bluuebunny/crossref_metadata_2025 huggingface dataset:
Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework
Authors: Jena Biswajit , Saxena Sanjay , Nayak Gopal Krishna , Balestrieri Antonella , Gupta Neha , Khanna Narinder N. , Laird John R. , Kalra Manudeep K. , Fouda Mostafa M. , Saba Luca , Suri Jasjit S.
Published: August 2022

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.
TNF Receptor Associated Factor 2 (TRAF2) Signaling in Cancer
Authors: Siegmund Daniela , Wagner Jennifer , Wajant Harald
Published: August 2022

Tumor necrosis factor (TNF) receptor associated factor-2 (TRAF2) has been originally identified as a protein interacting with TNF receptor 2 (TNFR2) but also binds to several other receptors of the TNF receptor superfamily (TNFRSF). TRAF2, often in concert with other members of the TRAF protein family, is involved in the activation of the classical NFκB pathway and the stimulation of various mitogen-activated protein (MAP) kinase cascades by TNFRSF receptors (TNFRs), but is also required to inhibit the alternative NFκB pathway. TRAF2 has also been implicated in endoplasmic reticulum (ER) stress signaling, the regulation of autophagy, and the control of cell death programs. TRAF2 fulfills its functions by acting as a scaffold, bringing together the E3 ligase cellular inhibitor of apoptosis-1 (cIAP1) and cIAP2 with their substrates and various regulatory proteins, e.g., deubiquitinases. Furthermore, TRAF2 can act as an E3 ligase by help of its N-terminal really interesting new gene (RING) domain. The finding that TRAF2 (but also several other members of the TRAF family) interacts with the latent membrane protein 1 (LMP1) oncogene of the Epstein–Barr virus (EBV) indicated early on that TRAF2 could play a role in the oncogenesis of B-cell malignancies and EBV-associated non-keratinizing nasopharyngeal carcinoma (NPC). TRAF2 can also act as an oncogene in solid tumors, e.g., in colon cancer by promoting Wnt/β-catenin signaling. Moreover, tumor cell-expressed TRAF2 has been identified as a major factor-limiting cancer cell killing by cytotoxic T-cells after immune checkpoint blockade. However, TRAF2 can also be context-dependent as a tumor suppressor, presumably by virtue of its inhibitory effect on the alternative NFκB pathway. For example, inactivating mutations of TRAF2 have been associated with tumor development, e.g., in multiple myeloma and mantle cell lymphoma. In this review, we summarize the various TRAF2-related signaling pathways and their relevance for the oncogenic and tumor suppressive activities of TRAF2. Particularly, we discuss currently emerging concepts to target TRAF2 for therapeutic purposes.
The Effect of Arsenic on the Photocatalytic Removal of Methyl Tet Butyl Ether (MTBE) Using Fe2O3/MgO Catalyst, Modeling, and Process Optimization
Authors: Mehdizadeh Akbar , Derakhshan Zahra , Abbasi Fariba , Samaei Mohammad Reza , Baghapour Mohammad Ali , Hoseini Mohammad , Lima Eder Claudio , Bilal Muhammad
Published: August 2022

MTBE is an aliphatic matter successfully removed from contaminated water by an advanced oxidation process. Additionally, arsenic is a toxic metalloid that is detected in some water supplies, such as in Iran. Concerning the oxidation potential of arsenic in an aqueous solution, it is expected that its interference in the photocatalytic removal of organic matter includes MTBE. Nevertheless, there is a lack of observation of this effect. In this study, the effect of arsenic on the photocatalytic removal of MTBE using an Fe2O3/MgO catalyst under UV radiation was investigated. Using an experimental design, modeling, and optimizing operational parameters, such as the arsenic and MTBE concentrations, catalyst dosage, pH, and reaction time, were studied. The synthesized nanocatalyst had a uniform and spherical morphological structure and contained 33.06% Fe2O3 and 45.06% MgO. The results indicate that the best model is related to the quadratic (p-value < 0.0001, R2 = 0.97) and that the effect of the MTBE concentration is greater than the others. The highest removal efficiency was taken in an initial concentration of 37.5 mg/L MTBE, 1.58 mg/L Fe2O3/MgO, pH 5, and a reaction time of 21.41 min without any As. The removal efficiency was negatively correlated with the initial MTBE concentration and pH, but it was positively associated with the Fe2O3/MgO dosage and reaction time. Finally, the presence of arsenic decreased the removal efficiency remarkably (90.90% As = 0.25 μg/L and 61% As = 500 μg/L). Consequently, MTBE was removed by the photocatalytic process caused by Fe2O3/MgO, but the presence of arsenic was introduced as a limiting factor. Therefore, pretreatment for the removal of arsenic and more details of this interference effect are suggested.
A Multi-Strain Probiotic Formulation Improves Intestinal Barrier Function by the Modulation of Tight and Adherent Junction Proteins
Authors: di Vito Raffaella , Conte Carmela , Traina Giovanna
Published: August 2022

