bluuebunny/crossref_metadata_2025 huggingface dataset:
Fuzzy Logic-Based Duty Cycle Controller for the Energy Management System of Hybrid Electric Vehicles with Hybrid Energy Storage System
Authors: Ishaque Muhammad Rafaqat , Khan Muhammad Adil , Afzal Muhammad Moin , Wadood Abdul , Oh Seung-Ryle , Talha Muhammad , Rhee Sang-Bong
Published: April 2021

Due to increasing fuel prices, the world is moving towards the use of hybrid electric vehicles (HEVs) because they are environmentally friendly, require less maintenance, and are a green technology. The energy management system (EMS) plays an important role in HEVs for the efficient storage of energy and control of the power flow mechanism. This paper deals with the design, modeling, and result-oriented approach for the development of EMS for HEVs using a fuzzy logic controller (FLC). Batteries and supercapacitors (SCs) are used as primary and secondary energy storage systems (ESSs), respectively. EMS consists of the ultra-power transfer algorithm (UPTA) and FLC techniques, which are used to control the power flow. The UPTA technique is used to charge the battery with the help of a single-ended primary inductor converter (SEPIC) during regenerative braking mode. The proposed research examines and compares the performance of FLC with a proportional integral (PI) controller by using MATLAB (Simulink) software. Three scenarios are built to confirm the efficiency of the proposed design. The simulation results show that the proposed design with FLC has a better response as its rise time (2.6 m) and settling time (1.47 µs) are superior to the PI controller.
The Role of Maintenance Operator in Industrial Manufacturing Systems: Research Topics and Trends
Authors: Tortora Alessia M. R. , Di Pasquale Valentina , Franciosi Chiara , Miranda Salvatore , Iannone Raffaele
Published: April 2021

Maintenance contributes to gaining high business performance, guarantees system availability and reliability as well as safe and sustainable operations. Maintenance activity effectiveness depends on competences and the skills of operators whose performance strongly affects maintenance and production operations. The research field of human issues in industrial maintenance was deeply addressed in the literature; however, the current industrial paradigm, which focusses on the integration of new technologies in conventional manufacturing operations to support human performance, sheds light on new challenges for enterprises and opportunities for research in this field. While some literature reviews in the field of human errors and human factors are available, no study investigated the main topics, research trends and challenges related to the role of maintenance operators in manufacturing systems. This paper addresses the current state-of-the-art role of maintenance operators in manufacturing systems, providing an overview of the main studies. A systematic literature review was carried out to identify significant papers. Then, a topic modelling algorithm was used to detect the main topics of the selected papers to provide the research trends of the subject. The identified topics provided interesting research insights on the human role in industrial maintenance. Research trends and further research opportunities were defined.
Comparative Study of Aortic Wall Stress According to Geometric Parameters in Abdominal Aortic Aneurysms
Authors: You Ji-Hun , Lee Chung Won , Huh Up , Lee Chi-Seung , Ryu Dongman
Published: April 2021

In abdominal aortic aneurysm (AAA), the rupture of the aortic tissue is directly related to wall stress. Thus, the investigation of maximum wall stress is a necessary procedure to predict the aortic rupture in AAA. In this study, computational simulations were performed to investigate the correlation between peak wall stress (PWS) and AAA geometry. The Holzapfel model and various orientations of the collagen fibers and thicknesses of the layers of the aorta were employed in the simulation. The material constants used in the Holzapfel model were estimated from the examination and analysis of the biaxial tensile test results of the normal abdominal aorta and AAA. The aneurysm diameter, height, neck angle, and iliac angle were selected as geometric factors affecting the AAA rupture. In addition, a simulation scenario was conceived and created based on the measurement results using the computed tomography data of patients with AAA. Accordingly, the correlation between the PWS and AAA geometry was estimated.
Assessment of Quality and Safety of Farm Level Produced Cheeses from Sheep and Goat Milk
Authors: Kováčová Mariana , Výrostková Jana , Dudriková Eva , Zigo František , Semjon Boris , Regecová Ivana
Published: April 2021

