bluuebunny/crossref_metadata_2025 huggingface dataset:
An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
Authors: Amin Al , Amin Mohammad Shafenoor , Cho Chongdu
Published: May 2023

Electric two-wheelers are becoming increasingly popular across the world, particularly in cities where their small size and flexibility make them a viable option for navigating congested streets. One of the most challenging aspects of e-mobility on two-wheelers is precisely calculating their range. This might be an issue for riders who must go long distances or who have limited access to charging stations. Various factors can influence an electric two-wheeler range, making it challenging to predict how far it can travel on a single charge. To tackle this problem, most of the manufacturers offer range predictions based on both test data and real-world usage scenarios. However, these estimates are customized for specific vehicle models and testing parameters that may not apply in all circumstances. Additionally, it can be challenging to obtain comprehensive technical specifications for two-wheelers available in the market, as most manufacturers do not provide detailed technical information. Hence, it is crucial to address the challenge of range prediction for two-wheelers in general, which can be advantageous for riders. In this paper, we discuss the precise prediction of the remaining range of electric two-wheelers even without knowing detailed e-mobility technical information. An application is also developed only for this research purpose, which can provide navigation services. Our approach concentrates on user behavior, weather, road conditions, and the vehicle’s performance history, which is gathered through the application. The collected data are used to train the selected ML model on the cloud. We applied various machine learning algorithms before deploying in the cloud where the SVM algorithm demonstrated outstanding performance, with a mean absolute error of 150 m for an average distance of 7.46 km. Furthermore, the model’s performance was evaluated after deployment and tested having 130 m error on average.
A Hybrid Deep Learning Framework Based on Diffusion Model and Deep Residual Neural Network for Defect Detection in Composite Plates
Authors: Huang Tianrui , Gao Yang , Li Zhenglin , Hu Yue , Xuan Fuzhen
Published: May 2023

The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies, such as deep learning, have become one of the main driving forces for state monitoring and predictive analysis of these structures. However, it is difficult to obtain sufficient data to train the deep learning model, which may fail to build an accurate and efficient SHM model. To overcome this problem, a new method based on Lamb waves and the diffusion model (DM) is proposed to realize the identification and classification of different defects for carbon-fiber-reinforced polymer (CFRP) structures. In this study, DM is used as the generation model of data enhancement, and the optimized and improved DDPM model is constructed in this experiment. The deep residual neural network (DenseNet) is used to identify and classify the defect features from the Lamb wave signals. Experimental and test results show that the deep learning framework designed in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify damage signals of common defects in CFRP composite plates.
Application of Time-Controlled Critical Point in Pressure Reducing Valves: A Case Study in North Spain
Authors: Ortega-Ballesteros Andrés , Muñoz-Rodríguez David , Aguilera-Ureña María-Jesús , de los Santos-Zarco Francisco Javier , Perea-Moreno Alberto-Jesus
Published: May 2023

Potable water utilities are currently making great efforts to reduce leakage rates and assure long-term supply to the population due to the challenges of climate change, growing population and water shortage scenarios that have been on them over the last years. One of the most employed methods to reduce leakage includes the installation of pressurereducing valves along the water distribution network and the utilization of pressure management schemes. Pressure management includes different types of control models, which are applied according to the requirements of each site. The most advanced and sophisticated scheme is critical point control, which relies on a flow signal from a measuring device or online communication between the critical point and the valve. This paper proposes the utilization of a seasonal autoregressive integrated moving average, or the SARIMA model, to correlate pressure at the outlet of the valve and pressure on the critical point of the area supplied, aiming to set a fixed pressure in the critical point. The SARIMA model is developed according to historical data logged in the field and then validated. Later, the SARIMA model was tested on a real location in the village of Noja, Spain. The analysis of the field test results prove that the proposed model is feasible to be used since there is no significance difference between the target values set in the critical point and the real values measured in the field. The research proves that the SARIMA model can be used as an alternative for critical point control in water distribution networks when no flow signal is available or when communication between the critical point and the pressure reducing valve is not an option.
Assessment of Interlocking Concrete Block Pavement with By-Products and Comparison with an Asphalt Pavement: A Review
Authors: Silva Webert , Picado-Santos Luís , Barroso Suelly , Cabral Antônio Eduardo , Stefanutti Ronaldo
Published: May 2023

This paper aims to review the performance analysis of interlocking concrete block pavement with by-products such as coconut fibers, and construction and demolition recycled materials, and to compare their skills with asphalt pavement, especially for light-traffic urban road applications. The focus is on mechanical behavior (mix parameters and the influence of by-products), pavement design, sustainability (the heat island effect), the management of contaminant concentrations within infiltration related to permeability, and Life Cycle Assessment. Considering the overall performance analysis, interlocking concrete block pavement was the most attractive alternative because it was approximately 33–44% cheaper in the maintenance process, cooler over a range of 2.2–15 °C, and more permeable by 0.4 cm/s to 0.6 cm/s than asphalt pavement, saving costs and improving drainage and human thermal comfort. However, asphalt pavement was around 35% cheaper during the construction phase (mainly due to energy consumption), and it presented a 32% lower nitrogen oxide concentration. This paper showed the advantages and disadvantages of both types of pavements. A further breakdown should be developed and integrated into the decision-making process about choosing between solutions.
Recycling Glass and Carbon Fibers for Reusable Components in the Automotive Sector through Additive Manufacturing
Authors: Romani Alessia , Caba Stefan , Suriano Raffaella , Levi Marinella
Published: May 2023

