bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products
Authors: Matsuzaka Yasunari , Kusakawa Shinji , Uesawa Yoshihiro , Sato Yoji , Satoh Mitsutoshi
Published: October 2021

Automated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine. Deep learning (DL) is a powerful method for classifying and recognizing images in cell biology, diagnostic medicine, and other fields because it automatically extracts the features from complex cell morphologies. In the present study, we construct prediction models that recognize cancer-cell contamination in continuous long-term (four-day) cell cultures. After dividing the whole dataset into Early- and Late-stage cell images, we found that Late-stage images improved the DL performance. The performance was further improved by optimizing the DL hyperparameters (batch size and learning rate). These findings are first report for the implement of DL-based systems in disease cell-type classification of human cell-processed therapeutic products (hCTPs), that are expected to enable the rapid, automatic classification of induced pluripotent stem cells and other cell treatments for life-threatening or chronic diseases.
Beekeeping in the Desert: Foraging Activities of Honey Bee during Major Honeyflow in a Hot-Arid Ecosystem
Authors: Alqarni Abdulaziz S. , Iqbal Javaid , Raweh Hael S. , Hassan Awad M. A. , Owayss Ayman A.
Published: October 2021

This study investigated the outgoing and pollen-gathering foraging activities of Apis mellifera jemenitica (AMJ) and Apis mellifera carnica (AMC) under a hot-arid environment in the presence of nectar-rich melliferous Ziziphus nummularia flora. The data revealed the differential effects of weather conditions and Z. nummularia flora on the foraging activities of the studied honey bee subspecies in the Rawdat-Khuraim oasis in central Saudi Arabia. Z. nummularia exhibited two flowering seasons, from June–July (season I) and August–October (season II), with a significantly higher mean flowering density observed during season II (404 flowers/m2) than during season I (235 flowers/m2). AMJ showed significantly higher foraging activities (outgoing and pollen-gathering) than AMC (exotic bees) during all months in each flowering season. The mean outgoing and pollen-gathering foraging rates of AMJ (32.40 ± 0.67 and 4.88 ± 0.40 workers/colony/min, respectively) were significantly higher than those of AMC (15.93 ± 1.20 and 2.39 ± 0.23 workers/colony/min, respectively). The outgoing and pollen-gathering foraging activities of the two subspecies fluctuated throughout the different times of day. Foraging activities were considerably high at sunrise (SR) and low at noon (N) during both flowering seasons. We also observed seasonal variations in the foraging activities of both bee subspecies. The mean foraging activities (outgoing and pollen-gathering) were slightly higher in season I (27.43 ± 1.21 and 4.46 ± 0.45 workers/colony/min, respectively) than in season II (21.71 ± 0.86 and 3.02 ± 0.22 workers/colony/min, respectively). The thermal window analysis revealed a significant difference between the flight activities (bees exiting and returning to the nest throughout the day) of AMJ and AMC; AMJ had a higher temperature threshold than AMC. The outgoing and pollen-gathering foraging activities within each bee subspecies were positively correlated. The present study can help researchers understand the performances of honeybees and the association of their performances with weather and nectar-rich flora conditions.
Non-Destructive Testing Applications for Steel Bridges
Authors: Khedmatgozar Dolati Seyed Saman , Caluk Nerma , Mehrabi Armin , Khedmatgozar Dolati Seyed Sasan
Published: October 2021

The growing population and increasing demand for surface transportation have highlighted the importance of maintaining safe and reliable civil infrastructures for daily use. Among all civil infrastructures, bridges are one of the most important elements in the transportation system. As such, to prevent any failures caused by aging and environmental impacts, bridges require periodic inspections. This becomes even more critical due to climate change and its effect on bridges, especially in the coastal regions. Most of the inspections conducted incorporate the visual type of evaluation due to its simplicity. However, with the current developments in new technologies, there is a need for more advanced techniques of structural health monitoring (SHM) methods to be incorporated in the maintenance programs for more accurate and efficient surveys. In this paper, non-destructive testing (NDT) methods applicable to steel bridges are reviewed, with a focus on methods applicable to local damage detection. Moreover, the methodology, advantages and disadvantages, and up-to-date research on NDT methods are presented. Furthermore, the application of novel NDT techniques using innovative sensors, drones, and robots for the rapid and efficient assessment of damages on small and large scales is emphasized. This study is deemed necessary as it compiles in one place the available information regarding NDT methods for in-service steel bridges. Access to such information is critical for researchers who intend to work on new or improved NDT techniques.
Design and Test of Load-Lifting Performance for Hydraulic Linkage of the High-Medium Horsepower Tractor
Authors: Cong Qian , Yang Zhengwen , Xu Jin , Ma Boshuai , Chen Tingkun , Zhang Xiaochao , Wang Lin , Ru Shaofeng
Published: October 2021