Consumption of sheep’s and goat’s milk and cheese is currently increasing. The production process of these types of cheese is being carried out by traditional domestic production at farm level. However, knowledge in the field of hygiene, technology and health safety of cheeses are still insufficient. This study aimed to examine the physical and chemical quality and microbiological safety of sheep’s and goat’s milk and cheeses made from them. The month of milking influenced the content of milk components (p < 0.001) in sheep’s milk and goat’s milk, but no changes in SCC content during the examined period were found (p > 0.05). Level of contamination by Enterobacteriaceae sp. and coagulase-positive staphylococci was lower than 5 log CFU/mL in sheep’s and goat’s milk. During the ripening time, the number of lactic acid bacteria significantly raised (p < 0.001). Ripening time statistically changed (p < 0.001) not just the microbial safety of cheeses but also the color (p < 0.01). Under the applicable regulations, the analyzed samples were evaluated as suitable for human consumption.
Automated Software Vulnerability Detection Based on Hybrid Neural Network
Authors: Li Xin , Wang Lu , Xin Yang , Yang Yixian , Tang Qifeng , Chen Yuling
Published: April 2021

Vulnerabilities threaten the security of information systems. It is crucial to detect and patch vulnerabilities before attacks happen. However, existing vulnerability detection methods suffer from long-term dependency, out of vocabulary, bias towards global features or local features, and coarse detection granularity. This paper proposes an automatic vulnerability detection framework in source code based on a hybrid neural network. First, the inputs are transformed into an intermediate representation with explicit structure information using lower level virtual machine intermediate representation (LLVM IR) and backward program slicing. After the transformation, the size of samples and the size of vocabulary are significantly reduced. A hybrid neural network model is then applied to extract high-level features of vulnerability, which learns features both from convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The former is applied to learn local vulnerability features, such as buffer size. Furthermore, the latter is utilized to learn global features, such as data dependency. The extracted features are made up of concatenated outputs of CNN and RNN. Experiments are performed to validate our vulnerability detection method. The results show that our proposed method achieves excellent results with F1-scores of 98.6% and accuracy of 99.0% on the SARD dataset. It outperforms state-of-the-art methods.
United Approach to Modelling of the Hot Deformation Behavior, Fracture, and Microstructure Evolution of Austenitic Stainless AISI 316Ti Steel
Authors: Churyumov Alexander Yu. , Medvedeva Svetlana V. , Mamzurina Olga I. , Kazakova Alena A. , Churyumova Tatiana A.
Published: April 2021

Hot deformation is one of the main technological stages of products made from metallic materials. It is strictly required to decrease the costs of developing optimized technologies at this stage without a significant decrease in the products’ quality. The present investigation offers an algorithm to unite three different models to predict the hot deformation behavior, fracture, and microstructure evolution. The hot compression and tension tests of the AISI 316Ti steel were conducted using the thermomechanical simulator Gleeble 3800 for the models’ construction. The strain-compensated constitutive model and the Johnson–Mehl–Avrami–Kolmogorov (JMAK)-type model of the grain structure evolution show a satisfactory accuracy of 4.38% and 6.9%, respectively. The critical values of the modified Rice and Tracy fracture criteria were determined using the experimental values of the relative cross-section reduction and finite element calculation of the stress triaxiality. The developed models were approved for the stainless AISI 316Ti steel by the hot torsion with tension test.
Machine Learning in Chronic Pain Research: A Scoping Review
Authors: Jenssen Marit Dagny Kristine , Bakkevoll Per Atle , Ngo Phuong Dinh , Budrionis Andrius , Fagerlund Asbjørn Johansen , Tayefi Maryam , Bellika Johan Gustav , Godtliebsen Fred
Published: April 2021

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.
Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
Authors: Pikoulis Erion-Vasilis , Ioannou Zafeiria-Marina , Paschou Mersini , Sakkopoulos Evangelos
Published: April 2021

Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. The aim of this paper is to present a qualitative assessment of the morphing attack issue, and the challenges it entails, highlighting both the technological and human aspects of the problem. Here, after the face morphing attack scenario is presented, the paper provides an overview of the relevant bibliography and recent advances towards two central directions. First, the morphing of face images is outlined with a particular focus on the three main steps that are involved in the process, namely, landmark detection, face alignment and blending. Second, the detection of morphing attacks is presented under the prism of the so-called on-line and off-line detection scenarios and whether the proposed techniques employ handcrafted features, using classical methods, or automatically generated features, using deep-learning-based methods. The paper, then, presents the evaluation metrics that are employed in the corresponding bibliography and concludes with a discussion on open challenges that need to be address for further advancing automatic detection of morphing attacks. Despite the progress being made, the general consensus of the research community is that significant effort and resources are needed in the near future for the mitigation of the issue, especially, towards the creation of datasets capturing the full extent of the problem at hand and the availability of reference evaluation procedures for comparing novel automatic attack detection algorithms.
Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019
Authors: Jakovlev Andrew R. , Smyshlyaev Sergei P. , Galin Vener Y.
Published: April 2021