This work explores the use of additive manufacturing (AM) to reprocess recycled glass and carbon fibers in the automotive sector. It aims to foster exploitation of recycled Glass Fiber Reinforced Polymers (rGFRPs) and recycled Carbon Fiber Reinforced Polymers (rCFRPs) through two manufacturing workflows: indirect Fused Filament Fabrication (FFF) and UV-assisted Direct Ink Writing (UV-DIW). An industrial case study on vehicle components has been considered by prototyping one real component. After the tensile tests, some molds were fabricated with a FFF 3D printer for the indirect 3D printing process to cast an epoxy-based thermosetting resin with rGFs and rCFs. The second technology consisted in fabricating the parts by hardening in-situ a photo- and thermal-curable thermosetting acrylic liquid resin with rGFs. These results validate the use of AM and recycled composites for applications in the automotive sector. These approaches may be implemented for customizable components for batches below 100 vehicles as the first step for their exploitation.
Experimental Study on the Effect of Hydrogen Addition on the Laminar Burning Velocity of Methane/Ammonia–Air Flames
Authors: Yasiry Ahmed , Wang Jinhua , Zhang Longkai , Dai Hongchao , Abdulraheem Ahmed A. A. , Shahad Haroun A. K. , Huang Zuohua
Published: May 2023

Variations in methane–ammonia blends with hydrogen enrichment can modify premixed flame behavior and play a crucial role in achieving ultra-low carbon emissions and sustainable energy consumption. Current combustion units may co-fire ammonia/methane/hydrogen, necessitating further investigation into flame characteristics to understand the behavior of multi-component fuels. This research aims to explore the potential of replacing natural gas with ammonia while making only minor adjustments to equipment and processes. The laminar burning velocity (LBV) of binary blends, such as ammonia–methane, ammonia–hydrogen, and hydrogen–methane–air mixtures, was investigated at an equivalence ratio of 0.8–1.2, within a constant volume combustion chamber at a pressure of 0.1 MPa and temperature of 298 K. Additionally, tertiary fuels were examined with varying hydrogen blending ratios ranging from 0% to 40%. The results show that the laminar burning velocity (LBV) increases as the hydrogen fraction increases for all mixtures, while methane increases the LBV during blending with ammonia. Hydrogen-ammonia blends are the most effective mixture for increasing LBV non-linearly. Enhancement parameters demonstrate the effect of ternary fuel, which behaves similarly to equivalent methane in terms of adiabatic flame temperature and LBV achieved at 40% hydrogen. Experimental data for neat and binary mixtures were validated by different kinetics models, which also showed good consistency. The ternary fuel mixtures were also validated with these models. The Li model may qualitatively predict well for ammonia-dominated fuel. The Shrestha model may overestimate results on the rich side due to the incomplete N2Hisub-mechanism, while lean and stoichiometric conditions have better predictions. The Okafor model is always overestimated.
Prediction of Urinary Tract Infection in IoT-Fog Environment for Smart Toilets Using Modified Attention-Based ANN and Machine Learning Algorithms
Authors: Alqahtani Abdullah , Alsubai Shtwai , Binbusayyis Adel , Sha Mohemmed , Gumaei Abdu , Zhang Yu-Dong
Published: May 2023

UTI (Urinary Tract Infection) has become common with maximum error rates in diagnosis. With the current progress on DM (Data Mining) based algorithms, several research projects have tried such algorithms due to their ability in making optimal decisions and efficacy in resolving complex issues. However, conventional research has failed to attain accurate predictions due to improper feature selection. To resolve such existing pitfalls, this research intends to employ suitable ML (Machine Learning)-based algorithms for predicting UTI in IoT-Fog environments, which will be applicable to a smart toilet. Additionally, bio-inspired algorithms have gained significant attention in recent eras due to their capability in resolving complex optimization issues. Considering this, the current study proposes MFB-FA (Modified Flashing Behaviour-based Firefly Algorithm) for feature selection. This research initializes the FF (Firefly) population and interchanges the constant absorption coefficient value with the chaotic maps as the chaos possesses an innate ability to evade getting trapped in local optima with the improvement in determining global optimum. Further, GM (Gaussian Map) is taken into account for moving all the FFs to a global optimum in an individual iteration. Due to such nature, this algorithm possesses a better optimization ability than other swarm intelligence approaches. Finally, classification is undertaken by the proposed MANN-AM (Modified Artificial Neural Network with Attention Mechanism). The main intention for proposing this network involves its ability to focus on small and significant data. Moreover, ANNs possess the ability for learning and modelling complex and non-linear relationships, in which the present study considers it. The proposed method is compared internally by using Random Forest, Naive Bayes and K-Nearest Neighbour to show the efficacy of the proposed model. The overall performance of this study is assessed with regard to standard performance metrics for confirming its optimal performance in UTI prediction. The proposed model has attained optimal values such as accuracy as 0.99, recall as 0.99, sensitivity as 1, precision as 1, specificity as 0.99 and f1-score as 0.99.
Adaptation of a Cogenerator with Induction Generator to an On/Off-Grid Operation Using a Power Electronic System
Authors: Kampik Marian , Fice Marcin , Jurkiewicz Andrzej
Published: May 2023