To improve the detection efficiency and safety of the tractor, the research proposed a device for detecting the loading–lifting performance of the lower link of the tractor based on the four-bar mechanism. According to the actual use requirements and the testing standards, the critical components in the device were designed. The dynamic analysis of the load-lifting device was carried out by dynamic simulation, and the component strength in the machine was checked by the finite element simulation method. The results showed that the designed device could realize the hooking and connection of the lower link without an artificial method. The average cost of the device was 5.13 s to realize the connection with the lower link, and it took 7.30 s to raise the lower hitch point to a set height, about 750 mm. The loading test showed that the device could keep the loading force of the lower link stable during the lifting process. The designed device could shorten the detection time of the tractor hydraulic linkage and improve the cost, safety, and efficiency of detection. The research could provide a reference for the design of hydraulic linkage detection devices for the large-medium horsepower tractors and help realize the intelligent detection of tractors.
Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
Authors: Huang Zhongkai , Zhang Dongmei , Zhang Dongming
Published: October 2021

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.
A Deep Convolutional Neural Network-Based Multi-Class Image Classification for Automatic Wafer Map Failure Recognition in Semiconductor Manufacturing
Authors: Zheng Huilin , Sherazi Syed Waseem Abbas , Son Sang Hyeok , Lee Jong Yun
Published: October 2021

Wafer maps provide engineers with important information about the root causes of failures during the semiconductor manufacturing process. Through the efficient recognition of the wafer map failure pattern type, the semiconductor manufacturing process and its product performance can be improved, as well as reducing the product cost. Therefore, this paper proposes an accurate model for the automatic recognition of wafer map failure types using a deep learning-based convolutional neural network (DCNN). For this experiment, we use WM811K, which is an open-source real-time wafer map dataset containing wafer map images of nine failure classes. Our research contents can be briefly summarized as follows. First, we use random sampling to extract 500 images from each class of the original image dataset. Then we propose a deep convolutional neural network model to generate a multi-class classification model. Lastly, we evaluate the performance of the proposed prediction model and compare it with three other popular machine learning-based models—logistic regression, random forest, and gradient boosted decision trees—and several well-known deep learning models—VGGNet, ResNet, and EfficientNet. Consequently, the comprehensive analysis showed that the performance of the proposed DCNN model outperformed those of other popular machine learning and deep learning-based prediction models.
Vehicle Delay Model Applied to Dynamic and Static Traffic Impact Analysis of Large Parking Lots
Authors: Wang Liya , Chen Jun , Cao Xiaofeng , Chen Jian , Zhang Chu
Published: October 2021

With the surge of motor vehicle ownership and land intensification, plenty of large parking lots emerge as the times demand. Although it solves the problem of insufficient parking spaces, it intensifies the interaction between dynamic and static traffic. This paper presented an impact assessment method for the interaction of dynamic and static traffic flow in parking lots. Firstly, the average vehicle delay was selected as the evaluation index. The delay effect caused by the interaction of dynamic and static traffic flow was determined according to the driving path of vehicles. Then, the average vehicle delay models of arrival vehicles, departure vehicles, and road vehicles in the parking lot were established. Finally, for the parameters difficult to determine directly in the delay model, this paper proposed a method to calibrate the model parameters by using the simulation experimental data on the VISSIM platform. The results showed that the errors of the three models are within the controllable range, and the delay model parameters had high reliability and feasibility. The delay models can provide a quantitative basis for the parking lot management department to formulate regulation strategies and realize more refined information guidance and navigation in the parking lot.
Advances in Industrial and Environmental Microbiology
Authors: Ciesielski Slawomir , Vaz-Moreira Ivone
Published: October 2021

Understanding microorganisms in terms of their functionality, diversity, and interactions with other organisms is crucial for better understanding of our Biosphere [...]
Investigation of Vehicle Stability with Consideration of Suspension Performance
Authors: Lukoševičius Vaidas , Makaras Rolandas , Rutka Arūnas , Keršys Robertas , Dargužis Andrius , Skvireckas Ramūnas
Published: October 2021

The issue of movement stability remains highly relevant considering increasing vehicle speeds. The evaluation of vehicle stability parameters and the modeling of specific movement modes is a complex task, as no universal evaluation criteria have been established. The main task in modeling car stability is an integrated assessment of the vehicle’s road interactions and identification of relationships. The main system affecting the vehicle’s road interaction is the suspension of the vehicle. Vehicle suspension is required to provide constant wheel to road surface contact, thus creating the preconditions for stability of vehicle movement. At the same time, it must provide the maximum possible body insulation against the effect of unevennesses on the road surface. Combining the two marginal prerequisites is challenging, and the issue has not been definitively solved to this day. Inaccurate alignment of the suspension and damping characteristics of the vehicle suspension impairs the stability of the vehicle, and passengers feel discomfort due to increased vibrations of the vehicle body. As a result, the driving speed is artificially restricted, the durability of the vehicle body is reduced, and the transported cargo is affected. In the study, analytical computational and experimental research methods were used. Specialized vehicle-road interaction assessment programs were developed for theoretical investigation. The methodology developed for assessing vehicle movement stability may be used for the following purposes: design and improvement of vehicle suspension and other mechanisms that determine vehicle stability; analysis of road spans assigned with characteristic vehicle movement settings; road accident situation analysis; design of road structures and establishment of certain operational restrictions on the road structures. A vehicle suspension test bench that included original structure mechanisms that simulate the effect of the road surface was designed and manufactured to test the results of theoretical calculations describing the work of the vehicle suspension and to study various suspension parameters. Experimental investigations were carried out by examining the vibrations of vehicle suspension elements caused by unevenness on the road surface.
Creating a Diagnostic Assistance System for Diseases in Kampo Medicine
Authors: Koike Reimei , Ogawa-Ochiai Keiko , Shirai Akiko , Hayashi Katsumi , Arimitsu Junsuke , Li Hongyang , Tsumura Norimichi
Published: October 2021

