bluuebunny/crossref_metadata_2025 huggingface dataset:
The Use of Commercial Microvolume Techniques for Feline Oocyte Vitrification
Authors: Nowak Agnieszka , Kochan Joanna , Kij-Mitka Barbara , Fryc Karolina , Witarski Wojciech
Published: December 2022

This project aimed to compare the three most popular commercial oocyte vitrification techniques to determine their suitability for the vitrification of felid germlines in rescue and conservation programs. The present study aimed to determine the viability and developmental competence of feline oocytes after IVM and vitrification using a commercial vitrification method. In the first experiment, oocytes were vitrified after in vitro maturation (IVM) using the Kitazato, Cryotech, and Vitrolife methods. The oocytes were stained with fluorescein diacetate and ethidium bromide to evaluate their viability. The differences between Vitrolife and the control, Cryotech and Kitazato were statistically significant (p < 0.05), and between the control and Kitazato, were highly significant (p < 0.01). There were no significant differences between the control and Cryotech, Vitrolife and Cryotech, or Kitazato and Vitrolife. In the second part of the experiment, oocytes, after IVM and vitrification using three commercial methods, were subjected to fertilization. After vitrification, IVF was performed. We observed 35% of embryonic divisions in the group where Vitrolife and Kitazato media were used and 45% in the control group. In the presented experiment, vitrification with Vitrolife media gave slightly better results for survival and fertilization, while in the case of emergency protocol vitrification, all of the above methods may be useful to protect material derived from valuable wild felids.
Pharmacoeconomic Analysis of the Different Therapeutic Approaches in Control of Bovine Mastitis: Phytotherapy and Antimicrobial Treatment
Authors: Kovačević Zorana , Mihajlović Jovan , Mugoša Snežana , Horvat Olga , Tomanić Dragana , Kladar Nebojša , Samardžija Marko
Published: December 2022

Mastitis in dairy cows is responsible for major economic losses on dairy farms worldwide as the most expensive and prevalent disease in dairy cattle. In spite of the fact that antibiotic therapy still remains the main treatment strategy for bovine mastitis, concerns about the shortcomings of this treatment approach are continuously raised. Hence, research on alternative treatments with increased effectiveness and reduced costs is needed. Therefore, we conducted a pharmacoeconomic analysis of conventional antibiotic vs. a proposed Phyto-Bomat treatment based on essential oils in bovine mastitis therapy. Treatments were compared from the farmer’s perspective in the domain of costs (expressed in total, direct and indirect, cost differences) and effectiveness (expressed in daily milk yield differences). Economic calculations were based on data from a dairy farm in Serbia. The average cost of conventional antibiotic treatment was estimated at EUR 80.32 consisting of therapy costs, veterinary services and milk rejection costs at EUR 16.54, EUR 17.68 and EUR 46.10, respectively. The average cost of Phyto-Bomat treatment was estimated at EUR 76.34 with therapy costs of EUR 34.34, veterinary costs of EUR 32.00 and rejection of milk costs of only EUR 10.00. Therefore, Phyto-Bomat results in cost savings approximating EUR 4 per each mastitis episode with the highest cost reductions obtained in milk rejection costs. This estimation of Phyto-Bomat’s economic benefits could be used as a starting point for the inclusion of this formulation as an alternative treatment approach with a focus on subclinical mastitis since it contributes to most of the financial losses.
Cleavage of Occludin by Cigarette Smoke-Elicited Cathepsin S Increases Permeability of Lung Epithelial Cells
Authors: Bigot Paul , Chesseron Simon , Saidi Ahlame , Sizaret Damien , Parent Christelle , Petit-Courty Agnès , Courty Yves , Lecaille Fabien , Lalmanach Gilles
Published: December 2022

Background. Chronic obstructive pulmonary disease (COPD) is an irreversible disease mainly caused by smoking. COPD is characterized by emphysema and chronic bronchitis associated with enhanced epithelial permeability. Hypothesis. Lung biopsies from smokers revealed a decreased expression level of occludin, which is a protein involved in the cohesion of epithelial tight junctions. Moreover, the occludin level correlated negatively with smoking history (pack-years), COPD grades, and cathepsin S (CatS) activity. Thus, we examined whether CatS could participate in the modulation of the integrity of human lung epithelial barriers. Methods and results. Cigarette smoke extract (CSE) triggered the upregulation of CatS by THP-1 macrophages through the mTOR/TFEB signaling pathway. In a co-culture model, following the exposure of macrophages to CSE, an enhanced level of permeability of lung epithelial (16HBE and NHBE) cells towards FITC-Dextran was observed, which was associated with a decrease in occludin level. Similar results were obtained using 16HBE and NHBE cells cultured at the air–liquid interface. The treatment of THP-1 macrophages by CatS siRNAs or by a pharmacological inhibitor restored the barrier function of epithelial cells, suggesting that cigarette smoke-elicited CatS induced an alteration of epithelial integrity via the proteolytic injury of occludin. Conclusions. Alongside its noteworthy resistance to oxidative stress induced by cigarette smoke oxidants and its deleterious elastin-degrading potency, CatS may also have a detrimental effect on the barrier function of epithelial cells through the cleavage of occludin. The obtained data emphasize the emerging role of CatS in smoking-related lung diseases and strengthen the relevance of targeting CatS in the treatment of emphysema and COPD.
Discovery of New Triterpenoids Extracted from Camellia oleifera Seed Cake and the Molecular Mechanism Underlying Their Antitumor Activity
Authors: Wu Zelong , Tan Xiaofeng , Zhou Junqin , Yuan Jun , Yang Guliang , Li Ze , Long Hongxu , Yi Yuhang , Lv Chenghao , Zeng Chaoxi , Qin Si
Published: December 2022