Cogeneration sources play a very important role in the power industry with dispersed renewable sources with forced generation (e.g., photovoltaics and wind generators). They also fit into the circular economy by increasing the efficiency of fuel use, including biogas from agricultural or livestock waste. The aim of our research was to develop an effective source of electricity powered by agricultural biogas. The most important features of such a source are: operation in on-grid and off-grid mode, as well as a low cost of the device and uncomplicated operation. In addition, in Europe, the source of electricity connected to the power grid must meet the technical requirements of the “Network Codes Requirements for Generators” (NC RfG) network code. The appropriate certificate is easier to obtain using a power converter system for the source. For this purpose, an induction generator with a converter system and a small battery was planned. A converter system was developed and built, and then tests were carried out in various operating modes. During the measurements, it was confirmed that the requirements for the quality of electricity for off-grid and on-grid operation modes were met. The assumed maximum time of voltage recovery after changing the operating mode, amounting to 40 ms, was not exceeded. Furthermore, the limit values of phase voltages with unsymmetrical load, amounting to ±10% of the rated voltage, were not exceeded. In the battery usage off-grid mode, the time after a step change in the load power was not longer than 2 s.
A Review of Digital Twin Technology for Electric and Autonomous Vehicles
Authors: Ali Wasim A. , Fanti Maria Pia , Roccotelli Michele , Ranieri Luigi
Published: May 2023

In the era of technological transformation, mobility and transportation systems are becoming more intelligent and greener. Thanks to powerful technologies and tools, electric and autonomous vehicles are spreading worldwide, substituting internal combustion engine vehicles and revolutionizing the way to drive. In this context, this paper is an extended version of the paper “Digital Twin in Intelligent Transportation Systems: a Review published in 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)”. The aim of this paper is to provide a comprehensive review of the literature from the last five years on the use of digital twin (DT) technology for Intelligent Transportation Systems (ITSs), focusing on electric and autonomous vehicles. In particular, with respect to the previous work, the focus has been expanded to include DT integration with other cutting-edge technologies, such as the Internet of Things (IoT), Big Data, artificial intelligence (AI), machine learning (ML), and 5G for ITS. Moreover, this paper presents a broad perspective on challenges in EV applications, including tracking, monitoring, battery and charge management, connectivity, security, and privacy. In addition, this paper discusses how DT can be used to effectively address the current issues in electric vehicle services, such as tracking, monitoring, battery and charge management, connectivity, security, and privacy.
HF-Induced Artificial Injection of Energetic Electrons from the Earth’s Radiation Belt as a Powerful Source for Modification of Ionized and Neutral Components of the Earth’s Atmosphere
Authors: Frolov Vladimir L. , Troitsky Arkady V.
Published: May 2023

It has been found in experiments at the SURA mid-latitude heating facility that the modification of the ionospheric F2 layer by powerful HF radio waves gives rise to artificial injection of energetic electrons from the Earth’s radiation belt into the atmosphere. The spectral, energy, and spatial characteristics of such an injection are presented in the paper. It is significant that the energetic electrons excite the atoms and molecules of the atmosphere to Rydberg energy levels, followed by the transition of the excited atoms and molecules to lower energy states, accompanied by the radiation of the microwave electromagnetic emissions. It has been shown that the artificial injection of energetic electrons can be considered as an independent powerful source of generation of secondary artificial turbulence, the effect of which manifests itself at ionospheric and mesospheric heights both near the heating facility and at a large distance from it up to a thousand or more kilometers. Examples of such generation are given.
Laser Attenuation and Ranging Correction in the Coal Dust Environment Based on Mie Theory and Phase Ranging Principle
Authors: Li Ben , Mao Shanjun , Zhang Hong
Published: May 2023