In healthy individuals, tight junction proteins (TJPs) maintain the integrity of the intestinal barrier. Dysbiosis and increased intestinal permeability are observed in several diseases, such as inflammatory bowel disease. Many studies highlight the role of probiotics in preventing intestinal barrier dysfunction. The present study aims to investigate the effects of a commercially available probiotic formulation of L. rhamnosus LR 32, B. lactis BL 04, and B. longum BB 536 (Serobioma, Bromatech s.r.l., Milan, Italy) on TJPs and the integrity of the intestinal epithelial barrier, and the ability of this formulation to prevent lipopolysaccharide-induced, inflammation-associated damage. An in vitro model of the intestinal barrier was developed using a Caco-2 cell monolayer. The mRNA expression levels of the TJ genes were analyzed using real-time PCR. Changes in the amounts of proteins were assessed with Western blotting. The effect of Serobioma on the intestinal epithelial barrier function was assessed using transepithelial electrical resistance (TEER) measurements. The probiotic formulation tested in this study modulates the expression of TJPs and prevents inflammatory damage. Our findings provide new insights into the mechanisms by which probiotics are able to prevent damage to the gut epithelial barrier.
Dental Ceramics: Fabrication Methods and Aesthetic Characterization
Authors: de Matos Jefferson David Melo , Lopes Guilherme Rocha Scalzer , Queiroz Daher Antonio , Nakano Leonardo Jiro Nomura , Ribeiro Nathália Carvalho Ramos , Barbosa Adriano Baldotto , Anami Lilian Costa , Bottino Marco Antonio
Published: August 2022

This study aimed to describe different staining protocols for the main dental ceramics. A bibliographic search was conducted in the main health databases PubMed and Scholar Google, in which 100 studies published were collected. In vitro and in silico studies, case reports, and systematic and literature reviews, on ceramic materials, were included. Therefore, articles that did not deal with the topic addressed were excluded. Ceramics can be classified into glass-matrix ceramics (etchable), polycrystalline (non-etchable), and hybrid ceramics. In this context, different fabrication methods, method indications, and characterization layers can be used for each ceramic group and numerous protocols differ according to the choice of material. Several ceramic systems are available, thus professionals in the prosthetic area need constant updates on dental ceramic restorations and their proper characterizations.
Deep Learning-Based Spectrum Reconstruction Method for Raman Spectroscopy
Authors: Zhou Qian , Zou Zhiyong , Han Lin
Published: August 2022

Raman spectroscopy, measured by a Raman spectrometer, is usually disturbed by the instrument response function and noise, which leads to certain measurement error and further affects the accuracy of substance identification. In this paper, we propose a spectral reconstruction method which combines the existing maximum a posteriori (MAP) method and deep learning (DL) to recover the degraded Raman spectrum. The proposed method first employs the MAP method to reconstruct the measured Raman spectra, so as to obtain preliminary estimated Raman spectra. Then, a convolutional neural network (CNN) is trained by using the preliminary estimated Raman spectra and the real Raman spectra to learn the mapping from the preliminary estimated Raman spectra to the real Raman spectra, so as to achieve a better spectral reconstruction effect than merely using the MAP method or a CNN. To prove the effectiveness of the proposed spectral reconstruction method, we employed the proposed method and some traditional spectral reconstruction methods to reconstruct the simulated and measured Raman spectra, respectively. The experimental results show that compared with traditional methods, the estimated Raman spectra reconstructed by the proposed method are closer to the real Raman spectra.
Fullerene Movement in a Carbon Nanocontainer
Authors: Bubenchikov Alexey M. , Bubenchikov Mikhail A. , Chelnokova Anna S. , Mamontov Dmitriy V. , Lun-Fu Alexander V.
Published: August 2022

The character of C60 fullerene motion inside a cylindrical carbon nanocontainer with flat graphene covers has been studied. A new trajectory approach to describe the three-dimensional motion of fullerene is presented, based on the use of motion equations for its center of mass in the field of van der Waals forces, as well as the rotational motion equations written in the absolute basis. A high-precision computational technology for implementing this approach is described. Calculations have shown the components of the C60 angular velocity change stepwise. This character of the change in rotation is determined by the impacts of the fullerene on the wall of the container. As a result of these impacts, the energy of rotation changes abruptly.
Interstitial Lung Fibrosis Following COVID-19 Pneumonia
Authors: Lazar Mihai , Barbu Ecaterina Constanta , Chitu Cristina Emilia , Tiliscan Catalin , Stratan Laurentiu , Arama Sorin Stefan , Arama Victoria , Ion Daniela Adriana
Published: August 2022

