bluuebunny/crossref_metadata_2025 huggingface dataset:
Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review
Authors: Jemioło Paweł , Storman Dawid , Mamica Maria , Szymkowski Mateusz , Żabicka Wioletta , Wojtaszek-Główka Magdalena , Ligęza Antoni
Published: March 2022

Our review aimed to assess the current state and quality of publicly available datasets used for automated affect and emotion recognition (AAER) with artificial intelligence (AI), and emphasising cardiovascular (CV) signals. The quality of such datasets is essential to create replicable systems for future work to grow. We investigated nine sources up to 31 August 2020, using a developed search strategy, including studies considering the use of AI in AAER based on CV signals. Two independent reviewers performed the screening of identified records, full-text assessment, data extraction, and credibility. All discrepancies were resolved by discussion. We descriptively synthesised the results and assessed their credibility. The protocol was registered on the Open Science Framework (OSF) platform. Eighteen records out of 195 were selected from 4649 records, focusing on datasets containing CV signals for AAER. Included papers analysed and shared data of 812 participants aged 17 to 47. Electrocardiography was the most explored signal (83.33% of datasets). Authors utilised video stimulation most frequently (52.38% of experiments). Despite these results, much information was not reported by researchers. The quality of the analysed papers was mainly low. Researchers in the field should concentrate more on methodology.
Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras
Authors: Molinaro Nunzia , Schena Emiliano , Silvestri Sergio , Massaroni Carlo
Published: March 2022

Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone’s camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min−1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.
Equivalent Circuit Model Extraction for a SAW Resonator: Below and above Room Temperature
Authors: Gugliandolo Giovanni , Marinković Zlatica , Crupi Giovanni , Campobello Giuseppe , Donato Nicola
Published: March 2022

In this work, a SAW resonator is characterized in terms of admittance (Y-) parameters in the temperature range spanning from 0 °C to 100 °C, with the aim of highlighting how its physical properties are affected by the temperature change. A lumped-element equivalent-circuit model is used to represent the device under test at the considered temperature conditions and a parameters extraction process based on a Lorentzian fitting is developed for the determination of the equivalent-circuit elements in the investigated temperature range. A very good agreement is observed between the performed measurements and the model simulations. The characterization process and the subsequent equivalent-circuit parameters extraction at different temperature values are described and discussed.
A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition
Authors: Syed Abbas Shah , Sierra-Sosa Daniel , Kumar Anup , Elmaghraby Adel
Published: March 2022

Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%.
Challenges of Large-Scale Multi-Camera Datasets for Driver Monitoring Systems
Authors: Ortega Juan Diego , Cañas Paola Natalia , Nieto Marcos , Otaegui Oihana , Salgado Luis
Published: March 2022

Tremendous advances in advanced driver assistance systems (ADAS) have been possible thanks to the emergence of deep neural networks (DNN) and Big Data (BD) technologies. Huge volumes of data can be managed and consumed as training material to create DNN models which feed functions such as lane keeping systems (LKS), automated emergency braking (AEB), lane change assistance (LCA), etc. In the ADAS/AD domain, these advances are only possible thanks to the creation and publication of large and complex datasets, which can be used by the scientific community to benchmark and leverage research and development activities. In particular, multi-modal datasets have the potential to feed DNN that fuse information from different sensors or input modalities, producing optimised models that exploit modality redundancy, correlation, complementariness and association. Creating such datasets pose a scientific and engineering challenge. The BD dimensions to cover are volume (large datasets), variety (wide range of scenarios and context), veracity (data labels are verified), visualization (data can be interpreted) and value (data is useful). In this paper, we explore the requirements and technical approach to build a multi-sensor, multi-modal dataset for video-based applications in the ADAS/AD domain. The Driver Monitoring Dataset (DMD) was created and partially released to foster research and development on driver monitoring systems (DMS), as it is a particular sub-case which receives less attention than exterior perception. Details on the preparation, construction, post-processing, labelling and publication of the dataset are presented in this paper, along with the announcement of a subsequent release of DMD material publicly available for the community.
Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images
Authors: Laiton-Bonadiez Camilo , Sanchez-Torres German , Branch-Bedoya John
Published: March 2022