The inadequate ventilation and complex environments in underground coal mines lead to a high concentration of dust particles. As a result, the health of the miners and the accuracy of laser rangefinder measurements are endangered. It is crucial to enhance the laser rangefinder’s efficiency to mitigate health risks and reduce labor intensity. In this study, we propose a laser power attenuation model and a ranging correction model to address the issues of laser power attenuation and inaccurate ranging in coal dust environments. The proposed models are based on theoretical analysis and practical experiments, and both are dependent on the dust particle size (<250 μm) and mass concentration. Firstly, we assessed the factors that caused laser power attenuation and demonstrated that our proposed model could accurately predict them (maximum residual of 0.06). Secondly, we obtained the connection between the attenuation coefficient and dust concentration by applying the Lambert–Beer law. Lastly, we established the ranging correction model by collecting laser wavelength information. The outcomes show that the root mean square error of the corrected values ranges between 0.27 and 0.47 mm. To summarize, our suggested model and correction technique can efficiently enhance the precision of laser rangefinder measurements, thus improving underground work in coal mines.
Artificial Periodic Irregularities and Temperature of the Lower Thermosphere
Authors: Bakhmetieva Nataliya V. , Grigoriev Gennadiy I. , Zhemyakov Ilia N. , Kalinina Elena E.
Published: May 2023

The results of temperature measurements in the lower thermosphere at altitudes of 90–130 km by the method of resonant scattering of radio waves on artificial periodic inhomogeneities (APIs) of the ionospheric plasma are presented. These inhomogeneities are created when the ionosphere is exposed to powerful HF radio emission. The temperature profile was obtained from measurements of the relaxation time of the API scattered signal. The data processes and the method of the temperature determination are given in detail. The height and temporal resolutions of the API technique are of the order of 1 km and 15 s, respectively, making it possible to study both fast and slow processes in the lower thermosphere. Large temperature variability at altitudes of 90–130 km during the day and from day to day, due to the propagation of atmospheric waves, has been confirmed. The temporal variations of the atmospheric parameters take place with periods from 15 min to some hours. There are often height profiles of the temperature with the wave-like variations and with the vertical scale of about 4–10 km. The irregular temperature profiles were observed above 100 km.
New Developments in Climate Change, Air Pollution, Pollen Allergy, and Interaction with SARS-CoV-2
Authors: D’Amato Gennaro , Annesi-Maesano Isabella , Biagioni Benedetta , Lancia Andrea , Cecchi Lorenzo , D’Ovidio Maria Concetta , D’Amato Maria
Published: May 2023

In recent years, the environmental impacts of climate change have become increasingly evident. Extreme meteorological events are influenced by climate change, which also alter the magnitude and pattern of precipitations and winds. Climate change can have a particularly negative impact on respiratory health, which can lead to the emergence of asthma and allergic respiratory illnesses. Pollen is one of the main components of the atmospheric bioaerosol and is able to induce allergic symptoms in certain subjects. Climate change affects the onset, length, and severity of the pollen season, with effects on pollen allergy. Higher levels of carbon dioxide (CO2) can lead to enhanced photosynthesis and a higher pollen production in plants. Pollen grains can also interact with air pollutants and be affected by thunderstorms and other extreme events, exacerbating the insurgence of respiratory diseases such as allergic rhinitis and asthma. The consequences of climate change might also favor the spreading of pandemics, such as the COVID-19 one.
Families of Orbits Produced by Three-Dimensional Central and Polynomial Potentials: An Application to the 3D Harmonic Oscillator
Authors: Kotoulas Thomas
Published: May 2023

We study three-dimensional potentials of the form V=U(xp+yp+zp), where U is an arbitrary function of C2-class, and p∈Z, which produces a preassigned two-parametric family of spatial regular orbits given in the solved form f(x,y,z) = c1, g(x,y,z) = c2 (c1, c2 = const). These potentials have to satisfy two linear PDEs, which are the basic equations of the 3D inverse problem of Newtonian dynamics. The functions f and g can be represented uniquely by the ”slope functions” α(x,y,z) and β(x,y,z). The orbital functions α(x,y,z) and β(x,y,z) have to satisfy three differential conditions according to the theory of the inverse problem. If these conditions are satisfied, then we can find such a potential analytically. We offer pertinent examples of potentials that are mainly used in physical problems. The values obtained for p lead to cases of well-known potentials, such as the Newtonian, cored, logarithmic, polynomial and quadratic ones. New families of orbits produced by the 3D harmonic oscillator are found. Pertinent examples are given and cover all cases. Two-dimensional potentials belong to a special category of potentials and are studied separately. The families of straight lines in 3D space are also examined.
Synthesis and Performance of NaTi2(PO4)3/VGCF@C Anode Composite Material for Aqueous Sodium-Ion Batteries
Authors: Ding Bo , Li Mingzhu , Zheng Fuzhou , Ma Yangzhou , Song Guangsheng , Guan Xiulong , Cao Yi , Wen Cuie
Published: May 2023