Background and Objectives: Pulmonary fibrosis represents a stage of normal physiologic response to inflammatory aggression, mostly self-limiting and reversible; however, numerous patients treated for SARS-CoV-2 pneumonia present after release from hospital residual lung fibrosis. In this article, we aim to present an optimization method for evaluating pulmonary fibrosis by quantitative analysis, to identify the risk factors/predictors for pulmonary fibrosis in patients with SARS-CoV-2 infection, and to characterize the impact of pulmonary fibrosis on the symptomatology of patients after release from the hospital. Materials and Methods: We performed a prospective observational study on 100 patients with severe forms of pneumonia, with a control group of 61 non-COVID normal patients. Results: We found persistent interstitial changes consistent with fibrotic changes in 69% of patients. The risk of fibrosis was proportional to the values of erythrocyte sedimentation rate (ESR), C reactive protein (CRP), and lactate dehydrogenase (LDH), and to the duration of hospitalization. The imaging parameters correlated with increased risk for interstitial fibrosis were the number of affected pulmonary lobes and the percent of interstitial pulmonary fibrosis. Conclusions: The main risk factors for pulmonary fibrosis post-COVID-19 identified in our study are increased ESR, CRP, LDH, duration of hospitalization and the severity of pneumonia.
Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders
Authors: Lahmiri Salim , Tadj Chakib , Gargour Christian
Published: August 2022

Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student t-test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions. Furthermore, distributions of multifractal characteristics are different across healthy and unhealthy conditions. Hence, this study improves the understanding of infant crying by providing a complete description of its intrinsic dynamics to better evaluate its health status.
Non-Reconciled Physical-Layer Keys-Assisted Secure Communication Scheme Based on Channel Correlation
Authors: Wang Meng , Huang Kaizhi , Wan Zheng , Sun Xiaoli , Jin Liang , Zhao Kai
Published: August 2022

Physical-layer key generation technology requires information reconciliation to correct channel estimation errors between two legitimate users. However, sending the reconciliation signals over the public channel increases the communication overhead and the risk of information leakage. Aiming at the problem, integrated secure communication schemes using non-reconciled keys have attracted extensive attention. These schemes exploit channel coding to correct both inconsistent keys and transmission error bits. Meanwhile, more redundant code bits must be added to correct errors, which results in a lower secure transmission rate. To address the problem, we analyze the merit of channel correlation between non-reconciled key generation and secure transmission. Inspired by this, we propose a non-reconciled physical-layer keys-assisted secure communication scheme based on channel correlation. First of all, the signal frame is designed to make use of channel correlation between non-reconciled key generation and secure transmission. Based on the channel correlation, non-reconciled keys are then generated from the wireless channel to encrypt transmitted data. Moreover, an adaptive coding algorithm based on the equivalent channel is presented to encode the data bits before encryption, to guarantee reliable transmission. Finally, theoretical analysis and simulations demonstrate the significant performance of the proposed scheme in terms of low bit error ratio and high secure transmission rate.
Addressing a New Class of Multi-Objective Passive Device Optimization for Radiofrequency Circuit Design
Authors: Passos Fabio , Roca Elisenda , Castro-López Rafael , Fernández Francisco V.
Published: August 2022

The design of radiofrequency circuits and systems lends itself to multi-objective optimization and the bottom-up composition of Pareto-optimal fronts. Conventional multi-objective optimization algorithms can effectively attain these fronts, which maximize or minimize a set of competing objective functions of interest. However, some of these real-life optimization problems reveal a non-conventional feature: there is one objective function that calls neither for minimization nor maximization. Instead, using the Pareto front demands this objective function to be swept across so that all its feasible values are available. Such a non-conventional feature, as shown here, emerges in the case of inductor optimization. The problem thus turns into a non-conventional one: determining how to find uniformly distributed feasible values of this function over the broadest possible range (typically unknown) while minimizing or maximizing the remaining competing objective functions. An NSGA-II-inspired algorithm is proposed that, based on the dynamic allocation of objective function slots and a modified dominance definition, can successfully return sets of solutions for inductor optimization problems with one sweeping objective. Furthermore, a mathematical benchmark function modeling this kind of problem is presented, which is also used to exhaustively test the proposed algorithm and obtain insight into its parameter settings.
Service Function Chain Deployment Method Based on Traffic Prediction and Adaptive Virtual Network Function Scaling
Authors: Hu Haiyan , Kang Qiaoyan , Zhao Shuo , Wang Jianfeng , Fu Youbin
Published: August 2022

