubynsky Andrij , Velychkovych Andrii , Hutsuliak Ivan , Boychuk Volodymyra , Kotsyubynsky Volodymyr , Ropyak Liubomyr
Published: October 2022

The mechanism of the influence of crystal inhomogeneities on the magnetic and domain microstructures of functional materials based on yttrium iron garnet heterostructures is an important subject of investigation due to the aim to predict parameters for manufacturingpurposes. A study of the structural and magnetic characteristics of a set of yttrium iron garnet films grown on gadolinium–gallium garnet substrate is presented. High-resolution X-ray diffractometry, Mössbauer spectroscopy, MFM, as well as ion implantation simulation and X-ray diffraction simulation were used together to determine the features of the effect of Ne+ ion implantation with different dose rates on the samples. The simulation of ion implantation with E = 82 keV showed energy loss profiles of Ne ions with subsequent defect formation up to amorphization of near-surface layers at high doses. Implantation creates two magnetically non-equivalent types of tetrahedrally located Fe3+ ions, which leads to a rotation of the total magnetic moment relative to the film surface and a change in the width of the magnetic domain stripes.
Improved Growth Techniques for Nonlinear Optical Crystal CsB3O5 Based on the Investigation of Defects
Authors: Feng Jingcheng , Chen Yuwei , Fan Feidi , Tu Heng , Zhang Guochun , Wu Yicheng
Published: October 2022

Growth defects in CsB3O5 (CBO) crystals grown using the seed-submerged growth technique (SSGT), which includes parallel grouping, growth step, and inclusion, were observed and analyzed. Dislocation was investigated using the chemical etching method for the first time. Relationships between defects and growth conditions are discussed, and ways to overcome growth defects are suggested. Using the cool-end compensation of thermocouples, a CBO crystal measuring 63 × 40 × 30 mm3 (weighing 190 g) was successfully grown using the SSGT. Adopting the top seeded solution growth (TSSG) method, a scattering centers-free CBO crystal measuring 75 × 52 × 46 mm3 (weighing 480 g) was obtained from the improved Cs2O–B2O3–MoO3 system with a molar ratio of (1–3):(1.5–3.5):(1–4), which is the largest CBO single crystal to date.
Effects of High-Intensity Interval Training (HIIT) on Patients with Musculoskeletal Disorders: A Systematic Review and Meta-Analysis with a Meta-Regression and Mapping Report
Authors: Cuenca-Martínez Ferran , Sempere-Rubio Núria , Varangot-Reille Clovis , Fernández-Carnero Josué , Suso-Martí Luis , Alba-Quesada Patricio , La Touche Roy
Published: October 2022

The aim was to assess the impact of high-intensity interval training (HIIT) on patients with musculoskeletal disorders. We conducted a search of Medline, Embase, PEDro, and Google Scholar. We conducted a meta-analysis to determine the effectiveness of HIIT on pain intensity, maximal oxygen consumption (VO2 max), disability, and quality of life (QoL). We employed the GRADE and PEDro scales to rate the quality, certainty, and applicability of the evidence. Results showed significant differences in pain intensity, with a moderate clinical-effect (SMD = −0.73; 95% CI: −1.40–−0.06), and in VO2 max, with a moderate clinical-effect (SMD = 0.69; 95% CI: 0.42–0.97). However, the meta-analysis showed no statistically significant results for disability (SMD = −0.34; 95% CI: −0.92–0.24) and QoL (SMD = 0.40; 95% CI: −0.80–1.60). We compared HIIT against other exercise models for reducing pain intensity and increasing VO2 max. The meta-analysis showed no significant differences in favour of HIIT. Meta-regression analysis revealed that pain intensity scores were negatively associated with VO2 max (R2 = 82.99%, p = 0.003). There is low-moderate evidence that the HIIT intervention for patients with musculoskeletal disorders can reduce pain intensity and increase VO2 max but has no effect on disability and QoL. Results also showed that HIIT was not superior to other exercise models in reducing pain intensity and increasing VO2 max.
Challenges and Strategies to Optimising the Quality of Small Bowel Magnetic Resonance Imaging in Crohn’s Disease
Authors: Bohra Anuj , Vasudevan Abhinav , Kutaiba Numan , Van Langenberg Daniel R.
Published: October 2022

