bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of an Ergonomic User Interface Design of Calcium Imaging Processing System
Authors: Jung Hayoung , Cui Xin , Kim Ha Lim , Li Mingkang , Liu Changhao , Zhang Shaomin , Yang Xiaopeng , Feng Linqing , You Heecheon
Published: February 2022

An optical brain-machine interface (O-BMI) system using calcium imaging has various advantages such as high resolution, a comprehensive view of large neural populations, abilities such as long-term stable recording, and applicability to freely behaving animals in neuroscience research. The present study developed an ergonomic user interface (UI) design, based on a use scenario for an O-BMI system that can be used for the acquisition and processing of calcium imaging in freely behaving rodents. The UI design was developed in three steps: (1) identification of design and function requirements of users, (2) establishment of a use scenario, and (3) development of a UI prototype. The UI design requirements were identified by a literature review, a benchmark of existing systems, and a focus group interview with five neuroscience researchers. Then, the use scenario was developed for tasks of data acquisition, feature extraction, and neural decoding for offline and online processing by considering the sequences of operations and needs of users. Lastly, a digital prototype incorporating an information architecture, graphic user interfaces, and simulated functions was fabricated. A usability test was conducted with five neuroscientists (work experience = 3.4 ± 1.1 years) and five ergonomic experts (work experience = 3.6 ± 2.7 years) to compare the digital prototypes with four existing systems (Miniscope, nVista, Mosaic, and Suite2p). The usability testing results showed that the ergonomic UI design was significantly preferred to the UI designs of the existing systems by reducing the task completion time by 10.1% to 70.2% on average, the scan path length by 14.4% to 88.7%, and perceived workload by 12.2% to 37.9%, increasing satisfaction by 11.3% to 74.3% in data acquisition and signal-extraction tasks. The present study demonstrates the significance of the user-centered design approach in the development of a system for neuroscience research. Further research is needed to validate the usability test results of the UI prototype as a corresponding real system is implemented.
Evaluating Attraction and Passage Efficiencies of Pool-Weir Type Fishways Based on Hydraulic Analysis
Authors: Baek Kyong-Oh , Lee Jeong-Min , Han Eun-Jin , Kim Young-Do
Published: February 2022

In this study, a hydraulic analysis technique is presented for evaluating the efficiency of a pool-weir type fishway. The River-2D model with the fish physical habitat module was used for the analysis of the attraction efficiency, and the weighted usable area proposed as an index of the efficiency. For the analysis of passage efficiency, the three-dimensional model, FLOW-3D, was used as an evaluating tool to describe the fluid behavior on a hydraulic structure with a free surface. The ice-harbor type fishway at Dalseong weir in the Nakdong River of Korea was selected as a test-site, and the efficiency was estimated using the hydraulic analysis. The results of River-2D analysis showed that the location of the fishway near the right bank now was not appropriate for attracting the target fish to the fishway due to the deep water depth. The 3D analysis results presented that the flow velocity field inside the fishway showed a similar plunging flow in each pool, but various flow patterns appeared locally.
Small Magnus Wind Turbine: Modeling Approaches
Authors: Lukin Aleksandr , Demidova Galina , Rassõlkin Anton , Lukichev Dmitry , Vaimann Toomas , Anuchin Alecksey
Published: February 2022

Renewables have passed the peak of the inflated expectation hype cycle for emerging technologies, but interest in the design of new energy conversion devices is still high due to widespread distributed energy systems for private households. Magnus effect-based wind turbine combines mechanical and electronic engineering that provides a broader wind speed range and potential maximum power point tracking for deeper grid integration. This paper provides a comparative analysis of Magnus effect-based wind turbine simulation models and the development of the numerical model for the maximum power point tracking algorithm. The advanced model contributes to the reduction of the number of actual tests required for the mechatronics system tuning and deals with sustainability-related challenges, such as climate change and the development of new renewable sources of energy.
Week-Wise Student Performance Early Prediction in Virtual Learning Environment Using a Deep Explainable Artificial Intelligence
Authors: Chen Hsing-Chung , Prasetyo Eko , Tseng Shian-Shyong , Putra Karisma Trinanda , Prayitno  , Kusumawardani Sri Suning , Weng Chien-Erh
Published: February 2022