With the development of network function virtualization (NFV), the resource management of service function chains (SFC) in the virtualized environment has gradually become a research hotspot. Usually, users hope that they can get the network services they want anytime and anywhere. The network service requests are dynamic and real-time, which requires that the SFC in the NFV environment can also meet the dynamically changing network service requests. In this regard, this paper proposes an SFC deployment method based on traffic prediction and adaptive virtual network function (VNF) scaling. Firstly, an improved network traffic prediction method is proposed to improve its prediction accuracy for dynamically changing network traffic. Secondly, the predicted traffic data is processed for the subsequent scaling of the VNF. Finally, an adaptive VNF scaling method is designed for the purpose of dynamic management of network virtual resources. The experimental results show that the method proposed in this paper can manage the network resources in dynamic scenarios. It can effectively improve the availability of network services, reduce the operating overhead and achieve a good optimization effect.
Performance Analysis Antenna Diversity Technique with Wavelet Transform Using Array Gain for Millimeter Wave Communication System
Authors: Parveen Nagma , Abdullah Khaizuran , Islam Md Rafiqul , Abbasi Muhammad Aashed Khan
Published: August 2022

Utilizing antenna diversity techniques has become a well-known approach to improve the performance of wireless communication systems. Multiple antenna arrays with half-length spacing, such as a uniform linear array (ULA), have been taken into consideration. Since 60 GHz is an unlicensed frequency band and ideal for local propagation, it is where the technology is being used. The transmitter and receiver both accomplish QAM modulation and demodulation. The performance in terms of bit error rate (BER) was tested in MATLAB simulation software for all antenna diversity scenarios: the single input and single output (SISO) DWT, multiple input and single output (MISO) DWT, single input and multiple output (SIMO) DWT, and multiple input and multiple output (MIMO) DWT. The MIMO DWT was shown to be the best of them. The performance of MIMO OFDM using various wavelets was also simulated, and the performance of the Haar wavelet transform was 2 dB better than that of the other wavelet transform. Compared to simulation results, the analytical results showed good agreement with little discrepancy.
A Hierarchical Federated Learning-Based Intrusion Detection System for 5G Smart Grids
Authors: Sun Xin , Tang Zhijun , Du Mengxuan , Deng Chaoping , Lin Wenbin , Chen Jinshan , Qi Qi , Zheng Haifeng
Published: August 2022

As the core component of smart grids, advanced metering infrastructure (AMI) provides the communication and control functions to implement critical services, which makes its security crucial to power companies and customers. An intrusion detection system (IDS) can be applied to monitor abnormal information and trigger an alarm to protect AMI security. However, existing intrusion detection models exhibit a low performance and are commonly trained on cloud servers, which pose a major threat to user privacy and increase the detection delay. To solve these problems, we present a transformer-based intrusion detection model (Transformer-IDM) to improve the performance of intrusion detection. In addition, we integrate 5G technology into the AMI system and propose a hierarchical federated learning intrusion detection system (HFed-IDS) to collaboratively train Transformer-IDM to protect user privacy in the core networks. Finally, extensive experimental results using a real-world intrusion detection dataset demonstrate that the proposed approach is superior to other existing approaches in terms of detection accuracy and communication cost for an IDS.
A Coherent Integrated TDOA Estimation Method for Target and Reference Signals
Authors: Ouyang Xinxin , Yao Shanfeng , Wan Qun
Published: August 2022

The performance of a time difference of arrival (TDOA) localization system is severely affected by time synchronization errors, and making use of reference signals is a common solution for the problem. The traditional method has two steps, first to measure the TDOAs of the target signal and reference signal separately, and next, to compensate the estimated target TDOA with the difference of the estimated reference TDOA and the true reference TDOA. Since the performance of the TDOA estimation is mainly decided by the frequency information, a coherent integration TDOA estimation method for the target signal and reference signal is proposed in this paper, based on cross correlation phase difference compensation, with use of the signals’ frequencies. First, as per the traditional method, the separated cross correlation functions of the target signal and reference signal were obtained by cross correlation, and the target TDOA and reference TDOA of the separate method were estimated. Next, the cross correlation phase was analyzed for each signal. Then the coherent integration cross correlation was obtained with phase compensation, from which the estimation of the target TDOA and reference TDOA could simultaneously be achieved. We performed simulation comparisons with the two methods, and showed that the proposed algorithm provided better performance.
The Impact of the COVID-19 Pandemic on the Global Web and Video Conferencing SaaS Market
Authors: Tudor Cristiana
Published: August 2022