Theasaponin derivatives, which are reported to exert antitumor activity, have been widely reported to exist in edible plants, including in the seed cake of Camellia oleifera (C.), which is extensively grown in south of China. The purpose of this study was to isolate new theasaponin derivatives from C. seed cake and explore their potential antitumor activity and their underlying molecular mechanism. In the present study, we first isolated and identified four theasaponin derivatives (compounds 1, 2, 3, and 4) from the total aglycone extract of the seed cake of Camellia oleifera by utilizing a combination of pre-acid-hydrolysis treatment and activity-guided isolation. Among them, compound 1 (C1) and compound 4 (C4) are newly discovered theasaponins that have not been reported before. The structures of these two new compounds were characterized based on comprehensive 1D and 2D NMR spectroscopy and high-resolution mass spectrometry, as well as data reported in the literature. Secondly, the cytotoxicity and antitumor property of the above four purified compounds were evaluated in selected typical tumor cell lines, Huh-7, HepG2, Hela, A549, and SGC7901, and the results showed that the ED50 value of C4 ranges from 1.5 to 11.3 µM, which is comparable to that of cisplatinum (CDDP) in these five cell lines, indicating that C4 has the most powerful antitumor activity among them. Finally, a preliminary mechanistic investigation was performed to uncover the molecular mechanism underlying the antitumor property of C4, and the results suggested that C4 may trigger apoptosis through the Bcl-2/Caspase-3 and JAK2/STAT3 pathways, and stimulate cell proliferation via the NF-κB/iNOS/COX-2 pathway. Moreover, it was surprising to find that C4 can inhibit the Nrf2/HO-1 pathway, which indicates that C4 has the potency to overcome the resistance to cancer drugs. Therefore, C1 and C4 are two newly identified theasaponin derivatives with antitumor activity from the seed cake of Camellia oleifera, and C4 is a promising antitumor candidate not only for its powerful antitumor activity, but also for its ability to function as an Nrf2 inhibitor to enhance the anticancer drug sensitivity.
Mitochondrial Peroxiredoxin III Protects against Non-Alcoholic Fatty Liver Disease Caused by a Methionine-Choline Deficient Diet
Authors: Park Jiyoung , Kim Nam Hee , Yi Ho Jin , Rhee Sue Goo , Woo Hyun Ae
Published: December 2022

Non-alcoholic fatty liver disease (NAFLD) is emerging as the most common chronic liver disease worldwide. In addition, NAFLD may increase the risk of cardiovascular and liver-related diseases, and displays features of metabolic syndrome. In NAFLD, oxidative stress is primarily caused by excessive free fatty acids. The oxidation of fatty acids is usually caused by β-oxidation of mitochondria under normal conditions, resulting in the production of energy. However, when the inflow of fatty acids in NAFLD becomes excessive, the β-oxidation of mitochondria becomes saturated and the oxidation process increases at sites including peroxisomes and microsomes, thereby increasing production of reactive oxygen species (ROS). Thus, hepatic mitochondrial ROS play an important role in the pathogenesis of NAFLD. Eliminating mitochondrial ROS may improve NAFLD, but the underlying mechanism remains unclear. We examined the effect of mitochondrial ROS on NAFLD by focusing on peroxiredoxin (Prx), an antioxidant protein that can remove hydrogen peroxide. The protective effect and pathological phenomenon of mitochondrial peroxiredoxin in methionine-choline deficient diet (MCD)-induced liver injury was assessed in a mouse model of NAFLD. In these mice, mitochondrial peroxiredoxin deficiency significantly increased hepatic steatosis and fibrosis. In addition, ablation of Prx III enhances susceptibility to MCD diet-induced oxidative stress and exacerbates NAFLD progression by promoting inflammation. The binding assay results also showed that Prx III-deficient mice had more severe liver damage than Prx III-abundant mice in MCD diet liver injury models. The present data suggest that mitochondrial peroxiredoxin III could be a therapeutic target for preventing and suppressing diet-induced NAFLD.
Perinatal Oxidative Stress and Kidney Health: Bridging the Gap between Animal Models and Clinical Reality
Authors: Tain You-Lin , Hsu Chien-Ning
Published: December 2022