In recent years, the use of deep learning-based models for developing advanced healthcare systems has been growing due to the results they can achieve. However, the majority of the proposed deep learning-models largely use convolutional and pooling operations, causing a loss in valuable data and focusing on local information. In this paper, we propose a deep learning-based approach that uses global and local features which are of importance in the medical image segmentation process. In order to train the architecture, we used extracted three-dimensional (3D) blocks from the full magnetic resonance image resolution, which were sent through a set of successive convolutional neural network (CNN) layers free of pooling operations to extract local information. Later, we sent the resulting feature maps to successive layers of self-attention modules to obtain the global context, whose output was later dispatched to the decoder pipeline composed mostly of upsampling layers. The model was trained using the Mindboggle-101 dataset. The experimental results showed that the self-attention modules allow segmentation with a higher Mean Dice Score of 0.90 ± 0.036 compared with other UNet-based approaches. The average segmentation time was approximately 0.038 s per brain structure. The proposed model allows tackling the brain structure segmentation task properly. Exploiting the global context that the self-attention modules incorporate allows for more precise and faster segmentation. We segmented 37 brain structures and, to the best of our knowledge, it is the largest number of structures under a 3D approach using attention mechanisms.
Optimal Wireless Distributed Sensor Network Design and Ad-Hoc Deployment in a Chemical Emergency Situation
Authors: Kendler Shai , Fishbain Barak
Published: March 2022

Industrial activities involve the manipulation of harmful chemicals. As there is no way to guarantee fail-safe operation, the means and response methods must be planned in advance to cope with a chemical disaster. In these situations, first responders assess the situation from the atmospheric conditions, but they have scant data on the source of the contamination, which curtails their response toolbox. Hence, a sensor deployment strategy needs to be formulated in real-time based on the meteorological conditions, sensor attributes, and resources. This work examined the tradeoff between sensor locations and their attributes. The findings show that if the sensor locations are optimal, the number is more important than quality, in that the sensors’ dynamic range is a significant factor when quantifying leaks but is less important if the goal is solely to locate the leak source/s. This methodology can be used for sensor location-allocation under real-life conditions and technological constraints.
A New Hypersonic Wind Tunnel Force Measurement System to Reduce Additional Bending Moment and Avoid Time-Varying Stiffness
Authors: Li Shichao , Liu Zihao , Zhao Fan , Gao Hongli
Published: March 2022

In order to improve traditional hypersonic wind tunnel airframe/propulsion integrated aerodynamic testing technology for hypersonic vehicles, a new force measurement system called the aerodynamic force measuring support (AFMS) was designed. The AFMS effectively overcomes the defect that the traditional internal box-balance occupies a large amount of internal space in the aircraft test model, which makes the airframe/propulsion integrated aerodynamic test more difficult. The AFMS also alleviates the interference of the additional bending moment caused by the non-coincidence between the calibration center of traditional external box-balance and the gravity center of the aircraft test model, innovatively designing a convex structure in the joint part of the force measuring system. Furthermore, the AFMS effectively overcomes the time-varying stiffness of joints caused by test model vibration in hypersonic wind tunnel testing, which eventually leads to test errors. Compared with the traditional box-balance, the AFMS proposed in this study has sufficient design space. This ensures more thorough aerodynamic decomposition of the AFMS and less interference between channels, whilst also having the advantages of the large support stiffness of traditional box-balance. Thus, the AFMS provides a new technical path for airframe/propulsion integrated aerodynamic testing of air-breathing hypersonic vehicles in a hypersonic wind tunnel.
On the Role of Structural Competency in the Healthcare of Migrant with Precarious Residency Status
Authors: Geeraert Jérémy
Published: March 2022

The literature on the health care of migrant patients has often emphasized the importance of cultural skills and cultural humility that caregivers must bring to their care. Recent work has emphasized the importance of adopting a structural reading of this competency. Based on two empirical surveys conducted in France and Germany in facilities providing access to care for migrants with precarious residency status, this article demonstrates the importance of competency linking in terms of what is produced by structures and institutions and what is produced during medical interactions between patients, medical professionals, and volunteers. The complexity of accessing health protection systems for migrants with precarious residency status is often the main structural and institutional barrier to care. To remove this barrier, health professionals can develop legal and administrative competency regarding residency and health rights. They can also develop institutional and practical competency regarding the possibilities of access to health care for people without health coverage in the local geographical context. Structural competency is also effective in deconstructing the stigma and discrimination that minority groups experience in the healthcare system.
Accuracy of Two-Dimensional Limit Equilibrium Methods in Predicting Stability of Homogenous Road-Cut Slopes
Authors: Sengani Fhatuwani , Allopi Dhiren
Published: March 2022