The COVID-19 pandemic related government interventions produced rapid decreases in worldwide economic and social activity, with multifaceted economic and social consequences. In particular, the disruption of key industries and significant lifestyle changes in the aftermath of the pandemic outbreak led to the exponential adoption of web and video conferencing Software as a Service (SaaS) programs and to the solutions-led video conferencing market growth. However, the magnitude and persistence of the COVID-19 pandemic impact on the video conferencing solutions segment remain uninvestigated. Building on previous evidence linking population web-search behavior, private consumption, and retail sales, this study sources and employs Google Trends data as an analytical and forecasting tool for the solutions segment of the videoconferencing market. It implements a univariate forecast evaluation approach that assesses the predictive performance of several statistical and machine-learning models for the relative search volume (RSV) in the two SaaS program leaders, Zoom and Teams. ETS is found to provide the best forecast of consumer GT search interest for both RSV series. A baseline level for the consumer interest over the first pandemic wave is subsequently produced with ETS and further serves to estimate the excess search interest over the February 2020–August 2020 period. Results indicate that the pandemic has created an excess or abnormal consumer interest in the global web and videoconferencing SaaS market that would not have occurred in the absence of the pandemic. Other findings indicate that the impact is persistent as the excess interest stabilized at higher levels than in the pre-pandemic period for both SaaS market leaders, although a higher saturation of the Zoom market is detected.
Assessment Criteria of Changes in Health Index Values over Time—A Transformer Population Study
Authors: Bohatyrewicz Patryk , Banaszak Szymon
Published: August 2022

The current use of health index algorithms is mainly limited to single assessments of the unit’s condition or the device comparison. The paper focuses on the changes in the health index values between the consecutive analyses. The algorithm used for this purpose was previously developed by the authors. The test group included 359 complete oil evaluation results from 86 power transformers monitored over several years. For each outcome, the influence of the sub-components of the main score was calculated. Additional health index increase simulations were performed based on the IEC 60599 standard guidelines. The highest increases and decreases in the total score were listed and analyzed to determine the main factors behind the changes. The study has shown that the changes in dissolved gases concentrations have a much more significant influence on the health index values than the changes in physicochemical properties of the oil and furfural content. Based on the magnitude of the observed changes and the simulation outcomes, the authors have proposed two assessment thresholds—the 50th percentile health index increase within a population as an alarm zone, and the 90th or 95th percentile increase as a pre-failure zone.
Numerical Simulation of Transient Combustion and the Acoustic Environment of Obstacle Vortex-Driven Flow
Authors: Bhutto Afaque Ahmed , Harijan Khanji , Hussain Mukkarum , Shah Syed Feroz , Kumar Laveet
Published: August 2022

Solid fuel combustion in a chamber does not necessarily occur at a constant rate and may show fluctuations due to variables such as varying burning rates, chamber pressure, and residual combustion. These variables can cause the fuel to burn disproportionately. The acoustic environment of obstacle vortex-driven flow due to transient combustion with pressure oscillations in a solid fuel chamber is numerically investigated in the present study. Solid fuel combustion is considered transient, and flow characteristics of the present problem are governed by large eddies shed from an obstacle. Since unsteady Reynolds-averaged Navier-Stokes (URANS) simulations are not appropriate to compute the present flow phenomenon, therefore, a detached eddy simulation (DES) is performed to precisely predict the flow behavior. Simulation of steady-state combustion is carried out to validate the numerical results with available experimental data from the literature. The simulation of transient combustion shows that if the combustion frequency is close to the chamber’s modal frequency of the chamber, its amplitude increases greatly and creates an acute acoustic environment. This will result in fuel savings. The amplitude of pressure oscillation up to 18% and 5% of mean pressure are evident at the first and second mode of forced oscillation frequencies respectively. Interestingly, it is also found that pressure oscillation always occurs at inlet mass flux disturbance frequency and not between the disturbance and natural frequency of the chamber. As a result, it is evident that the combustion process or chamber configuration could be modified to ensure that both frequencies are far away enough to interact and create both a harsh acoustic environment and sufficient fuel to burn disproportionately.
Validated Analytical Modeling of Eccentricity and Dynamic Displacement in Diesel Engines with Flexible Crankshaft
Authors: Elmoselhy Salah A. M. , Faris Waleed F. , Rakha Hesham A.
Published: August 2022

In spite of the fact that the flexibility of the crankshaft of diesel engines exhibits notable nonlinearities, analytical modeling of such nonlinearities is not yet realized. The present study thus analytically models the effect of eccentricity on flexible crankshaft and piston secondary motion. The eccentricity of the crankshaft is modeled as the summation of the hydrodynamic eccentricity and the dynamic mass eccentricity of the crankshaft. The study also models the absolute value of the vibrational dynamic displacement of the center of the crankshaft. The paper proves that such dynamic displacement of the center of the crankshaft is sensitive to the changes in its independent variables. It was found that the most influential parameters on the dynamic displacement of the center of the crankshaft due to vibration are the natural frequency and the eccentricity of the crankshaft. The modeling of the dynamic displacement in a flexible crankshaft was validated using a case study based on the eccentricity of the crankshaft showing a relative error of 4%, which is less than the relative error in the CMEM and GT-Power. Furthermore, the analytical modeling of the dynamic displacement in the flexible crankshaft was validated using another case study based on fatigue analysis of the crankshaft showing a relative error of 9%, which is less than that the relative error in Newman’s model of diesel engine fuel consumption and Lansky’s model of diesel engine cylinders. The paper also presents a proposed approach of fatigue failure analysis for vehicular dynamic components and presents a proposed nanostructure of crankshafts for improving such fatigue performance. The developed models would help develop efficient diesel engines and help prolong their service life.
Research on the Characteristics of the Solid–Liquid Two-Phase Flow Field of a Submersible Mixer Based on CFD-DEM
Authors: Tian Fei , Zhang Erfeng , Yang Chen , Shi Weidong , Chen Yonghua
Published: August 2022