This study combines self-prepared NaTi2(PO4)3 (NTP) with commercial vapor-grown carbon fiber (VGCF) using a solid state calcination, then coats it with carbon to synthesize the composite anode material NaTi2(PO4)3/VGCF@C (NTP/VGCF@C). The microstructure and electrochemical properties of the composite material were then analyzed using microstructure analysis and electrochemical testing equipment. Single phase NTP shows nanoparticles with a polyhedral structure, and there is good contact at the interface between the nanoparticles and the VGCFs. The carbon coating formed on the NTP particles displays a nearly 6.5 nm thick layer of amorphous carbon. From the coin-cell battery performance measurements, after 850 cycles, the composite material NTP/VGCF@C exhibits an excellent retention rate of 96.3% compared to that of the pure NTP material when the current density is 200 mA/g. As a result, the composite material and lithium manganate (denoted as LMO) were assembled into an LMO-NTP/VGCF@C aqueous sodium-ion soft pack full battery system. The full battery shows an initial capacity of 31.07 mAh at a rate of 0.5C, and a reversible discharge capacity retention rate of 95.8% after 480 cycles, exhibiting a good long-cycle stability performance.
Application of Artificial Intelligence for Fraudulent Banking Operations Recognition
Authors: Mytnyk Bohdan , Tkachyk Oleksandr , Shakhovska Nataliya , Fedushko Solomiia , Syerov Yuriy
Published: May 2023

This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.
Tailoring the Microarchitectures of 3D Printed Bone-like Scaffolds for Tissue Engineering Applications
Authors: Zenobi Eleonora , Merco Miriam , Mochi Federico , Ruspi Jacopo , Pecci Raffaella , Marchese Rodolfo , Convertino Annalisa , Lisi Antonella , Del Gaudio Costantino , Ledda Mario
Published: May 2023

Material extrusion (MEX), commonly referred to as fused deposition modeling (FDM) or fused filament fabrication (FFF), is a versatile and cost-effective technique to fabricate suitable scaffolds for tissue engineering. Driven by a computer-aided design input, specific patterns can be easily collected in an extremely reproducible and repeatable process. Referring to possible skeletal affections, 3D-printed scaffolds can support tissue regeneration of large bone defects with complex geometries, an open major clinical challenge. In this study, polylactic acid scaffolds were printed resembling trabecular bone microarchitecture in order to deal with morphologically biomimetic features to potentially enhance the biological outcome. Three models with different pore sizes (i.e., 500, 600, and 700 µm) were prepared and evaluated by means of micro-computed tomography. The biological assessment was carried out seeding SAOS-2 cells, a bone-like cell model, on the scaffolds, which showed excellent biocompatibility, bioactivity, and osteoinductivity. The model with larger pores, characterized by improved osteoconductive properties and protein adsorption rate, was further investigated as a potential platform for bone-tissue engineering, evaluating the paracrine activity of human mesenchymal stem cells. The reported findings demonstrate that the designed microarchitecture, better mimicking the natural bone extracellular matrix, favors a greater bioactivity and can be thus regarded as an interesting option for bone-tissue engineering.
Optimal Design of Array Coils for Multi-Target Adjustable Electromagnetic Brain Stimulation System
Authors: Wang Tingyu , Yan Lele , Yang Xinsheng , Geng Duyan , Xu Guizhi , Wang Alan
Published: May 2023

Temporal interference magnetic stimulation is a novel noninvasive deep brain neuromodulation technology that can solve the problem of balance between focus area and stimulation depth. However, at present, the stimulation target of this technology is relatively single, and it is difficult to realize the coordinated stimulation of multiple brain regions, which limits its application in the modulation of multiple nodes in the brain network. This paper first proposes a multi-target temporal interference magnetic stimulation system with array coils. The array coils are composed of seven coil units with an outer radius of 25 mm, and the spacing between coil units is 2 mm. Secondly, models of human tissue fluid and the human brain sphere are established. Finally, the relationship between the movement of the focus area and the amplitude ratio of the difference frequency excitation sources under time interference is discussed. The results show that in the case of a ratio of 1:5, the peak position of the amplitude modulation intensity of the induced electric field has moved 45 mm; that is, the movement of the focus area is related to the amplitude ratio of the difference frequency excitation sources. The conclusion is that multi-target temporal interference magnetic stimulation with array coils can simultaneously stimulate multiple network nodes in the brain region; rough positioning can be performed by controlling the conduction of different coils, fine-tuning the position by changing the current ratio of the conduction coils, and realizing accurate stimulation of multiple targets in the brain area.
Transcriptome Analysis and Identification of Sesquiterpene Synthases in Liverwort Jungermannia exsertifolia
Authors: Yan Xiaoguang , Li Yukun , Li Weiguo , Liang Dongmei , Nie Shengxin , Chen Ruiqi , Qiao Jianjun , Wen Mingzhang , Caiyin Qinggele
Published: May 2023

