bluuebunny/crossref_metadata_2025 huggingface dataset:
Cannabis sativa L. Bioactive Compounds and Their Protective Role in Oxidative Stress and Inflammation
Authors: Kopustinskiene Dalia M. , Masteikova Ruta , Lazauskas Robertas , Bernatoniene Jurga
Published: March 2022

Cannabis (Cannabis sativa L.) plants from the family Cannabidaceae have been used since ancient times, to produce fibers, oil, and for medicinal purposes. Psychoactive delta-9-tetrahydrocannabinol (THC) and nonpsychoactive cannabidiol (CBD) are the main pharmacologically active compounds of Cannabis sativa. These compounds have, for a long time, been under extensive investigation, and their potent antioxidant and inflammatory properties have been reported, although the detailed mechanisms of their actions have not been fully clarified. CB1 receptors are suggested to be responsible for the analgesic effect of THC, while CB2 receptors may account for its immunomodulatory properties. Unlike THC, CBD has a very low affinity for both CB1 and CB2 receptors, and behaves as their negative allosteric modulator. CBD activity, as a CB2 receptor inverse agonist, could be important for CBD anti-inflammatory properties. In this review, we discuss the chemical properties and bioavailability of THC and CBD, their main mechanisms of action, and their role in oxidative stress and inflammation.
An Oversampling Method for Class Imbalance Problems on Large Datasets
Authors: Rodríguez-Torres Fredy , Martínez-Trinidad José F. , Carrasco-Ochoa Jesús A.
Published: March 2022

Several oversampling methods have been proposed for solving the class imbalance problem. However, most of them require searching the k-nearest neighbors to generate synthetic objects. This requirement makes them time-consuming and therefore unsuitable for large datasets. In this paper, an oversampling method for large class imbalance problems that do not require the k-nearest neighbors’ search is proposed. According to our experiments on large datasets with different sizes of imbalance, the proposed method is at least twice as fast as 8 the fastest method reported in the literature while obtaining similar oversampling quality.
Partial Replacement of Petroleum Coke with Modified Biocoke during Production of Anodes Used in the Aluminum Industry: Effect of Additive Type
Authors: Amara Belkacem , Kocaefe Duygu , Kocaefe Yasar , Bhattacharyay Dipankar , Côté Jules , Gilbert André
Published: March 2022

In order to reduce greenhouse gas (GHG) emissions, biocoke modified with different additives was used to replace part of the petroleum coke. Previously, a number of researchers attempted to manufacture anodes using biocoke. However, the majority of these efforts were unsuccessful because the quality of the anodes deteriorated with this replacement. The deterioration was due to the weak interactions between the pitch and biocoke compared to those between the pitch and petroleum coke. In this study, a chemical modification of biocoke was carried out using three additives (i.e., A(1), A(2), and A(3)) with the aim of improving biocoke–pitch interactions to prevent the deterioration of anode quality. The results of this study showed that biocoke–pitch interactions improved when the biocoke was modified with A(1) and A(3). The anodes containing biocoke modified with these two additives had properties similar to those of the standard anode (i.e., without biocoke). The utilization of additive A(2) did not show the same trend.
Multiple Sound Source Localization, Separation, and Reconstruction by Microphone Array: A DNN-Based Approach
Authors: Chen Long , Chen Guitong , Huang Lei , Choy Yat-Sze , Sun Weize
Published: March 2022

Synchronistical localization, separation, and reconstruction for multiple sound sources are usually necessary in various situations, such as in conference rooms, living rooms, and supermarkets. To improve the intelligibility of speech signals, the application of deep neural networks (DNNs) has achieved considerable success in the area of time-domain signal separation and reconstruction. In this paper, we propose a hybrid microphone array signal processing approach for the nearfield scenario that combines the beamforming technique and DNN. Using this method, the challenge of identifying both the sound source location and content can be overcome. Moreover, the use of a sequenced virtual sound field reconstruction process enables the proposed approach to be quite suitable for a sound field which contains a dominant, stronger sound source and masked, weaker sound sources. Using this strategy, all traceable, mainly sound, sources can be discovered by loops in a given sound field. The operational duration and accuracy of localization are further improved by substituting the broadband weighted multiple signal classification (BW-MUSIC) method for the conventional delay-and-sum (DAS) beamforming algorithm. The effectiveness of the proposed method for localizing and reconstructing speech signals was validated by simulations and experiments with promising results. The localization results were accurate, while the similarity and correlation between the reconstructed and original signals was high.
Task-Aware Feature Composition for Few-Shot Relation Classification
Authors: Deng Sinuo , Shi Ge , Feng Chong , Wang Yashen , Liao Lejian
Published: March 2022