Submersible mixers are widely used in the sewage treatment process in various fields, such as agriculture and industry. They are mainly responsible for pushing flow and mixing activated sludge particles in a pool. Based on the CFD-DEM coupling method under the Euler–Lagrange framework, the solid–liquid two-phase flow of a submersible mixer was simulated in this paper, and the motion characteristics and distribution laws of particles in the pool were studied in axial, horizontal, and lateral directions, respectively. An evaluation method of distribution uniformity was proposed to analyze the velocity distribution of the flow field, the velocity distribution of particles, and the mixing uniformity of particles. The results show that the movement process of activated sludge particles in the pool can be roughly divided into three stages: the horizontal development stage, absorption–injection stage, and reflux-mixing stage, in which the reflux-mixing stage is the main stage for the uniform distribution of particles in the whole flow field. Particle accumulation occurs mainly in the dead zones of the flow field. Distribution of particles in the axial direction has the most homogeneous extent. Vortices can be generated near pool walls, causing accumulation of particles. This method can be a good guide for engineering practice.
Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
Authors: Lombardo Gianfranco , Pellegrino Mattia , Adosoglou George , Cagnoni Stefano , Pardalos Panos M. , Poggi Agostino
Published: August 2022

Predicting corporate bankruptcy is one of the fundamental tasks in credit risk assessment. In particular, since the 2007/2008 financial crisis, it has become a priority for most financial institutions, practitioners, and academics. The recent advancements in machine learning (ML) enabled the development of several models for bankruptcy prediction. The most challenging aspect of this task is dealing with the class imbalance due to the rarity of bankruptcy events in the real economy. Furthermore, a fair comparison in the literature is difficult to make because bankruptcy datasets are not publicly available and because studies often restrict their datasets to specific economic sectors and markets and/or time periods. In this work, we investigated the design and the application of different ML models to two different tasks related to default events: (a) estimating survival probabilities over time; (b) default prediction using time-series accounting data with different lengths. The entire dataset used for the experiments has been made available to the scientific community for further research and benchmarking purposes. The dataset pertains to 8262 different public companies listed on the American stock market between 1999 and 2018. Finally, in light of the results obtained, we critically discuss the most interesting metrics as proposed benchmarks for future studies.
Establishment and Validation of Reference Genes of Brassica napus L. for Digital PCR Detection of Genetically Modified Canola
Authors: Long Likun , Xing Zhenjuan , He Yuxuan , Yan Wei , Li Congcong , Xia Wei , Dong Liming , Zhao Ning , Ma Yue , Xie Yanbo , Liu Na , Li Feiwu
Published: August 2022

As an effective tool for genetically modified organism (GMO) quantification in complex matrices, digital PCR (dPCR) has been widely used for the quantification of genetically modified (GM) canola events; however, little is known about the quantification of GM canola events using endogenous reference gene (ERG) characteristics by dPCR. To calculate and quantify the content of GM canola using endogenous reference gene (ERG) characteristics, the suitability of several ERGs of canola, such as cruciferin A (CruA), acetyl-CoA carboxylase (BnAcc), phosphoenolpyruvate carboxylase (PEP), cruciferin storage (BnC1), oleoyl hydrolase (Fat(A)), and high-mobility-group protein I/Y (HMG-I/Y), was investigated by droplet dPCR. BnAcc and BnC1 were more specific and stable in copy number in the genome of Brassica napus L. than the other genes. By performing intra-laboratory validation of the suitability of ERG characteristics for the quantification of GM canola events, the ddPCR methods for BnAcc and BnC1 were comprehensively demonstrated in dPCR assays. The methods could provide technical support for GM labeling regulations.
Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging
Authors: Mao Yilin , Li He , Wang Yu , Fan Kai , Song Yujie , Han Xiao , Zhang Jie , Ding Shibo , Song Dapeng , Wang Hui , Ding Zhaotang
Published: August 2022