Although limit equilibrium methods are widely used by engineers and scientists in predicting the stability of homogenous slopes, their use has been demonstrated to present significant errors due to the violation of kinematic and static admissibility. The concern is often voiced regarding the accuracy of limit equilibrium methods (LEMs) solutions in predicting the stability of homogenous slopes. There are no exact limit equilibrium solutions or charts available that could be used to check the LEMs solutions. The present study has used the rigorous upper and lower bounds solutions of limit analysis based on finite element formulations of the bound theorems to benchmark and develop an accuracy classification chart of limit equilibrium methods in predicting the stability of the homogenous slope. Six case studies of homogenous road-cut slopes that vary with material properties were used and the effect of the increase in material strength with depth was considered. The results of LEMs and limit analysis solutions have shown that Janbu simplified limit equilibrium solutions are closely related to those of rigorous upper bound solutions with an accuracy error ranging from 1 to 7% in various slope materials. Meanwhile, the Corp of Engineer 2 limit equilibrium solutions were found to overestimate among other methods, with an accuracy error ranging from 12 to 17% in various cases. Based on the results of the study an accuracy error classification chart of LEMs is developed.
LTE/NR V2X Communication Modes and Future Requirements of Intelligent Transportation Systems Based on MR-DC Architectures
Authors: González Edgar E. , Garcia-Roger David , Monserrat Jose F.
Published: March 2022

This paper deals with the potential of Third Generation Partnership (3GPP) Project mobile cellular standards to enable vehicular communications. Starting from 3GPP Release 15, and Release 16 specifications for Vehicle-to-Everything (V2X) communications, the different communication modes, interfaces and use cases for V2X based on Long Term Evolution (LTE) and New Radio (NR) are analyzed. This research also studies the potential beneficial impact on V2X of a network that is aware of the underlying Multi-RAT Dual Connectivity (MR-DC) architecture. The methodology followed in this work consists of a review of 3GPP standards for vehicular communications based on mobile networks. The performance evaluation of the communication modes was performed through simulations taking into account resource allocation schemes, packet transmission frequencies, packet size, vehicle density and other parameters defined in the standard. In order to perform simulations of the decentralized communication mode (mode 4), a simulator based on OMNeT++ was configured. For the centralized mode (mode 3), an analytical model in MATLAB was used to configure different simulation scenarios. The results obtained indicate that LTE networks can only support basic V2X use cases because they do not demand strict potential requirements. Simulations showed that the centralized mode offers better performance than mode 4; however, it requires cellular network coverage. More advanced use cases are key for a future Intelligent Transport System (ITS), high-performance networks (i.e., Fifth Generation (5G), NR) are expected to coexist gradually with LTE in the V2X landscape. Therefore, in order to meet the strict requirements for latency, transmission speed and reliability, MR-DC architectures combining different radio access technologies, communication modes and connection interfaces should be deployed. In addition, operation in multi-operator and cross-border scenarios must be guaranteed.
Context-Based Decision Support System for Energy Efficiency in Industrial Plants
Authors: Neves-Silva Rui , Camarinha-Matos Luis M.
Published: March 2022

Industrial companies must actively pursue more energy efficiency in their processes, with impacts on both costs and the environment, and ultimately business performance. This article explores the influence of context around the manufacturing process on energy consumption. By creating awareness of this influence in a quantified way, it is possible, via a structured decision process, to find opportunities and derive solutions to improve energy performance. This work introduces a method developed in the scope of the LifeSaver project, which is based on the visualization of energy consumption data against benchmark/average values. The overall approach is supported by a software platform which offers a set of functionalities covering the complete approach, from the detection of the consumption pattern to the implementation of improvement solutions. The approach was tested in two industrial business cases. The first one illustrates the approach by showing the influence of the human factor on the energy performance in cement production. The second case deals with finding opportunities on the selection of the operation point, and its impact on peak load management. The proposed approach and developed system demonstrate a positive direct impact on reducing energy consumption and consequent carbon dioxide emissions. Furthermore, the operation of the implemented case studies has an important indirect effect on bringing awareness to the impact of small actions on general energy efficiency.
Ambidexterity in a Rapidly Changing Environment of China: Top Management Team Decision Making and Sustained Performance
Authors: Zu Jiewei , Wang Jianan , Ma Jun
Published: March 2022

The market requires Internet companies to achieve unit ambidexterity to adapt to a rapidly changing market environment. Studies have discussed the background, behavior, and performance of corporate ambidexterity but have not focused on formulating ambidextrous decisions by the top management team. We implemented inductive multiple case studies by utilizing five Chinese Internet companies as subjects. Results show that if the senior managers consider the contingency between business and environment from the perspective of the entire industry to achieve goals, resource allocation, and co-evolution, and effectively coordinate the internal conflict of the decision-making process, then the company could attain sustained performance. The newly constructed theoretical framework emphasizes the role of contingency and strategy behavior, rather than deterministically interpreting the outcome based on the personal judgment of senior managers and the embeddedness of units.
SARS-CoV-2 Dynamics in the Mucus Layer of the Human Upper Respiratory Tract Based on Host–Cell Dynamics
Authors: Li Hanyu , Kuga Kazuki , Ito Kazuhide
Published: March 2022