Early prediction of students’ learning performance and analysis of student behavior in a virtual learning environment (VLE) are crucial to minimize the high failure rate in online courses during the COVID-19 pandemic. Nevertheless, traditional machine learning models fail to predict student performance in the early weeks due to the lack of students’ activities’ data in a week-wise timely manner (i.e., spatiotemporal feature issues). Furthermore, the imbalanced data distribution in the VLE impacts the prediction model performance. Thus, there are severe challenges in handling spatiotemporal features, imbalanced data sets, and a lack of explainability for enhancing the confidence of the prediction system. Therefore, an intelligent framework for explainable student performance prediction (ESPP) is proposed in this study in order to provide the interpretability of the prediction results. First, this framework utilized a time-series weekly student activity data set and dealt with the VLE imbalanced data distribution using a hybrid data sampling method. Then, a combination of convolutional neural network (CNN) and long short-term memory (LSTM) was employed to extract the spatiotemporal features and develop the early prediction deep learning (DL) model. Finally, the DL model was explained by visualizing and analyzing typical predictions, students’ activities’ maps, and feature importance. The numerical results of cross-validation showed that the proposed new DL model (i.e., the combined CNN-LSTM and ConvLSTM), in the early prediction cases, performed better than the baseline models of LSTM, support vector machine (SVM), and logistic regression (LR) models.
Particle Dynamics-Based Stochastic Modeling of Carbon Particle Charging in the Flow Capacitor Systems
Authors: Summer Faiza , Torop Janno , Aabloo Alvo , Kyritsakis Andreas , Zadin Veronika
Published: February 2022

Aqueous electrochemical flow capacitors (EFCs) have demonstrated high-power capabilities and safety at low cost, making them promising energy storage devices for grid applications. A primary performance metric of an EFC is the steady-state electrical current density it can accept or deliver. Performance prediction, design improvements, and up-scaling are areas in which modeling can be useful. In this paper, a novel stochastic superparticle (SP) modeling approach was developed and applied to study the charging of carbon electrodes in the EFC system, using computational superparticles representing real carbon particles. The model estimated the exact values of significant operating parameters of an EFC, such as the number of particles in the flow channel and the number of electrolytic ions per carbon particle. Optimized model parameters were applied to three geometrical designs of an EFC to estimate their performance. The modeling approach allowed study of the charge per carbon particle to form the electric double-layer structure. The linear relationship between the concentration of SPs and the ionic charge was observed when optimized at a constant voltage of 0.75 V. The simulation results are in excellent agreement with experimental data, providing a deep insight into the performance of an EFC and identifying limiting parameters for both engineers and material scientists to consider.
Measurements of Aquatic Particle Volume Scattering Function up to 178.5° in the East China Sea
Authors: Wu Chaofan , Tao Bangyi , Guo Yilu , Huang Haiqing , Mao Zhihua , Song Hong , Pan Delu
Published: February 2022

Particulate volume scattering function (VSF), especially at angles larger than 170°, is of particular importance for interpreting ocean optical remote sensing signals and underwater imagery. In this study, a laboratory-based VSF instrument (VSFlab) adopting the periscopic optical system was developed to obtain VSF measurements from 1°–178.5°. In the VSFlab, a new prism design that simply combines a single prism and a neutral density filter was proposed to more efficiently reduce the stray light in the backward direction, while a detailed calibration procedure was given. A full validation based on standard beads of various sizes and a comparison with the results from LISST-VSF and POLVSM indicated that the VSFlab can provide reliable results from 1° to 178.5°. VSFlab measurements in the East China Sea (ECS) exhibited a moderate increase (not more than 5 times) in VSF from 170° to 178.5° rather than a sharp increase of more than one order of magnitude presented in other instrument results measured in other coastal regions. The estimates of the particulate backscattering coefficient using single angle scattering measurements near 120° or 140° and suitable χp were justified. Two types of the VSFs with different size distribution and shape parameters in the ECS can be distinguished based on the variability of χp after 155°. The measured VSF could provide a basis for the parameterization of VSF in the radiative transfer model and the variability of χp in the backward direction had the potential to be used to characterize the particles in the coastal region of the ECS.
Automatic Identification of Emotional Information in Spanish TV Debates and Human–Machine Interactions
Authors: de Velasco Mikel , Justo Raquel , Inés Torres María
Published: February 2022

Automatic emotion detection is a very attractive field of research that can help build more natural human–machine interaction systems. However, several issues arise when real scenarios are considered, such as the tendency toward neutrality, which makes it difficult to obtain balanced datasets, or the lack of standards for the annotation of emotional categories. Moreover, the intrinsic subjectivity of emotional information increases the difficulty of obtaining valuable data to train machine learning-based algorithms. In this work, two different real scenarios were tackled: human–human interactions in TV debates and human–machine interactions with a virtual agent. For comparison purposes, an analysis of the emotional information was conducted in both. Thus, a profiling of the speakers associated with each task was carried out. Furthermore, different classification experiments show that deep learning approaches can be useful for detecting speakers’ emotional information, mainly for arousal, valence, and dominance levels, reaching a 0.7F1-score.
A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea
Authors: Park So-Hyun , Kim Mi-Yeon , Kim Yeon-Ji , Park Young-Ho
Published: February 2022

In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys; hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively.
Construction Risk Assessment of Deep Foundation Pit Projects Based on the Projection Pursuit Method and Improved Set Pair Analysis
Authors: Zhang Long , Li Hongbing
Published: February 2022