The aim of this study was to propose a method to assess images of the tongue captured using a polarized light camera for diagnostic use in Kampo medicine. Glossy and non-glossy images of the tongue were captured simultaneously using a polarizing camera and a polarizing plate. Data augmentation was performed by modulating the color and gloss, resulting in an increase in the number of images from 11 to 275. To create a data set, the values for which diseases were evaluated by Kampo doctors for all tongue images were taken as the correct values and combined with the features extracted from the tongue images. Using this data set, we constructed a diagnostic support module to evaluate diseases. The resulting mean absolute error of the assessment was 0.44 for qi deficiency, 0.42 for blood deficiency, 0.33 for blood stagnation, 0.36 for yin deficiency, and 0.55 for fluid stagnation, suggesting that the diagnostic assistance module was accurate, and our proposed learning and data augmentation methods were effective.
Handling Imbalanced Datasets for Robust Deep Neural Network-Based Fault Detection in Manufacturing Systems
Authors: Kafunah Jefkine , Ali Muhammad Intizar , Breslin John G.
Published: October 2021

Over the recent years, Industry 4.0 (I4.0) technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and the presence of Industrial Big Data (IBD) have helped achieve intelligent Fault Detection (FD) in manufacturing. Notably, data-driven approaches in FD apply Deep Learning (DL) techniques to help generate insights required for monitoring complex manufacturing processes. However, due to the ratio of instances where actual faults occur, FD datasets tend to be imbalanced, leading to training challenges that result in inefficient DL-based FD models. In this paper, we propose Dual Logits Weights Perturbation (DLWP) loss, a method featuring weight vectors for improved dataset generalization in FD systems. The weight vectors act as hyperparameters adjusted on a case-by-case basis to regulate focus accorded to individual minority classes during training. In particular, our proposed method is suitable for imbalanced datasets from safety-related FD tasks as it generates DL models that minimize false negatives. Subsequently, we integrate human experts into the workflow as a strategy to help safeguard the system. A subset of the results, model predictions with uncertainties exceeding a preset threshold, are considered a preliminary output subject to cross-checking by human experts. We demonstrate that DLWP achieves improved Recall, AUC, F1 scores.
A Simple Approach for Counting CD4+ T Cells Based on a Combination of Magnetic Activated Cell Sorting and Automated Cell Counting Methods
Authors: Vo Ngoc Duc , Nguyen Anh Thi Van , Le Hoi Thi , Nguyen Nam Hoang , Pham Huong Thi Thu
Published: October 2021

Frequent tests for CD4+ T cell counting are important for the treatment of patients with immune deficiency; however, the routinely used fluorescence-activated cell-sorting (FACS) gold standard is costly and the equipment is only available in central hospitals. In this study, we developed an alternative simple approach (shortly named as the MACS-Countess system) for CD4+ T cell counting by coupling magnetic activated cell sorting (MACS) to separate CD4+ T cells from blood, followed by counting the separated cells using CountessTM, an automated cell-counting system. Using the cell counting protocol, 25 µL anti-CD4 conjugated magnetic nanoparticles (NP-CD4, BD Bioscience) were optimized for separating CD4+ T cells from 50 µL of blood in PBS using a DynamagTM-2 magnet, followed by the introduction of 10 µL separated cells into a CountessTM chamber slide for automated counting of CD4+ T cells. To evaluate the reliability of the developed method, 48 blood samples with CD4+ T cell concentrations ranging from 105 to 980 cells/µL were analyzed using both MACS-Countess and FACS. Compared with FACS, MACS-Countess had a mean bias of 3.5% with a limit of agreement (LoA) ranging from −36.4% to 43.3%, which is close to the reliability of the commercial product, PIMA analyzer (Alere), reported previously (mean bias 0.2%; LoA ranging from −42% to 42%, FACS as reference). Further, the MACS-Countess system requires very simple instruments, including only a magnet and an automated cell counter, which are affordable for almost every lab located in a limited resource region.
A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition
Authors: Otis Martin J.-D. , Vandewynckel Julien
Published: October 2021

Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.
Single-Walled Carbon Nanotube-Enhanced Bagasse-Epoxy Hybrid Composites under Varied Low Tensile Strain Rates
Authors: Khieng Tan Ke , Debnath Sujan , Anwar Mahmood , Pramanik Alokesh , Basak Animesh Kumar
Published: October 2021

