bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Concomitant Nrf2 Targeting and Stem Cell Therapy in Cerebrovascular Disease
Authors: Gordon Jonah , Lockard Gavin , Monsour Molly , Alayli Adam , Borlongan Cesario V.
Published: July 2022

Despite the reality that a death from cerebrovascular accident occurs every 3.5 min in the United States, there are few therapeutic options which are typically limited to a narrow window of opportunity in time for damage mitigation and recovery. Novel therapies have targeted pathological processes secondary to the initial insult, such as oxidative damage and peripheral inflammation. One of the greatest challenges to therapy is the frequently permanent damage within the CNS, attributed to a lack of sufficient neurogenesis. Thus, recent use of cell-based therapies for stroke have shown promising results. Unfortunately, stroke-induced inflammatory and oxidative damage limit the therapeutic potential of these stem cells. Nuclear factor erythroid 2-related factor 2 (Nrf2) has been implicated in endogenous antioxidant and anti-inflammatory activity, thus presenting an attractive target for novel therapeutics to enhance stem cell therapy and promote neurogenesis. This review assesses the current literature on the concomitant use of stem cell therapy and Nrf2 targeting via pharmaceutical and natural agents, highlighting the need to elucidate both upstream and downstream pathways in optimizing Nrf2 treatments in the setting of cerebrovascular disease.
5G Electromagnetic Radiation Attenuates Skin Melanogenesis In Vitro by Suppressing ROS Generation
Authors: Kim Kyuri , Lee Young Seung , Kim Nam , Choi Hyung-Do , Lim Kyung-Min
Published: July 2022

Recently, the impacts of 5G electromagnetic radiation (EMR) with 28 GHz on human health have been attracting public attention with the advent of 5G wireless communication. Here, we report that 5G (28 GHz) EMR can attenuate the skin pigmentation in murine melanoma cells (B16F10) and a 3D pigmented human epidermis model (Melanoderm™). B16 cells were exposed to 5G (28 GHz) with or without α-MSH for 4 h per day. Interestingly, 5G attenuated α-MSH-induced melanin synthesis. Fontana–Masson staining confirmed that the dendritic formation of α-MSH stimulated B16 cells was diminished by 5G exposure. To confirm the anti-melanogenic effect of 5G EMR, MelanoDerm™ was irradiated with 5G at a power intensity of 10 W/m2 for 4 h a day for 16 days and melanin distribution was detected with Fontana–Masson staining, which supported the anti-melanogenic effect of 5G EMR. Consistently, 5G EMR suppressed α-MSH induced upregulation of melanogenic enzymes; tyrosinase, TRP-1, and TRP-2. Of note, 5G EMR attenuated ROS production stimulated by α-MSH and H2O2, suggesting that 5G EMR may dissipate ROS generation, which is pivotal for the melanin synthesis. Collectively, we demonstrated that 5G EMR can attenuate skin pigmentation by attenuating ROS generation.
Noninvasive Diagnosis of the Mitochondrial Function of Doxorubicin-Induced Cardiomyopathy Using In Vivo Dynamic Nuclear Polarization–Magnetic Resonance Imaging
Authors: Mizuta Yukie , Akahoshi Tomohiko , Eto Hinako , Hyodo Fuminori , Murata Masaharu , Tokuda Kentaro , Eto Masatoshi , Yamaura Ken
Published: July 2022

Doxorubicin (DOX) induces dose-dependent cardiotoxicity via oxidative stress and abnormal mitochondrial function in the myocardium. Therefore, a noninvasive in vivo imaging procedure for monitoring the redox status of the heart may aid in monitoring diseases and developing treatments. However, an appropriate technique has yet to be developed. In this study, we demonstrate a technique for detecting and visualizing the redox status of the heart using in vivo dynamic nuclear polarization–magnetic resonance imaging (DNP–MRI) with 3-carbamoyl-PROXYL (CmP) as a molecular imaging probe. Male C57BL/6N mice were administered DOX (20 mg/kg) or saline. DNP–MRI clearly showed a slower DNP signal reduction in the DOX group than in the control group. Importantly, the difference in the DNP signal reduction rate between the two groups occurred earlier than that detected by physiological examination or clinical symptoms. In an in vitro experiment, KCN (an inhibitor of complex IV in the mitochondrial electron transport chain) and DOX inhibited the electron paramagnetic resonance change in H9c2 cardiomyocytes, suggesting that the redox metabolism of CmP in the myocardium is mitochondrion-dependent. Therefore, this molecular imaging technique has the potential to monitor the dynamics of redox metabolic changes in DOX-induced cardiomyopathy and facilitate an early diagnosis of this condition.
Evolution of the Complex Supply Chain Network Based on Deviation from the Power-Law Distribution
Authors: Qian Xiaodong , Dai Yufan
Published: July 2022