Accurately evaluating the construction risk of deep foundation pit projects is crucial to formulate science-based risk response measures. Here, we propose a novel construction risk assessment method for deep foundation pit projects. A construction risk evaluation index system based on a work breakdown structure-risk breakdown structure matrix was established to deal with the complex risks of deep foundation pit construction. The projection pursuit method optimized by particle swarm optimization was used to extract the structural features from the evaluation data to obtain objective index weights. The calculation method of the five-element connection number in the set pair analysis was improved to evaluate the static construction risk. The partial derivatives of the five-element connection number were utilized to assess the dynamic construction risk. The Qi ‘an Fu deep foundation pit project in China was selected as a case study. The results show that the construction risk was acceptable and decreased during the construction period, which was consistent with actual conditions, demonstrating the effectiveness of this novel method. The proposed model showed better performance than classical methods (analytic hierarchy process, entropy weight method, classical set pair analysis, fuzzy comprehensive evaluation, gray clustering method, backpropagation neural network, and support vector machine).
Explainable Machine Learning for Lung Cancer Screening Models
Authors: Kobylińska Katarzyna , Orłowski Tadeusz , Adamek Mariusz , Biecek Przemysław
Published: February 2022

Modern medicine is supported by increasingly sophisticated algorithms. In diagnostics or screening, statistical models are commonly used to assess the risk of disease development, the severity of its course, and expected treatment outcome. The growing availability of very detailed data and increased interest in personalized medicine are leading to the development of effective but complex machine learning models. For these models to be trusted, their predictions must be understandable to both the physician and the patient, hence the growing interest in the area of Explainable Artificial Intelligence (XAI). In this paper, we present selected methods from the XAI field in the example of models applied to assess lung cancer risk in lung cancer screening through low-dose computed tomography. The use of these techniques provides a better understanding of the similarities and differences between three commonly used models in lung cancer screening, i.e., BACH, PLCOm2012, and LCART. For the presentation of the results, we used data from the Domestic Lung Cancer Database. The XAI techniques help to better understand (1) which variables are most important in which model, (2) how they are transformed into model predictions, and facilitate (3) the explanation of model predictions for a particular screenee.
Global Assessment of Bridge Passage in Relation to Oversized and Excessive Transport: Case Study Intended for Slovakia
Authors: Gnap Jozef , Jagelčák Juraj , Marienka Peter , Frančák Marcel , Vojteková Mária
Published: February 2022

The development of an economy and, in particular, the construction of new infrastructure as well as industrial enterprises creates demand for the road transport of oversized freight that exceeds the maximum permissible total mass of vehicle combinations with its share on the axles. Failure to comply with the defined technological processes and a deficiency in the assessment of permitting such forms of transportation can have a large adverse effect, predominantly on the lifetime of bridges in a road network, which can have international implications as well. There is no legislation adopted by the EU Member States, which would at least partially unify the authorisation procedures of these forms of transportation and, therefore, it results in problems when crossing borders and leads to differences related to the assessment of bridge passages. If there is no systematic inspection of this kind of transportation, it can lead to permanent damage of these bridges as well. Currently, and not only in Slovakia but also in other states, the assessment of bridge passage for certain routes is used for heavy and oversized transportation. It means that if we use 100 transports, 100 assessments of individual routes are needed, although some are the same routes or the same vehicles/vehicle combinations used for a number of transports. Thus, the authors designed a global assessment for bridge passage in relation to heavy and oversized road transport while verifying it in the conditions of the EU Member State from Central Europe–Slovakia. Roads are full of different types of vehicles/vehicle combinations for which the axle loads and distances of the axles (wheelbases) are important. Thus, there were vehicle/vehicle combinations parameters (big data) observed, for which the routes relating to heavy and/or oversized transportation were assessed from 1 January 2016 to 31 December 2020 in Slovakia. The global assessment of bridge passage introduces an entirely new approach within the procedure for obtaining a special permission for road use as well as within transport use itself. Given the low presence of freight with an abnormal axle load or enormous total mass, it is appropriate to define the limited conditions under which it would be possible to implement the global assessment in practice as well.
Global Vibration Intensity Assessment Based on Vibration Source Localization on Construction Sites: Application to Vibratory Sheet Piling
Authors: Wang Shiguang , Zhu Songye
Published: February 2022

Various construction activities (such as piling) often generate high-intensity ground vibrations that adversely affect the surrounding environment. A common way of assessing vibration impact is to conduct on-site ground vibration monitoring at several selected locations. However, as vibration sources are often not pinpointed in the construction process, this approach cannot predict the vibration intensities at locations other than those monitored points. Therefore, the localization of vibration sources (e.g., vibratory sheet pile driving location) is crucial to quantify the corresponding vibration intensities in a broad area. This paper investigates a time-based source localization method based on wave propagation characteristics derived via three-dimensional finite element modeling of vibratory sheet pile driving in an infinite half-space soil domain. Satisfactory accuracy in the localization of the vibratory driving sources was achieved in all investigated numerical examples. Field validation tests were also conducted on a construction site with ongoing vibratory sheet pile driving work. A site-specific empirical formula was adopted to model the attenuation of measured vibration intensities with the increasing distance from the localized vibration source. As such, the combined utilization of the estimated vibration source location and the adopted empirical formula can achieve vibration intensity assessment in a broad surrounding area rather than being confined to a few monitored points.
Digital Photoelastic Analysis of TAD-Supported Maxillary Arch Distalization
Authors: Nishii Yasushi , Sameshima Glenn T. , Tachiki Chie
Published: February 2022