The liverwort Jungermannia exsertifolia is one of the oldest terrestrial plants and rich in structurally specific sesquiterpenes. There are several sesquiterpene synthases (STSs) with non-classical conserved motifs that have been discovered in recent studies on liverworts; these motifs are rich in aspartate and bind with cofactors. However, more detailed sequence information is needed to clarify the biochemical diversity of these atypical STSs. This study mined J. exsertifolia sesquiterpene synthases (JeSTSs) through transcriptome analysis using BGISEQ-500 sequencing technology. A total of 257,133 unigenes was obtained, and the average length was 933 bp. Among them, a total of 36 unigenes participated in the biosynthesis of sesquiterpenes. In addition, the in vitro enzymatic characterization and heterologous expression in Saccharomyces cerevisiae showed that JeSTS1 and JeSTS2 produced nerolidol as the major product, while JeSTS4 could produce bicyclogermacrene and viridiflorol, suggesting a specificity of J. exsertifolia sesquiterpene profiles. Furthermore, the identified JeSTSs had a phylogenetic relationship with a new branch of plant terpene synthases, the microbial terpene synthase-like (MTPSL) STSs. This work contributes to the understanding of the metabolic mechanism for MTPSL-STSs in J. exsertifolia and could provide an efficient alternative to microbial synthesis of these bioactive sesquiterpenes.
Towards Novel Biomimetic In Vitro Models of the Blood–Brain Barrier for Drug Permeability Evaluation
Authors: Mármol Inés , Abizanda-Campo Sara , Ayuso Jose M. , Ochoa Ignacio , Oliván Sara
Published: May 2023

Current available animal and in vitro cell-based models for studying brain-related pathologies and drug evaluation face several limitations since they are unable to reproduce the unique architecture and physiology of the human blood–brain barrier. Because of that, promising preclinical drug candidates often fail in clinical trials due to their inability to penetrate the blood–brain barrier (BBB). Therefore, novel models that allow us to successfully predict drug permeability through the BBB would accelerate the implementation of much-needed therapies for glioblastoma, Alzheimer’s disease, and further disorders. In line with this, organ-on-chip models of the BBB are an interesting alternative to traditional models. These microfluidic models provide the necessary support to recreate the architecture of the BBB and mimic the fluidic conditions of the cerebral microvasculature. Herein, the most recent advances in organ-on-chip models for the BBB are reviewed, focusing on their potential to provide robust and reliable data regarding drug candidate ability to reach the brain parenchyma. We point out recent achievements and challenges to overcome in order to advance in more biomimetic in vitro experimental models based on OOO technology. The minimum requirements that should be met to be considered biomimetic (cellular types, fluid flow, and tissular architecture), and consequently, a solid alternative to in vitro traditional models or animals.
Chondroprotective Effects of Chondrogenic Differentiated Adipose-Derived Mesenchymal Stem Cells Sheet on Degenerated Articular Cartilage in an Experimental Rabbit Model
Authors: Taninaka Atsushi , Kabata Tamon , Hayashi Katsuhiro , Kajino Yoshitomo , Inoue Daisuke , Ohmori Takaaki , Ueoka Ken , Yamamuro Yuki , Kataoka Tomoyuki , Saiki Yoshitomo , Yanagi Yu , Ima Musashi , Iyobe Takahiro , Tsuchiya Hiroyuki
Published: May 2023

Adipose-derived stem cells (ADSCs) have been studied for many years as a therapeutic option for osteoarthritis (OA); however, their efficacy remains insufficient. Since platelet-rich plasma (PRP) induces chondrogenic differentiation in ADSCs and the formation of a sheet structure by ascorbic acid can increase the number of viable cells, we hypothesized that the injection of chondrogenic cell sheets combined with the effects of PRP and ascorbic acid may hinder the progression of OA. The effects of induction of differentiation by PRP and formation of sheet structure by ascorbic acid on changes in chondrocyte markers (collagen II, aggrecan, Sox9) in ADSCs were evaluated. Changes in mucopolysaccharide and VEGF-A secretion from cells injected intra-articularly in a rabbit OA model were also evaluated. ADSCs treated by PRP strongly chondrocyte markers, including type II collagen, Sox9, and aggrecan, and their gene expression was maintained even after sheet-like structure formation induced by ascorbic acid. In this rabbit OA model study, the inhibition of OA progression by intra-articular injection was improved by inducing chondrocyte differentiation with PRP and sheet structure formation with ascorbic acid in ADSCs.
Machine Learning-Based Prediction of Mental Well-Being Using Health Behavior Data from University Students
Authors: Abdul Rahman Hanif , Kwicklis Madeline , Ottom Mohammad , Amornsriwatanakul Areekul , H. Abdul-Mumin Khadizah , Rosenberg Michael , Dinov Ivo D.
Published: May 2023

Background: Since the onset of the COVID-19 pandemic in early 2020, the importance of timely and effective assessment of mental well-being has increased dramatically. Machine learning (ML) algorithms and artificial intelligence (AI) techniques can be harnessed for early detection, prognostication and prediction of negative psychological well-being states. Methods: We used data from a large, multi-site cross-sectional survey consisting of 17 universities in Southeast Asia. This research work models mental well-being and reports on the performance of various machine learning algorithms, including generalized linear models, k-nearest neighbor, naïve Bayes, neural networks, random forest, recursive partitioning, bagging, and boosting. Results: Random Forest and adaptive boosting algorithms achieved the highest accuracy for identifying negative mental well-being traits. The top five most salient features associated with predicting poor mental well-being include the number of sports activities per week, body mass index, grade point average (GPA), sedentary hours, and age. Conclusions: Based on the reported results, several specific recommendations and suggested future work are discussed. These findings may be useful to provide cost-effective support and modernize mental well-being assessment and monitoring at the individual and university level.
The Predatory Stink Bug Arma custos (Hemiptera: Pentatomidae) Produces a Complex Proteinaceous Venom to Overcome Caterpillar Prey
Authors: Qu Yuli , Walker Andrew A. , Meng Ling , Herzig Volker , Li Baoping
Published: May 2023