The production demand of high-performance polymer composites utilizing natural and renewable resources, especially agricultural waste fibres, is rapidly growing. However, these polymers’ mechanical properties are strain rate-dependent due to their viscoelastic nature. Particularly, for natural fibre-reinforced polymer composites (NFPCs), the involvement of fillers has caused rather complex failure mechanisms under different strain rates. Moreover, unevenly and micro-sized bagasse-reinforced polymer composites often cause the formation of micro-cracks and voids in composites. Consequently, the rates of crack initiation and propagation of these composites become extremely sensitive. This, in turn, causes low and unpredictable tensile performance at higher tensile crosshead speeds, even within the low strain rate range. In this study, single-walled carbon nanotubes (SWCNTs) were applied to enhance the bagasse-epoxy composites’ strength. The effects of the weightage in the SWCNT loadings on the composites’ tensile properties were subsequently investigated under low strain rates of 0.0005 s−1, 0.005 s−1 and 0.05 s−1. The composites’ failure shifted to a higher distribution (65.7% improvement, from 37.23 to 61.68 MPa, across strain rates) due to the addition of 0.05% SWCNTs, as indicated in a Weibull distribution plot. The high aspect ratio and strong interface adhesion of SWCNTs in and toward the epoxy matrix contributed significantly to the composites’ strengths. However, a further increase in SWCNT content in the tested composites caused early embrittlement due to agglomeration. The toughness and characteristic strength improved significantly as the strain rate increased. A scanning electron microscopic (SEM) analysis revealed that the SWCNTs’ high aspect ratios and large surface areas improved the interface bonding between the filler and matrix. However, higher SWCNT loadings (0.15% and 0.25%) caused a reverse effect in the same properties of these composites under the same strain rate variations, due to agglomeration. Finally, an empirical relationship was developed to describe the strain rate effect of tensile properties containing 0.05% SWCNT-reinforced bagasse-epoxy composites.
Estimation of Biogas Generated in Two Landfills in South-Central Ecuador
Authors: Poma Paulina , Usca Marco , Polanco María , Toulkeridis Theofilos , Mestanza-Ramón Carlos
Published: October 2021

The landfill is a final disposal technique to confine municipal solid waste (MSW), where organic matter is degraded generating leachate and biogas composed of methane gases (CH4), carbon dioxide (CO2) and other gases that contribute to global warming. The objective of the current research was to estimate the amount of biogas generated through the LandGEM 3.03 mathematical model to determine the amount of electrical energy generated and the number of homes that would be supplied with electrical energy from 2021 to 2144. As a result of the application, it was estimated that in the Pichacay landfill, the highest point of biogas generation in 2053 would be 76,982,177 (m3/year) that would generate 81,226,339.36 (kWh/year), and would supply 5083 homes with electricity. Similarly, in the Las Iguanas landfill, the highest point would be 693,975,228 (m3/year) of biogas that produces 73,223,5296.7 (kWh/year) and would supply electricity to 45,825 homes. Of the performed gas analyses in the Pichacay landfill in 2020, an average of 51.49% CH4, 40.35% CO2, 1.75% O2 and 17.8% H2S was presented, while in the Las Iguanas landfill, for 2020 and 2021, we obtained an average of 51.88/CH4, 36.62% CO2, 1.01% O2 and 187.58 ppm H2S. Finally, the biogas generated by being harnessed minimizes the impacts related to global warming and climate change and would contribute electricity to the nearby communities.
Low-Energy Electron Elastic Collisions with Actinide Atoms Am, Cm, Bk, Es, No and Lr: Negative-Ion Formation
Authors: Msezane Alfred Z. , Felfli Zineb
Published: October 2021

The rigorous Regge-pole method is used to investigate negative-ion formation in actinide atoms through electron elastic total cross sections (TCSs) calculation. The TCSs are found to be characterized generally by negative-ion formations, shape resonances and Ramsauer-Townsend(R-T) minima, and they exhibit both atomic and fullerene molecular behavior near the threshold. Additionally, a polarization-induced metastable cross section with a deep R-T minimum is identified near the threshold in the Am, Cm and Bk TCSs, which flips over to a shape resonance appearing very close to the threshold in the TCSs for Es, No and Lr. We attribute these new manifestations to size effects and orbital collapse significantly impacting the polarization interaction. From the TCSs unambiguous and reliable ground, metastable and excited states negative-ion binding energies (BEs) for Am−, Cm−, Bk−, Es−, No− and Lr− anions formed during the collisions are extracted and compared with existing electron affinities (EAs) of the atoms. The novelty of the Regge-pole approach is in the extraction of the negative-ion BEs from the TCSs. We conclude that the existing theoretical EAs of the actinide atoms and the recently measured EA of Th correspond to excited anionic BEs.
Multi-Layer Fuzzy Sustainable Decision Approach for Outsourcing Manufacturer Selection in Apparel and Textile Supply Chain
Authors: Wang Chia-Nan , Pham Thuy-Duong Thi , Nhieu Nhat-Luong
Published: October 2021