A thorough understanding of the inhalation dynamics of infectious aerosols indoors and infection dynamics within the host by inhaled viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in the assessment and control of infection risks indoors. Here, by combining computational fluid–particle dynamics (CFPD) and host–cell dynamics (HCD), SARS-CoV-2 infection dynamics in the mucus layer of the human upper airway were studied. To reproduce the diffusive and convective transport of the virus in the nasal cavity–nasopharynx by mucociliary motion, a three-dimensional (3D)-shell model with a mucus layer was developed. The initial virus concentrations for HCD calculation were estimated based on the deposition distribution of droplets with representative sizes analyzed by CFPD. To develop a new HCD model, the target-cell-limited model was integrated with the convection–diffusion equation. Additionally, the sensitivity of the infection rate β to the infection dynamics was systematically investigated. The results showed that the time series of SARS-CoV-2 concentration in the mucus layer strongly depended on diffusion, convection, and β. Although the SARS-CoV-2 dynamics obtained here have not been verified by corresponding clinical data, they can preliminarily reveal its transmission mode in the upper airway, which will contribute to the prevention and treatment of coronavirus disease 2019.
The Lockean Proviso and Orbital Sustainability—An Anthropological View
Authors: Mocrei-Rebrean Lucian
Published: March 2022

Over the last decades, we have witnessed the gradual commercialization of the Earth orbit. The exponential development of private space activities makes this distant natural field, with the overcoming of technological difficulties, more and more hospitable to free initiative and entrepreneurship. However, the orbital space is considered global commons. Through the imaginary case method, we intend to ponder on possible ways to legally regulate the exploitation of the orbital space, namely the application of Pigouvian taxes, on the sustainability of the orbital environment, through ethical considerations originating from the application of the Lockean proviso. Although they are designed to cover the damage caused by that particular polluting activity, which is difficult to estimate and, in our case, almost impossible to quantify in the long run, the Pigouvian taxes are the result of a proactive logic. The tension between civilization and nature turns the world outside the Earth into a wilderness destined for humanization, another area of exercise of the liberal self. Non-legal reasons for the sustainability of the orbital environment may arise from observing the Lockean principle of fair ownership. Between the prohibition of an unreasonable destruction of nature’s goods and the equitable access to extra-terrestrial resources, the human desire for appropriation updates the proviso destined for the colonization of America in the twenty-first century. Given that there are currently no plans to clean the technological waste in orbit, adopting the conservation of the orbital environment as an ethical principle could help to formulate a more environmentally responsible liberalism, as part of a long-term agenda of exploitation in the vicinity of our planet.
Implementation of a Smart Contract on a Consortium Blockchain for IoT Applications
Authors: Lin Ting , Huan Ziyi , Shi Yongcan , Yang Xu
Published: March 2022

Advancements in cryptography and computer science have given birth to blockchain technology. One of the most exciting evolutions of blockchain is the advancements in smart contract technology. Smart contracts can be used for a broad range of use cases, not just financial transactions. Smart contract technology on the public blockchain, represented by Ethereum, due to its public and opaque nature, is not a good choice for many scenarios that do not require full disclosure, such as many IoT applications. On the other hand, the existing blockchain smart contract system still has a strong connection with virtual currency, which also limits its application in non-financial scenarios. In order to solve the above problems and explore more of the potential of smart contracts for the IoT application domain, this paper mainly explores the construction of a smart contract system based on consortium blockchains associated with no virtual currency. Based on the smart contract system designed in this project, blockchain can be more easily applied in payment, product traceability, authority authentication, and other fields. Through a certain centralized way, the system is easier to manage, can reduce the management expenditure, and the power and other resource consumption is less, which is conducive to environmental protection. Results show that our smart contract system has the potential for IoT usage in the future.
Enhancing Transformative Learning and Innovation Skills Using Remote Learning for Sustainable Architecture Design
Authors: Avsec Stanislav , Jagiełło-Kowalczyk Magdalena , Żabicka Agnieszka
Published: March 2022