Relation classification tends to struggle when training data are limited or when it needs to adapt to unseen categories. In such challenging scenarios, recent approaches employ the metric-learning framework to measure similarities between query and support examples and to determine relation labels of the query sentences based on the similarities. However, these approaches treat each support class independently from one another, never looking at the entire task as a whole. Because of this, they are constrained to using a shared set of features for all meta-tasks, which hinders the ability to compose discriminative features for the task at hand. For example, if two similar relation types occur in a meta-task, the model needs to construct more detailed, task-related features instead of common features shared by all tasks. In this paper, we propose a novel task-aware relation classification model to tackle this issue. We first build a task embedding component to capture task-specific information, after which two mechanisms, such as task-specific gate and gated feature combination methods, are proposed to utilize the task-specific information to guide feature composition dynamically for each meta-task. Experiment results show that our model improves performance considerably over high performing baseline systems on both FewRel 1.0 and FewRel 2.0 benchmarks. Moreover, our proposed methods can be incorporated into metric-learning-based methods and significantly improve their performance.
Comparative Stress Analysis of Polyetherketoneketone (PEKK) Telescopic Crowns Supported by Different Primary Crown Materials
Authors: Tribst João Paulo Mendes , Dal Piva Amanda Maria de Oliveira , Syed Azeem Ul Yaqin , Alrabiah Mohammed , Al-Aali Khulud A. , Vohra Fahim , Abduljabbar Tariq
Published: March 2022

The present study aimed to investigate the stress distribution of secondary telescopic crowns made of polyetherketoneketone (PEKK) combined with different primary crown (PC) materials (Zirconia, CoCr, Titanium, and PEKK) using finite element analysis. The geometric model was composed of bone tissue, periodontal ligament, root dentin, cement layer, primary crown, and secondary telescopic crown (SC). A total of four models were evaluated in which the secondary crowns were simulated in PEKK. The models were designed in CAD software and exported to the computer aided engineering software for the statistic structural analysis simulation. The materials were considered isotropic, with linear behavior and elastic properties. The model was fixed in the bone base and the load was applied at the occlusal surface of the crowns with 600 N. The results were required in von-Mises stress for the primary crown, secondary crown, cement layer, and Equivalent Strain to the periodontal ligament and bone tissue. Results show that the material influenced the stress distribution. The higher the PC elastic modulus, the higher the stress magnitude on the SC and cement layer. In the present study, the use of milled high-density polymer for primary crown presented a promising biomechanical behavior as an alternative material for double-crown design.
Applicability of Machine Learning for Vessel Dimension Survey with a Minimum Number of Common Points
Authors: Garczyńska Ilona , Tomczak Arkadiusz , Stępień Grzegorz , Kasyk Lech , Ślączka Wojciech , Kogut Tomasz
Published: March 2022

This paper presents the challenges encountered in the dimensional control of ships, platforms, and offshore units. This novel approach utilizes machine learning (MLP—Multilayer Perceptron Neural Network) for three-dimensional (3D) spatial coordinate transformations when only three common points are known. The proposed method was verified based on laboratory and field data. The main issue was to provide a sufficient number of valid training points. The oversampling method was used to meet this criterion. The achieved results indicate equal or better accuracy when the points were located inside the adjustment points array. In the case where the points lay outside this array, no improvement in the accuracy of the transformation was observed. The neural approach restores the transformation symmetry, and in some cases, such as the study of deformation of engineering objects, breaks the symmetry rather than restoring it.
Performance Analysis of RCU-Style Non-Blocking Synchronization Mechanisms on a Manycore-Based Operating System
Authors: Kim Changhui , Choi Euteum , Han Mingyun , Lee Seongjin , Kim Jaeho
Published: March 2022

There have been recent advances in multi-core machines with tens and hundreds of cores, and there is an increasing emphasis on the software structure. Many different synchronization mechanism techniques have been developed to improve the performance and the scalability of manycore systems. As the non-blocking algorithms are promising in overcoming performance limits in traditional lock-based blocking synchronization mechanisms, we are observing an increased usage ratio and a number of non-blocking synchronization algorithms. For example, the usage ratio of RCU increased sharply in recent years. Since RCU exhibits low write performance and is difficult to use, the research community introduced RLU and MV-RLU synchronization algorithms to address the issues. RLU and MV-RLU, which are called RCU-style synchronization mechanisms, are promising in terms of providing easy-to-use APIs (Application Programming Interfaces) and better performance in manycore machines. To expand the applicability of RCU-style mechanisms, we need to measure the performance and analyze their measurements in various environments. To meet the goal, we evaluate them at the user and kernel level in sv6 variant, which is a research operating system on a manycore system. In order to enable RCU-style synchronization algorithms in sv6 variant, we implemented and modified some of the libraries and memory allocators in sv6 variant. We use micro-benchmarks that exploit a linked list and hash table to measure the performance while experimenting with parameters of the benchmarks and types of data structures. In most of the experiments, we observed that MV-RLU is scalable. MV-RLU exhibits about thirteen times better throughput than RCU in the case of running 70 threads. In addition, we compare the operation procedures and APIs of each RCU-style synchronization algorithm to analyze the pros and cons of the algorithms.
Study of Underwater and Wave Gliders on the Basis of Simplified Mathematical Models
Authors: Rozhdestvensky Kirill
Published: March 2022