The apparel and textile industry are known as a key sector in the structure of many economies around the world. In particular, the influence of foreign outsourcing manufacturers on textile supply chains has been recognized for decades. The outsourcing manufacturers are multi-criteria selected and changed by supply chain managers from time to time in search of the most efficient state for the entire supply chain. This is a known concern with the community and there is large interest in studying the apparel and textile outsourcing manufacturer problems. Aiming at reinforcing the selection methods, this study develops a three-layer fuzzy multiple criteria decision-making approach that leverages the strengths from the original methods. In turn through the layers, the hierarchy and weights of criteria and sub-criteria, which includes sustainability factors, are determined by the fuzzy analytic hierarchy process (FAHP) method. Next, the results from the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) process determine the outsourcing manufacturer’s performance via expert linguistics judgments. Then, data envelopment analysis (DEA) models are applied for the purpose of evaluating the outsourcing manufacturer’s overall performance along with other quantitative effectiveness. This approach is applied to the problem of selecting the apparel and textile outsourcing manufacturers in Vietnam, one of the places that makes the necessity of this problem grow. The third position in the world apparel and textile export ranking, as well as the trend of shifting labor-intensive production systems to Southeast Asia make the necessity of Vietnam outsourcing manufacturer selection problem grow. The results of this study also classified manufacturers into groups as a support for selection decisions. Analysis of quantitative uncertainties using simulation tools and forecasting techniques can strengthen the solutions in future related studies.
General Properties on Differential Sets of a Graph
Authors: Basilio Ludwin A. , Bermudo Sergio , Hernández-Gómez Juan C. , Sigarreta José M.
Published: October 2021

Let G=(V,E) be a graph, and let β∈R. Motivated by a service coverage maximization problem with limited resources, we study the β-differential of G. The β-differential of G, denoted by ∂β(G), is defined as ∂β(G):=max{|B(S)|−β|S|suchthatS⊆V}. The case in which β=1 is known as the differential of G, and hence ∂β(G) can be considered as a generalization of the differential ∂(G) of G. In this paper, upper and lower bounds for ∂β(G) are given in terms of its order |G|, minimum degree δ(G), maximum degree Δ(G), among other invariants of G. Likewise, the β-differential for graphs with heavy vertices is studied, extending the set of applications that this concept can have.
Metal Oxide Nanoparticles and Nanotubes: Ultrasmall Nanostructures to Engineer Antibacterial and Improved Dental Adhesives and Composites
Authors: Balhaddad Abdulrahman A. , Garcia Isadora M. , Mokeem Lamia , Alsahafi Rashed , Collares Fabrício Mezzomo , Sampaio de Melo Mary Anne
Published: October 2021

Advances in nanotechnology have unlocked exclusive and relevant capabilities that are being applied to develop new dental restorative materials. Metal oxide nanoparticles and nanotubes perform functions relevant to a range of dental purposes beyond the traditional role of filler reinforcement—they can release ions from their inorganic compounds damaging oral pathogens, deliver calcium phosphate compounds, provide contrast during imaging, protect dental tissues during a bacterial acid attack, and improve the mineral content of the bonding interface. These capabilities make metal oxide nanoparticles and nanotubes useful for dental adhesives and composites, as these materials are the most used restorative materials in daily dental practice for tooth restorations. Secondary caries and material fractures have been recognized as the most common routes for the failure of composite restorations and bonding interface in the clinical setting. This review covers the significant capabilities of metal oxide nanoparticles and nanotubes incorporated into dental adhesives and composites, focusing on the novel benefits of antibacterial properties and how they relate to their translational applications in restorative dentistry. We pay close attention to how the development of contemporary antibacterial dental materials requires extensive interdisciplinary collaboration to accomplish particular and complex biological tasks to tackle secondary caries. We complement our discussion of dental adhesives and composites containing metal oxide nanoparticles and nanotubes with considerations needed for clinical application. We anticipate that readers will gain a complete picture of the expansive possibilities of using metal oxide nanoparticles and nanotubes to develop new dental materials and inspire further interdisciplinary development in this area.
Enhancement of the Aroma Compound 2-Acetyl-1-pyrroline in Thai Jasmine Rice (Oryza sativa) by Rhizobacteria under Salt Stress
Authors: Chinachanta Kawiporn , Shutsrirung Arawan , Herrmann Laetitia , Lesueur Didier , Pathom-aree Wasu
Published: October 2021