Magnetic resonance enterography (MRE) is one of the most highly utilised tools in the assessment of patients with small bowel Crohn’s disease (CD). As a non-invasive modality, it has both patient and procedure-related advantages over ileocolonoscopy which is the current gold standard for Crohn’s disease activity assessment. MRE relies upon high-quality images to ensure accurate disease activity assessment; however, few studies have explored the impact of image quality on the accuracy of small bowel CD activity assessment. Bowel distension and motion artifacts are two key imaging parameters that impact the quality of images obtained through MRE. Multiple strategies have been employed to both minimise the effects of motion artifacts and improve bowel distension. This review discusses the definitions of bowel distension and motion artifacts within the literature with a particular focus on current strategies to improve bowel distension and limit motion artifacts in MRE.
Coronary Arteries Aneurysms: A Case-Based Literature Review
Authors: Vadalà Giuseppe , Di Caccamo Leandro , Alaimo Chiara , Di Fazio Luca , Ferraiuoli Giovanni , Buccheri Giancarlo , Sucato Vincenzo , Galassi Alfredo Ruggero
Published: October 2022

Coronary artery aneurysm (CAA) is an abnormal dilatation of a coronary artery segment; those coronary artery aneurysms that are very large in size are defined as giant. However, a standardized dimension cut-off to define giant CAAs is still missing. The reported prevalence of coronary aneurysms in the population who underwent coronary angiography ranges from 0.3% to 5%, and often CAAs are found in patient with aneurysms in other sites, such as the ascending or abdominal aorta. In half of the cases an atherosclerotic etiology could be recognized; often, CAA is found in the context of acute coronary syndrome. Seldomly, CAA is found at the autopsy of patients who died due to sudden cardiac death. Currently, very few data exist about CAA management and their prognostic relevance; moreover, CAA treatment is still not clearly codified, but rather case-based. Indeed, currently there are no published dedicated studies exploring the best medical therapy, i.e., with antiplatelets or anticoagulant agents rather than an interventional approach such as an endovascular or surgical technique. In this review, through two clinical cases, the current evidence regarding diagnostic tools and treatment options of CAAs will be described.
Pneumonia and Pulmonary Thromboembolism Classification Using Electronic Health Records
Authors: Siordia-Millán Sinhue , Torres-Ramos Sulema , Salido-Ruiz Ricardo A. , Hernández-Gordillo Daniel , Pérez-Gutiérrez Tracy , Román-Godínez Israel
Published: October 2022

Pneumonia and pulmonary thromboembolism (PTE) are both respiratory diseases; their diagnosis is difficult due to their similarity in symptoms, medical subjectivity, and the large amount of information from different sources necessary for a correct diagnosis. Analysis of such clinical data using computational tools could help medical staff reduce time, increase diagnostic certainty, and improve patient care during hospitalization. In addition, no studies have been found that analyze all clinical information on the Mexican population in the Spanish language. Therefore, this work performs automatic diagnosis of pneumonia and pulmonary thromboembolism using machine-learning tools along with clinical laboratory information (structured data) and clinical text (unstructured data) obtained from electronic health records. A cohort of 173 clinical records was obtained from the Mexican Social Security Institute. The data were preprocessed, transformed, and adjusted to be analyzed using several machine-learning algorithms. For structured data, naïve Bayes, support vector machine, decision trees, AdaBoost, random forest, and multilayer perceptron were used; for unstructured data, a BiLSTM was used. K-fold cross-validation and leave-one-out were used for evaluation of structured data, and hold-out was used for unstructured data; additionally, 1-vs.-1 and 1-vs.-rest approaches were used. Structured data results show that the highest AUC-ROC was achieved by the naïve Bayes algorithm classifying PTE vs. pneumonia (87.0%), PTE vs. control (75.1%), and pneumonia vs. control (85.2%) with the 1-vs.-1 approach; for the 1-vs.-rest approach, the best performance was reported in pneumonia vs. rest (86.3%) and PTE vs. rest (79.7%) using naïve Bayes, and control vs. diseases (79.8%) using decision trees. Regarding unstructured data, the results do not present a good AUC-ROC; however, the best F1-score were scored for control vs. disease (72.7%) in the 1-vs.-rest approach and control vs. pneumonia (63.6%) in the 1-to-1 approach. Additionally, several decision trees were obtained to identify important attributes for automatic diagnosis for structured data, particularly for PTE vs. pneumonia. Based on the experiments, the structured datasets present the highest values. Results suggest using naïve Bayes and structured data to automatically diagnose PTE vs. pneumonia. Moreover, using decision trees allows the observation of some decision criteria that the medical staff could consider for diagnosis.
Automated Detection of Cervical Carotid Artery Calcifications in Cone Beam Computed Tomographic Images Using Deep Convolutional Neural Networks
Authors: Ajami Maryam , Tripathi Pavani , Ling Haibin , Mahdian Mina
Published: October 2022