The objective of this study was to determine whether the distribution of compressional and tensional stress around tooth roots is influenced by the position of a temporary anchorage device and the length of the retraction hook during the distalization of the maxillary dentition. A photoelastic orthodontic model was made of photoelastic epoxy resin. Six combinations of three retraction hook lengths and two posterior Temporary skeletal anchorage devices (TAD) positions were established. Stress was applied through an elastic chain for each of the combinations. Digital photoelastic stress analysis measured the compression, tensional stress, and direction around the tooth root. Using this novel photoelastic model, we found that the distribution of compressional and tensional stress during the retraction of the maxillary dentition was significantly influenced by the position of the TAD and the length of the retraction hook.
Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review
Authors: Ho Phuong Thao , Albajez José Antonio , Santolaria Jorge , Yagüe-Fabra José A.
Published: February 2022

Augmented Reality (AR) has gradually become a mainstream technology enabling Industry 4.0 and its maturity has also grown over time. AR has been applied to support different processes on the shop-floor level, such as assembly, maintenance, etc. As various processes in manufacturing require high quality and near-zero error rates to ensure the demands and safety of end-users, AR can also equip operators with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. However, there is currently no systematic review paper about AR technology enhancing the quality sector. The purpose of this paper is to conduct a systematic literature review (SLR) to conclude about the emerging interest in using AR as an assisting technology for the quality sector in an industry 4.0 context. Five research questions (RQs), with a set of selection criteria, are predefined to support the objectives of this SLR. In addition, different research databases are used for the paper identification phase following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology to find the answers for the predefined RQs. It is found that, in spite of staying behind the assembly and maintenance sector in terms of AR-based solutions, there is a tendency towards interest in developing and implementing AR-assisted quality applications. There are three main categories of current AR-based solutions for quality sector, which are AR-based apps as a virtual Lean tool, AR-assisted metrology and AR-based solutions for in-line quality control. In this SLR, an AR architecture layer framework has been improved to classify articles into different layers which are finally integrated into a systematic design and development methodology for the development of long-term AR-based solutions for the quality sector in the future.
Characteristic Analysis, Simulation, and Experimental Comparison of Two Kinds of Circular Magnet Array in Energy Harvesting
Authors: Guo Shengkai , Gao Shiqiao , Jin Lei , Du Xueda , Wu Qinghe , Shang Jie
Published: February 2022

The circular Halbach array is of great application value in electromagnetic energy conversion. The Halbach array can be formed with multiple magnets according to a specific method. The magnetic flux intensity of the magnet array is concentrated and enhanced in a certain area. The concentrated magnetic flux intensity could lead to a higher rate of change in the magnetic flux when the magnet and the coil move relatively. The spatial arrangement, structural dimensions, and residual flux density of the Halbach array are the main factors affecting its performance. Some areas with almost no magnetic induction in the Halbach array affect the performance of electromagnetic energy conversion. This paper conducted a comparative study of the Halbach array and the two-directional magnet array in terms of energy conversion. The root mean square (RMS) value and phase of the induced voltage in the coil were analyzed by a finite element method. Simulation shows that the RMS value of the induced voltage in the coil is equal when the gap is 1.55 mm and the REMEH adopts either of the two magnet arrays. The two-directional array has advantages in the energy harvesting performance when the gap is less than 1.55 mm, while the Halbach array has advantages in the energy harvesting performance when the gap is greater than 1.55 mm. The experimental verification of the designed prototype was implemented in this study. The experimental voltage is in good agreement with the simulation voltage. The threshold value of the gap in the experiment is about 2 mm, which is slightly higher than the simulation value. This characteristic has a certain reference value for the application of the Halbach array in the electromagnetic field.
Ozone Therapy for Early Childhood Caries (ECC) Treatment: An In Vivo Prospective Study
Authors: Luppieri Valentina , Manfra Antonio , Ronfani Luca , Chermetz Maddalena , Cadenaro Milena
Published: February 2022

The present study aimed to evaluate the effectiveness of ozone therapy in (1) increasing the compactness of decayed dentin (main aim), (2) reducing dentin hypersensitivity, (3) decreasing the salivary bacterial count, and (4) improving the quality of life in ECC-affected children during a 3-month follow-up. A total of 20 subjects uncooperative with conventional restorative treatment were treated with a 60 s ozone application/week (OzoneDTA®) for 4 weeks. Patients were evaluated at T0 = baseline; T1 = after ozone cycle and T2, T3, T4 = 1, 2, 3-month follow-up, respectively. Dentin compactness was evaluated according to the Affected Dentine Scale (ADS) proposed by the authors; dentin hypersensitivity was recorded with the Wong–Baker Faces Pain Rating Scale (WBFPRS); salivary bacteria were measured with the Saliva Check Mutans Test and quality of life with the Early Childhood Oral Health Impact Scale (ECOHIS). ADS values and salivary bacteria count results were analyzed using non-parametric tests. Statistically significant differences were detected for both parameters between T0 and T1 (p < 0.01). At T1, dentin hypersensitivity was resolved and quality of life improved. Ozone therapy is a simple and painless approach, which might be useful for controlling caries’ progression in uncooperative children until appropriate cooperation is achieved.
A Pancancer Analysis of the Oncogenic Role of S100 Calcium Binding Protein A7 (S100A7) in Human Tumors
Authors: Peng Ge , Tsukamoto Saya , Okumura Ko , Ogawa Hideoki , Ikeda Shigaku , Niyonsaba François
Published: February 2022