Thai jasmine rice (Oryza sativa L. KDML105), particularly from inland salt-affected areas in Thailand, is both domestically and globally valued for its unique aroma and high grain quality. The key aroma compound, 2-acetyl-1-pyrroline (2AP), has undergone a gradual degradation due to anthropogenic soil salinization driven by excessive chemical input and climate change. Here, we propose a cheaper and an ecofriendly solution to improve the 2AP levels, based on the application of plant growth-promoting rhizobacteria (PGPR). In the present study, nine PGPR isolates from rice rhizosphere were investigated for the 2AP production in liquid culture and the promotion potential for 2AP content in KDML105 rice seedlings under four NaCl concentrations (0, 50, 100, and 150 mM NaCl). The inoculation of 2AP-producing rhizobacteria resulted in an increase in 2AP content in rice seedling leaves with the maximum enhancement from Sinomonas sp. ORF15-23 at 50 mM NaCl (19.6 µg·kg−1), corresponding to a 90.2% increase as compared to the control. Scanning electron microscopy confirmed the colonization of Sinomonas sp. ORF15-23 in the roots of salinity-stressed KDML105 seedlings. Our results provide evidence that Sinomonas sp. ORF15-23 could be a promising PGPR isolate in promoting aroma level of Thai jasmine rice KDML105 under salt stress.
In Silico Prediction of the Phosphorylation of NS3 as an Essential Mechanism for Dengue Virus Replication and the Antiviral Activity of Quercetin
Authors: Alomair Lamya , Almsned Fahad , Ullah Aman , Jafri Mohsin S.
Published: October 2021

Dengue virus infection is a global health problem for which there have been challenges to obtaining a cure. Current vaccines and anti-viral drugs can only be narrowly applied in ongoing clinical trials. We employed computational methods based on structure-function relationships between human host kinases and viral nonstructural protein 3 (NS3) to understand viral replication inhibitors’ therapeutic effect. Phosphorylation at each of the two most evolutionarily conserved sites of NS3, serine 137 and threonine 189, compared to the unphosphorylated state were studied with molecular dynamics and docking simulations. The simulations suggested that phosphorylation at serine 137 caused a more remarkable structural change than phosphorylation at threonine 189, specifically located at amino acid residues 49–95. Docking studies supported the idea that phosphorylation at serine 137 increased the binding affinity between NS3 and nonstructural Protein 5 (NS5), whereas phosphorylation at threonine 189 decreased it. The interaction between NS3 and NS5 is essential for viral replication. Docking studies with the antiviral plant flavonoid Quercetin with NS3 indicated that Quercetin physically occluded the serine 137 phosphorylation site. Taken together, these findings suggested a specific site and mechanism by which Quercetin inhibits dengue and possible other flaviviruses.
Angiogenic Potential of VEGF Mimetic Peptides for the Biofunctionalization of Collagen/Hydroxyapatite Composites
Authors: Wang Suya , Umrath Felix , Cen Wanjing , Reinert Siegmar , Alexander Dorothea
Published: October 2021

Currently, the focus on bioinspired concepts for the development of tissue engineering constructs is increasing. For this purpose, the combination of collagen (Coll) and hydroxyapatite (HA) comes closest to the natural composition of the bone. In order to confer angiogenic properties to the scaffold material, vascular endothelial growth factor (VEGF) is frequently used. In the present study, we used a VEGF mimetic peptide (QK) and a modified QK-peptide with a poly-glutamic acid tag (E7-QK) to enhance binding to HA, and analyzed in detail binding efficiency and angiogenic properties. We detected a significantly higher binding efficiency of E7-QK peptides to hydroxyapatite particles compared to the unmodified QK-peptide. Tube formation assays revealed similar angiogenic functions of E7-QK peptide (1µM) as induced by the entire VEGF protein. Analyses of gene expression of angiogenic factors and their receptors (FLT-1, KDR, HGF, MET, IL-8, HIF-1α, MMP-1, IGFBP-1, IGFBP-2, VCAM-1, and ANGPT-1) showed higher expression levels in HUVECs cultured in the presence of 1 µM E7-QK and VEGF compared to those detected in the negative control group without any angiogenic stimuli. In contrast, the expression of the anti-angiogenic gene TIMP-1 showed lower mRNA levels in HUVECs cultured with E7-QK and VEGF. Sprouting assays with HUVEC spheroids within Coll/HA/E7-QK scaffolds showed significantly longer sprouts compared to those induced within Coll/HA/QK or Coll/HA scaffolds. Our results demonstrate a significantly better functionality of the E7-QK peptide, electrostatically bound to hydroxyapatite particles compared to that of unmodified QK peptide. We conclude that the used E7-QK peptide represents an excellently suited biomolecule for the generation of collagen/hydroxyapatite composites with angiogenic properties.
Data Mining of Molecular Simulations Suggest Key Amino Acid Residues for Aggregation, Signaling and Drug Action
Authors: Gurunathan Vaibhav , Hamre John , Klimov Dmitri K. , Jafri Mohsin Saleet
Published: October 2021