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.
Modelling Software Architecture for Visual Simultaneous Localization and Mapping
Authors: Mishra Bhavyansh , Griffin Robert , Sevil Hakki Erhan
Published: April 2021

Visual simultaneous localization and mapping (VSLAM) is an essential technique used in areas such as robotics and augmented reality for pose estimation and 3D mapping. Research on VSLAM using both monocular and stereo cameras has grown significantly over the last two decades. There is, therefore, a need for emphasis on a comprehensive review of the evolving architecture of such algorithms in the literature. Although VSLAM algorithm pipelines share similar mathematical backbones, their implementations are individualized and the ad hoc nature of the interfacing between different modules of VSLAM pipelines complicates code reuseability and maintenance. This paper presents a software model for core components of VSLAM implementations and interfaces that govern data flow between them while also attempting to preserve the elements that offer performance improvements over the evolution of VSLAM architectures. The framework presented in this paper employs principles from model-driven engineering (MDE), which are used extensively in the development of large and complicated software systems. The presented VSLAM framework will assist researchers in improving the performance of individual modules of VSLAM while not having to spend time on system integration of those modules into VSLAM pipelines.
A Relation-Theoretic Matkowski-Type Theorem in Symmetric Spaces
Authors: Ali Based , Imdad Mohammad , Sessa Salvatore
Published: April 2021

In this paper, we present a fixed-point theorem in R-complete regular symmetric spaces endowed with a locally T-transitive binary relation R using comparison functions that generalizes several relevant existing results. In addition, we adopt an example to substantiate the genuineness of our newly proved result. Finally, as an application of our main result, we establish the existence and uniqueness of a solution of a periodic boundary value problem.
Pseudo-Lucas Functions of Fractional Degree and Applications
Authors: Cesarano Clemente , Natalini Pierpaolo , Ricci Paolo
Published: April 2021

In a recent article, the first and second kinds of multivariate Chebyshev polynomials of fractional degree, and the relevant integral repesentations, have been studied. In this article, we introduce the first and second kinds of pseudo-Lucas functions of fractional degree, and we show possible applications of these new functions. For the first kind, we compute the fractional Newton sum rules of any orthogonal polynomial set starting from the entries of the Jacobi matrix. For the second kind, the representation formulas for the fractional powers of a r×r matrix, already introduced by using the pseudo-Chebyshev functions, are extended to the Lucas case.
Calcium Signaling Derangement and Disease Development and Progression
Authors: Sivaraman Vijay , Onyenwoke Rob U.
Published: April 2021

The importance of intracellular calcium (Ca2+) in regulating integral biological functions such as cell division, cell motility, autophagy, apoptosis and gene transcription through its capacity as a ubiquitous second messenger is clear [...]
Identification of a Resistance Mechanism to IGF-IR Targeting in Human Triple Negative MDA-MB-231 Breast Cancer Cells
Authors: Tsui Jennifer , Qi Shu , Perrino Stephanie , Leibovitch Matthew , Brodt Pnina
Published: April 2021