The currently used educational technology with artificial-intelligence-powered solutions, although rather instrumental, may lead to discontinuity in learning, as it lacks social and emotional value, which is an essential part of education for sustainable development and results in an immersive experience through which higher-order thinking skills can be adopted. This paper aims to explore transformative learning (TL) and innovation skill improvement accommodated by transactional distance theory in a 16-week remote sustainable architecture design course. The analysis identified the following: (a) significant progress in students’ attitudes toward uncertainty and criticality while social support differs due to the influence of classmates, faculty staff, teamwork, writing and reading assignments, promoters from industry and extracurricular activities; (b) significant progress in TL achievement while innovation skill development differs significantly across the groups in which online collaborative learning was found as an influencer in creativity and motivation; (c) self-efficacy influenced by feedback in and on actions, such as essay and other writing assignments, verbal persuasions and positive social comparisons; (d) lack of development of situational awareness, continuity of learning and interactions/situations to empower teammates in handling conflicts to develop leadership ability; (e) decrease in risk-taking ability, especially in a group of students in which social support was limited due to the absence of challenging situations and tasks. The results support the use of remote intervention directed at prosocial motivations and action-focused group goals.
FabLabs: The Road to Distributed and Sustainable Technological Training through Digital Manufacturing
Authors: García-Ruiz María-Elena , Lena-Acebo Francisco-Javier
Published: March 2022

The fast expansion of digital culture has fostered the creation of makerspaces such as fabrication laboratories (FabLabs) that, thanks to their flexibility and their use of open source tools, strengthen the sense of community and produce true transformations within those communities. Despite their relevance, few studies focus on the characterization of these environments. This paper presents the results of the FabLab Global Survey, aimed at understanding the characteristics of FabLabs through the visions of their managers, or “FabManagers”. The results show an enormous diversity of approaches within the FabLab movement that cannot be extrapolated to a single characteristic element, but that allow its global compression. Their properties reveal them as transforming elements that eliminate technological and cultural barriers, empowering user communities and optimizing learning processes regarding digital technology. FabLab activities allow not only economic and industrial development thanks to innovative projects, but also a digital technology approach for young students and the inclusion of minorities at risk, thus eliminating old cultural and social barriers.
Application of the Simplex-Centroid Mixture Design to Biomass Charcoal Powder Formulation Ratio for Biomass Charcoal Briquettes
Authors: Hwangdee Phisamas , Charee Singrun , Kheowkrai Watcharin , Junsiri Chaiyan , Laloon Kittipong
Published: March 2022

This study aims to increase the quality and value of raw materials with a low higher heating value, HHV (secondary ingredients), but which is abundantly available throughout the year by mixing it with high HHV materials (main ingredients) to obtain quality and standardized charcoal products in accordance with the industrial product standards as approved for commercial use. As for the ingredients, charcoal A is Eucalyptus bark coal (EuBC) with an average HHV of 3779.98 cal/g, charcoal B is rice husk coal (RHC) with an average HHV of 4863.29 cal/g, and charcoal C is charcoal from a biomass power plant (CBPP) with an average HHV of 5991.18 cal/g. The results from the simplex-centroid mixture design method allowed increased quality and value of the biomass charcoal powder (raw material) that has a low heating value but is sufficiently available throughout the year due to the mixing of secondary ingredients with raw materials that have a high heating value (main ingredient). The charcoal briquettes production must be qualified and meet the industrial product standards, and be approved for commercial use.
Systematic Review on Identification and Prediction of Deep Learning-Based Cyber Security Technology and Convergence Fields
Authors: Hwang Seung-Yeon , Shin Dong-Jin , Kim Jeong-Joon
Published: March 2022

Recently, as core technologies leading the fourth industrial revolution, such as the Internet of Things (IoT), 5G, the cloud, and big data, have promoted smart convergence across national socio-economic infrastructures, cyber systems are expanding and becoming complex, and they are not effective in responding to cyber safety risks and threats using security technology solutions limited to a single system. Therefore, we developed cyber security technology that combines machine learning and AI technology to solve complex problems related to cyber safety. In this regard, this study aims to identify technology development trends to prevent the risks and threats of various cyber systems by monitoring major cyber security convergence fields and technologies through the symmetrical thesis and patent analysis. Because thesis information can explain the superiority of technology and patent information can explain the usefulness of a technology, they can be effectively used for analyzing and predicting technology development trends. Therefore, in this study, latent Dirichlet allocation is applied to extract text-document-based technical topics for the symmetrical thesis and patent information to identify security convergence fields and technologies for cyber safety. In addition, it elucidates cyber security convergence fields and technology trends by applying a dynamic topic model and long short-term memory, which are useful for analyzing technological changes and predicting trends. Based on these results, cyber security administrators, system operators, and developers can effectively identify and respond to trends in related technologies to reduce threats, and companies and experts developing cyber security solutions can present a new security approach.
A Domain-Specific, Model Based Systems Engineering Approach for Cyber-Physical Systems
Authors: Neureiter Christian , Binder Christoph
Published: March 2022