Both underwater and wave gliders are known as autonomous unmanned energy-saving vehicles which have recently found applications for monitoring the world ocean. The paper under consideration discusses simplified mathematical models of these platforms enabling the straightforward parametric investigation into relationships between their parameters and performance. In its first part the paper discusses equations describing the motion of an underwater glider (UG) in a vertical plane as a basis for derivations relating geometric, kinematic and hydrodynamic characteristics of UG and its lifting system with relative differential buoyancy and pitch angle. Obtained therewith are formulae for the estimation of the UG glide path speed, lift-to-drag ratio, range of navigation and endurance. The approach is exemplified for typical cases of the UG conceived as winged bodies of revolution and flying wings. The calculated results feature dependencies of the UG speed on its configuration and volume as well as on the angle of attack for different magnitudes of relative buoyancy. Also considered is an optimal mode of operation, based on the maximization of the lift-to-drag ratio. The second part of the paper is dedicated to the estimation of the thrust and speed of a wave glider (WG), comprising a surface module (float) and underwater module represented by a wing, with the use of a simplified mathematical modeling intended to clarify the influence of the parameters upon the performance of the WG. The derivations led to an equation of forced oscillations of the vehicle accounting for the interaction of the upper and lower modules, connected by a rigid umbilical. The exciting impact of progressive waves of a given length and amplitude is found through the calculation of the variation of a buoyancy force in accordance with the Froude–Krylov hypothesis. The derivatives of time-varying lift with respect to kinematic parameters, entering the equation of vertical motion of the WG, as well as coefficients of instantaneous and time-averaged thrust force, are found by resorting to the oscillating hydrofoil theory. The derivation of the available thrust and the approximate calculation of the drag of the vehicle with account of wave and viscous components enable the evaluation of the speed of the WG for the prescribed geometry of the craft and wave motion parameters.
Antibacterial Activity of Green Synthesised Silver Nanoparticles on Saccharomyces cerevisiae
Authors: Kharchenko Yugin , Lastovetska Liudmyla , Maslak Valeriia , Sidorenko Marina , Vasylenko Volodymyr , Shydlovska Olga
Published: March 2022

Green synthesis of nanoparticles is a widely researched and popular direction in the development of nanotechnology. It is a simple, cheap and effective method for obtaining nanoparticles with interesting biological properties. In light of the development of antibiotic resistance to important clinical strains of bacteria, this method was used in the present study to obtain silver nanoparticles with antibacterial activity. The aim of this study was to synthesise silver nanoparticles with antibacterial action by yeast in a process known as “green synthesis”. We are also considering the prospect of using silver nanoparticles as an antibacterial substance for drug development. The production of nanoparticles was confirmed by UV spectroscopy. Staphylococcus aureus ATCC 25923 and Escherichia coli ATCC 25922 test strains and Staphylococcus aureus 1536 and Klebsiella pneumoniae 520 clinical isolates were used to study the antibacterial effect. The effect of synthesised nanoparticles on the metabolic activity of bacterial cells and their ability to adhere, as well as the minimum inhibitory concentrations (MICs) of synthesised nanoparticles for each of the strains, were determined. Following UV spectroscopy, the nanoparticles obtained were found to have a pronounced peak in optical absorption at 400 nm, corresponding to the plasmon resonance of silver nanoparticles, and demonstrated a high antibacterial effect against all the strains studied.
Effects of SRC and IKKβ Kinase Inhibition in Ischemic Factors Modeling In Vitro and In Vivo
Authors: Loginova Maria M. , Novozhilova Maria O. , Urazov Mark D. , Yarkov Roman S. , Krivonosov Mikhail I. , Kravchenko Galina A. , Mitroshina Elena V. , Vedunova Maria V.
Published: March 2022

The search for new molecular targets whose modulation can reduce nerve cell dysfunction and neuronal death during ischemic damage is one of the most significant issues in both fundamental and clinical neurobiology. Various kinase enzymes are often considered to be such promising targets since they are involved in key molecular cascades that regulate cell adaptation to stress factors. Our work is devoted to the study of the role of two kinases—SRC and IKKβ—in maintaining the neural networks’ functional activity under a hypoxic condition in vivo and in vitro. SRC kinase is a cytoplasmic non-receptor protein tyrosine kinase. It is involved in the regulation of cell proliferation and differentiation; its expression in nerve cells changes during hypoxia. IKKβ kinase is involved in the regulation of the activity of the transcription factor NF-κB, which is a pleiotropic regulator of many cellular signaling pathways. We have shown that blockade of SRC and IKKβ kinases by selective inhibitors maintains cell viability in modeling hypoxic damage in vitro but does not allow for the preservation of the bioelectrical activity of neurons. Studies in vivo have shown the neuroprotective effect of SRC but not IKKβ kinase inhibition in the modeling of cerebral ischemia in mice.
An Overview of the Seismic Elastic Response Spectra and Their Application According to Mexican, U.S., and International Building Codes
Authors: Gonzalez Arturo , Calderon Julio
Published: March 2022