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.
Effects of Extrusion on Starch Molecular Degradation, Order–Disorder Structural Transition and Digestibility—A Review
Authors: Huang Xiaoyue , Liu Hongsheng , Ma Yue , Mai Shihua , Li Cheng
Published: August 2022

Extrusion is a thermomechanical technology that has been widely used in the production of various starch-based foods and can transform raw materials into edible products with unique nutritional characteristics. Starch digestibility is a crucial nutritional factor that can largely determine the human postprandial glycemic response, and frequent consumption of foods with rapid starch digestibility is related to the occurrence of type 2 diabetes. The extrusion process involves starch degradation and order–disorder structural transition, which could result in large variance in starch digestibility in these foods depending on the raw material properties and processing conditions. It provides opportunities to modify starch digestibility by selecting a desirable combination of raw food materials and extrusion settings. This review firstly introduces the application of extrusion techniques in starch-based food production, while, more importantly, it discusses the effects of extrusion on the alteration of starch structures and consequentially starch digestibility in various foods. This review contains important information to generate a new generation of foods with slow starch digestibility by the extrusion technique.
Toxoplasma gondii in Foods: Prevalence, Control, and Safety
Authors: Marín-García Pablo-Jesús , Planas Nuria , Llobat Lola
Published: August 2022

Toxoplasma gondii is an obligate intracellular parasite that causes toxoplasmosis, with approximately one third of the population around the world seropositive. The consumption of contaminated food is the main source of infection. These include meat products with T. gondii tissue cysts, and dairy products with tachyzoites. Recently, contamination has been detected in fresh products with oocysts and marine products. Despite the great health problems that are caused by T. gondii, currently there are no standardized methods for its detection in the food industry. In this review, we analyze the current detection methods, the prevalence of T. gondii in different food products, and the control measures. The main detection methods are bioassays, cell culture, molecular and microscopic techniques, and serological methods, but some of these do not have applicability in the food industry. As a result, emerging techniques are being developed that are aimed at the detection of multiple parasites simultaneously that would make their application more efficient in the industry. Since the prevalence of this parasite is high in many products (meat and milk, marine products, and vegetables), it is necessary to standardize detection methods, as well as implement control measures.
“It Was a Smoke Dream”: Affective Aesthetics in Women’s Literature of the Irish Civil War
Authors: McDaid Ailbhe
Published: August 2022

The formal, ideological, and narrative elements constituting the aesthetics of hope and disappointment in women’s writing of the Irish revolution offer new insights into the gendered experience of conflict. By arguing that women’s writing in this period complicates and expands existing classifications of conflict literature, this paper proposes to trace a network of alternative connection, built out of subjective gendered experiences of political and social upheaval. Drawing on theories of affect and emotion with reference to Rosamond Jacob’s The Troubled House (1938), Margaret Barrington’s My Cousin Justin (1939) and Dorothy Macardle’s The Uninvited (1942), this article suggests that appraisal of textual interconnection can thicken our understanding of the conceptual tools engaged by women writers to record, relay, and refract the personal and political implications of early-twentieth century Ireland.
The Novel Association of Early Apoptotic Circulating Tumor Cells with Treatment Outcomes in Breast Cancer Patients
Authors: Grigoryeva Evgeniya S. , Tashireva Liubov A. , Alifanov Vladimir V. , Savelieva Olga E. , Vtorushin Sergey V. , Zavyalova Marina V. , Cherdyntseva Nadezhda V. , Perelmuter Vladimir M.
Published: August 2022

Stemness and epithelial–mesenchymal plasticity are widely studied in the circulating tumor cells of breast cancer patients because the roles of both processes in tumor progression are well established. An important property that should be taken into account is the ability of CTCs to disseminate, particularly the viability and apoptotic states of circulating tumor cells (CTCs). Recent data demonstrate that apoptosis reversal promotes the formation of stem-like tumor cells with pronounced potential for dissemination. Our study focused on the association between different apoptotic states of CTCs with short- and long-term treatment outcomes. We evaluated the association of viable CTCs, CTCs with early features of apoptosis, and end-stage apoptosis/necrosis CTCs with clinicopathological parameters of breast cancer patients. We found that the proportion of circulating tumor cells with features of early apoptosis is a perspective prognosticator of metastasis-free survival, which also correlates with the neoadjuvant chemotherapy response in breast cancer patients. Moreover, we establish that apoptotic CTCs are associated with the poor response to neoadjuvant chemotherapy, and metastasis-free survival expressed at least two stemness markers, CD44 and CD133.
Gut Metabolites and Breast Cancer: The Continuum of Dysbiosis, Breast Cancer Risk, and Potential Breast Cancer Therapy
Authors: Jaye Kayla , Chang Dennis , Li Chun Guang , Bhuyan Deep Jyoti
Published: August 2022