Triple negative breast cancer (TNBC) is associated with unfavorable prognosis and high relapse rates following chemotherapy. There is an urgent need to develop effective targeted therapy for this BC subtype. The type I insulin-like growth factor receptor (IGF-IR) was identified as a potential target for BC management. We previously reported on the production of the IGF-Trap, a soluble IGF-1R fusion protein that reduces the bioavailability of circulating IGF-1 and IGF-2 to the cognate receptor, impeding signaling. In nude mice xenotransplanted with the human TNBC MDA-MB-231 cells, we found variable responses to this inhibitor. We used this model to investigate potential resistance mechanisms to IGF-targeted therapy. We show here that prolonged exposure of MDA-MB-231 cells to the IGF-Trap in vitro selected a resistant subpopulation that proliferated unhindered in the presence of the IGF-Trap. We identified in these cells increased fibroblast growth factor receptor 1 (FGFR1) activation levels that sensitized them to the FGFR1-specific tyrosine kinase inhibitor PD166866. Treatment with this inhibitor caused cell cycle arrest in both the parental and resistant cells, markedly increasing cell death in the latter. When combined with the IGF-Trap, an increase in cell cycle arrest was observed in the resistant cells. Moreover, FGFR1 silencing increased the sensitivity of these cells to IGF-Trap treatment in vivo. Our data identify increased FGFR1 signaling as a resistance mechanism to targeted inhibition of the IGF-IR and suggest that dual IGF-1R/FGFR1 blockade may be required to overcome TNBC cell resistance to IGF-axis inhibitors.
Interaction of Ligands for PET with the Dopamine D3 Receptor: In Silico and In Vitro Methods
Authors: Hsieh Chia-Ju , Riad Aladdin , Lee Ji Youn , Sahlholm Kristoffer , Xu Kuiying , Luedtke Robert R. , Mach Robert H.
Published: April 2021

[18F]Fallypride and [18F]Fluortriopride (FTP) are two different PET radiotracers that bind with sub-nanomolar affinity to the dopamine D3 receptor (D3R). In spite of their similar D3 affinities, the two PET ligands display very different properties for labeling the D3R in vivo: [18F]Fallypride is capable of binding to D3R under “baseline” conditions, whereas [18F]FTP requires the depletion of synaptic dopamine in order to image the receptor in vivo. These data suggest that [18F]Fallypride is able to compete with synaptic dopamine for binding to the D3R, whereas [18F]FTP is not. The goal of this study was to conduct a series of docking and molecular dynamic simulation studies to identify differences in the ability of each molecule to interact with the D3R that could explain these differences with respect to competition with synaptic dopamine. Competition studies measuring the ability of each ligand to compete with dopamine in the β-arrestin assay were also conducted. The results of the in silico studies indicate that FTP has a weaker interaction with the orthosteric binding site of the D3R versus that of Fallypride. The results of the in silico studies were also consistent with the IC50 values of each compound in the dopamine β-arrestin competition assays. The results of this study indicate that in silico methods may be able to predict the ability of a small molecule to compete with synaptic dopamine for binding to the D3R.
An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies
Authors: Gao Bei , Chi Liang , Zhu Yixin , Shi Xiaochun , Tu Pengcheng , Li Bing , Yin Jun , Gao Nan , Shen Weishou , Schnabl Bernd
Published: April 2021

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.
Determinants of Schizophrenia Endophenotypes Based on Neuroimaging and Biochemical Parameters
Authors: Bryll Amira , Krzyściak Wirginia , Karcz Paulina , Pilecki Maciej , Śmierciak Natalia , Szwajca Marta , Skalniak Anna , Popiela Tadeusz J.
Published: April 2021

Despite extensive research, there is no convincing evidence of a reliable diagnostic biomarker for schizophrenia beyond clinical observation. Disorders of glutamatergic neurotransmission associated with N-methyl-D-aspartate (NMDA) receptor insufficiency, neuroinflammation, and redox dysregulation are the principal common mechanism linking changes in the periphery with the brain, ultimately contributing to the emergence of negative symptoms of schizophrenia that underlie differential diagnosis. The aim of the study was to evaluate the influence of these systems via peripheral and cerebral biochemical indices in relation to the patient’s clinical condition. Using neuroimaging diagnostics, we were able to define endophenotypes of schizophrenia based on objective laboratory data that form the basis of a personalized approach to diagnosis and treatment. The two distinguished endophenotypes differed in terms of the quality of life, specific schizophrenia symptoms, and glutamatergic neurotransmission metabolites in the anterior cingulate gyrus. Our results, as well as further studies of the excitatory or inhibitory balance of microcircuits, relating the redox systems on the periphery with the distant regions of the brain might allow for predicting potential biomarkers of neuropsychiatric diseases, including schizophrenia. To the best of our knowledge, our study is the first to identify an objective molecular biomarker of schizophrenia outcome.
Mitochondrion-Dependent Cell Death in TDP-43 Proteinopathies
Authors: Lucini Chantal B. , Braun Ralf J.
Published: April 2021