The power-law distribution is an important descriptive characteristic of scale-free complex supply chain networks (SCN). The power-law distribution and deviation phenomena of SCN nodes are explored in combination with complex network theory, so it is imporant to accurately characterize the dynamic characteristics of network evolution on a time scale. Based on the analysis of the topological structure and evolutionary characteristics of the small-world network and scale-free SCN, the single and double power-law distribution and evolutionary dynamic characteristics of the complex SCN are further analyzed, and the deviation phenomenon of the power-law distribution is analyzed. On the premise of setting three parameters of new nodes, new edges, and node reconnection in the process of network evolution, the power-law distribution deviation evolution model under a complex network environment is constructed, and then the parameters of the SCN evolution model are analyzed. Combined with numerical simulation and model simulation, the evolution of SCN with two kinds of power-law deviation is analyzed. The results show that the deviation of the two-stage power-law distribution is not caused by the process of adding nodes or connecting edges, while it has a certain influence on the change of the power index, and the deviation of the power-law distribution in SCN increases with the extension of the evolution time. When p1=0, the single power-law distribution of SCN tends to change to a δ distribution when the time step is large enough.
Halophytes as Medicinal Plants against Human Infectious Diseases
Authors: Ferreira Maria João , Pinto Diana C. G. A. , Cunha Ângela , Silva Helena
Published: July 2022

Halophytes have long been used for medicinal purposes. However, for many decades, their use was entirely empirical, with virtually no knowledge of the bioactive compounds underlying the different applications. In recent decades, the growing problem of antibiotic resistance triggered the research on alternative antimicrobial approaches, and halophytes, along with other medicinal plants, regained attention as an underexplored pharmacological vein. Furthermore, the high nutritional/nutraceutical/pharmacological value of some halophytic species may represent added value to the emerging activity of saline agriculture and targeted modification of the rhizosphere, with plant-growth-promoting bacteria being attempted to be used as a tool to modulate the plant metabolome and enhance the expression of interesting metabolites. The objective of this review is to highlight the potential of halophytes as a valuable, and still unexplored, source of antimicrobial compounds for clinical applications. For that, we provide a critical perspective on the empirical use of halophytes in traditional medicine and a state-or-the-art overview of the most relevant plant species and metabolites related with antiviral, antifungal and antibacterial activities.
Natural Time Analysis of Global Seismicity
Authors: Christopoulos Stavros-Richard G. , Varotsos Panayiotis K. , Perez-Oregon Jennifer , Papadopoulou Konstantina A. , Skordas Efthimios S. , Sarlis Nicholas V.
Published: July 2022

Natural time analysis enables the introduction of an order parameter for seismicity, which is just the variance of natural time χ, κ1=⟨χ2⟩−⟨χ⟩2. During the last years, there has been significant progress in the natural time analysis of seismicity. Milestones in this progress are the identification of clearly distiguishable minima of the fluctuations of the order parameter κ1 of seismicity both in the regional and global scale, the emergence of an interrelation between the time correlations of the earthquake (EQ) magnitude time series and these minima, and the introduction by Turcotte, Rundle and coworkers of EQ nowcasting. Here, we apply all these recent advances in the global seismicity by employing the Global Centroid Moment Tensor (GCMT) catalog. We show that the combination of the above three milestones may provide useful precursory information for the time of occurrence and epicenter location of strong EQs with M≥8.5 in GCMT. This can be achieved with high statistical significance (p-values of the order of 10−5), while the epicentral areas lie within a region covering only 4% of that investigated.
Study on Share Rate of Support Structure for Super-Large Span Twin Tunnels with Small Interval
Authors: Zhao Xuwei , Sun Keguo , Zhen Yingzhou , Hong Yiqin , Zhou Huichao
Published: July 2022

Super-large span twin tunnels, which can relieve traffic burden and improve the efficiency of roads, have been rapidly popularized in urban construction. Hence, it is very important to master the share rate of support structure in this sort of tunnel correctly. In this paper, some field monitoring and numerical simulation work has been carried out in such a tunnel in Daling, China. The content of field monitoring consisted of the vault settlement, stress of the concrete, and the sum pressure. Firstly, the calculation formula of rock pressure in this kind of tunnel had been deduced, which was based on the theoretical sliding failure mode. Then, the calculated rock pressure was applied to the load structure model step by step with 10% intervals (10%, 20%, …). By comparing the monitoring and the simulation results on vault settlement, the share rate of the primary support was ensured to be 40%. Similarly, by comparing the result on safety factors, the share rate of the secondary lining was obtained (40%). The remained load was born mainly by the surrounding rock itself and the advanced support. Finally, the reliability of the results were verified by the sum pressure. This paper can provide reference for the effective design of support structure in super-large span twin tunnels.
Visualizing Collaboration in Teamwork: A Multimodal Learning Analytics Platform for Non-Verbal Communication
Authors: Noël René , Miranda Diego , Cechinel Cristian , Riquelme Fabián , Primo Tiago Thompsen , Munoz Roberto
Published: July 2022