The design of earthquake-resistant structures by applying traditional or performance-based seismic analysis methods involves two large variables: the seismic risk of the area and the physical characteristics of the structure. Before analyzing any building, it is required to determine the seismicity in the location to evaluate the accelerations that the structure must withstand by deterministic or probabilistic methods. The typical results of a seismic study are the uniform hazard spectra in the rock layer related to a specific return period and structural damping. The building codes use different methods to obtain the elastic response spectra, seeking to offer simple procedures by using parametric factors to consider the soil type; however, the procedure tends to return conservative estimates. On the other hand, site-specific spectra offer accurate and less conservative acceleration values, with the disadvantage of an extensive and costly mathematical process, justifying their application mostly to important structures. This review article gives the state of the art of seismic elastic response spectra using probabilistic seismic hazard maps as inputs, taking into account the importance of the structure and the soil type, according to Mexican, U.S., and international building codes.
Road Infrastructure Challenges Faced by Automated Driving: A Review
Authors: Mihalj Tomislav , Li Hexuan , Babić Dario , Lex Cornelia , Jeudy Mathieu , Zovak Goran , Babić Darko , Eichberger Arno
Published: March 2022

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS.
Insights into Recent Studies on Biotransformation and Pharmacological Activities of Ginsenoside Rd
Authors: Song Xiaoping , Wang Lina , Fan Daidi
Published: March 2022

It is well known that ginsenosides—major bioactive constituents of Panax ginseng—are attracting more attention due to their beneficial pharmacological activities. Ginsenoside Rd, belonging to protopanaxadiol (PPD)-type ginsenosides, exhibits diverse and powerful pharmacological activities. In recent decades, nearly 300 studies on the pharmacological activities of Rd—as a potential treatment for a variety of diseases—have been published. However, no specific, comprehensive reviews have been documented to date. The present review not only summarizes the in vitro and in vivo studies on the health benefits of Rd, including anti-cancer, anti-diabetic, anti-inflammatory, neuroprotective, cardioprotective, ischemic stroke, immunoregulation, and other pharmacological effects, it also delves into the inclusion of potential molecular mechanisms, providing an overview of future prospects for the use of Rd in the treatment of chronic metabolic diseases and neurodegenerative disorders. Although biotransformation, pharmacokinetics, and clinical studies of Rd have also been reviewed, clinical trial data of Rd are limited; the only data available are for its treatment of acute ischemic stroke. Therefore, clinical evidence of Rd should be considered in future studies.
Retinal Circular RNA hsa_circ_0087207 Expression Promotes Apoptotic Cell Death in Induced Pluripotent Stem Cell-Derived Leber’s Hereditary Optic Neuropathy-like Models
Authors: Yang Yi-Ping , Chang Yuh-Lih , Lai Yun-Hsien , Tsai Ping-Hsing , Hsiao Yu-Jer , Nguyen Long Hoang , Lim Xue-Zhen , Weng Chang-Chi , Ko Yu-Ling , Yang Chang-Hao , Hwang De-Kuang , Chen Shih-Jen , Chiou Shih-Hwa , Chiou Guang-Yuh , Wang An-Guor , Chien Yueh
Published: March 2022

Backgrounds: Leber’s hereditary optic neuropathy (LHON) is known as an inherited retinal disorder characterized by the bilateral central vision loss and degeneration of retinal ganglion cells (RGCs). Unaffected LHON carriers are generally asymptomatic, suggesting that certain factors may contribute to the disease manifestations between carriers and patients who carry the same mutated genotypes. Methods: We first aimed to establish the iPSC-differentiated RGCs from the normal healthy subject, the carrier, and the LHON patient and then compared the differential expression profile of circular RNAs (CircRNAs) among RGCs from these donors in vitro. We further overexpressed or knocked down the most upregulated circRNA to examine whether this circRNA contributes to the distinct phenotypic manifestations between the carrier- and patient-derived RGCs. Results: iPSCs were generated from the peripheral blood cells from the healthy subject, the carrier, and the LHON patient and successfully differentiated into RGCs. These RGCs carried equivalent intracellular reactive oxygen species, but only LHON-patient iPSC-derived RGCs exhibited remarkable apoptosis. Next-generation sequencing and quantitative real-time PCR revealed the circRNA hsa_circ_0087207 as the most upregulated circRNA in LHON-patient iPSC-derived RGCs. Overexpression of hsa_circ_0087207 increased the apoptosis in carrier iPSC-derived RGCs, while knockdown of hsa_circ_0087207 attenuated the apoptosis in LHON-patient iPSC-derived RGCs. Predicted by bioinformatics approaches, hsa_circ_0087207 acts as the sponge of miR-665 to induce the expression of a variety of apoptosis-related genes in LHON patient iPSC-derived RGCs. Conclusions: Our data indicated that hsa_circ_0087207 upregulation distinguishes the disease phenotype manifestations between iPSC-derived RGCs generated from the LHON patient and carrier. Targeting the hsa_circ_0087207/miR-665 axis might hold therapeutic promises for the treatment of LHON.
MicroRNAs: Novel Targets in Hepatic Ischemia–Reperfusion Injury
Authors: Ingram Holly , Dogan Murat , Eason James , Kuscu Cem , Kuscu Canan
Published: March 2022