Predatory stink bugs capture prey by injecting salivary venom from their venom glands using specialized stylets. Understanding venom function has been impeded by a scarcity of knowledge of their venom composition. We therefore examined the proteinaceous components of the salivary venom of the predatory stink bug Arma custos (Fabricius, 1794) (Hemiptera: Pentatomidae). We used gland extracts and venoms from fifth-instar nymphs or adult females to perform shotgun proteomics combined with venom gland transcriptomics. We found that the venom of A. custos comprised a complex suite of over a hundred individual proteins, including oxidoreductases, transferases, hydrolases, ligases, protease inhibitors, and recognition, transport and binding proteins. Besides the uncharacterized proteins, hydrolases such as venom serine proteases, cathepsins, phospholipase A2, phosphatases, nucleases, alpha-amylases, and chitinases constitute the most abundant protein families. However, salivary proteins shared by and unique to other predatory heteropterans were not detected in the A. custos venom. Injection of the proteinaceous (>3 kDa) venom fraction of A. custos gland extracts or venom into its prey, the larvae of the oriental armyworm Mythimna separata (Walker, 1865), revealed insecticidal activity against lepidopterans. Our data expand the knowledge of heteropteran salivary proteins and suggest predatory asopine bugs as a novel source for bioinsecticides.
The Potential of Microalgae–Bacteria Consortia to Restore Degraded Soils
Authors: Gonzalez-Gonzalez Lina M. , de-Bashan Luz E.
Published: May 2023

Soil restoration is one of the biggest challenges of this century. Besides the negative impacts of climate change, the current increase in food demands has put severe pressure on soil resources, resulting in a significant area of degraded land worldwide. However, beneficial microorganisms, such as microalgae and plant growth-promoting bacteria, have an outstanding ability to restore soil health and fertility. In this mini-review, we summarize state-of-the-art knowledge on these microorganisms as amendments that are used to restore degraded and contaminated soils. Furthermore, the potential of microbial consortia to maximize beneficial effects on soil health and boost the production of plant-growth-promoting compounds within a mutualistic interaction is discussed.
AIF1: Function and Connection with Inflammatory Diseases
Authors: De Leon-Oliva Diego , Garcia-Montero Cielo , Fraile-Martinez Oscar , Boaru Diego Liviu , García-Puente Luis , Rios-Parra Antonio , Garrido-Gil Maria J. , Casanova-Martín Carlos , García-Honduvilla Natalio , Bujan Julia , Guijarro Luis G. , Alvarez-Mon Melchor , Ortega Miguel A.
Published: May 2023

Macrophages are a type of immune cell distributed throughout all tissues of an organism. Allograft inflammatory factor 1 (AIF1) is a calcium-binding protein linked to the activation of macrophages. AIF1 is a key intracellular signaling molecule that participates in phagocytosis, membrane ruffling and F-actin polymerization. Moreover, it has several cell type-specific functions. AIF1 plays important roles in the development of several diseases: kidney disease, rheumatoid arthritis, cancer, cardiovascular diseases, metabolic diseases and neurological disorders, and in transplants. In this review, we present a comprehensive review of the known structure, functions and role of AIF1 in inflammatory diseases.
Early Exotic Vegetation Development Is Affected by Vine Plants and Bird Activity at Rapidly Exposed Floodplains in South Korea
Authors: Park Jae-Hoon , Park Ji-Won , Kim Yoon-Seo , Lee Jung-Min , Kim Eui-Joo , Jeon Bo-Yeon , Kim Se-Hee , You Young-Han
Published: May 2023

For the study on the relationships between the seed dispersal of exotic plants and bird population, flora, avifauna, vegetation patches, and the dynamics of seed banks were investigated in and around the exposed floodplains of the large rivers, and the causes of exotic vegetation development were determined with respect to plant life form, bird population characteristics, and landscape using multivariate analysis. The number of dominant exotic plant species observed in exposed areas was higher than that observed in an abandoned field and paddy field undergoing secondary succession. Additionally, the area occupied by exotic vegetation in exposed areas increased with the increase in number of vine plants and small terrestrial birds, whereas the relationship between vine and runner plants was inversely proportional. Therefore, to control exotic plants in exposed floodplains surrounding large rivers, it is necessary to remove vines and shrubs along the waterfront where small resident birds carrying plant seeds live and to maintain and manage runner plant populations. Furthermore, implementing an ecological landscape management strategy, such as afforestation through the planting of trees, may also be effective.
Emerging Intrinsic Therapeutic Targets for Metastatic Breast Cancer
Authors: Li Jiawei , Goh Eyleen L. K. , He Ji , Li Yan , Fan Zhimin , Yu Zhigang , Yuan Peng , Liu Dong-Xu
Published: May 2023