Developing communication skills in collaborative contexts is of special interest for educational institutions, since these skills are crucial to forming competent professionals for today’s world. New and accessible technologies open a way to analyze collaborative activities in face-to-face and non-face-to-face situations, where collaboration and student attitudes are difficult to measure using traditional methods. In this context, Multimodal Learning Analytics (MMLA) appear as an alternative to complement the evaluation and feedback of core skills. We present a MMLA platform to support collaboration assessment based on the capture and classification of non-verbal communication interactions. The developed platform integrates hardware and software, including machine learning techniques, to detect spoken interactions and body postures from video and audio recordings. The captured data is presented in a set of visualizations, designed to help teachers to obtain insights about the collaboration of a team. We performed a case study to explore if the visualizations were useful to represent different behavioral indicators of collaboration in different teamwork situations: a collaborative situation and a competitive situation. We discussed the results of the case study in a focus group with three teachers, to get insights in the usefulness of our proposal. The results show that the measurements and visualizations are helpful to understand differences in collaboration, confirming the feasibility the MMLA approach for assessing and providing collaboration insights based on non-verbal communication.
In Silico Study and Excito-Repellent Activity of Vitex negundo L. Essential Oil against Anopheles gambiae
Authors: Okoli Bamidele J. , Eltayb Wafa Ali , Gyebi Gideon A. , Ghanam Amr R. , Ladan Zakari , Oguegbulu Joseph C. , Abdalla Mohnad
Published: July 2022

(1) Background: Essential oil from Vitex negundo is known to have repellent and insecticidal properties toward the Anopheles gambiae and this is linked to its monoterpene and sesquiterpene content. In this work, an effort is made to delineate the constitution of V. negundo essential oil (VNEO) and their interaction with odorant-binding proteins (OBPs) of A. gambiae and hence access its repellent efficiency as cost-effective and safer malaria vector control alternatives. (2) Methods: Anopheles species authentication was performed by genomic DNA analysis and was subjected to behavioral analysis. GC-MS profiling was used to identify individual components of VNEO. Anopheles OBPs were obtained from the RCSB protein data bank and used for docking studies. Determination of ligand efficiency metrics and QSAR studies were performed using Hyper Chem Professional 8.0.3, and molecular dynamics simulations were performed using the Desmond module. (3) Results: GC-MS analysis of VNEO showed 28 compounds (monoterpenes, 80.16%; sesquiterpenes, 7.63%; and unknown constituents, 10.88%). The ligand efficiency metrics of all four ligands against the OBP 7 were within acceptable ranges. β-selinene (−12.2 kcal/mol), β-caryophellene (−9.5 kcal/mol), sulcatone (−10.9 kcal/mol), and α-ylangene (−9.3 kcal/mol) showed the strongest binding affinities for the target proteins. The most stable hydrophobic interactions were observed between β-selinene (Phe111 and Phe120), Sulcatone (Phe54 and Phe120), and α-ylangene (Phe111), while only sulcatone (Tyr49) presented H-bond interactions in the simulated environment. (4) Conclusions: Sulcatone and β-caryophyllene presented the best log p values, 6.45 and 5.20, respectively. These lead phytocompounds can be used in their purest as repellent supplement or as a natural anti-mosquito agent in product formulations.
Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
Authors: Guo Yulong , Bi Qingsheng , Li Yuan , Du Chenggong , Huang Junchang , Chen Weiqiang , Shi Lingfei , Ji Guangxing
Published: July 2022

Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color.
Research on the Decoupling of the Parallel Vehicle Tilting and Steering Mechanism
Authors: Gao Ruolin , Li Haitao , Wei Wenjun , Wang Ya
Published: July 2022

Active tilting vehicles tilt to the inside of the corner when the vehicle is steering. The tilting motion improves the steering and roll stability of the vehicle. The steering mechanism and the tilting mechanism of the vehicle are connected in parallel. The transmission of the steering mechanism is influenced by the movements of the tilting mechanism. In order to solve this problem, a parallel mechanism is proposed in this paper. It consists of a spatial steering mechanism and a tilting mechanism in parallel. A mathematical model of the parallel mechanism with the wheel alignment parameters has been established. The model calculates the decoupling conditions of the parallel mechanism. In this study, a decoupling method for the parallel mechanism is proposed. A prototype of the parallel mechanism was designed according to the proposed method. The prototype was found to reduce the influence of vehicle tilting on the outer and inner wheel steering angles by up to 0.64% and 0.78%, respectively. The steering geometry correction rate of the prototype is between 1.198 and 0.961. The correctness of the model was verified by experimentation on the prototype. The proposed method can effectively decouple the tilting motion and steering motion of the vehicle and make the wheels on both sides satisfy the Ackerman steering condition.
Possibilities of River Water Temperature Reconstruction Using Statistical Models in the Context of Long-Term Thermal Regime Changes Assessment
Authors: Sojka Mariusz , Ptak Mariusz
Published: July 2022