In the last decade, pieces of evidence for TDP-43-mediated mitochondrial dysfunction in neurodegenerative diseases have accumulated. In patient samples, in vitro and in vivo models have shown mitochondrial accumulation of TDP-43, concomitantly with hallmarks of mitochondrial destabilization, such as increased production of reactive oxygen species (ROS), reduced level of oxidative phosphorylation (OXPHOS), and mitochondrial membrane permeabilization. Incidences of TDP-43-dependent cell death, which depends on mitochondrial DNA (mtDNA) content, is increased upon ageing. However, the molecular pathways behind mitochondrion-dependent cell death in TDP-43 proteinopathies remained unclear. In this review, we discuss the role of TDP-43 in mitochondria, as well as in mitochondrion-dependent cell death. This review includes the recent discovery of the TDP-43-dependent activation of the innate immunity cyclic GMP-AMP synthase/stimulator of interferon genes (cGAS/STING) pathway. Unravelling cell death mechanisms upon TDP-43 accumulation in mitochondria may open up new opportunities in TDP-43 proteinopathy research.
Diagnostic Classification and Biomarker Identification of Alzheimer’s Disease with Random Forest Algorithm
Authors: Song Minseok , Jung Hyeyoom , Lee Seungyong , Kim Donghyeon , Ahn Minkyu
Published: April 2021

Random Forest (RF) is a bagging ensemble model and has many important advantages, such as robustness to noise, an effective structure for complex multimodal data and parallel computing, and also provides important features that help investigate biomarkers. Despite these benefits, RF is not used actively to predict Alzheimer’s disease (AD) with brain MRIs. Recent studies have reported RF’s effectiveness in predicting AD, but the test sample sizes were too small to draw any solid conclusions. Thus, it is timely to compare RF with other learning model methods, including deep learning, particularly with large amounts of data. In this study, we tested RF and various machine learning models with regional volumes from 2250 brain MRIs: 687 normal controls (NC), 1094 mild cognitive impairment (MCI), and 469 AD that ADNI (Alzheimer’s Disease Neuroimaging Initiative database) provided. Three types of features sets (63, 29, and 22 features) were selected, and classification accuracies were computed with RF, Support vector machine (SVM), Multi-layer perceptron (MLP), and Convolutional neural network (CNN). As a result, RF, MLP, and CNN showed high performances of 90.2%, 89.6%, and 90.5% with 63 features. Interestingly, when 22 features were used, RF showed the smallest decrease in accuracy, −3.8%, and the standard deviation did not change significantly, while MLP and CNN yielded decreases in accuracy of −6.8% and −4.5% with changes in the standard deviation from 3.3% to 4.0% for MLP and 2.1% to 7.0% for CNN, indicating that RF predicts AD more reliably with fewer features. In addition, we investigated the importance of the features that RF provides, and identified the hippocampus, amygdala, and inferior lateral ventricle as the major contributors in classifying NC, MCI, and AD. On average, AD showed smaller hippocampus and amygdala volumes and a larger volume of inferior lateral ventricle than those of MCI and NC.
Long-Term Brain Disorders in Post Covid-19 Neurological Syndrome (PCNS) Patient
Authors: Nuzzo Domenico , Cambula Gaetano , Bacile Ignazio , Rizzo Manfredi , Galia Massimo , Mangiapane Paola , Picone Pasquale , Giacomazza Daniela , Scalisi Luca
Published: April 2021

In the recent pandemic disease, called COVID-19, the role of neurologists and neurobiologists represents a chance to study key features of brain infection and deepen neurological manifestations of COVID-19 and other coronavirus infections. In fact, many studies suggest brain damage during infection and persistent neurological symptoms after COVID-19 infection. Reverse transcription PCR test, antibody tests, Computed Tomography (CT) of the lung, and Magnetic Resonance (MR) of the brain of the patient were periodically performed during this case report for eight months after infection. The aim of this article is to describe the prolonged neurological clinical consequences related to COVID-19. We believe it is clinically clear that we can define a post-acute COVID-19 neurological syndrome. Therefore, in patients after a severe clinical condition of COVID-19, a deepening of persistent neurological signs is necessary.
CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG
Authors: Neng Wenpeng , Lu Jun , Xu Lei
Published: April 2021

In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.
Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment
Authors: Sepasgozar Samad M. E.
Published: April 2021