The aim of this study was to determine if a convolutional neural network (CNN) can be trained to automatically detect and localize cervical carotid artery calcifications (CACs) in CBCT. A total of 56 CBCT studies (15,257 axial slices) were utilized to train, validate, and test the deep learning model. The study comprised of two steps: Step 1: Localizing axial slices that are below the C2–C3 disc space. For this step the openly available Inception V3 architecture was trained on the ImageNet dataset of real-world images, and retrained on 40 CBCT studies. Step 2: Detecting CACs in slices from step 1. For this step, two methods were implemented; Method A: Segmentation neural network trained using small patches at random coordinates of the original axial slices; Method B: Segmentation neural network trained using two larger patches at fixed coordinates of the original axial slices with an improved loss function to account for class imbalance. Our approach resulted in 94.2% sensitivity and 96.5% specificity. The mean intersection over union metric for Method A was 76.26% and Method B improved this metric to 82.51%. The proposed CNN model shows the feasibility of deep learning in the detection and localization of CAC in CBCT images.
Emergency Remote Education and Its Impact on Higher Education: A Temporary or Permanent Shift in Instruction?
Authors: Linnes Cathrine , Ronzoni Giulio , Agrusa Jerome , Lema Joseph
Published: October 2022

The COVID-19 pandemic has led to problems and upheaval throughout the higher-education sector, with university campuses ceasing face-to-face instruction and with assessments shifting to an online model for a few years. As a result, the pandemic prompted educators to teach online, utilizing online lectures, narrated power points, audio snippets, podcasts, instant messaging, and interactive videos, whereas traditional universities had primarily relied on in-person courses. Evaluations, which included assignments and multiple-choice questions, were conducted online, forcing lecturers to reconsider how deliverables were set up to prevent students from having easy access to the answers in a textbook or online. Learning from college students’ experiences throughout this time period will assist higher-education stakeholders (administration, faculty, and students) in adapting future online course delivery selections for higher education. In this study, we investigated the experiences of students learning from a distance, as well as aspects of their learning. We provide recommendations for higher education. The COVID-19 pandemic has clearly resulted in the largest distance-learning experiment in history.
Motives of Student Teachers in Academic Teacher Education for Secondary Education: Research in Flanders (Belgium) on the Motivation to Become and to Remain a Teacher
Authors: Pauwels Katho , Van Loon Helena , Tanghe Els , Schelfhout Wouter
Published: October 2022

The increasing shortage of teachers in secondary education, in Flanders (Belgium) as well as worldwide, is partly due to an insufficient inflow of new teachers. This stimulates research on the motivation of students to become and remain teachers. Our research focuses on the motives of student teachers in academic teacher education for secondary education to become a graduated teacher and how this evolves during teacher education, taking into account different profiles of students. Surveys, with closed and open questions based on our theoretical framework, were conducted at different points in time during teacher education as a basis for mixed method analyses. Compared to the period between the start and the end of the teacher education courses, it appeared that graduated student teachers attached more importance to the motives ‘subject orientation’ and ‘educational orientation’. The research shows a shift to a more realistic educational orientation among graduated students. ‘Work dynamic’ aspects as a motive to work in education were generally considered equally important by women and men but combination possibilities were more important for men. Specifically related to students with a ‘teacher in training’ [TiT] statute, graduated TiT-students considered the motive ‘idealism’ more important than non-TiT-students. Related to working students, ‘work perspective’ appeared to be more important compared to non-working students. These insights into the evolving motives of student teachers have a number of possible implications for the organization of teacher education, which can also contribute to reducing the teacher shortage. Teacher education can put extra emphasis on positive and qualitative hands-on experiences by performing a well-considered selection of internship schools and mentors. At the same time, teacher education can offer even more space and time for student teachers to dedicate to internships, by keeping other activities such as deadlines and teaching moments during internships to a minimum.
Detection Performance Evaluation for Marine Wireless Sensor Networks
Authors: Hu Qi , Liu Yaobo , Mao Ruoxin , Yang Chaoqun
Published: October 2022