Water temperature in rivers is the key property determining the biotic and abiotic processes occurring in these ecosystems. In many regions of the world, the significant lack of measurement data is a serious problem. This paper presents reconstruction of water temperature for selected Polish rivers with monitoring discontinued in the period 2015–2020. Information regarding air temperature and water temperature in lakes provided the basis for the comparison of three models: multiple linear regression, random forest regression, and multilayer perceptron network. The results show that the best reconstruction results were obtained with a multilayer perceptron network model based on water temperatures in the lake and air temperatures from three meteorological stations. The average values of mean error, root mean square error and standard error were for the rivers in Poland: 1.52 °C, 5.03%, and 0.47 °C. The course of mean yearly water temperature in the years 1987–2020 showed a statistically significant increase from 0.18 to 0.49 °C per decade. The results show that the largest increases occurred in June, August, September, November, and December.
Evaluation of Genetic Damage and Antigenotoxic Effect of Ascorbic Acid in Erythrocytes of Orochromis niloticus and Ambystoma mexicanum Using Migration Groups as a Parameter
Authors: Moya Carlos Alvarez , Silva Mónica Reynoso , Ramírez Lucia Barrientos , Radillo José de Jesús Vargas
Published: July 2022

The comet assay system is an efficient method used to assess DNA damage and repair; however, it currently provides the average result and, unfortunately, the heterogeneity of DNA damage loses relevance. To take advantage of this heterogeneity, migration groups (MGs) of cell comets can be formed. In this study, genetic damage was quantified in erythrocytes of Oreochromis niloticus and Ambystoma mexicanum exposed to ethyl methanesulfonate (ethyl methanesulfonate (EMS) 2.5, 5, and 10 mM over two hours) and ultraviolet C radiation (UV-C) for 5, 10, and 15 min using the tail length, tail moment, and migration group parameters. Additionally, blood cells were exposed to UV-C radiation for 5 min and treated post-treatment at 5, 10, and 15 mM ascorbic acid (AA) for two hours. With the MG parameter, it was possible to observe variations in the magnitude of genetic damage. Our data indicate that MGs help to detect basal and induced genetic damage or damage reduction with approximately the same efficiency of the tail length and tail moment parameters. MGs can be a complementary parameter used to assess DNA integrity in species exposed to mutagens.
A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
Authors: Yan Hongyu , Xie Suchao , Jing Kunkun , Feng Zhejun
Published: July 2022

Since the invention of the train, the problem of train noise has been a constraint on the development of trains. With increases in train speed, the main noise from high-speed trains has changed from rolling noise to aerodynamic noise, and the noise level and noise frequency range have also changed significantly. This paper provides a comprehensive overview of recent advances in the development of high-speed train noise. Firstly, the train noise composition is summarized; next, the main research methods for train noise, which include real high-speed train noise tests, wind tunnel tests, and numerical simulations, are reviewed and discussed. We also discuss the current methods of noise reduction for trains and summarize the progress in current research and the limitations of train body panels and railroad sound barrier technology. Finally, the article introduces the development and potential future applications of acoustic metamaterials and proposes application scenarios of acoustic metamaterials for the specific needs of railroad sound barriers and train car bodies. This synopsis provides a useful platform for researchers and engineers to cope with problems of future high-speed rail noise in the future.
A Finite Element Model for Monitoring the Displacement of Pipelines in Landslide Regions by Discrete FBG Strain Sensors
Authors: Magisano Domenico , Mastroianni Marisa , Leonetti Leonardo , Madeo Antonio , Garcea Giovanni , Gagliardi Gianfranco , Casavola Alessandro , Vecchio Giuseppe , Ferrini Francesco , Pierro Alessio , Colloca Roberta , Muraca Emanuel
Published: July 2022

This study investigates a system for monitoring displacements of underground pipelines in landslide-prone regions. This information is an important alarm indicator, not only to prevent the failure of the line itself but also to mitigate the direct consequences of landslides on buildings and infrastructures in the affected area. Specifically, a numerical processing tool coupled with a data acquisition system is proposed. The starting point is the measurement of axial strain at three points of discrete sections of the pipeline by Fiber Bragg grating sensors, used to approximate the trend of mean axial strain and bending curvatures along the pipe axis. A finite element analysis based on a 3D geometrically exact beam model is developed for computing the deformed configuration corresponding to the input strain field. After assigning the boundary conditions, a mixed iterative scheme is used for a quick solution to the nonlinear problem. Firstly, the tool is validated theoretically with benchmarks on beam-like structures undergoing large deflections. Then, experimental results are produced on a monitored pipe buried in a wedge of land subject to an artificial slide. The overall sensor-modeling system, with zero displacements far from the landslide as a boundary condition, provides a satisfactory displacement trend with a mean error of about 18% with just three effective monitored sections in the affected pipe stretch of 18 m. The acquisition and processing tool is implemented in a web application as a real-time alarm system.
H&E Multi-Laboratory Staining Variance Exploration with Machine Learning
Authors: Prezja Fabi , Pölönen Ilkka , Äyrämö Sami , Ruusuvuori Pekka , Kuopio Teijo
Published: July 2022