The complex association between the gut microbiome and cancer development has been an emerging field of study in recent years. The gut microbiome plays a crucial role in the overall maintenance of human health and interacts closely with the host immune system to prevent and fight infection. This review was designed to draw a comprehensive assessment and summary of recent research assessing the anticancer activity of the metabolites (produced by the gut microbiota) specifically against breast cancer. In this review, a total of 2701 articles were screened from different scientific databases (PubMed, Scopus, Embase and Web of Science) with 72 relevant articles included based on the predetermined inclusion and exclusion criteria. Metabolites produced by the gut microbial communities have been researched for their health benefits and potential anticancer activity. For instance, the short-chain fatty acid, butyrate, has been evaluated against multiple cancer types, including breast cancer, and has demonstrated anticancer potential via various molecular pathways. Similarly, nisin, a bacteriocin, has presented with a range of anticancer properties primarily against gastrointestinal cancers, with nominal evidence supporting its use against breast cancer. Comparatively, a natural purine nucleoside, inosine, though it has not been thoroughly investigated as a natural anticancer agent, has shown promise in recent studies. Additionally, recent studies demonstrated that gut microbial metabolites influence the efficacy of standard chemotherapeutics and potentially be implemented as a combination therapy. Despite the promising evidence supporting the anticancer action of gut metabolites on different cancer types, the molecular mechanisms of action of this activity are not well established, especially against breast cancer and warrant further investigation. As such, future research must prioritise determining the dose-response relationship, molecular mechanisms, and conducting animal and clinical studies to validate in vitro findings. This review also highlights the potential future directions of this field.
Blockade of Platelet Glycoprotein Ibα Augments Neuroprotection in Orai2-Deficient Mice during Middle Cerebral Artery Occlusion
Authors: Bieber Michael , Schuhmann Michael , Bellut Maximilian , Stegner David , Heinze Katrin , Pham Mirko , Nieswandt Bernhard , Stoll Guido
Published: August 2022

During ischemic stroke, infarct growth before recanalization diminishes functional outcome. Hence, adjunct treatment options to protect the ischemic penumbra before recanalization are eagerly awaited. In experimental stroke targeting two different pathways conferred protection from penumbral tissue loss: (1) enhancement of hypoxic tolerance of neurons by deletion of the calcium channel subunit Orai2 and (2) blocking of detrimental lymphocyte–platelet responses. However, until now, no preclinical stroke study has assessed the potential of combining neuroprotective with anti-thrombo-inflammatory interventions to augment therapeutic effects. We induced focal cerebral ischemia in Orai2-deficient (Orai2-/-) mice by middle cerebral artery occlusion (MCAO). Animals were treated with anti-glycoprotein Ib alpha (GPIbα) Fab fragments (p0p/B Fab) blocking GPIbα–von Willebrand factor (vWF) interactions. Rat immunoglobulin G (IgG) Fab was used as the control treatment. The extent of infarct growth before recanalization was assessed at 4 h after MCAO. Moreover, infarct volumes were determined 6 h after recanalization (occlusion time: 4 h). Orai2 deficiency significantly halted cerebral infarct progression under occlusion. Inhibition of platelet GPIbα further reduced primary infarct growth in Orai2-/- mice. During ischemia–reperfusion, upon recanalization, mice were likewise protected. All in all, we show that neuroprotection in Orai2-/- mice can be augmented by targeting thrombo-inflammation. This supports the clinical development of combined neuroprotective/anti-platelet strategies in hyper-acute stroke.
Salt Dependence of DNA Binding Activity of Human Transcription Factor Dlx3
Authors: Jin Ho-Seong , Son Juyeon , Seo Yeo-Jin , Choi Seo-Ree , Ahn Hye-Bin , Go Youyeon , Lim Juhee , Oh Kwang-Im , Ryu Kyoung-Seok , Lee Joon-Hwa
Published: August 2022

Distal-less 3 (Dlx3) is a homeobox-containing transcription factor and plays a crucial role in the development and differentiation process. Human Dlx3 consists of two transactivation domains and a homeobox domain (HD) that selectively binds to the consensus site (5′-TAATT-3′) of the DNA duplex. Here, we performed chemical shift perturbation experiments on Dlx3-HD in a complex with a 10-base-paired (10-bp) DNA duplex under various salt conditions. We also acquired the imino proton spectra of the 10-bp DNA to monitor the changes in base-pair stabilities during titration with Dlx3-HD. Our study demonstrates that Dlx3-HD selectively recognizes its consensus DNA sequences through the α3 helix and L1 loop regions with a unique dynamic feature. The dynamic properties of the binding of Dlx3-HD to its consensus DNA sequence can be modulated by varying the salt concentrations. Our study suggested that this unique structural and dynamic feature of Dlx3-HD plays an important role in target DNA recognition, which might be associated with tricho-dento-osseous syndrome.