Model Based Systems Engineering as a scientific discipline tries to address the increasing complexity of today’s cyber-physical systems by utilizing different kinds of models. In practical application, however, this approach is often constrained to SysML-based object modeling. Even though this appears to be a suitable approach for dealing with complexity, various restrictions limit stakeholder acceptance. Considering scientific discussions in the context of modeling shows two different schools of thought. On the one hand, arguments for more formalized and rigorous concepts can be found, where on the other hand, the need for more stakeholder-oriented and easier-to-understand concepts is postulated. As both are reasonable, the question of integration arises. To address this aspect, we developed the concept of Domain Specific Systems Engineering. Our research in this field lasted for nearly a decade, and different aspects have been investigated. This paper contributes a summary of the overall approach that integrates the various aspects investigated so far. Thus, the underlying concepts are explained, and the corresponding modeling stack and tool-chain are described in more detail. Further, the practical experiences from various case studies are summarized, and identified shortcomings are discussed.
Differences in PLA2 Constitution Distinguish the Venom of Two Endemic Brazilian Mountain Lanceheads, Bothrops cotiara and Bothrops fonsecai
Authors: Nachtigall Pedro G. , Freitas-de-Sousa Luciana A. , Mason Andrew J. , Moura-da-Silva Ana M. , Grazziotin Felipe G. , Junqueira-de-Azevedo Inácio L. M.
Published: March 2022

Interspecific differences in snake venom compositions can result from distinct regulatory mechanisms acting in each species. However, comparative analyses focusing on identifying regulatory elements and patterns that led to distinct venom composition are still scarce. Among venomous snakes, Bothrops cotiara and Bothrops fonsecai represent ideal models to complement our understanding of the regulatory mechanisms of venom production. These recently diverged species share a similar specialized diet, habitat, and natural history, but each presents a distinct venom phenotype. Here, we integrated data from the venom gland transcriptome and miRNome and the venom proteome of B. fonsecai and B. cotiara to better understand the regulatory mechanisms that may be acting to produce differing venom compositions. We detected not only the presence of similar toxin isoforms in both species but also distinct expression profiles of phospholipases A2 (PLA2) and some snake venom metalloproteinases (SVMPs) and snake venom serine proteinases (SVSPs) isoforms. We found evidence of modular expression regulation of several toxin isoforms implicated in venom divergence and observed correlated expression of several transcription factors. We did not find strong evidence for miRNAs shaping interspecific divergence of the venom phenotypes, but we identified a subset of toxin isoforms whose final expression may be fine-tuned by specific miRNAs. Sequence analysis on orthologous toxins showed a high rate of substitutions between PLA2s, which indicates that these toxins may be under strong positive selection or represent paralogous toxins in these species. Our results support other recent studies in suggesting that gene regulation is a principal mode of venom evolution across recent timescales, especially among species with conserved ecotypes.
Optimization of Biodegradation Characteristics of Sphingopyxis sp. YF1 against Crude Microcystin-LR Using Response Surface Methodology
Authors: Massey Isaac Yaw , Peng Tangjian , Danping Cai , Yang Fei
Published: March 2022

Sphingopyxis sp. YF1 has proven to be efficient in biodegrading microcystin (MC)-leucine (L) and arginine (R) (MC-LR); however, the optimal environmental factors to biodegrade the toxin have not been investigated. In this study, the biodegrading characteristics of strain YF1 against MC-LR were assessed under diverse environmental factors, including temperature (20, 30 or 40 °C), pH (5, 7 or 9) and MC-LR concentration (1, 3 or 5 µg/mL). Data obtained from the single-factor experiment indicated that MC-LR biodegradation by strain YF1 was temperature-, pH- and MC-LR-concentration-dependent, and the maximal biodegradation rate occurred at 5 µg/mL/h. Proposing Box-Behnken Design in response surface methodology, the influence of the three environmental factors on the biodegradation efficiency of MC-LR using strain YF1 was determined. A 17-run experiment was generated and carried out, including five replications performed at the center point. The ANOVA analysis demonstrated that the model was significant, and the model prediction of MC-LR biodegradation was also validated with the experimental data. The quadratic statistical model was established to predict the interactive effects of the environmental factors on MC-LR biodegradation efficiency and to optimize the controlling parameters. The optimal conditions for MC-LR biodegradation were observed at 30 °C, pH 7 and 3 µg/mL MC-LR, with a biodegradation efficiency of 100% after 60 min. The determination of the optimal environmental factors will help to unveil the detailed biodegradation mechanism of MC-LR by strain YF1 and to apply it into the practice of eliminating MC-LR from the environment.
VZV Infection of Primary Human Adrenal Cortical Cells Produces a Proinflammatory Environment without Cell Death
Authors: Niemeyer Christy S. , Mescher Teresa , Bubak Andrew N. , Medina Eva M. , Hassell James E. , Nagel Maria A.
Published: March 2022