Hepatic ischemia–reperfusion injury (IRI) is one of the main factors for early allograft dysfunction (EAD), which may lead to graft rejection, graft loss, or shortened graft life in liver transplantation. Hepatic IRI appears to be inevitable during the majority of liver procurement and transportation of donor organs, resulting in a cascade of biological changes. The activation of signaling pathways during IRI results in the up- and downregulation of genes and microRNAs (miRNAs). miRNAs are ~21 nucleotides in length and well-characterized for their role in gene regulations; they have recently been used for therapeutic approaches in addition to their role as biomarkers for many diseases. miRNAs that are associated with hepatic IRI in in vitro and in vivo animal models are comprehensively summarized in this review. In those studies, the manipulation of miRNAs has been shown for the inhibition of aggravated immune response, reduction of apoptosis, stimulation of tissue repair, and enhancement of cell recovery to attenuate liver damage. Therefore, the utilization of liver-specific miRNA holds great potential as a therapeutic agent to improve early allograft dysfunction, hepatic injury, and patient outcome.
Serum Levels of Stromal Cell-Derived Factor-1α and Vascular Endothelial Growth Factor Predict Clinical Outcomes in Head and Neck Squamous Cell Carcinoma Patients Receiving TPF Induction Chemotherapy
Authors: Chen Yen-Hao , Chien Chih-Yen , Wang Yu-Ming , Li Shau-Hsuan
Published: March 2022

Chemokines, such as stromal cell-derived factor-1α (SDF-1α) and vascular endothelial growth factor (VEGF), are associated with clinical outcomes in several cancer types. This study aimed to investigate the role of SDF-1α and VEGF in the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) who underwent TPF induction chemotherapy (docetaxel, cisplatin, and 5-fluorouracil). A total of 77 HNSCC patients were enrolled and circulating SDF-1α and VEGF values were examined at two time points for each patient, including pre-TPF treatment (treatment-naïve) and post-TPF treatment but before chemoradiotherapy. The median progression-free survival (PFS) and overall survival (OS) were 18.1 and 32.9 months, respectively. Decreased SDF-1α and VEGF levels after TPF treatment, post-TPF SDF-1α < 1500 pg/mL and VEGF value < 150 pg/mL were independent prognostic factors for better PFS and OS in univariate and multivariate analyses. A combination of SDF-1α and VEGF values may predict clinical outcomes significantly. Our study confirmed the role of SDF-1α and VEGF in the disease progression of HNSCC, and that decreased SDF-1α and VEGF after TPF treatment and lower post-TPF SDF-1α and VEGF values were associated with better prognosis in HNSCC patients who received induction chemotherapy with TPF followed by chemoradiotherapy.
Geometric Feature Extraction for Identification and Classification of Overlapping Cells for Leukaemia
Authors: Kiu Siew Ming , Wang Yin Chai
Published: March 2022

This paper describes the study of overlapping leukaemia cells based on geometric features for identification and classification. Geometric features of blood cells are proposed to identify and classify overlapping cells into groups based on different overlapping degrees and the number of overlapped cells. In the proposed method, the percentage of average accuracy for identifying overlapping cells reached 98 percent. The proposed method can segment white blood cells from the overlapping cells with an overlapping degree of 70 percent. Improved Watershed Algorithm successfully increased 36.89 percent of accuracy in WBC segmentation. It reduced 46.12 percent of the over-segmentation problem. Tests of cell counting are conducted in the two stages, which are before and after the process of identification and classification of overlapping cells. The average percentage of total cell count is 83.31 percent, the average percentage of WBC counting is 84.8 percent, and the average percentage of RBC counting is 60.55 percent. The proposed method is efficient in identifying and classifying overlapping cells for increasing the accuracy of cell counting.
Better Ideation Task Results in Web-Based Idea Management Systems
Authors: Mikelsone Elina , Spilbergs Aivars , Segers Jean-Pierre , Volkova Tatjana , Liela Elita
Published: March 2022