Oxidative stress arises when the generation of reactive oxygen species or reactive nitrogen species overwhelms antioxidant systems. Developing kidneys are vulnerable to oxidative stress, resulting in adult kidney disease. Oxidative stress in fetuses and neonates can be evaluated by assessing various biomarkers. Using animal models, our knowledge of oxidative-stress-related renal programming, the molecular mechanisms underlying renal programming, and preventive interventions to avert kidney disease has grown enormously. This comprehensive review provides an overview of the impact of perinatal oxidative stress on renal programming, the implications of antioxidant strategies on the prevention of kidney disease, and the gap between animal models and clinical reality.
Adding High-Intensity Interval Training to Classical Resistance Training Does Not Impede the Recovery from Inactivity-Induced Leg Muscle Weakness
Authors: Venckunas Tomas , Brazaitis Marius , Snieckus Audrius , Mickevicius Mantas , Eimantas Nerijus , Subocius Andrejus , Mickeviciene Dalia , Westerblad Håkan , Kamandulis Sigitas
Published: December 2022

Inactivity is known to induce muscle weakness, and chronically increased levels of reactive oxygen species (ROS) are proposed to have a central causative role in this process. Intriguingly, high-intensity interval training (HIIT), which involves bursts of high ROS production, can have positive effects in pathological conditions with chronically increased ROS. Here, young male volunteers were exposed to 3 weeks of unloading of the dominant leg followed by 3 weeks of resistance training without (Ctrl group) or with the addition of all-out cycling HIIT. Changes in muscle thickness were assessed by ultrasonography, and contractile function was studied by measuring the torque during maximal voluntary contractions (MVC). The results show an ~6% decrease in vastus lateralis thickness after the unloading period, which was fully restored after the subsequent training period in both the Ctrl and HIIT groups. MVC torque was decreased by ~11% after the unloading period and recovered fully during the subsequent training period in both groups. All-out cycling performance was improved by the 3 weeks of HIIT. In conclusion, the decline in muscle size and function after 3 weeks of unloading was restored by 3 weeks of resistance training regardless of whether it was combined with HIIT.
Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
Authors: Maraveas Chrysanthos
Published: December 2022

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example, AI-based irrigation and soil fertilizer application enabled farmers to realize higher returns on investment on fertilizer application and gross returns above the fertilizer cost, higher yields, and resource use efficiency. Similarly, AI image detection techniques led to the early diagnosis of powdery mildew. The precise operation of agricultural robots was supported by the integration of light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras in place of the traditional GPS (geographic positioning systems) technologies, which are prone to errors. However, critical challenges remained unresolved, including cost, disparities between research and development (R&D) innovations and technology commercialization, energy use, the tradeoff between accuracy and computational speeds, and technology gaps between the Global North and South. In general, the value of this review is that it surveys the literature on the maturity level of various AI technologies in smart greenhouses and offers a state-of-the-art picture of how far the technologies have successfully been applied in agriculture and what can be done to optimize their usability.
A Numerical Investigation to Calculate Ultimate Limit State Capacity of Cable Bolts Subjected to Impact Loading
Authors: Tahmasebinia Faham , Yang Adam , Feghali Patrick , Skrzypkowski Krzysztof
Published: December 2022

As rock bursts are unavoidable in deep mines and excavations with high in-situ stresses, ground support systems are implemented to manage and mitigate rock bursts. Cable bolts are commonly used as reinforcing elements in ground support systems, which are subject to dynamic loads in burst-prone excavations. To design an efficient cable bolt in burst-prone conditions, shear and energy absorption capacity must be considered. Numerical modelling is an advantageous method of repeatable testing and it is inexpensive and non-destructive. This study develops a statically and dynamically loaded numerical model of a double shear test in ABAQUS/Explicit. A total of 36 static and 576 dynamic tests are carried out, which examine the influence of bolt diameter, steel yield and ultimate strength, dynamic load velocity and dynamic load mass on the displacement, shear force and energy absorption capacity of cable bolts. As bolt diameter and steel yield strength increases, the maximum shear force resisted and bolt displacement increases. Similarly, as the mass and velocity of the dynamic load increases, the amount of energy absorbed by the cable bolt increases. The main novelty of the current research is to suggest a reliable computational tool to investigate the influence of the different key parameters in the cable bolts on the ultimate capacity. The suggested method is a significantly cost-effective technique compared with the experimental investigations.
Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
Authors: Ren Guanghao , Wang Yun , Shi Zhenyun , Zhang Guigang , Jin Feng , Wang Jian
Published: December 2022