Virus infection of adrenal glands can disrupt secretion of mineralocorticoids, glucocorticoids, and sex hormones from the cortex and catecholamines from the medulla, leading to a constellation of symptoms such as fatigue, dizziness, weight loss, nausea, and muscle and joint pain. Specifically, varicella zoster virus (VZV) can produce bilateral adrenal hemorrhage and adrenal insufficiency during primary infection or following reactivation. However, the mechanisms by which VZV affects the adrenal glands are not well-characterized. Herein, we determined if primary human adrenal cortical cells (HAdCCs) infected with VZV support viral replication and produce a proinflammatory environment. Quantitative PCR showed VZV DNA increasing over time in HAdCCs, yet no cell death was seen at 3 days post-infection by TUNEL staining or Western Blot analysis with PARP and caspase 9 antibodies. Compared to conditioned supernatant from mock-infected cells, supernatant from VZV-infected cells contained significantly elevated IL-6, IL-8, IL-12p70, IL-13, IL-4, and TNF-α. Overall, VZV can productively infect adrenal cortical cells in the absence of cell death, suggesting that these cells may be a potential reservoir for ongoing viral replication and proinflammatory cytokine production, leading to chronic adrenalitis and dysfunction.
Zoonotic Origins of Human Metapneumovirus: A Journey from Birds to Humans
Authors: Jesse Sonja T. , Ludlow Martin , Osterhaus Albert D. M. E.
Published: March 2022

Metapneumoviruses, members of the family Pneumoviridae, have been identified in birds (avian metapneumoviruses; AMPV’s) and humans (human metapneumoviruses; HMPV’s). AMPV and HMPV are closely related viruses with a similar genomic organization and cause respiratory tract illnesses in birds and humans, respectively. AMPV can be classified into four subgroups, A–D, and is the etiological agent of turkey rhinotracheitis and swollen head syndrome in chickens. Epidemiological studies have indicated that AMPV also circulates in wild bird species which may act as reservoir hosts for novel subtypes. HMPV was first discovered in 2001, but retrospective studies have shown that HMPV has been circulating in humans for at least 50 years. AMPV subgroup C is more closely related to HMPV than to any other AMPV subgroup, suggesting that HMPV has evolved from AMPV-C following zoonotic transfer. In this review, we present a historical perspective on the discovery of metapneumoviruses and discuss the host tropism, pathogenicity, and molecular characteristics of the different AMPV and HMPV subgroups to provide increased focus on the necessity to better understand the evolutionary pathways through which HMPV emerged as a seasonal endemic human respiratory virus.
Circular RNA Profiles in Viremia and ART Suppression Predict Competing circRNA–miRNA–mRNA Networks Exclusive to HIV-1 Viremic Patients
Authors: Zucko Dora , Hayir Abdullgadir , Grinde Kelsey , Boris-Lawrie Kathleen
Published: March 2022

Since the onset of the HIV-1/AIDS epidemic in 1981, 75 million people have been infected with the virus, and the disease remains a public health crisis worldwide. Circular RNAs (circRNAs) are derived from excised exons and introns during backsplicing, a form of alternative splicing. The relevance of unconventional, non-capped, and non-poly(A) transcripts to transcriptomics studies remains to be routinely investigated. Knowledge gaps to be filled are the interface between host-encoded circRNAs and viral replication in chronically progressed patients and upon treatment with antiviral drugs. We implemented a bioinformatic pipeline and repurpose publicly archived RNA sequence reads from the blood of 19 HIV-1-positive patients that previously compared transcriptomes during viremia and viremia suppression by antiretroviral therapy (ART). The in silico analysis identified viremic patients’ circRNA that became undetectable after ART. The circRNAs originated from a subset of host genes enriched in the HDAC biological pathway. These circRNAs and parental mRNAs held in common a small collection of miRNA response elements (MREs), some of which were present in HIV-1 mRNAs. The function of the MRE-containing target mRNA enriched the RNA polymerase II GO pathway. To visualize the interplay between individual circRNA–miRNA–target mRNA, important for HIV-1 and potentially other diseases, an Interactive Circos tool was developed to efficiently parse the intricately competing endogenous network of circRNA–miRNA–mRNA interactions originating from seven circRNA singled out in viremic versus non-viremic patients. The combined downregulation of the identified circRNAs warrants investigation as a novel antiviral targeting strategy.
Genetic Characterization of Small Ruminant Lentiviruses (SRLVs) Circulating in Naturally Infected Sheep in Central Italy
Authors: Arcangeli Chiara , Torricelli Martina , Sebastiani Carla , Lucarelli Daniele , Ciullo Marcella , Passamonti Fabrizio , Giammarioli Monica , Biagetti Massimo
Published: March 2022