Alzheimer’s disease, the most common form of dementia, currently has no cure. There are only temporary treatments that reduce symptoms and the progression of the disease. Alzheimer’s disease is characterized by the prevalence of plaques of aggregated amyloid β (Aβ) peptide. Recent treatments to prevent plaque formation have provided little to relieve disease symptoms. Although there have been numerous molecular simulation studies on the mechanisms of Aβ aggregation, the signaling role has been less studied. In this study, a total of over 38,000 simulated structures, generated from molecular dynamics (MD) simulations, exploring different conformations of the Aβ42 mutants and wild-type peptides were used to examine the relationship between Aβ torsion angles and disease measures. Unique methods characterized the data set and pinpointed residues that were associated in aggregation and others associated with signaling. Machine learning techniques were applied to characterize the molecular simulation data and classify how much each residue influenced the predicted variant of Alzheimer’s Disease. Orange3 data mining software provided the ability to use these techniques to generate tables and rank the data. The test and score module coupled with the confusion matrix module analyzed data with calculations of specificity and sensitivity. These methods evaluating frequency and rank allowed us to analyze and predict important residues associated with different phenotypic measures. This research has the potential to help understand which specific residues of Aβ should be targeted for drug development.
PCNA Ubiquitylation: Instructive or Permissive to DNA Damage Tolerance Pathways?
Authors: Che Jun , Hong Xin , Rao Hai
Published: October 2021

DNA lesions escaping from repair often block the DNA replicative polymerases required for DNA replication and are handled during the S/G2 phases by the DNA damage tolerance (DDT) mechanisms, which include the error-prone translesion synthesis (TLS) and the error-free template switching (TS) pathways. Where the mono-ubiquitylation of PCNA K164 is critical for TLS, the poly-ubiquitylation of the same residue is obligatory for TS. However, it is not known how cells divide the labor between TLS and TS. Due to the fact that the type of DNA lesion significantly influences the TLS and TS choice, we propose that, instead of altering the ratio between the mono- and poly-Ub forms of PCNA, the competition between TLS and TS would automatically determine the selection between the two pathways. Future studies, especially the single integrated lesion “i-Damage” system, would elucidate detailed mechanisms governing the choices of specific DDT pathways.
Styrene Maleic Acid Copolymer-Based Micellar Formation of Temoporfin (SMA@ mTHPC) Behaves as A Nanoprobe for Tumor-Targeted Photodynamic Therapy with A Superior Safety
Authors: Fang Jun , Gao Shanghui , Islam Rayhanul , Nema Hinata , Yanagibashi Rina , Yoneda Niho , Watanabe Natsumi , Yasuda Yuki , Nuita Naoki , Zhou Jian-Rong , Yokomizo Kazumi
Published: October 2021

Tumor-targeted photodynamic therapy (PDT) using polymeric photosensitizers is a promising anticancer therapeutic strategy. Previously, we developed several polymeric nanoprobes for PDT using different polymers and PDT agents. In the study, we synthesized a styrene maleic acid copolymer (SMA) micelle encapsulating temoporfin (mTHPC) that is a clinically used PDT drug, SMA@mTHPC, with a hydrodynamic size of 98 nm, which showed high water solubility. SMA@mTHPC maintained stable micelle formation in physiological aqueous solutions including serum; however, the micelles could be disrupted in the presence of detergent (e.g., Tween 20) as well as lecithin, the major component of cell membrane, suggesting micelles will be destroyed and free mTHPC will be released during intracellular uptake. SMA@mTHPC showed a pH-dependent release profile, for which a constant release of ≈20% per day was found at pH 7.4, and much more release occurred at acidic pH (e.g., 6.5, 5.5), suggesting extensive release of free mTHPC could occur in the weak acidic environment of a tumor and further during internalization into tumor cells. In vitro cytotoxicity assay showed a lower cytotoxicity of SMA@mTHPC than free mTHPC; however, similar in vivo antitumor effects were observed by both SMA@mTHPC and free THPC. More importantly, severe side effects (e.g., body weight loss, death of the mice) were found during free mTHPC treatment, whereas no apparent side effects were observed for SMA@mTHPC. The superior safety profile of SMA@mTHPC was mostly due to its micelle formation and the enhanced permeability and retention (EPR) effect-based tumor accumulation, as well as the tumor environment-responsive release properties. These findings suggested SMA@mTHPC may become a good candidate drug for targeted PDT with high safety.
Strategies Targeting Type 2 Inflammation: From Monoclonal Antibodies to JAK-Inhibitors
Authors: Matucci Andrea , Vivarelli Emanuele , Nencini Francesca , Maggi Enrico , Vultaggio Alessandra
Published: October 2021