With the rapid growth of the aviation fields, the remaining useful life (RUL) estimation of aero-engine has become the focus of the industry. Due to the shortage of existing prediction methods, life prediction is stuck in a bottleneck. Aiming at the low efficiency of traditional estimation algorithms, a more efficient neural network is proposed by using Convolutional Neural Networks (CNN) to replace Long-Short Term Memory (LSTM). Firstly, multi-sensor degenerate information fusion coding is realized with the convolutional autoencoder (CAE). Then, the temporal convolutional network (TCN) is applied to achieve efficient prediction with the obtained degradation code. It does not depend on the iteration along time, but learning the causality through a mask. Moreover, the data processing is improved to further improve the application efficiency of the algorithm. ExtraTreesClassifier is applied to recognize when the failure first develops. This step can not only assist labelling, but also realize feature filtering combined with tree model interpretation. For multiple operation conditions, new features are clustered by K-means++ to encode historical condition information. Finally, an experiment is carried out to evaluate the effectiveness on the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets provided by the National Aeronautics and Space Administration (NASA). The results show that the proposed algorithm can ensure high-precision prediction and effectively improve the efficiency.
An Ensemble-Based Machine Learning Approach for Cyber-Attacks Detection in Wireless Sensor Networks
Authors: Ismail Shereen , El Mrabet Zakaria , Reza Hassan
Published: December 2022

Wireless Sensor Networks (WSNs) are the key underlying technology of the Internet of Things (IoT); however, these networks are energy constrained. Security has become a major challenge with the significant increase in deployed sensors, necessitating effective detection and mitigation approaches. Machine learning (ML) is one of the most effective methods for building cyber-attack detection systems. This paper presents a lightweight ensemble-based ML approach, Weighted Score Selector (WSS), for detecting cyber-attacks in WSNs. The proposed approach is implemented using a blend of supervised ML classifiers, in which the most effective classifier is promoted dynamically for the detection process to gain higher detection performance quickly. We compared the performance of the proposed approach to three classical ensemble techniques: Boosting-based, Bagging-based, and Stacking-based. The performance comparison was conducted in terms of accuracy, probability of false alarm, probability of detection, probability of misdetection, model size, processing time, and average prediction time per sample. We applied two independent feature selection techniques. We utilized the simulation-based labeled dataset, WSN-DS, that comprises samples of four internal network-layer Denial of Service attack types: Grayhole, Blackhole, Flooding, and TDMA scheduling, in addition to normal traffic. The simulation revealed promising results for our proposed approach.
Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants
Authors: Faqeerzada Mohammad Akbar , Park Eunsoo , Kim Taehyun , Kim Moon Sung , Baek Insuck , Joshi Rahul , Kim Juntae , Cho Byoung-Kwan
Published: December 2022

Ginseng is a perennial herbaceous plant that has been widely consumed for medicinal and dietary purposes since ancient times. Ginseng plants require shade and cool temperatures for better growth; climate warming and rising heat waves have a negative impact on the plants’ productivity and yield quality. Since Republic of Korea’s temperature is increasing beyond normal expectations and is seriously threatening ginseng plants, an early-stage non-destructive diagnosis of stressed ginseng plants is essential before symptomatic manifestation to produce high-quality ginseng roots. This study demonstrated the potential of fluorescence hyperspectral imaging to achieve the early high-throughput detection and prediction of chlorophyll composition in four varieties of heat-stressed ginseng plants: Chunpoong, Jakyeong, Sunil, and Sunmyoung. Hyperspectral imaging data of 80 plants from these four varieties (temperature-sensitive and temperature-resistant) were acquired before and after exposing the plants to heat stress. Additionally, a SPAD-502 meter was used for the non-destructive measurement of the greenness level. In accordance, the mean spectral data of each leaf were extracted from the region of interest (ROI). Analysis of variance (ANOVA) was applied for the discrimination of heat-stressed plants, which was performed with 96% accuracy. Accordingly, the extracted spectral data were used to develop a partial least squares regression (PLSR) model combined with multiple preprocessing techniques for predicting greenness composition in ginseng plants that significantly correlates with chlorophyll concentration. The results obtained from PLSR analysis demonstrated higher determination coefficients of R2val = 0.90, and a root mean square error (RMSE) of 3.59%. Furthermore, five proposed bands (683 nm, 688 nm, 703 nm, 731 nm, and 745 nm) by stepwise regression (SR) were developed into a PLSR model, and the model coefficients were used to create a greenness-level concentration in images that showed differences between the control and heat-stressed plants for all varieties.
Research Progress and Applications of 2D Antimonene
Authors: Zhong Tingting , Zeng Lina , Li Zaijin , Sun Li , Qiao Zhongliang , Qu Yi , Liu Guojun , Li Lin
Published: December 2022

Antimonene has attracted much attention due to its excellent properties such as high carrier mobility, excellent thermoelectric performance and high stability. In order to verify its theoretical advantages, a large number of experimental studies have been carried out and its applications explored. This paper mainly introduces the experimental preparation of antimonene by mechanical exfoliation, liquid phase exfoliation and epitaxial growth, summarizes the advantages and disadvantages of each method, and describes the applications of antimonene in sensor, battery, medicine and laser. Finally, prospects have been made to the future applications of antimonene in photoelectric field.
Correction: Lin et al. Study on the Development and Growth of Coral Larvae. Appl. Sci. 2022, 12, 5255
Authors: Lin Chiahsin , Kang Chia-Ming , Huang Chih-Yang , Li Hsing-Hui , Tsai Sujune
Published: December 2022