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.
Succinate Pathway in Head and Neck Squamous Cell Carcinoma: Potential as a Diagnostic and Prognostic Marker
Authors: Terra Ximena , Ceperuelo-Mallafré Victoria , Merma Carla , Benaiges Ester , Bosch Ramon , Castillo Paola , Flores Joan Carles , León Xavier , Valduvieco Izaskun , Basté Neus , Cámara Marina , Lejeune Marylène , Gumà Josep , Vendrell Joan , Vilaseca Isabel , Fernández-Veledo Sonia , Avilés-Jurado Francesc Xavier
Published: April 2021

Head and neck squamous cell carcinoma (HNSCC) is characterized by high rates of mortality and treatment-related morbidity, underscoring the urgent need for innovative and safe treatment strategies and diagnosis practices. Mitochondrial dysfunction is a hallmark of cancer and can lead to the accumulation of tricarboxylic acid cycle intermediates, such as succinate, which function as oncometabolites. In addition to its role in cancer development through epigenetic events, succinate is an extracellular signal transducer that modulates immune response, angiogenesis and cell invasion by activating its cognate receptor SUCNR1. Here, we explored the potential value of the circulating succinate and related genes in HNSCC diagnosis and prognosis. We determined the succinate levels in the serum of 66 pathologically confirmed, untreated patients with HNSCC and 20 healthy controls. We also surveyed the expression of the genes related to succinate metabolism and signaling in tumoral and nontumoral adjacent tissue and in normal mucosa from 50 patients. Finally, we performed immunohistochemical analysis of SUCNR1 in mucosal samples. The results showed that the circulating levels of succinate were higher in patients with HNSCC than in the healthy controls. Additionally, the expression of SUCNR1, HIF-1α, succinate dehydrogenase (SDH) A, and SDHB was higher in the tumor tissue than in the matched normal mucosa. Consistent with this, immunohistochemical analysis revealed an increase in SUCNR1 protein expression in tumoral and nontumoral adjacent tissue. High SUCNR1 and SDHA expression levels were associated with poor locoregional control, and the locoregional recurrence-free survival rate was significantly lower in patients with high SUCNR1 and SDHA expression than in their peers with lower levels (77.1% [95% CI: 48.9–100.0] vs. 16.7% [95% CI: 0.0–44.4], p = 0.018). Thus, the circulating succinate levels are elevated in HNSCC and high SUCNR1/SDHA expression predicts poor locoregional disease-free survival, identifying this oncometabolite as a potentially valuable noninvasive biomarker for HNSCC diagnosis and prognosis.
Overview of Prognostic Systems for Hepatocellular Carcinoma and ITA.LI.CA External Validation of MESH and CNLC Classifications
Authors: Vitale Alessandro , Farinati Fabio , Finotti Michele , Di Renzo Chiara , Brancaccio Giuseppina , Piscaglia Fabio , Cabibbo Giuseppe , Caturelli Eugenio , Missale Gabriele , Marra Fabio , Sacco Rodolfo , Giannini Edoardo , Trevisani Franco , Cillo Umberto ,   Associazione Italiana per lo Studio del Fegato (AISF) HCC Special Interest Group,   Italian Liver Cancer (ITA.LI.CA) Study Group
Published: April 2021

Prognostic assessment in patients with HCC remains an extremely difficult clinical task due to the complexity of this cancer where tumour characteristics interact with degree of liver dysfunction, patient general health status, and a large span of available treatment options. Several prognostic systems have been proposed in the last three decades, both from the Asian and European/North American countries. Prognostic scores, such as the CLIP score and the recent MESH score, have been generated on a solid statistical basis from real life population data, while staging systems, such as the BCLC scheme and the recent CNLC classification, have been created by experts according to recent HCC prognostic evidences from the literature. A third category includes combined prognostic systems that can be used both as prognostic scores and staging systems. A recent example is the ITA.LI.CA prognostic system including either a prognostic score and a simplified staging system. This review focuses first on an overview of the main prognostic systems for HCC classified according to the above three categories, and, second, on a comprehensive description of the methodology required for a correct comparison between different systems in terms of prognostic performance. In this second section the main studies in the literature comparing different prognostic systems are described in detail. Lastly, a formal comparison between the last prognostic systems proposed for each of the above three categories is performed using a large Italian database including 6882 HCC patients in order to concretely apply the comparison rules previously described.
Cancer Stem Cells Are Possible Key Players in Regulating Anti-Tumor Immune Responses: The Role of Immunomodulating Molecules and MicroRNAs
Authors: Tomei Sara , Ibnaof Ola , Ravindran Shilpa , Ferrone Soldano , Maccalli Cristina
Published: April 2021