Detection performance evaluation is one of the inevitable problems for marine wireless sensor networks (MWSNs) deployed for target detection. However, it is a very complicated problem since it associates many different aspects, such as emitter power, range, radar cross-section, weather, geography, working mode, and so on. Targeting this problem, this paper incorporates the Poisson point process model into describing the ranges from sensors to targets. The relationship between sensors and a target is built from the perspective of detection probabilities. Then, a new consistent, conservative target detection probability evaluation is derived within a CFAR framework, and the further global detection probability of the whole MWSN on the target is developed. Additionally, the rationality of this modeling approach is demonstrated via simulation results, which is in accord with the actual situation.
Graph Neural Networks for Intelligent Modelling in Network Management and Orchestration: A Survey on Communications
Authors: Tam Prohim , Song Inseok , Kang Seungwoo , Ros Seyha , Kim Seokhoon
Published: October 2022

The advancing applications based on machine learning and deep learning in communication networks have been exponentially increasing in the system architectures of enabled software-defined networking, network functions virtualization, and other wired/wireless networks. With data exposure capabilities of graph-structured network topologies and underlying data plane information, the state-of-the-art deep learning approach, graph neural networks (GNN), has been applied to understand multi-scale deep correlations, offer generalization capability, improve the accuracy metrics of prediction modelling, and empower state representation for deep reinforcement learning (DRL) agents in future intelligent network management and orchestration. This paper contributes a taxonomy of recent studies using GNN-based approaches to optimize the control policies, including offloading strategies, routing optimization, virtual network function orchestration, and resource allocation. The algorithm designs of converged DRL and GNN are reviewed throughout the selected studies by presenting the state generalization, GNN-assisted action selection, and reward valuation cooperating with GNN outputs. We also survey the GNN-empowered application deployment in the autonomous control of optical networks, Internet of Healthcare Things, Internet of Vehicles, Industrial Internet of Things, and other smart city applications. Finally, we provide a potential discussion on research challenges and future directions.
High-Definition Map Representation Techniques for Automated Vehicles
Authors: Ebrahimi Soorchaei Babak , Razzaghpour Mahdi , Valiente Rodolfo , Raftari Arash , Fallah Yaser Pourmohammadi
Published: October 2022

Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment’s information. Among these efforts is the high-definition (HD) map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD maps for automated vehicles. The current state of autonomous vehicle (AV) mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy.
Low-Dose COVID-19 CT Image Denoising Using Batch Normalization and Convolution Neural Network
Authors: Diwakar Manoj , Singh Prabhishek , Karetla Girija Rani , Narooka Preeti , Yadav Arvind , Maurya Rajesh Kumar , Gupta Reena , Arias-Gonzáles José Luis , Singh Mukund Pratap , Shetty Dasharathraj K. , Paul Rahul , Naik Nithesh
Published: October 2022

Computed tomography (CT) is used in medical applications to produce digital medical imaging of the human body and is acquired by the reconstruction process, where X-rays are the key component of CT imaging. The present coronavirus outbreak has spawned new medical device and technology research fields. COVID-19 most severely affects people with poor immunity; children and pregnant women are more susceptible. A CT scan will be required to assess the infection’s severity. As a result, to reduce the radiation levels significantly there is a need to minimize the CT scan noise. The quality of CT images may degrade in the form of noisy images due to low radiation levels. Hence, this study proposes a novel denoising methodology for COVID-19 CT images with a low dose, where a convolution neural network (CNN) and batch normalization were utilized for denoising. From different output metrics such as peak signal-to-noise ratio (PSNR) and image quality index (IQI), the accuracy of the resulting CT images was checked and evaluated, where IQI obtained the best results in terms of 99% accuracy. The findings were also compared with the outcomes of related recent research in the domain. After a detailed review of the findings, it was noted that the proposed algorithm in the present study performed better in comparision to the existing literature.
Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles Using Pre-Trained Deep Neural Network
Authors: Abu Al-Haija Qasem , Alsulami Abdulaziz A.
Published: October 2022