There was an error in the original publication [...]
Investigations on Factors Affecting 3D-Printed Holes Dimensional Accuracy and Repeatability
Authors: Popescu Diana , Gheorghe Amza Cătălin , Marinescu Rodica , Cristiana Iacob Mariana , Luminiţa Căruţaşu Nicoleta
Published: December 2022

This paper investigates the impact of several factors related to manufacturing, design, and post-processing on the dimensional accuracy of holes built in the additively manufactured parts obtained by material extrusion process (MEX). Directly fabricated holes in the 3D prints are commonly used for joining with other parts by means of mechanical fasteners, thus producing assemblies or larger parts, or have other functional purposes such as guiding the drill in the case of patient-personalized surgical guides. However, despite their spread use and importance, the relationship between the 3D-printed holes’ accuracy and printing settings is not well documented in the literature. Therefore, in this research, test parts were manufactured by varying the number of shells, printing speed, layer thickness, and axis orientation angles for evaluating their effect on the dimensional accuracy of holes of different diameters. In the same context of limited existing information, the influence of material, 3D printer, and slicing software is also investigated for determining the dimensional accuracy of hole-type features across different manufacturing sites, a highly relevant aspect when using MEX to produce spare or end-use parts in a delocalized production paradigm. The results of this study indicated that the layer thickness is the most relevant influence factor for the diameter accuracy, followed by the number of shells around the holes. Considering the tested values, the optimal set of values found as optimizing the accuracy and printing time was 0.2 mm layer thickness, two shells, and 50 mm/s printing speed for the straight holes. Data on the prints manufactured on different MEX equipment and slicers indicated no statistically significant difference between the diameters of the holes. The evaluation of 3D-printed polylactic acid test parts mimicking a surgical template device with inclined holes showed that the medical decontamination process had more impact on the holes’ dimensional variability than on their dimensional accuracy.
Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation
Authors: Dharmawan I Dewa Made Oka , Lee Jinyi
Published: December 2022

Grain boundaries (GBs), which are among the mechanical properties of a material, are a microstructural aspect that contributes to the overall behavior of metal. A deep understanding of the behavior of the GBs’ deformation, dislocation, and fracture will encourage the rapid development of new materials and lead to the better operation and maintenance of materials during their designed lifetimes. In this study, an integrated image processing toolset is proposed to provide an expeditious approach to extracting GBs, tracking their location, and identifying their internal deformation. This toolset consists of three integrated algorithms: image stitching, grain matching, and boundary extraction. The algorithms are designed to simultaneously integrate high and low spatial resolution images for gathering high-precision boundary coordinates and effectively reconstructing a view of the entire material surface for the tracing of the grain location. This significantly reduces the time needed to acquire the dataset owing to the ability of the low spatial resolution lens to capture wider areas as the base image. The high spatial resolution lens compensates for any weakness of the base image by capturing views of specific sections, thereby increasing the observation flexibility. One application successfully described in this paper is tracking the direction of the metal grain deformation in global coordinates by stacking a specific grain before and after the deformation. This allows observers to calculate the direction of the grain deformation by comparing the overlapping areas after the material experiences a load. Ultimately, this toolset is expected to lead to further applications in terms of observing fascinating phenomena in materials science and engineering.
Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization
Authors: Zhang Jianping , Zhu Yingqi , Chen Dong
Published: December 2022

It is crucial to understand the characteristics of wind resources and optimize wind resources in the area that is being considered for offshore wind farm development. Based on the improved particle swarm optimization (IPSO) and the back propagation neural network (BPNN), the IPSO-BP hybrid intelligent algorithm model was established. The assessment of wind resource characteristics in the eastern waters of China, including average wind speed, extreme wind speed, wind power density, effective wind energy hours and wind direction distribution were all calculated. Additionally, the wind speed throughout the different years in Luchao Port, a famous seaport in China, was predicted. The results revealed that the wind power density is approximately 300 W/m2 all year round and that the effective wind energy hours take up about 92% per hour. It was also identified that the wind direction distribution is stable in Luchao Port, implying that there are better wind energy resource reserves in this region. The IPSO-BP model has a strong tracking performance for wind speed changes, and can accurately predict the wind speed change in a short period. In addition, the prediction error of the IPSO-BP model is smaller when the time of training data is closer to the target one, and it can be controlled within a 5% range.
A Sequence-Based Hyper-Heuristic for Traveling Thieves
Authors: Rodríguez Daniel , Cruz-Duarte Jorge M. , Ortiz-Bayliss José Carlos , Amaya Ivan
Published: December 2022