Cancer cells endowed with stemness properties and representing a rare population of cells within malignant lesions have been isolated from tumors with different histological origins. These cells, denominated as cancer stem cells (CSCs) or cancer initiating cells (CICs), are responsible for tumor initiation, progression and resistance to therapies, including immunotherapy. The dynamic crosstalk of CSCs/CICs with the tumor microenvironment orchestrates their fate and plasticity as well as their immunogenicity. CSCs/CICs, as observed in multiple studies, display either the aberrant expression of immunomodulatory molecules or suboptimal levels of molecules involved in antigen processing and presentation, leading to immune evasion. MicroRNAs (miRNAs) that can regulate either stemness properties or their immunological profile, with in some cases dual functions, can provide insights into these mechanisms and possible interventions to develop novel therapeutic strategies targeting CSCs/CICs and reverting their immunogenicity. In this review, we provide an overview of the immunoregulatory features of CSCs/CICs including miRNA profiles involved in the regulation of the interplay between stemness and immunological properties.
Tumor Margin Contains Prognostic Information: Radiomic Margin Characteristics Analysis in Lung Adenocarcinoma Patients
Authors: Lee Geewon , Park Hyunjin , Lee Ho Yun , Ahn Joong Hyun , Sohn Insuk , Lee Seung-Hak , Kim Jhingook
Published: April 2021

We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. We enrolled 334 patients who underwent complete resection for lung adenocarcinoma. A quantitative computed tomography analysis was performed, and 76 radiomic margin characteristics were extracted. The radiomic margin characteristics were correlated with overall survival. The selected clinical variables and radiomic margin characteristics were used to calculate a prognostic model with subsequent internal and external validation. Nearly all of the radiomic margin characteristics showed excellent reproducibility. The least absolute shrinkage and selection operator (LASSO) method was used to select eight radiomic margin characteristics. When compared to the model with clinical variables only (C-index = 0.738), the model incorporating clinical variables and radiomic margin characteristics (C-index = 0.753) demonstrated a higher C-index for predicting overall survival. In the model integrating both clinical variables and radiomic margin characteristics, convexity, a Laplace of Gaussian (LoG) kurtosis of 3, and the roundness factor were each independently predictive of overall survival. In addition, radiomic margin characteristics were also correlated with the micropapillary subtype, and the sphericity value was able to predict the presence of the micropapillary subtype. In conclusion, our study showed that radiomic margin characteristics helped predict overall survival in patients with lung adenocarcinomas, thus implying that the tumor margin contains prognostic information.
Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning
Authors: Panagopoulou Maria , Karaglani Makrina , Manolopoulos Vangelis G. , Iliopoulos Ioannis , Tsamardinos Ioannis , Chatzaki Ekaterini
Published: April 2021

DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982–1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921–1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920–1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.
Conformation-Specific Inhibitory Anti-MMP-7 Monoclonal Antibody Sensitizes Pancreatic Ductal Adenocarcinoma Cells to Chemotherapeutic Cell Kill
Authors: Mohan Vishnu , Gaffney Jean P. , Solomonov Inna , Levin Maxim , Klepfish Mordehay , Akbareian Sophia , Grünwald Barbara , Dym Orly , Eisenstein Miriam , Yu Kenneth H. , Kelsen David P. , Krüger Achim , Edwards Dylan R. , Sagi Irit
Published: April 2021

Matrix metalloproteases (MMPs) undergo post-translational modifications including pro-domain shedding. The activated forms of these enzymes are effective drug targets, but generating potent biological inhibitors against them remains challenging. We report the generation of anti-MMP-7 inhibitory monoclonal antibody (GSM-192), using an alternating immunization strategy with an active site mimicry antigen and the activated enzyme. Our protocol yielded highly selective anti-MMP-7 monoclonal antibody, which specifically inhibits MMP-7′s enzyme activity with high affinity (IC50 = 132 ± 10 nM). The atomic model of the MMP-7-GSM-192 Fab complex exhibited antibody binding to unique epitopes at the rim of the enzyme active site, sterically preventing entry of substrates into the catalytic cleft. In human PDAC biopsies, tissue staining with GSM-192 showed characteristic spatial distribution of activated MMP-7. Treatment with GSM-192 in vitro induced apoptosis via stabilization of cell surface Fas ligand and retarded cell migration. Co-treatment with GSM-192 and chemotherapeutics, gemcitabine and oxaliplatin elicited a synergistic effect. Our data illustrate the advantage of precisely targeting catalytic MMP-7 mediated disease specific activity.
Serum Biomarkers for the Prediction of Hepatocellular Carcinoma
Authors: Debes José , Romagnoli Pablo , Prieto Jhon , Arrese Marco , Mattos Angelo , Boonstra André ,   on behalf of the ESCALON Consortium
Published: April 2021