Web-based idea management systems (IMS) have helped many organisations to adapt their creativity processes to the new way of working in response to the global pandemic. IMS provide a systematic and manageable idea generation and evaluation process in the virtual environment. Many well-known organisations in various industries, for example, Etsy, Panasonic, Sony, Electrolux, and Volvo, use IMS. In this paper, the authors identified a clear research gap—what kind of ideation tasks creates the best idea quality and quantity. The aim of this research is to find out how the different ideation task elements influence the quality and quantity of generated ideas. The following methods were used to fill the gap: (1) literature review (data collection: systematic data collection from scientific databases; data analysis: through content analysis) and (2) global survey of n > 500 organisations with web-based IMS experience (data collection: survey; data analysis: statistical inference). The results provide insight into: (1) correlations between the number of created ideation tasks and idea quality and quantity, (2) how results are affected by the duration of ideation tasks, and (3) analysis of different ideation task types and their impact on results.
Complement Proteins C5/C5a, Cathepsin D and Prolactin in Chondrocytes: A Possible Crosstalk in the Pathogenesis of Osteoarthritis
Authors: Silawal Sandeep , Wagner Miriam , Roth Dominik , Bertsch Thomas , Schwarz Silke , Willauschus Maximilian , Gesslein Markus , Triebel Jakob , Schulze-Tanzil Gundula
Published: March 2022

Introduction: Both increased activity of the complement system (CS) and the role of the pituitary hormone prolactin (PRL) are implicated in osteoarthritis (OA) pathogenesis. Besides, Cathepsin D (CatD) activity is increased in the context of OA and can exert not only proteolytic but also non-proteolytic effects on cells. For the first time, possible crosstalk between two separate humoral systems: the CS and the PRL hormone systems in chondrocytes are examined together. Methods: Primary human articular chondrocytes (hAC) were stimulated with complement protein C5 (10 µg /mL), PRL (25 ng/mL), CatD (100 ng/mL), or anaphylatoxin C5a (25 ng/mL) for 24 h or 72 h, while unstimulated cells served as controls. In addition, co-stimulations of C5 or PRL with CatD were carried out under the same conditions. The influence of the stimulants on cell viability, cell proliferation, and metabolic activity of hAC, the chondrosarcoma cell line OUMS-27, and endothelial cells of the human umbilical cord vein (HUVEC) was investigated. Gene expression analysis of C5a receptor (C5aR1), C5, complement regulatory protein CD59, PRL, PRL receptor (PRLR), CatD, and matrix metal-loproteinases (MMP)-13 were performed using real-time PCR. Also, collagen type (Col) I, Col II, C5aR1, CD59, and PRL were detected on protein level using immunofluorescence labeling. Results: The stimulation of the hAC showed no significant impairment of the cell viability. C5, C5a, and PRL induced cell growth in OUMS-27 and HUVEC, but not in chondrocytes. CatD, as well as C5, significantly reduced the gene expression of CatD, C5aR1, C5, and CD59. PRLR gene expression was likewise impaired by C5, C5a, and PRL+CatD stimulation. On the protein level, CatD, as well as C5a, decreased Col II as well as C5aR1 synthesis. Conclusions: The significant suppression of the C5 gene expression under the influence of PRL+CatD and that of CD59 via PRL+/−CatD and conversely a suppression of the PRLR gene expression via C5 alone or C5a stimulation indicates an interrelation between the two mentioned systems. In addition, CatD and C5, in contrast to PRL, directly mediate possible negative feedback of their own gene expression.
Deciphering Haplotypic Variation and Gene Expression Dynamics Associated with Nutritional and Cooking Quality in Rice
Authors: Rana Nitika , Kumawat Surbhi , Kumar Virender , Bansal Ruchi , Mandlik Rushil , Dhiman Pallavi , Patil Gunvant B. , Deshmukh Rupesh , Sharma Tilak Raj , Sonah Humira
Published: March 2022

Nutritional quality improvement of rice is the key to ensure global food security. Consequently, enormous efforts have been made to develop genomics and transcriptomics resources for rice. The available omics resources along with the molecular understanding of trait development can be utilized for efficient exploration of genetic resources for breeding programs. In the present study, 80 genes known to regulate the nutritional and cooking quality of rice were extensively studied to understand the haplotypic variability and gene expression dynamics. The haplotypic variability of selected genes were defined using whole-genome re-sequencing data of ~4700 diverse genotypes. The analytical workflow identified 133 deleterious single-nucleotide polymorphisms, which are predicted to affect the gene function. Furthermore, 788 haplotype groups were defined for 80 genes, and the distribution and evolution of these haplotype groups in rice were described. The nucleotide diversity for the selected genes was significantly reduced in cultivated rice as compared with that in wild rice. The utility of the approach was successfully demonstrated by revealing the haplotypic association of chalk5 gene with the varying degree of grain chalkiness. The gene expression atlas was developed for these genes by analyzing RNA-Seq transcriptome profiling data from 102 independent sequence libraries. Subsequently, weighted gene co-expression meta-analyses of 11,726 publicly available RNAseq libraries identified 19 genes as the hub of interactions. The comprehensive analyses of genetic polymorphisms, allelic distribution, and gene expression profiling of key quality traits will help in exploring the most desired haplotype for grain quality improvement. Similarly, the information provided here will be helpful to understand the molecular mechanism involved in the development of nutritional and cooking quality traits in rice.
Spectroscopic Study of Phytosynthesized Ag Nanoparticles and Their Activity as SERS Substrate
Authors: Dzhagan Volodymyr , Smirnov Oleksandr , Kovalenko Mariia , Mazur Nazar , Hreshchuk Oleksandr , Taran Nataliya , Plokhovska Svitlana , Pirko Yaroslav , Yemets Alla , Yukhymchuk Volodymyr , Zahn Dietrich R. T.
Published: March 2022