A plethora of combinatorial optimization problems can be linked to real-life decision scenarios. Even nowadays, more diverse and complex problems are popping up. One of these problems is the traveling thief problem (TTP), which combines elements from the knapsack and traveling salesperson problems. Hence, it is paramount to keep improving solvers to tackle combinatorial problems. Among recent proposals, hyper-heuristics have proven useful since they seek to combine the strengths of more straightforward solvers. This paper proposes a sequence-based selection hyper-heuristic and assesses its feasibility when solving the TTP. Our proposal can be represented by an array of operators selecting a city or an item. In the first case, the solution moves to a new city and thus advances the tour. In the second one, the thief agent picks an item within the current city and tries to store it in its knapsack. We generate several sets of TTP instances with different parameters to validate our approach and analyze the model’s performance. Our data reveal that the proposed approach outperforms randomly generated sequences. Moreover, our approach finds general sequences that surpass sequences specialized for each instance. We believe this is noteworthy and represents a stepping stone towards achieving a more robust solver for complex problems.
Study of the Influence of the Dielectrophoretic Force on the Preferential Growth of Bacterial Biofilms in 3D Printed Microfluidic Devices
Authors: Csapai Alexandra , Toc Dan Alexandru , Pascalau Violeta , Tosa Nicoleta , Tripon Septimiu , Ciorîță Alexandra , Mihaila Razvan Marian , Mociran Bogdan , Costache Carmen , Popa Catalin O.
Published: December 2022

Understanding the effect of different electric potentials upon the preferential formation of biofilms inside microfluidic devices could represent a step forward in comprehending the mechanisms that govern biofilm formation and growth. 3D printed microfluidic devices were used to investigate the influence of the dielectrophoretic forces on the formation and growth of Staphylococcus aureus ATCC 25923 biofilms. Bacterial suspensions of 2.5 McF were pushed through microfluidic channels while simultaneously applying various potential differences between 10 and 60 V. The overall electric field distribution within the channel was simulated using the COMOSL software. The effect of the electric potential variation on the preferential biofilm formation was determined using an adjusted microtiter plate technique, as well as a qualitative method, Scanning Electron Microscopy (SEM). SEM images were used to describe the morphology of the biofilm surface. The conclusions show that the dielectrophoretic forces, resulting due to inhomogeneity of the electric field, have more visible effects upon the cells up to 40 V. Above this magnitude, due to a more homogenous distribution of the electric field, the formation and growth of the biofilm become more uniform. At around 60 V, the distance between the high electric gradient regions decreases, leading to an almost uniform distribution of the electric field and, therefore, to a shift from dielectrophoretic to electrophoretic forces acting upon the bacterial cells.
Robust Feedback Linearization Control Design for Five-Link Human Biped Robot with Multi-Performances
Authors: Chi Kuang-Hui , Hsiao Yung-Feng , Chen Chung-Cheng
Published: December 2022

The study first proposes the difficult nonlinear convergent radius and convergent rate formulas and the complete derivations of a mathematical model for the nonlinear five-link human biped robot (FLHBR) system which has been a challenge for engineers in recent decades. The proposed theorem simultaneously has very distinctive superior advantages including the stringent almost disturbance decoupling feature that addresses the major deficiencies of the traditional singular perturbation approach without annoying “complete” conditions for the discriminant function and the global exponential stability feature without solving the impractical Hamilton–Jacobi equation for the traditional H-infinity technique. This article applies the feedback linearization technique to globally stabilize the FLHBR system that greatly improved those shortcomings of nonlinear function approximator and make the effective working range be global for whole state space, whereas the traditional Jacobian linearization technique is valid only for areas near the equilibrium point. In order to make some comparisons with traditional approaches, first example of the representative ones, that cannot be addressed well for the pioneer paper, is shown to demonstrate the fact that the effectiveness of the proposed main theorem is better than the traditional singular perturbation technique. Finally, we execute a second simulation example to compare the proposed approach with the traditional PID approach. The simulation results show that the transient behaviors of the proposed approach including the peak time, the rise time, the settling time and the maximum overshoot specifications are better than the traditional PID approach.
Cultural Landscape Reproduction of Typical Religious Architecture in Qingjiangpu Based on Scene Theory
Authors: Mao Wei , Hong Shuai , Chai Tengfei , Shen Junchao , Shen Jie
Published: December 2022

Scenes are important carriers of cultural expression. Cultural landscapes reveal specific cultural connotations through various scenes, and people understand and give things cultural connotations through scenes. In recent years, new techniques for visualizing cultural landscape heritage have been made possible by the advent of mapping and geographic information technology. The Beijing-Hangzhou Grand Canal’s culture is a “living” cultural legacy. As one of the key links in the canal’s cultural chain, Qingjiangpu is crucial to reproducing its cultural landscape. This paper first discusses the relationship between scene theory and the cultural landscape. Starting from the five elements of scene theory, through the collection of online text data and the corresponding data obtained from questionnaire research, the paper analyzed the scene constructed by the cultural landscape and the urban spirituality embodied by the scene. Through the deep excavation of cultural landscape and its historical context, the theoretical framework of “node-neighbor-city” cultural landscape reproduction is proposed. Taking the ancient city of Qingjiangpu as an example, the cultural landscape has been reproduced at different scales and in different dimensions through various technical means. This study can provide a theoretical basis and practical reference for the research of cultural landscape reproduction.
Machine-Learning Application for a Likelihood Ratio Estimation Problem at LHC
Authors: Auricchio Silvia , Cirotto Francesco , Giannini Antonio
Published: December 2022