In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists’ time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue samples stained by the method routinely used in each laboratory. The samples were digitized and summarized as red, green, and blue channel histograms. Dimensions were reduced using principal component analysis. The data projected by principal components were inserted into the k-means clustering algorithm and the k-nearest neighbors classifier with the laboratories as the target. The k-means silhouette index indicated that K = 2 clusters had the best separability in all tissue types. The supervised classification result showed laboratory effects and tissue-type bias. Both supervised and unsupervised approaches suggested that tissue type also affected inter-laboratory variance. We suggest tissue type to also be considered upon choosing the staining and color-normalization approach.
AutoElbow: An Automatic Elbow Detection Method for Estimating the Number of Clusters in a Dataset
Authors: Onumanyi Adeiza James , Molokomme Daisy Nkele , Isaac Sherrin John , Abu-Mahfouz Adnan M.
Published: July 2022

The elbow technique is a well-known method for estimating the number of clusters required as a starting parameter in the K-means algorithm and certain other unsupervised machine-learning algorithms. However, due to the graphical output nature of the method, human assessment is necessary to determine the location of the elbow and, consequently, the number of data clusters. This article presents a simple method for estimating the elbow point, thus, enabling the K-means algorithm to be readily automated. First, the elbow-based graph is normalized using the graph’s minimum and maximum values along the ordinate and abscissa coordinates. Then, the distance between each point on the graph to the minimum (i.e., the origin) and maximum reference points, and the “heel” of the graph are calculated. The estimated elbow location is, thus, the point that maximizes the ratio of these distances, which corresponds to an approximate number of clusters in the dataset. We demonstrate that the strategy is effective, stable, and adaptable over different types of datasets characterized by small and large clusters, different cluster shapes, high dimensionality, and unbalanced distributions. We provide the clustering community with a description of the method and present comparative results against other well-known methods in the prior state of the art.
ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
Authors: Kim Youngsoo , Lee Gunwoo , Jeong Jongpil
Published: July 2022

The semiconductor etch cluster facility is the most used facility platform in the semiconductor manufacturing process. Optimizing cluster facilities can depend on production schedules and can have a direct impact on productivity. According to the diversity of semiconductor processes, the complexity of optimization is also increasing. Various optimization methods have been studied in many papers for optimizing such a complex cluster facility. However, there is a lack of discussion of how these methods can apply to practical semiconductor manufacturing fabs and the actual performance results. Even now, data analysis and optimal parameter derivation for maximizing the productivity of cluster manufacturing in semiconductor manufacturing fabs are continuing. In this study, we propose an automated method for data collection and analysis of the cluster, which used to be done manually. In addition, the derivation of optimization parameters and application to facilities are addressed. This automated method could improve the manual analysis methods, such as simulation through data analysis using machine learning algorithms. It could also solve the inefficiency caused by manual analysis performed within the network inside the semiconductor manufacturing fabs.
Design Exploration of Bamboo Shells Structures by Using Parametric Tools
Authors: Estrada Meza María Guadalupe , González Meza Edwin , Chi Pool Doris Abigail , McNamara Trujillo Jaqueline Sofía
Published: July 2022

Bamboo is a promising material for structural use due to its mechanical properties and rapid growth. Currently, standards and codes to support the analysis and calculation of bamboo structures are still scarce. Even international standards do not include specifications for the design of complex geometries. This work aimed to verify the use of parametric tools to design complex shapes. First, the NSR-10 Colombian code was used to analytically solve and design the mechanical behavior of double-curved shells. Then, the results were compared to those calculated with the add-on Karamba3D. Lastly, verification was considered successful when both results coincided. Here, it should be noticed that the resultant data might lead to plastic deformation of the cylinder head. However, this work did not intend to formulate structural designs whose beams effectively support the requested stresses but focused on the verification of software applications. Once the software was verified, this work further analyzed the structural behavior of two double curvature geometries simulated with bamboo, showing that digital simulation tools have the potential to be applied during the early structural design phases.
A Deep Learning Method for Early Detection of Diabetic Foot Using Decision Fusion and Thermal Images
Authors: Munadi Khairul , Saddami Khairun , Oktiana Maulisa , Roslidar Roslidar , Muchtar Kahlil , Melinda Melinda , Muharar Rusdha , Syukri Maimun , Abidin Taufik Fuadi , Arnia Fitri
Published: July 2022