Keyless systems have replaced the old-fashioned methods of inserting physical keys into keyholes to unlock the door, which are inconvenient and easily exploited by threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, keyless systems are also susceptible to being compromised by a threat actor who intercepts the transmitted signal and performs a replay attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.
Autonomous Air Combat Maneuvering Decision Method of UCAV Based on LSHADE-TSO-MPC under Enemy Trajectory Prediction
Authors: Tan Mulai , Tang Andi , Ding Dali , Xie Lei , Huang Changqiang
Published: October 2022

In this paper, an autonomous UCAV air combat maneuvering decision method based on LSHADE-TSO optimization in a model predictive control framework is proposed, along with enemy trajectory prediction. First, a sliding window recursive prediction method for multi-step enemy trajectory prediction using a Bi-LSTM network is proposed. Second, Model Predictive Control (MPC) theory is introduced, and when combined with enemy trajectory prediction, a UCAV maneuver decision model based on the MPC framework is proposed. The LSHADE-TSO algorithm is proposed by combining the LSHADE and TSO algorithms, which overcomes the problem of traditional sequential quadratic programming falling into local optimum when solving complex nonlinear models. The LSHADE-TSO-MPC air combat maneuver decision method is then proposed, which combines the LSHADE-TSO algorithm with the MPC framework and employs the LSHADE-TSO algorithm as the optimal control sequence solver. To validate the effectiveness of the maneuvering decision method proposed in this paper, it is tested against the test maneuver and the LSHADE-TSO decision algorithm, respectively, and the experimental results show that the maneuvering decision method proposed in this paper can beat the opponent and win the air combat using the same weapons and flight platform. Finally, to demonstrate that LSHADE-TSO can better exploit the decision-making ability of the MPC model, LSHADE-TSO is compared to various optimization algorithms based on the MPC model, and the results show that LSHADE-TSO-MPC can not only help obtain air combat victory faster but also demonstrates better decision-making ability.
Minimal-Approximation-Based Adaptive Event-Triggered Control of Switched Nonlinear Systems with Unknown Control Direction
Authors: Cao Yumeng , Zhao Ning , Xu Ning , Zhao Xudong , Alsaadi Fawaz E.
Published: October 2022

In this paper, the adaptive neural network event-triggered tracking problem is investigated for a class of uncertain switched nonlinear systems with unknown control direction and average dwell time switching. To reduce the communication network traffic, an event-triggering mechanism based on the tracking error is explored in the controller-to-actuator channel. Additionally, the minimal approximation technology, which designs virtual control laws as the unavailable intermediate signals, is introduced to reduce the difficulty of the controller design process. Compared with the existing adaptive backstepping designs using the filtering technology, the virtual controllers are recursed into a lumped nonlinear function to settle the explosion of complexity, and one neural network is employed in the recursive process. Meanwhile, a boundedness lemma on Nussbaum function is given to address the unknown control direction under the minimal approximation design framework. The stability of the overall closed-loop system is rigorously proved by the Lyapunov stability theory, and the rationality of the proposed strategy is verified by a simulation example. According to the proposed event-triggered mechanism, 81.25% of the communication resources are saved in the simulation example.
Optimization of Bioactive Compound Extraction from Eggplant Peel by Response Surface Methodology: Ultrasound-Assisted Solvent Qualitative and Quantitative Effect
Authors: Shahabi Mohammadabadi Shiva , Goli Mohammad , Naji Tabasi Sara
Published: October 2022