Background: Although emerging studies support the relationship between S100 calcium binding protein A7 (S100A7) and various cancers, no pancancer analysis of S100A7 is available thus far. Methods: We investigated the potential oncogenic roles of S100A7 across 33 tumors based on datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Moreover, a survival prognosis analysis was performed with the gene expression profiling interactive analysis (GEPIA) web server and Kaplan–Meier plotter, followed by the genetic alteration analysis of S100A7 and enrichment analysis of S100A7-related genes. Results: S100A7 was highly expressed in most types of cancers, and remarkable associations were found between S100A7 expression and the prognosis of cancer patients. S100A7 expression was associated with the expression of DNA methyltransferase and mismatch repair genes in head and neck squamous cell carcinoma, the infiltration of CD8+ T cells and cancer-associated fibroblasts in different tumors. Moreover, glycosaminoglycan degradation and lysosome-associated functions were involved in the functional mechanisms of S100A7. Conclusions: The current pancancer study shows a relatively integrative understanding of the carcinogenic involvement of S100A7 in numerous types of cancers.
Probing Genome-Scale Model Reveals Metabolic Capability and Essential Nutrients for Growth of Probiotic Limosilactobacillus reuteri KUB-AC5
Authors: Namrak Thanawat , Raethong Nachon , Jatuponwiphat Theeraphol , Nitisinprasert Sunee , Vongsangnak Wanwipa , Nakphaichit Massalin
Published: February 2022

Limosilactobacillus reuteri KUB-AC5 displays the hallmark features of probiotic properties for food and feed industries. Optimization of cultivation condition for the industrial production is important to reach cell concentration and cost reduction. Considering the strain-specific growth physiology, metabolic capability, and essential nutrients of L. reuteri KUB-AC5, the genome-scale metabolic model (GSMM) of L. reuteri KUB-AC5 was developed. Hereby, the GSMM of iTN656 was successfully constructed which contained 656 genes, 831 metabolites, and 953 metabolic reactions. The iTN656 model could show a metabolic capability under various carbon sources and guide potentially 14 essential single nutrients (e.g., vitamin B complex and amino acids) and 2 essential double nutrients (pairwise glutamine-glutamate and asparagine-aspartate) for L. reuteri KUB-AC5 growth through single and double omission analysis. Promisingly, the iTN656 model was further integrated with transcriptome data suggesting that putative metabolic routes as preferable paths e.g., sucrose uptake, nucleotide biosynthesis, urea cycle, and glutamine transporter for L. reuteri KUB-AC5 growth. The developed GSMM offers a powerful tool for multi-level omics analysis, enabling probiotic strain optimization for biomass overproduction on an industrial scale.
Blue Laser Irradiation Decreases the ATP Level in Mouse Skin and Increases the Production of Superoxide Anion and Hypochlorous Acid in Mouse Fibroblasts
Authors: Nakayama Eiko , Kushibiki Toshihiro , Mayumi Yoshine , Azuma Ryuichi , Ishihara Miya , Kiyosawa Tomoharu
Published: February 2022

Photobiomodulation studies have reported that blue light irradiation induces the production of reactive oxygen species. We investigated the effect of blue laser (405 nm) irradiation on the ATP levels in mouse skin and determined the types of reactive oxygen species and reactive nitrogen species using cultured mouse fibroblasts. Blue laser irradiation caused a decrease in the ATP level in the mouse skin and triggered the generation of superoxide anion and hypochlorous acid, whereas nitric oxide and peroxynitrite were not detected. Moreover, blue laser irradiation resulted in reduced cell viability. It is believed that the decrease in the skin ATP level due to blue light irradiation results from the increased levels of oxidative stress due to the generation of reactive oxygen species. This method of systematically measuring the levels of reactive oxygen species and reactive nitrogen species may be useful for understanding the effects of irradiation conditions.
RRM2 Alleviates Doxorubicin-Induced Cardiotoxicity through the AKT/mTOR Signaling Pathway
Authors: Jiao Yuheng , Li Yanyan , Zhang Jiayan , Zhang Song , Zha Yafang , Wang Jian
Published: February 2022