Bronchial asthma and its frequent comorbidity chronic rhinosinusitis (CRS), are characterized by an inflammatory process at lower and upper respiratory tract, with a variability in terms of clinical presentations (phenotypes) and distinct underpin pathophysiological mechanisms (endotypes). Based on the characteristics of inflammation, bronchial asthma can be distinguished into type 2 (eosinophilic) or nontype 2 (noneosinophilic) endotypes. In type 2 asthma endotype, the pathogenic mechanism is sustained by an inflammatory process driven by Th2 cells, type 2 innate lymphoid cells (ILC2) and type 2 cytokines, which include interleukin (IL)-4, IL-5, IL-9 and IL-13. The definition of asthma and chronic rhinusinusitis phenotype/endotype is crucial, taking into account the availability of novel biologic agents, such as monoclonal antibodies targeting the classical type 2 cytokines. Recently, new therapeutic strategies have been proposed and analyzed in preliminary clinical trials. Among them Janus kinase (JAK) inhibitors, now largely used for the treatment of other chronic inflammatory diseases such as rheumatoid arthritis and inflammatory bowel diseases, is receiving great relevance. The rationale of this strategy derives from the data that JAK is a tyrosine kinase involved in the signaling of T cell receptor and of several cytokines that play a role in allergic respiratory disease, such as IL-2, IL-4 and IL-9. In this review, we discuss whether treatment with biological agents and JAK inhibitors may be equally effective in controlling type 2 inflammatory process in both asthma and CRS.
High-Throughput, Label-Free Isolation of White Blood Cells from Whole Blood Using Parallel Spiral Microchannels with U-Shaped Cross-Section
Authors: Mehran Amirhossein , Rostami Peyman , Saidi Mohammad Said , Firoozabadi Bahar , Kashaninejad Navid
Published: October 2021

Rapid isolation of white blood cells (WBCs) from whole blood is an essential part of any WBC examination platform. However, most conventional cell separation techniques are labor-intensive and low throughput, require large volumes of samples, need extensive cell manipulation, and have low purity. To address these challenges, we report the design and fabrication of a passive, label-free microfluidic device with a unique U-shaped cross-section to separate WBCs from whole blood using hydrodynamic forces that exist in a microchannel with curvilinear geometry. It is shown that the spiral microchannel with a U-shaped cross-section concentrates larger blood cells (e.g., WBCs) in the inner cross-section of the microchannel by moving smaller blood cells (e.g., RBCs and platelets) to the outer microchannel section and preventing them from returning to the inner microchannel section. Therefore, it overcomes the major limitation of a rectangular cross-section where secondary Dean vortices constantly enforce particles throughout the entire cross-section and decrease its isolation efficiency. Under optimal settings, we managed to isolate more than 95% of WBCs from whole blood under high-throughput (6 mL/min), high-purity (88%), and high-capacity (360 mL of sample in 1 h) conditions. High efficiency, fast processing time, and non-invasive WBC isolation from large blood samples without centrifugation, RBC lysis, cell biomarkers, and chemical pre-treatments make this method an ideal choice for downstream cell study platforms.
Treatment for Anomia in Bilingual Speakers with Progressive Aphasia
Authors: Grasso Stephanie M. , Peña Elizabeth D. , Kazemi Nina , Mirzapour Haideh , Neupane Rozen , Bonakdarpour Borna , Gorno-Tempini Maria Luisa , Henry Maya L.
Published: October 2021

Anomia is an early and prominent feature of primary progressive aphasia (PPA) and other neurodegenerative disorders. Research investigating treatment for lexical retrieval impairment in individuals with progressive anomia has focused primarily on monolingual speakers, and treatment in bilingual speakers is relatively unexplored. In this series of single-case experiments, 10 bilingual speakers with progressive anomia received lexical retrieval treatment designed to engage relatively spared cognitive-linguistic abilities and promote word retrieval. Treatment was administered in two phases, with one language targeted per phase. Cross-linguistic cognates (e.g., rose and rosa) were included as treatment targets to investigate their potential to facilitate cross-linguistic transfer. Performance on trained and untrained stimuli was evaluated before, during, and after each phase of treatment, and at 3, 6, and 12 months post-treatment. Participants demonstrated a significant treatment effect in each of their treated languages, with maintenance up to one year post-treatment for the majority of participants. Most participants showed a significant cross-linguistic transfer effect for trained cognates in both the dominant and nondominant language, with fewer than half of participants showing a significant translation effect for noncognates. A gradual diminution of translation and generalization effects was observed during the follow-up period. Findings support the implementation of dual-language intervention approaches for bilingual speakers with progressive anomia, irrespective of language dominance.
Anxiety, Depression and Posttraumatic Stress Disorder after Terrorist Attacks: A General Review of the Literature
Authors: Rigutto Claudia , Sapara Adegboyega O. , Agyapong Vincent I. O.
Published: October 2021

Terrorism, though not well-defined, is a violent act that has been shown to have longstanding effects on the mental health of those who witness it. The aim of this general literature review is to explore the effect that terrorism has on posttraumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders, as well as the bio-psycho-social determinants that mediate its impact. This paper describes the prevalence, risk factors, protective factors, common presentations and interventions identified for PTSD, depression and anxiety disorders occurring following terrorist attacks. We conducted a literature search in MEDLINE using a number of keywords detailed below. After applying inclusion and exclusion criteria, we kept 80 articles, which we summarized in tabular form. A majority of articles found detailed the impact of terrorism on PTSD, and took place in a Western, mainly American setting. The main factors that impacted the presentation of mental illness include gender, ethnicity, social supports, socioeconomic status, level of preparedness, level of exposure, pre-existing trauma and mental illness, and subsequent life stressors. The main intervention detailed in this article as showing evidence post-terrorism is trauma-focused cognitive-behavioural therapy. This study highlights the importance of this topic, and in particular, its implications for public health policy and practice.