Hepatocellular carcinoma (HCC) is a leading cause of global cancer death. Major etiologies of HCC relate to chronic viral infections as well as metabolic conditions. The survival rate of people with HCC is very low and has been attributed to late diagnosis with limited treatment options. Combining ultrasound and the biomarker alpha-fetoprotein (AFP) is currently one of the most widely used screening combinations for HCC. However, the clinical utility of AFP is controversial, and the frequency and operator-dependence of ultrasound lead to a variable degree of sensitivity and specificity across the globe. In this review, we summarize recent developments in the search for non-invasive serum biomarkers for early detection of HCC to improve prognosis and outcome for patients. We focus on tumor-associated protein markers, immune mediators (cytokines and chemokines), and micro-RNAs in serum or circulating extracellular vesicles and examine their potential for clinical application.
Ex Vivo High Salt Activated Tumor-Primed CD4+T Lymphocytes Exert a Potent Anti-Cancer Response
Authors: Tiriveedhi Venkataswarup , Ivy Michael T. , Myles Elbert L. , Zent Roy , Rathmell Jeffrey C. , Titze Jens
Published: April 2021

Cell based immunotherapy is rapidly emerging as a promising cancer treatment. A modest increase in salt (sodium chloride) concentration in immune cell cultures is known to induce inflammatory phenotypic differentiation. In our current study, we analyzed the ability of salt treatment to induce ex vivo expansion of tumor-primed CD4 (cluster of differentiation 4)+T cells to an effector phenotype. CD4+T cells were isolated using immunomagnetic beads from draining lymph nodes and spleens from tumor bearing C57Bl/6 mice, 28 days post-injection of Py230 syngeneic breast cancer cells. CD4+T cells from non-tumor bearing mice were isolated from splenocytes of 12-week-old C57Bl/6 mice. These CD4+T cells were expanded ex vivo with five stimulation cycles, and each cycle comprised of treatment with high salt (Δ0.035 M NaCl) or equimolar mannitol controls along with anti-CD3/CD28 monoclonal antibodies for the first 3 days, followed by the addition of interleukin (IL)-2/IL-7 cytokines and heat killed Py230 for 4 days. Ex vivo high salt treatment induced a two-fold higher Th1 (T helper type 1) expansion and four-fold higher Th17 expansion compared to equimolar mannitol treatment. Importantly, the high salt expanded CD4+T cells retained tumor-specificity, as demonstrated by higher in vitro cytotoxicity against Py230 breast cancer cells and reduced in vivo syngeneic tumor growth. Metabolic studies revealed that high salt treatment enhanced the glycolytic reserve and basal mitochondrial oxidation of CD4+T cells, suggesting a role of high salt in enhanced pro-growth anabolic metabolism needed for inflammatory differentiation. Mechanistic studies demonstrated that the high salt induced switch to the effector phenotype was mediated by tonicity-dependent transcription factor, TonEBP/NFAT5. Using a transgenic murine model, we demonstrated that CD4 specific TonEBP/NFAT5 knock out (CD4cre/creNFAT5flox/flox) abrogated the induction of the effector phenotype and anti-tumor efficiency of CD4+T cells following high salt treatment. Taken together, our data suggest that high salt-mediated ex vivo expansion of tumor-primed CD4+T cells could induce effective tumor specific anti-cancer responses, which may have a novel cell-based cancer immunotherapeutic application.
Is ALDH1L1 Elevated in Lung Cancer? Comment on: Lee, S.-H.; et al. “The Combination of Loss of ALDH1L1 Function and Phenformin Treatment Decreases Tumor Growth in KRAS-Driven Lung Cancer” Cancers 2020, 12, 1382
Authors: Krupenko Sergey A. , Sharma Jaspreet
Published: April 2021

We read with interest the article by Lee et al [...]