Doxorubicin (DOX) is an effective chemotherapeutic agent that plays an unparalleled role in cancer treatment. However, its serious dose-dependent cardiotoxicity, which eventually contributes to irreversible heart failure, has greatly limited the widespread clinical application of DOX. A previous study has demonstrated that the ribonucleotide reductase M2 subunit (RRM2) exerts salutary effects on promoting proliferation and inhibiting apoptosis and autophagy. However, the specific function of RRM2 in DOX-induced cardiotoxicity is yet to be determined. This study aimed to elucidate the role and potential mechanism of RRM2 on DOX-induced cardiotoxicity by investigating neonatal primary cardiomyocytes and mice treated with DOX. Subsequently, the results indicated that RRM2 expression was significantly reduced in mice hearts and primary cardiomyocytes. Apoptosis and autophagy-related proteins, such as cleaved-Caspase3 (C-Caspase3), LC3B, and beclin1, were distinctly upregulated. Additionally, RRM2 deficiency led to increased autophagy and apoptosis in cells. RRM2 overexpression, on the contrary, alleviated DOX-induced cardiotoxicity in vivo and in vitro. Consistently, DIDOX, an inhibitor of RRM2, attenuated the protective effect of RRM2. Mechanistically, we found that AKT/mTOR inhibitors could reverse the function of RRM2 overexpression on DOX-induced autophagy and apoptosis, which means that RRM2 could have regulated DOX-induced cardiotoxicity through the AKT/mTOR signaling pathway. In conclusion, our experiment established that RRM2 could be a potential treatment in reversing DOX-induced cardiac dysfunction.
Circulating Chemerin and Its Kinetics May Be a Useful Diagnostic and Prognostic Biomarker in Critically Ill Patients with Sepsis: A Prospective Study
Authors: Karampela Irene , Christodoulatos Gerasimos Socrates , Vallianou Natalia , Tsilingiris Dimitrios , Chrysanthopoulou Evangelia , Skyllas George , Antonakos Georgios , Marinou Ioanna , Vogiatzakis Evaggelos , Armaganidis Apostolos , Dalamaga Maria
Published: February 2022

Chemerin, a novel adipokine, is a potent chemoattractant molecule with antimicrobial properties, implicated in immune responses. Our aim was to investigate circulating chemerin and its kinetics, early in sepsis in critically ill patients and its association with severity and prognosis. Serum chemerin was determined in a cohort of 102 critically ill patients with sepsis during the first 48 h from sepsis onset and one week later, and in 102 age- and gender-matched healthy controls. Patients were followed for 28 days and their outcomes were recorded. Circulating chemerin was significantly higher in septic patients at onset compared to controls (342.3 ± 108.1 vs. 200.8 ± 40.1 μg/L, p < 0.001). Chemerin decreased significantly from sepsis onset to one week later (342.3 ± 108.1 vs. 308.2 ± 108.5 μg/L, p < 0.001), but remained higher than in controls. Chemerin was higher in patients presenting with septic shock than those with sepsis (sepsis onset: 403.2 ± 89.9 vs. 299.7 ± 99.5 μg/L, p < 0.001; one week after: 374.9 ± 95.3 vs. 261.6 ± 91.9 μg/L, p < 0.001), and in nonsurvivors than survivors (sepsis onset: 427.2 ± 96.7 vs. 306.9 ± 92.1 μg/L, p < 0.001; one week after: 414.1 ± 94.5 vs. 264.2 ± 79.9 μg/L, p < 0.001). Moreover, patients with septic shock and nonsurvivors, presented a significantly lower absolute and relative decrease in chemerin one week after sepsis onset compared to baseline (p < 0.001). Based on ROC curve analyses, the diagnostic performance of chemerin (AUC 0.78, 95% CI 0.69–0.87) was similar to C-reactive protein (CRP) (AUC 0.78, 95% CI 0.68–0.87) in discriminating sepsis severity. However, increased chemerin at sepsis onset and one week later was an independent predictor of 28-day mortality (sepsis onset: HR 3.58, 95% CI 1.48–8.65, p = 0.005; one week after: HR 10.01, 95% CI 4.32–23.20, p < 0.001). Finally, serum chemerin exhibited significant correlations with the severity scores, white blood cells, lactate, CRP and procalcitonin, as well as with biomarkers of glucose homeostasis, but not with cytokines and soluble urokinase-type plasminogen activator receptor (suPAR). Circulating chemerin is increased early in sepsis and its kinetics may have diagnostic and prognostic value in critically ill patients. Further studies are needed to shed light on the role of chemerin in sepsis.
Dynamic and Systemic Perspective in Autism Spectrum Disorders: A Change of Gaze in Research Opens to A New Landscape of Needs and Solutions
Authors: Panisi Cristina , Marini Marina
Published: February 2022