Breast cancer is now the most common cancer worldwide, and it is also the main cause of cancer-related death in women. Survival rates for female breast cancer have significantly improved due to early diagnosis and better treatment. Nevertheless, for patients with advanced or metastatic breast cancer, the survival rate is still low, reflecting a need for the development of new therapies. Mechanistic insights into metastatic breast cancer have provided excellent opportunities for developing novel therapeutic strategies. Although high-throughput approaches have identified several therapeutic targets in metastatic disease, some subtypes such as triple-negative breast cancer do not yet have an apparent tumor-specific receptor or pathway to target. Therefore, exploring new druggable targets in metastatic disease is a high clinical priority. In this review, we summarize the emerging intrinsic therapeutic targets for metastatic breast cancer, including cyclin D-dependent kinases CDK4 and CDK6, the PI3K/AKT/mTOR pathway, the insulin/IGF1R pathway, the EGFR/HER family, the JAK/STAT pathway, poly(ADP-ribose) polymerases (PARP), TROP-2, Src kinases, histone modification enzymes, activated growth factor receptors, androgen receptors, breast cancer stem cells, matrix metalloproteinases, and immune checkpoint proteins. We also review the latest development in breast cancer immunotherapy. Drugs that target these molecules/pathways are either already FDA-approved or currently being tested in clinical trials.
Dopamine Transporter Deficient Rodents: Perspectives and Limitations for Neuroscience
Authors: Savchenko Artem , Targa Giorgia , Fesenko Zoia , Leo Damiana , Gainetdinov Raul R. , Sukhanov Ilya
Published: May 2023

The key element of dopamine (DA) neurotransmission is undoubtedly DA transporter (DAT), a transmembrane protein responsible for the synaptic reuptake of the mediator. Changes in DAT’s function can be a key mechanism of pathological conditions associated with hyperdopaminergia. The first strain of gene-modified rodents with a lack of DAT were created more than 25 years ago. Such animals are characterized by increased levels of striatal DA, resulting in locomotor hyperactivity, increased levels of motor stereotypes, cognitive deficits, and other behavioral abnormalities. The administration of dopaminergic and pharmacological agents affecting other neurotransmitter systems can mitigate those abnormalities. The main purpose of this review is to systematize and analyze (1) known data on the consequences of changes in DAT expression in experimental animals, (2) results of pharmacological studies in these animals, and (3) to estimate the validity of animals lacking DAT as models for discovering new treatments of DA-related disorders.
In-Situ Fabrication of Electroactive Cu2+-Trithiocyanate Complex and Its Application for Label-Free Electrochemical Aptasensing of Thrombin
Authors: Wang Zehao , Gao Ningning , Chen Zhenmao , Gao Feng , Wang Qingxiang
Published: May 2023

The preparation of an electroactive matrix for the immobilization of the bioprobe shows great promise to construct the label-free biosensors. Herein, the electroactive metal-organic coordination polymer has been in-situ prepared by pre-assembly of a layer of trithiocynate (TCY) on a gold electrode (AuE) through Au-S bond, followed by repetitive soaking in Cu(NO3)2 solution and TCY solutions. Then the gold nanoparticles (AuNPs) and the thiolated thrombin aptamers were successively assembled on the electrode surface, and thus the electrochemical electroactive aptasensing layer for thrombin was achieved. The preparation process of the biosensor was characterized by an atomic force microscope (AFM), attenuated total reflection-Fourier transform infrared (ATR-FTIR), and electrochemical methods. Electrochemical sensing assays showed that the formation of the aptamer-thrombin complex changed the microenvironment and the electro-conductivity of the electrode interface, causing the electrochemical signal suppression of the TCY-Cu2+ polymer. Additionally, the target thrombin can be label-free analyzed. Under optimal conditions, the aptasensor can detect thrombin in the concentration range from 1.0 fM to 1.0 μM, with a detection limit of 0.26 fM. The spiked recovery assay showed that the recovery of the thrombin in human serum samples was 97.2–103%, showing that the biosensor is feasible for biomolecule analysis in a complex sample.
The Role of The Rostral Ventromedial Medulla in Stress Responses
Authors: Pagliusi Marco , Gomes Felipe V.
Published: May 2023

The rostral ventromedial medulla (RVM) is a brainstem structure critical for the descending pain modulation system involved in both pain facilitation and inhibition through its projection to the spinal cord. Since the RVM is well connected with pain- and stress-engaged brain structures, such as the anterior cingulate cortex, nucleus accumbens, and amygdala, its involvement in stress responses has become a matter of great interest. While chronic stress has been proposed as a trigger of pain chronification and related psychiatric comorbidities due to maladaptive stress responses, acute stress triggers analgesia and other adaptative responses. Here we reviewed and highlighted the critical role of the RVM in stress responses, mainly in acute stress-induced analgesia (SIA) and chronic stress-induced hyperalgesia (SIH), providing insights into pain chronification processes and comorbidity between chronic pain and psychiatric disorders.