Diabetes mellitus (DM) is one of the major diseases that cause death worldwide and lead to complications of diabetic foot ulcers (DFU). Improper and late handling of a diabetic foot patient can result in an amputation of the patient’s foot. Early detection of DFU symptoms can be observed using thermal imaging with a computer-assisted classifier. Previous study of DFU detection using thermal image only achieved 97% of accuracy, and it has to be improved. This article proposes a novel framework for DFU classification based on thermal imaging using deep neural networks and decision fusion. Here, decision fusion combines the classification result from a parallel classifier. We used the convolutional neural network (CNN) model of ShuffleNet and MobileNetV2 as the baseline classifier. In developing the classifier model, firstly, the MobileNetV2 and ShuffleNet were trained using plantar thermogram datasets. Then, the classification results of those two models were fused using a novel decision fusion method to increase the accuracy rate. The proposed framework achieved 100% accuracy in classifying the DFU thermal images in binary classes of positive and negative cases. The accuracy of the proposed Decision Fusion (DF) was increased by about 3.4% from baseline ShuffleNet and MobileNetV2. Overall, the proposed framework outperformed in classifying the images compared with the state-of-the-art deep learning and the traditional machine-learning-based classifier.
Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
Authors: Sherbert Kyle M. , Naimipour Naveed , Safavi Haleh , Shaw Harry C. , Soltanalian Mojtaba
Published: July 2022

Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, typically manifested as signal sparsity. Compressive sensing’s vast repertoire of applications in areas such as communications and image reconstruction stems from the traditional approach of utilizing non-linear optimization to exploit the sparsity assumption by selecting the lowest-weight (i.e., maximum sparsity) signal consistent with all acquired measurements. Recent efforts in the literature consider instead a data-driven approach, training tensor networks to learn the structure of signals of interest. The trained tensor network is updated to “project” its state onto one consistent with the measurements taken, and is then sampled site by site to “guess” the original signal. In this paper, we take advantage of this computing protocol by formulating an alternative “quantum” protocol, in which the state of the tensor network is a quantum state over a set of entangled qubits. Accordingly, we present the associated algorithms and quantum circuits required to implement the training, projection, and sampling steps on a quantum computer. We supplement our theoretical results by simulating the proposed circuits with a small, qualitative model of LIDAR imaging of earth forests. Our results indicate that a quantum, data-driven approach to compressive sensing may have significant promise as quantum technology continues to make new leaps.
An MHD Marangoni Boundary Layer Flow and Heat Transfer with Mass Transpiration and Radiation: An Analytical Study
Authors: Anusha Thippeswamy , Mahesh Rudraiah , Mahabaleshwar Ulavathi Shettar , Laroze David
Published: July 2022

This examination is carried out on the two-dimensional magnetohydrodynamic problem for a steady incompressible flow over a porous medium. The Cu−Al2O3 nanoparticles are added to the water base fluid in order to improve thermal efficiency. The transverse magnetic field with strength B0 is applied. The governing equations formed for the defined flow form a system of partial differential equations that are then converted to a system of ordinary differential equations upon applying the suitable similarity transformations. On analytically solving the obtained system, the solutions for velocity profile and temperature distribution are obtained in terms of exponential and Gamma functions, respectively. In addition, the physical parameter of interest, the local Nusselt number, is obtained. The results are analyzed through plotting graphs, and the effect of different parameters is analyzed. Furthermore, we observe that the suction/injection parameter enhances the axial velocity. The porous and radiation parameters enhance the temperature distribution, and the suction/injection parameter suppresses the temperature distribution.
The Classification of Reflectance Anisotropy and Its Application in Albedo Retrieval
Authors: Zhao Mengzhuo , Zhang Hu , Chen Cancan , Wang Chenxia , Liu Yan , Li Juan , Cui Tiejun
Published: July 2022

The land surface albedo reflects the ability of the surface to reflect solar radiation and is a critical physical variable in the study of the Earth’s energy budget and global climate change. Algorithms for the retrieval of albedo usually require multi-angle measurements due to surface anisotropy. However, most of the satellites cannot currently provide sufficient and well-distributed observations; therefore, the accuracy of remotely sensed albedo is constrained. Based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo product (MCD43A1), this study proposed a method to further subdivide reflectance anisotropy and build an updated database of BRDF archetype, using both the Anisotropic Flat Index (AFX) and Perpendicular Anisotropic Flat Index (PAFX). The BRDF archetypes were used to fit the corresponding MODIS BRDF, and the optimal number of BRDF archetype categories was determined according to the tendency of fitting error. The effect of surface anisotropy and observation noise on albedo retrieval were explored based on simulated MODIS reflectance. Finally, the BRDF archetype A2P2 was taken as prior knowledge to retrieve albedo from a different number of MODIS observations, and the result was validated by the high-quality MODIS albedo product. The results show that the fitting error between BRDF archetypes and MODIS BRDF shows a rapid decline when introducing the PAFX in the classification process. A 3-by-3 matrix of BRDF archetypes, which occupy 73.44% and 70.13% of the total decline in the red and NIR band, can be used to represent the characteristics of reflectance anisotropy. The archetype A2P2 may be used as prior knowledge to improve the albedo retrieval from insufficient observations. The validation results based on MODIS observations show that the archetype A2P2-based albedo can reach root-mean-square errors (RMSEs) of no more than 0.03.
Home Health Care Planning with the Consideration of Flexible Starting/Ending Points and Service Features
Authors: Khodabandeh Pouria , Kayvanfar Vahid , Rafiee Majid , Werner Frank
Published: July 2022