Anthocyanin pigments, which the peel of eggplant is rich in, contribute to food quality because of their function in color, appearance, and nutritional advantages. For the first time, this study aimed to optimize the composition of the extracting solvent as three factors: factor A (ratio of ethanol to methanol 0–100% v/v), factor B (ratio of water to alcohol 0–100% v/v), and factor C (citric acid in the final solvent 0–1% w/v) using response surface methodology (RSM), central composite design (CCD) with α 2, and two repeats in axial and factorial points and four central points, for maximum total phenolic content, total anthocyanin content, extraction yield, antioxidant activity in terms of DPPH radical scavenging activity and ferric reducing antioxidant power (FRAP) assay of the eggplant peel dry extract assisted by ultrasound (200 watts power, frequency of 28 kHz) in 60 °C for 45 min has been investigated. The best optimal formulas determined using RSM for the final solvent comprised optimal formula 1 (i.e., ethanol-to-methanol ratio 59% and water-to-alcohol ratio 0%, and citric acid in final solvent 0.47%), and optimal formula 2 (i.e., ethanol-to-methanol ratio 67% and water-to-alcohol ratio 0%, and citric acid in final solvent 0.56%). In general, an alcoholic–acidic extract of eggplant peel made with an ethanol–methanol solvent including citric acid can be used in the food industry as a natural source of antioxidants and pigment.
A Novel Module Promotes Horizontal Gene Transfer in Azorhizobium caulinodans ORS571
Authors: Li Mingxu , Chen Qianqian , Wu Chuanhui , Li Yiyang , Wang Sanle , Chen Xuelian , Qiu Bowen , Li Yuxin , Mao Dongmei , Lin Hong , Yu Daogeng , Cao Yajun , Huang Zhi , Cui Chunhong , Zhong Zengtao
Published: October 2022

Azorhizobium caulinodans ORS571 contains an 87.6 kb integrative and conjugative element (ICEAc) that conjugatively transfers symbiosis genes to other rhizobia. Many hypothetical redundant gene fragments (rgfs) are abundant in ICEAc, but their potential function in horizontal gene transfer (HGT) is unknown. Molecular biological methods were employed to delete hypothetical rgfs, expecting to acquire a minimal ICEAc and consider non-functional rgfs as editable regions for inserting genes related to new symbiotic functions. We determined the significance of rgf4 in HGT and identified the physiological function of genes designated rihF1a (AZC_3879), rihF1b (AZC_RS26200), and rihR (AZC_3881). In-frame deletion and complementation assays revealed that rihF1a and rihF1b work as a unit (rihF1) that positively affects HGT frequency. The EMSA assay and lacZ-based reporter system showed that the XRE-family protein RihR is not a regulator of rihF1 but promotes the expression of the integrase (intC) that has been reported to be upregulated by the LysR-family protein, AhaR, through sensing host’s flavonoid. Overall, a conservative module containing rihF1 and rihR was characterized, eliminating the size of ICEAc by 18.5%. We propose the feasibility of constructing a minimal ICEAc element to facilitate the exchange of new genetic components essential for symbiosis or other metabolic functions between soil bacteria.
Systematic Analysis of Immune Infiltration and Predicting Prognosis in Clear Cell Renal Cell Carcinoma Based on the Inflammation Signature
Authors: Zhang Yuke , Shi Chunliu , Chen Yue , Wang Hongwei , Chen Feng , Han Ping
Published: October 2022

Clear cell renal cell carcinoma (ccRCC) is the most frequent kind of kidney malignancy. Inflammation is a physiological response of the immune system to harmful stimuli. Notably, the role inflammation plays in ccRCC is still unknown. In this study, consensus clustering analysis sorted the ccRCC specimens from the TCGA dataset into C1 and C2 clusters. The C2 cluster comprised ccRCC specimens with a high TNM stage and tumor grade. These specimens were characterized by the activation of the inflammatory response and an immunosuppressive microenvironment. A seven-gene inflammation-related risk signature was designed employing the LASSO and Cox regression analyses for the inflammation-related genes. The ccRCC specimens were classified into two groups with high and low risk by calculating the risk scores. The specimens in the group with high risk showed a poor prognosis and were positively correlated with immune inhibitory factors. Moreover, a nomogram was created by incorporating inflammation-related risk signatures and clinical characteristics. The ROC and DCA curves indicated a satisfactory efficiency of the nomogram for predicting the survival outcomes. Furthermore, we identified the potential therapeutic drug molecules through CMap analysis. The findings of our study may act as a guide for further research on new prognostic biomarkers and therapies.