The affordable and scalable synthesis of noble metal nanoparticles that are biocompatible without additional functionalization steps has been a growing field of research, stimulated by numerous prospective applications of these NPs. In the case of phytosynthesized or biogenic noble metal NPs, the mechanism of NP stabilization by biomolecules contained in each particular plant extract or living organism determines the possible applications of these NPs. In this work, we investigated Ag NPs synthesized in water with plant extracts of common toothwort (Lathraea squamaria) and two species of pepper (Capsicum annuum and Capsicum chinense). From FTIR and XPS, we drew conclusions about the composition of the functional groups and molecules that stabilize NPs in each extract, such as polysaccharide compounds (pectins, cellulose, glycosides and phenolic acids). Distinct characteristic IR features of amide I and amide II proteins were observed, which are common in plant extracts, while features of amide III were not distinctly observed in our extracts. A Raman spectroscopy study revealed weak own-SERS activity of the biomolecules of the extract and high efficiency of the NPs in the enhancement of “external” analytes, such as dyes and antibodies. This is the first report of the efficient SERS application of phytosynthesized Ag NPs.
Design of Open Code Software to Downs and Steiner Lateral Cephalometric Analysis with Tracing Landmarks
Authors: Reyes Salgado Juan José
Published: March 2022

Cephalometric analysis is an excellent instrument in clinical diagnosis, treatment, and recovery from surgery. Nowadays, efforts to develop computerized dental X-ray image Cephalometric analysis systems for clinical and education usages. Much commercial software is created, but its high cost is unaffordable for some undergraduate students or low-income medical institutions; sure, the best option is the use of open source software alternatives. The study aimed to design free software Cephalopoint that applies vector algebra to perform the accuracy and precision of Cephalometric analysis. Three tests were used to validate the measurements made: accuracy test, consisting of comparing three selected cases and reply 32 times using the manual and software technique measurement; time test, consisted in obtaining the average time used to complete manual and software techniques of the previous test; and statistical test, consisted of measuring and applying the statistical analysis of 42 random cases for each method only using the software technique. The results showed high repeatability and no significant difference between manual tracing and software techniques. All the variables calculated with the software technique exhibited a normal distribution. Cephalopoint is excellent software for accurate and precise Cephalometric measurements. Moreover, it significantly decreased the measurement time compared with the manual.
Student Approaches and Performance in Element Sequencing Tasks Using 2D and Augmented Reality Formats
Authors: McCord Kieren H. , Ayer Steven K. , Perry Logan A. , Patil Karan R. , London Jeremi S. , Khoury Vanessa , Wu Wei
Published: March 2022

In civil and construction engineering education research, a focus has been on using 3D models to support students’ design comprehension. Despite this trend, the predominant mode of design communication in the industry relies on 2D plans and specifications, which typically supersede other modes of communication. Rather than focusing on the presentation of less common 3D content as an input to support students’ understanding of a design, this paper explores more common 2D inputs, but compares different visualization formats of student output in two educational interventions. In the first intervention, students document a construction sequence for wood-framed elements in a 2D worksheet format. In the second, students work with the same wood-framed design, but document their sequence through an augmented reality (AR) format where their physical interactions move full-scale virtual elements as if they were physically constructing the wood frame. Student approaches and performance were analyzed using qualitative attribute coding of video, audio, and written documentation of the student experience. Overall, results showed that the 2D worksheet format was simple to implement and was not mentally demanding to complete, but often corresponded with a lack of critical checks and a lack of mistake recognition from the students. The AR approach challenged students more in terms of cognitive load and completion rates but showed the potential for facilitating mistake recognition and self-remediation through visualization. These results suggest that when students are tasked with conceptualizing construction sequences from 2D documentation, the cognitive challenges associated with documenting a sequence in AR may support their recognition of their own mistakes in ways that may not be effectively supported through 2D documentation as an output for documenting and planning a construction sequence. The results presented in this paper provide insights on student tendencies, behaviors, and perceptions related to defining construction sequences from 2D documentation in order for educators to make informed decisions regarding the use of similar learning activities to prepare their students for understanding the 2D design documents used in industry.
Structured Design of Complex Hardware Microarchitectures Based on Explicit Generic Implementations of Custom Microarchitectural Mechanisms
Authors: Antonov Alexander
Published: March 2022