One of the recently proposed strategies in health systems is providing services to patients at home and improving the service quality in addition to reducing the health system costs. In the real world, some services, such as biological tests or blood sampling, force the nurses to start or end his/her route from/at the laboratory instead of the depot, changing the whole optimal planning. The effect of these special service requirements and features has not been considered so far. In this study, a new mathematical model is suggested considering the flexibility of starting/ending places of each nurse’s route according to the specific characteristics of each service. Then, several sets of problems in various sizes are solved using the proposed model, where the results confirm the efficiency of the proposed approach. In addition, some sensitivity analyses are performed on the parameters of the required features of the services, followed by some managerial insights and directions for future studies.
Fractional Dynamics of a Measles Epidemic Model
Authors: Abboubakar Hamadjam , Fandio Rubin , Sofack Brandon Satsa , Ekobena Fouda Henri Paul
Published: July 2022

In this work, we replaced the integer derivative with Caputo derivative to model the transmission dynamics of measles in an epidemic situation. We began by recalling some results on the local and global stability of the measles-free equilibrium point as well as the local stability of the endemic equilibrium point. We computed the basic reproduction number of the fractional model and found that is it equal to the one in the integer model when the fractional order ν = 1. We then performed a sensitivity analysis using the global method. Indeed, we computed the partial rank correlation coefficient (PRCC) between each model parameter and the basic reproduction number R0 as well as each variable state. We then demonstrated that the fractional model admits a unique solution and that it is globally stable using the Ulam–Hyers stability criterion. Simulations using the Adams-type predictor–corrector iterative scheme were conducted to validate our theoretical results and to see the impact of the variation of the fractional order on the quantitative disease dynamics.
Comparative Analysis of the Upper Respiratory Bacterial Communities of Pigs with or without Respiratory Clinical Signs: From Weaning to Finishing Phase
Authors: Rampelotto Pabulo Henrique , dos Santos Anne Caroline Ramos , Muterle Varela Ana Paula , Takeuti Karine Ludwig , Loiko Márcia Regina , Mayer Fabiana Quoos , Roehe Paulo Michel
Published: July 2022

A prospective study was conducted to identify bacterial communities in the nasal and laryngeal cavities of pigs with or without clinical signs of respiratory disease in a longitudinal fashion, from weaning to the finishing phase. Nasal and laryngeal swabs were collected from asymptomatic pigs (n = 30), as well as from pigs with clinical signs of respiratory disease (n = 30) at the end of the weaning (T1—33 days) phase, end of the nursery phase (T2—71 days), and finishing (T3—173 days). Total DNA was extracted from each sample, and the V4 hypervariable region of the 16S rRNA gene was amplified and sequenced with the Illumina MiSeq platform. Principal coordinates analysis indicated no significant differences between the nasal and laryngeal bacterial communities. Nevertheless, the microbiota composition in the upper respiratory tract (URT) was clearly distinct between animals, with or without signs of respiratory disease, particularly at post-weaning and the end of nursery. In pigs with clinical signs of respiratory disease, Actinobacillus, Streptococcus Porphyromonas, Veillonella, and an unclassified genus of Pasteurellaceae were more abundant than in pigs with no signs. Metabolic prediction identified 28 differentially abundant pathways, mainly related to carbohydrate, energy, amino acid, anaerobic, and nucleotide metabolism in symptomatic pigs (especially in T2). These findings provide evidence that the composition of the URT bacterial microbiota differs significantly when comparing pigs with or without respiratory clinical signs after weaning, and this difference is maintained in the nursery phase; such differences, however, were not evident at the finishing phase.
Promising Biomolecules with High Antioxidant Capacity Derived from Cryptophyte Algae Grown under Different Light Conditions
Authors: Abidizadegan Maryam , Blomster Jaanika , Fewer David , Peltomaa Elina
Published: July 2022