The first step for a harmonious bio-psycho-social framework in approaching autism spectrum disorders (ASD) is overcoming the conflict between the biological and the psychosocial perspective. Biological research can provide clues for a correct approach to clinical practice, assuming that it would lead to the conceptualization of a pathogenetic paradigm able to account for epidemiologic and clinical findings. The upward trajectory in ASD prevalence and the systemic involvement of other organs besides the brain suggest that the epigenetic paradigm is the most plausible one. The embryo-fetal period is the crucial window of opportunity for keeping neurodevelopment on the right tracks, suggesting that women’s health in pregnancy should be a priority. Maladaptive molecular pathways beginning in utero, in particular, a vicious circle between the immune response, oxidative stress/mitochondrial dysfunction, and dysbiosis-impact neurodevelopment and brain functioning across the lifespan and are the basis for progressive multisystemic disorders that account for the substantial health loss and the increased mortality in ASD. Therefore, the biological complexity of ASD and its implications for health requires the enhancement of clinical skills on these topics, to achieve an effective multi-disciplinary healthcare model. Well-balanced training courses could be a promising starting point to make a change.
Frequency Fitting Optimization Using Evolutionary Algorithm in Cochlear Implant Users with Bimodal Binaural Hearing
Authors: Saadoun Alexis , Schein Antoine , Péan Vincent , Legrand Pierrick , Aho Glélé Ludwig Serge , Bozorg Grayeli Alexis
Published: February 2022

Optimizing hearing in patients with a unilateral cochlear implant (CI) and contralateral acoustic hearing is a challenge. Evolutionary algorithms (EA) can explore a large set of potential solutions in a stochastic manner to approach the optimum of a minimization problem. The objective of this study was to develop and evaluate an EA-based protocol to modify the default frequency settings of a MAP (fMAP) of the CI in patients with bimodal hearing. Methods: This monocentric prospective study included 27 adult CI users (with post-lingual deafness and contralateral functional hearing). A fitting program based on EA was developed to approach the best fMAP. Generated fMAPs were tested by speech recognition (word recognition score, WRS) in noise and free-field-like conditions. By combining these first fMAPs and adding some random changes, a total of 13 fMAPs over 3 generations were produced. Participants were evaluated before and 45 to 60 days after the fitting by WRS in noise and questionnaires on global sound quality and music perception in bimodal binaural conditions. Results: WRS in noise improved with the EA-based fitting in comparison to the default fMAP (41.67 ± 9.70% versus 64.63 ± 16.34%, respectively, p = 0.0001, signed-rank test). The global sound quality and music perception were also improved, as judged by ratings on questionnaires and scales. Finally, most patients chose to keep the new fitting definitively. Conclusions: By modifying the default fMAPs, the EA improved the speech discrimination in noise and the sound quality in bimodal binaural conditions.
The Neuropathology of Autoimmune Ataxias
Authors: Clark H. Brent
Published: February 2022

Autoimmune-mediated ataxia has been associated with paraneoplastic disease, gluten enteropathy, Hashimoto thyroiditis as well as autoimmune disorders without a known associated disease. There have been relatively few reports describing the neuropathology of these conditions. This review is an attempt to consolidate those reports and determine the ways in which autoimmune ataxias can be neuropathologically differentiated from hereditary or other sporadic ataxias. In most instances, particularly in paraneoplastic forms, the presence of inflammatory infiltrates is a strong indicator of autoimmune disease, but it was not a consistent finding in all reported cases. Therefore, clinical and laboratory findings are important for assessing an autoimmune mechanism. Such factors as rapid rate of clinical progression, presence of known autoantibodies or the presence of a malignant neoplasm or other autoimmune disease processes need to be considered, particularly in cases where inflammatory changes are minimal or absent and the pathology is largely confined to the cerebellum and its connections, where the disease can mimic hereditary or other sporadic ataxias.
Peptides against Low Density Lipoprotein (LDL) Aggregation Inhibit Intracellular Cholesteryl Ester Loading and Proliferation of Pancreatic Tumor Cells
Authors: Benitez-Amaro Aleyda , Martínez-Bosch Neus , Manero-Rupérez Noemí , Claudi Lene , La Chica Lhoëst Maria , Soler Marta , Ros-Blanco Lia , Navarro Pilar , Llorente-Cortés Vicenta
Published: February 2022

Dyslipidemia, metabolic disorders and/or obesity are postulated as risk factors for pancreatic ductal adenocarcinoma (PDAC). The majority of patients with these metabolic alterations have low density lipoproteins (LDLs) with increased susceptibility to become aggregated in the extracellular matrix (ECM). LDL aggregation can be efficiently inhibited by low-density lipoprotein receptor-related protein 1 (LRP1)-based peptides. The objectives of this work were: (i) to determine if aggregated LDLs affect the intracellular cholesteryl ester (CE)/free cholesterol (FC) ratio and/or the tumor pancreatic cell proliferation, using sphingomyelinase-modified LDL particles (Aggregated LDL, AgLDL); and (ii) to test whether LRP1-based peptides, highly efficient against LDL aggregation, can interfere in these processes. For this, we exposed human pancreatic cancer cell lines (PANC-1, RWP-1 and Capan-1) to native (nLDL) or AgLDLs in the absence or presence of LRP1-based peptides (DP3) or irrelevant peptides (IP321). Results of thin-layer chromatography (TLC) following lipid extraction indicate that AgLDLs induce a higher intracellular CE/FC ratio than nLDL, and that DP3 but not IP321 counteracts this effect. AgLDLs also increase PANC-1 cell proliferation, which is inhibited by the DP3 peptide. Our results indicate that AgLDL-induced intracellular CE accumulation plays a crucial role in the proliferation of pancreatic tumor cell lines. Peptides with anti-LDL aggregation properties may thus exhibit anti-tumor effects.
Precision Targets for Intercepting the Lethal Progression of Prostate Cancer: Potential Avenues for Personalized Therapy
Authors: Christenson Max , Song Chung-Seog , Liu Ya-Guang , Chatterjee Bandana
Published: February 2022