In typical computer system design flows, hardware microarchitecture is commonly considered a dynamically developing domain, where abstract mechanisms remain mostly in conceptual form and exhibit poor source code reuse in designs. However, many important basic mechanisms appear to be pervasive and persistent. This paper summarizes original efforts to propose generic implementations of microarchitectural mechanisms for in-order and out-of-order microarchitectures and various demo designs based on these implementations. The basic EDA components proposed for this design approach are “Kernel IP (KIP) cores”—open-source collection of programmable hardware generators inferred from microarchitectural templates. KIP cores feasibility for their role is demonstrated from the viewpoints of abstract representation of microarchitectural mechanisms, support of functional differentiation of designs, and generic optimizations. Experimental designing according to this approach has shown useful separation of concerns in hardware design activities, lower design effort, and increased formalization and code reuse.
SeAttE: An Embedding Model Based on Separating Attribute Space for Knowledge Graph Completion
Authors: Liang Zongwei , Yang Junan , Liu Hui , Huang Keju , Qu Lingzhi , Cui Lin , Li Xiang
Published: March 2022

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. The tensor decomposition models are an embedding family with good performance in link prediction. The previous tensor decomposition models do not consider the problem of attribute separation. These models mainly explore particular regularization to improve performance. No matter how sophisticated the design of tensor decomposition models is, the performance is theoretically under the basic tensor decomposition model. Moreover, the unnoticed task of attribute separation in the traditional models is just handed over to the training. However, the amount of parameters for this task is tremendous, and the model is prone to overfitting. We investigate the design approaching the theoretical performance of tensor decomposition models in this paper. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Therefore, the comparison of actual triples needs first to separate specific attribute dimensions, which is ignored by existing models. Inspired by this observation, we design a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion (SeAttE). The major novelty of this paper is that SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. We also prove that RESCAL, DisMult and ComplEx are special cases of SeAttE in this paper. Furthermore, we classify existing tensor decomposition models for subsequent researchers. Experiments on the benchmark datasets show that SeAttE has achieved state-of-the-art among tensor decomposition models.
Online Learning for Reference-Based Super-Resolution
Authors: Chae Byungjoo , Park Jinsun , Kim Tae-Hyun , Cho Donghyeon
Published: March 2022

Online learning is a method for exploiting input data to update deep networks in the test stage to derive potential performance improvement. Existing online learning methods for single-image super-resolution (SISR) utilize an input low-resolution (LR) image for the online adaptation of deep networks. Unlike SISR approaches, reference-based super-resolution (RefSR) algorithms benefit from an additional high-resolution (HR) reference image containing plenty of useful features for enhancing the input LR image. Therefore, we introduce a new online learning algorithm, using several reference images, which is applicable to not only RefSR but also SISR networks. Experimental results show that our online learning method is seamlessly applicable to many existing RefSR and SISR models, and that improves performance. We further present the robustness of our method to non-bicubic degradation kernels with in-depth analyses.
Facial Identity Verification Robust to Pose Variations and Low Image Resolution: Image Comparison Based on Anatomical Facial Landmarks
Authors: Hong Yu-Jin
Published: March 2022

Face comparison/face mapping is one of the promising methods in face biometrics which needs relatively little effort compared with face identification. Various factors may be used to verify whether two faces are of the same person, among which facial landmarks are one of the most objective indicators due to the same anatomical definition for every face. This study identified major landmarks from 2D and 3D facial images of the same Korean individuals and calculated the distance between the reciprocal landmarks of two images to examine their acceptable range for identifying an individual to obtain standard values from diverse facial angles and image resolutions. Given that reference images obtained in the real-world could be from various angles and resolutions, this study created a 3D face model from multiple 2D images of different angles, and oriented the 3D model to the angle of the reference image to calculate the distance between reciprocal landmarks. In addition, we used the super-resolution method of artificial intelligence to address the inaccurate assessments that low-quality videos can yield. A portion of the process was automated for speed and convenience of face analysis. We conclude that the results of this study could provide a standard for future studies regarding face-to-face analysis to determine if different images are of the same person.
Thin Circularly Polarized Slot Array Antenna for High-Band UWB Applications
Authors: Takigawa Ryo , Kai Shunsuke , Kanaya Haruichi
Published: March 2022

This paper presents a thin 2 × 2 circularly polarized slot array antenna that operates in the high-band ultra-wideband (UWB) system. The proposed antenna is composed of three parts: a top metal part that consists of an L-shaped slot structure, a dielectric substrate, and a bottom floating metal layer. The design model and characteristics of the antenna element and array antenna are investigated through simulation and measurement. Experimental results show that the maximum gain of the prototype array antenna is 6.94 dBi at 9.02 GHz. The antenna size is 29.2 mm × 31.7 mm, and its thickness is 1.636 mm. By using a slot antenna structure, a thin circularly polarized antenna (thickness: 1.636 mm) with high gain was demonstrated. The antenna characteristics qualify the antenna to be used in high-band UWB applications.