High-energy physics is now entering a new era. Current particle experiments, such as ATLAS at the Large Hadron Collider at CERN, offer the possibility of discovering new and interesting physics’ phenomena beyond the Standard Model, the theoretical framework which describes fundamental interactions between particles. In this paper a machine-learning algorithm to deal with the estimate of the expected background processes distribution, a very demanding task for particle physics analyses, is described. A new technique is exploited, inspired by the well-known likelihood ratio estimation problem, called direct importance estimation in statistics. First, its theoretical formulation is discussed, then its performances in two ATLAS analyses are described.
The Applications of Plasma Techniques
Authors: Jasiński Mariusz
Published: December 2022

This Special Issue “The Applications of Plasma Techniques” in the section “Optics and Lasers” of the journal Applied Sciences intends to provide a description of plasmas, plasma devices and processes related to plasma applications in a broad sense [...]
Air Pollution and COVID-19 Mortality in Brazil
Authors: Damasceno Reizane Maria , Cicerelli Rejane Ennes , Almeida Tati de , Requia Weeberb J.
Published: December 2022

Long-term exposure to poor air quality has been associated with respiratory viral infections such as influenza and measles. Some epidemiological studies in North America, Europe, and Asia also suggest that exposure to air pollution is associated with increased SARS-CoV-2 infection and mortality associated with COVID-19. However, few studies have been conducted on this topic in South America. In this study, we investigate the association between ambient PM2.5, NO2, and O3 and their interaction with COVID-19-associated deaths in Brazil, the largest country in South America. We apply a negative binomial mixed model with zero inflation using municipality-level COVID-19 deaths as the endpoint and the long-term average of PM2.5, NO2, and O3 as the exposure. We added a random intercept by state to account for potential correlations between communities within the same state. After adjusting the model for several potential confounders, including meteorological variables, demographic characteristics, socioeconomic aspects, and healthcare conditions, we found a positive association between PM2.5 and COVID-19 deaths in Brazil, where a 1 μg/m3 increase in the long-term average of PM2.5 was associated with a 10.22% (95% CI: 9.35; 11.09) increase in COVID-19 deaths. In contrast, O3 and NO2 showed negative associations. As suggested by the literature in other countries, our results suggest that air pollution is an important cofactor that increases the risk of death from COVID-19 in Brazil. The effects of air pollution in Brazil are pronounced, indicating the need for improved air quality control policies.
Children’s Exposure to Radon in Schools and Kindergartens in the Republic of Moldova
Authors: Coretchi Liuba , Ene Antoaneta , Virlan Serghei , Gincu Mariana , Ababii Aurelia , Capatina Angela , Overcenco Alla , Sargu Valentin
Published: December 2022

This work presents the results from measurements of radon concentrations in primary and high school education institutions—including their gymnasiums—from the Chisinau municipality and various rayons from the central and southern part of the Republic of Moldova. In the research carried out during the years of 2013–2014 and 2021, there were 78 (29 + 49) premises included, respectively, and 149 and 23,805 investigations were performed using RTM-1642 (active measurements) and RadonEye+2 devices (passive measurements). The results show an essential variability for the studied radio-stressogenic factor, depending on the geological conditions of the location of the premises and the age of the building. Thus, during 2013–2014, the minimum concentration of radon detected was 26 Bq m−3, and the maximum detected was 607 Bq m−3. In 2021, the results denote an indicator variability in the range of 17.4–657.9 Bq m−3 for early education institutions, with an average value of 127.6 Bq m−3, and denote a range of 231.8–1129.3 Bq m−3, with an average value of 665.4 Bq m−3), for high school education institutions and their gymnasiums. The effective annual dose for the children in a classroom varies between an interval of 0.21–4.88 mSv y−1 (average 1.19 mSv y−1) and 0.14–9.08 mSv y−1 (average 1.29 mSv y−1) for the 2013–2014 and 2021 surveys, respectively.
Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties
Authors: Gnjatović Milan , Maček Nemanja , Saračević Muzafer , Adamović Saša , Joksimović Dušan , Karabašević Darjan
Published: December 2022

This paper introduces a heuristic for multiple sequence alignment aimed at improving real-time object recognition in short video streams with uncertainties. It builds upon the idea of the progressive alignment but is cognitively economical to the extent that the underlying edit distance approach is adapted to account for human working memory limitations. Thus, the proposed heuristic procedure has a reduced computational complexity compared to optimal multiple sequence alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation conducted in real-life settings demonstrated a significant improvement in number recognition accuracy in short video streams under uncertainties caused by noise and incompleteness. The second line of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine learning approaches, which are typically not tailored to the task of object sequence recognition from a limited number of frames of incomplete data recorded in a dynamic scene situation.
Clinical Application of Bioresorbable, Synthetic, Electrospun Matrix in Wound Healing
Authors: MacEwan Matthew , Jeng Lily , Kovács Tamás , Sallade Emily
Published: December 2022