Organ-confined prostate cancer of low-grade histopathology is managed with radiation, surgery, active surveillance, or watchful waiting and exhibits a 5-year overall survival (OS) of 95%, while metastatic prostate cancer (PCa) is incurable, holding a 5-year OS of 30%. Treatment options for advanced PCa—metastatic and non-metastatic—include hormone therapy that inactivates androgen receptor (AR) signaling, chemotherapy and genome-targeted therapy entailing synthetic lethality of tumor cells exhibiting aberrant DNA damage response, and immune checkpoint inhibition (ICI), which suppresses tumors with genomic microsatellite instability and/or deficient mismatch repair. Cancer genome sequencing uncovered novel somatic and germline mutations, while mechanistic studies are revealing their pathological consequences. A microRNA has shown biomarker potential for stratifying patients who may benefit from angiogenesis inhibition prior to ICI. A 22-gene expression signature may select high-risk localized PCa, which would not additionally benefit from post-radiation hormone therapy. We present an up-to-date review of the molecular and therapeutic aspects of PCa, highlight genomic alterations leading to AR upregulation and discuss AR-degrading molecules as promising anti-AR therapeutics. New biomarkers and druggable targets are shaping innovative intervention strategies against high-risk localized and metastatic PCa, including AR-independent small cell-neuroendocrine carcinoma, while presenting individualized treatment opportunities through improved design and precision targeting.
Activation of ABCC Genes by Cisplatin Depends on the CoREST Occurrence at Their Promoters in A549 and MDA-MB-231 Cell Lines
Authors: Sobczak Maciej , Strachowska Magdalena , Gronkowska Karolina , Robaszkiewicz Agnieszka
Published: February 2022

Although cisplatin-based therapies are common among anticancer approaches, they are often associated with the development of cancer drug resistance. This phenomenon is, among others, caused by the overexpression of ATP-binding cassette, membrane-anchored transporters (ABC proteins), which utilize ATP to remove, e.g., chemotherapeutics from intracellular compartments. To test the possible molecular basis of increased expression of ABCC subfamily members in a cisplatin therapy mimicking model, we generated two cisplatin-resistant cell lines derived from non-small cell lung cancer cells (A549) and triple-negative breast cancer cells (MDA-MB-231). Analysis of data for A549 cells deposited in UCSC Genome Browser provided evidence on the negative interdependence between the occurrence of the CoREST complex at the gene promoters and the overexpression of ABCC genes in cisplatin-resistant lung cancer cells. Pharmacological inhibition of CoREST enzymatic subunits—LSD1 and HDACs—restored gene responsiveness to cisplatin. Overexpression of CoREST-free ABCC10 in cisplatin-resistant phenotypes was caused by the activity of EP300 that was enriched at the ABCC10 promoter in drug-treated cells. Cisplatin-induced and EP300-dependent transcriptional activation of ABCC10 was only possible in the presence of p53. In summary, the CoREST complex prevents the overexpression of some multidrug resistance proteins from the ABCC subfamily in cancer cells exposed to cisplatin. p53-mediated activation of some ABCC genes by EP300 occurs once their promoters are devoid of the CoREST complex.
UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia
Authors: Weijler Lisa , Kowarsch Florian , Wödlinger Matthias , Reiter Michael , Maurer-Granofszky Margarita , Schumich Angela , Dworzak Michael N.
Published: February 2022

Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in ALL as well as in AML. Machine learning techniques have been emerging in the field of automated MRD quantification with the objective of superseding subjective and time-consuming manual analysis of FCM-MRD data. In contrast to ALL, where supervised multi-class classification methods have been successfully deployed for MRD detection, AML poses new challenges: AML is rarer (with fewer available training data) than ALL and much more heterogeneous in its immunophenotypic appearance, where one-class classification (anomaly detection) methods seem more suitable. In this work, a new semi-supervised approach based on the UMAP algorithm for MRD detection utilizing only labels of blast free FCM samples is presented. The method is tested on a newly gathered set of AML FCM samples and results are compared to state-of-the-art methods. We reach a median F1-score of 0.794, while providing a transparent classification pipeline with explainable results that facilitates inter-disciplinary work between medical and technical experts. This work shows that despite several issues yet to overcome, the merits of automated MRD quantification can be fully exploited also in AML.