The accumulation and production of biochemical compounds in microalgae are influenced by available light quality and algal species-specific features. In this study, four freshwater cryptophyte strains (Cryptomonas ozolinii, C. pyrenoidifera, C. curvata, and C. sp. (CPCC 336)) and one marine strain (Rhodomonas salina) were cultivated under white (control), blue, and green (experimental conditions) lights. Species-specific responses to light quality were detected, i.e., the color of light significantly affected cryptophyte biomass productivity and biochemical compositions, but the optimal light for the highest chemical composition with high antioxidant capacity was different for each algal strain. Overall, the highest phycoerythrin (PE) content (345 mg g−1 dry weight; DW) was reached by C. pyrenoidifera under green light. The highest phenolic (PC) contents (74, 69, and 66 mg g−1 DW) were detected in C. curvata under control conditions, in C. pyrenoidifera under green light, and in C. ozolinii under blue light, respectively. The highest exopolysaccharide (EPS) content (452 mg g−1 DW) was found in C. curvata under the control light. In terms of antioxidant activity, the biochemical compounds from the studied cryptophytes were highly active, with IC50 -values < 50 µg mL−1. Thus, in comparison to well-known commercial microalgal species, cryptophytes could be considered a possible candidate for producing beneficial biochemical compounds.
Association between Cervical Microbiota and HPV: Could This Be the Key to Complete Cervical Cancer Eradication?
Authors: Cascardi Eliano , Cazzato Gerardo , Daniele Antonella , Silvestris Erica , Cormio Gennaro , Di Vagno Giovanni , Malvasi Antonio , Loizzi Vera , Scacco Salvatore , Pinto Vincenzo , Cicinelli Ettore , Maiorano Eugenio , Ingravallo Giuseppe , Resta Leonardo , Minoia Carla , Dellino Miriam
Published: July 2022

The heterogeneity of the cervico-vaginal microbiota can be appreciated in various conditions, both pathological and non-pathological, and can vary according to biological and environmental factors. Attempts are still in course to define the interaction and role of the various factors that constitute this community of commensals in immune protection, inflammatory processes, and the onset of precancerous lesions of the cervical epithelium. Despite the many studies on the relationship between microbiota, immunity, and HPV-related cervical tumors, further aspects still need to be probed. In this review article, we will examine the principal characteristics of microorganisms commonly found in cervico-vaginal specimens (i) the factors that notoriously condition the diversity and composition of microbiota, (ii) the role that some families of organisms may play in the onset of HPV-dysplastic lesions and in neoplastic progression, and (iii) possible diagnostic-therapeutic approaches.
Changes in Subjective Time and Self during Meditation
Authors: Linares Gutiérrez Damisela , Schmidt Stefan , Meissner Karin , Wittmann Marc
Published: July 2022

This study examined the effects of meditative states in experienced meditators on present-moment awareness, subjective time, and self-awareness while assessing meditation-induced changes in heart-rate variability and breathing rate. A sample of 22 experienced meditators who practiced meditation techniques stressing awareness of the present moment (average 20 years of practice) filled out subjective scales pertaining to sense of time and the bodily self and accomplished a metronome task as an operationalization of present-moment awareness before and after a 20 min meditation session (experimental condition) and a 20 min reading session (control condition) according to a within-subject design. A mixed pattern of increased sympathetic and parasympathetic activity was found during meditation regarding heart-rate measures. Breathing intervals were prolonged during meditation. Participants perceived their body boundaries as less salient during meditation than while reading the story; they also felt time passed more quickly and they paid less attention to time during meditation. No significant differences between conditions became apparent for the metronome task. This is probably the first quantitative study to show how the experience of time during a meditation session is altered together with the sense of the bodily self.
Quantitative Proteomics and Network Analysis of Differentially Expressed Proteins in Proteomes of Icefish Muscle Mitochondria Compared with Closely Related Red-Blooded Species
Authors: Katyal Gunjan , Ebanks Brad , Dowle Adam , Shephard Freya , Papetti Chiara , Lucassen Magnus , Chakrabarti Lisa
Published: July 2022

Antarctic icefish are extraordinary in their ability to thrive without haemoglobin. We wanted to understand how the mitochondrial proteome has adapted to the loss of this protein. Metabolic pathways that utilise oxygen are most likely to be rearranged in these species. Here, we have defined the mitochondrial proteomes of both the red and white muscle of two different icefish species (Champsocephalus gunnari and Chionodraco rastrospinosus) and compared these with two related red-blooded Notothenioids (Notothenia rossii, Trematomus bernacchii). Liquid Chromatography-Mass spectrometry (LC-MS/MS) was used to generate and examine the proteomic profiles of the two groups. We recorded a total of 91 differentially expressed proteins in the icefish red muscle mitochondria and 89 in the white muscle mitochondria when compared with the red-blooded related species. The icefish have a relatively higher abundance of proteins involved with Complex V of oxidative phosphorylation, RNA metabolism, and homeostasis, and fewer proteins for striated muscle contraction, haem, iron, creatine, and carbohydrate metabolism. Enrichment analyses showed that many important pathways were different in both red muscle and white muscle, including the citric acid cycle, ribosome machinery and fatty acid degradation. Life in the Antarctic waters poses extra challenges to the organisms that reside within them. Icefish have successfully inhabited this environment and we surmise that species without haemoglobin uniquely maintain their physiology. Our study highlights the mitochondrial protein pathway differences between similar fish species according to their specific tissue oxygenation idiosyncrasies.