Small ruminant lentiviruses (SRLVs) represent a very heterogeneous group of ss-RNA viruses that infect sheep and goats worldwide. They cause important, deleterious effects on animal production and limit the animal trade. SRLVs show a high genetic variability due to high mutation rate and frequent recombination events. Indeed, five genotypes (A–E) and several subtypes have been detected. The aim of this work was to genetically characterize SRLVs circulating in central Italy. On this basis, a phylogenetic study on the gag-pol genetic region of 133 sheep, collected from 19 naturally infected flocks, was conducted. In addition, to evaluate the frequency of mutation and the selective pressure on this region, a WebLogo 3 analysis was performed, and the dN/dS ratio was computed. The results showed that 26 samples out of 133 were clustered in genotype A and 106 samples belonged to genotype B, as follows: A9 (n = 8), A11 (n = 10), A24 (n = 7), B1 (n = 2), B2 (n = 59), and B3 (n = 45). No recombination events were found. Mutations were localized mainly in the VR-2 region, and the dN/dS ratio of 0.028 indicated the existence of purifying selection. Since the genetic diversity of SRLVs could make serological identification difficult, it is important to perform molecular characterization to ensure a more reliable diagnosis, to maintain flock health status, and for the application of local and national control programs.
Gene Expression Profile Induced by Two Different Variants of Street Rabies Virus in Mice
Authors: Appolinário Camila M. , Daly Janet M. , Emes Richard D. , Marchi Fabio Albuquerque , Ribeiro Bruna Leticia Devidé , Megid Jane
Published: March 2022

Pathogenicity and pathology of rabies virus (RABV) varies according to the variant, but the mechanisms are not completely known. In this study, gene expression profile in brains of mice experimentally infected with RABV isolated from a human case of dog rabies (V2) or vampire bat-acquired rabies (V3) were analyzed. In total, 138 array probes associated with 120 genes were expressed differentially between mice inoculated with V2 and sham-inoculated control mice at day 10 post-inoculation. A single probe corresponding to an unannotated gene was identified in V3 versus control mice. Gene ontology (GO) analysis revealed that all of the genes upregulated in mice inoculated with V2 RABV were involved in the biological process of immune defense against pathogens. Although both variants are considered pathogenic, inoculation by the same conditions generated different gene expression results, which is likely due to differences in pathogenesis between the dog and bat RABV variants. This study demonstrated the global gene expression in experimental infection due to V3 wild-type RABV, from the vampire bat Desmodus rotundus, an important source of infection for humans, domestic animals and wildlife in Latin America.
Social Support Mediates the Association between Attachment Style and Psychological Distress during COVID-19 in Israel
Authors: Adar Tal , Davidof May , Elkana Odelia
Published: March 2022

Aim: The aim of this study was to examine the relationships between attachment style, social support, and psychological distress (i.e., depression and anxiety) during the COVID-19 lockdown of the third wave in Israel. Specifically, we examined whether social support mediates the well-documented relationship between attachment style and psychological distress. Methods: An online survey was administered from 3 January to 6 February, 2021, while a strict lockdown was in place. The sample included 288 Israelis ranging between the ages of 18–78, recruited by snowball sampling. Psychological distress was evaluated by Patients Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder questionnaire (GAD-7); attachment style by the Experiences in Close Relationships (ECR-36), and social support by the Multi-dimensional Perceived Social Support (MSPSS). A mediation model was applied with social support mediating the association between attachment style and depression and anxiety. Results: Significant correlations were found between attachment style and psychological distress, and between social support and psychological distress. Social support partially mediated the associations between attachment style and psychological distress (Depression: p < 0.001, confidence interval [CI] = 0.4018, 1.7468; Anxiety: p < 0.001, confidence interval [CI] = 0.0493, 0.9822). These results remained the same while controlling for age. Conclusion: Our findings suggest that the secure attachment style serves as a protective factor against psychological distress and vice versa; insecure attachment style serves as a risk factor for developing psychological distress during a peak period of COVID-19. Nevertheless, social support played a central role in the association between attachment style and psychological distress, thus, individuals with an insecure attachment may thus be helped by offering them social support during a crisis, which in turn may increase their well-being.