Electrospun polymeric matrices have long been investigated as constructs for use in regenerative medicine, yet relatively few have been commercialized for human clinical use. In 2017, a novel electrospun matrix, composed of two synthetic biocompatible polymers, polyglactin 910 (PLGA 10:90) and polydioxanone (PDO) of varying pore and fiber sizes (i.e., hybrid-scale) was developed and cleared by the FDA for human clinical use. The present review aims to explain the mechanism of action and review the preclinical and clinical results to summarize the efficacy of the matrix across multiple use cases within the wound care setting, including an assessment of over 150 wounds of varying etiologies treated with the synthetic matrix. Clinical data demonstrated effective use of the synthetic hybrid-scale fiber matrix across a variety of wound etiologies, including diabetic foot and venous leg ulcers, pressure ulcers, burns, and surgical wounds. This review represents a comprehensive clinical demonstration of a synthetic, electrospun, hybrid-scale matrix and illustrates its value and versatility across multiple wound etiologies.
Disease–Gene Networks of Skin Pigmentation Disorders and Reconstruction of Protein–Protein Interaction Networks
Authors: D’Arcy Cian , Bass Olivia , Junk Philipp , Sevrin Thomas , Oliviero Giorgio , Wynne Kieran , Halasz Melinda , Kiel Christina
Published: December 2022

Melanin, a light and free radical absorbing pigment, is produced in melanocyte cells that are found in skin, but also in hair follicles, eyes, the inner ear, heart, brain and other organs. Melanin synthesis is the result of a complex network of signaling and metabolic reactions. It therefore comes as no surprise that mutations in many of the genes involved are associated with various types of pigmentation diseases and phenotypes (‘pigmentation genes’). Here, we used bioinformatics tools to first reconstruct gene-disease/phenotype associations for all pigmentation genes. Next, we reconstructed protein–protein interaction (PPI) networks centered around pigmentation gene products (‘pigmentation proteins’) and supplemented the PPI networks with protein expression information obtained by mass spectrometry in a panel of melanoma cell lines (both pigment producing and non-pigment producing cells). The analysis provides a systems network representation of all genes/ proteins centered around pigmentation and melanin biosynthesis pathways (‘pigmentation network map’). Our work will enable the pigmentation research community to experimentally test new hypothesis arising from the pigmentation network map and to identify new targets for drug discovery.
Ligand Binding Properties of Odorant-Binding Protein OBP5 from Mus musculus
Authors: Moitrier Lucie , Belloir Christine , Lalis Maxence , Hou Yanxia , Topin Jérémie , Briand Loïc
Published: December 2022

Odorant-binding proteins (OBPs) are abundant soluble proteins secreted in the nasal mucus of a variety of species that are believed to be involved in the transport of odorants toward olfactory receptors. In this study, we report the functional characterization of mouse OBP5 (mOBP5). mOBP5 was recombinantly expressed as a hexahistidine-tagged protein in bacteria and purified using metal affinity chromatography. The oligomeric state and secondary structure composition of mOBP5 were investigated using gel filtration and circular dichroism spectroscopy. Fluorescent experiments revealed that mOBP5 interacts with the fluorescent probe N-phenyl naphthylamine (NPN) with micromolar affinity. Competitive binding experiments with 40 odorants indicated that mOBP5 binds a restricted number of odorants with good affinity. Isothermal titration calorimetry (ITC) confirmed that mOBP5 binds these compounds with association constants in the low micromolar range. Finally, protein homology modeling and molecular docking analysis indicated the amino acid residues of mOBP5 that determine its binding properties.
Milk Polysialic Acid Levels Rapidly Decrease in Line with the N-Acetylneuraminic Acid Concentrations during Early Lactation in Dairy Cows
Authors: Hinterseher Julia , Günther Juliane , Zlatina Kristina , Isernhagen Lisa , Viergutz Torsten , Wirthgen Elisa , Hoeflich Andreas , Vernunft Andreas , Galuska Sebastian Peter
Published: December 2022

Sialylated milk oligosaccharides and glycoconjugates have several positive effects on the mucosal barrier, the gut microbiome, and an effective immune system. For this reason, they are important biomolecules for mammary gland health and optimal development of offspring. In milk, the major sialic acid, N-acetylneuraminic acid (Neu5Ac), can be attached as monosialyl-residues or as polymers. To investigate the sialylation processes during lactation of German Holstein cows, we analyzed udder tissue in addition to milk at different time points of lactation. The analysis of the milk samples revealed that both the levels of Neu5Ac and its polymer, polysialic acid (polySia), rapidly decreased during the first three days of lactation, and a high interindividual variance was observed. In mature milk, however, the sialylation status remains relatively constant. The results indicate that mammary gland epithelial cells are one source for milk polySia, since immunohistochemistry of udder tissue exhibited strong polySia staining in these cells. Furthermore, both polysialyltransferases, ST8SiaII and ST8SiaIV, are expressed. Based on known functions of monosialyl residues and polySia, we discuss the potential impact of these biomolecules and the consequences of the heterogeneous sialylation status of milk in relation to udder health and offspring health.