bluuebunny/crossref_metadata_2025 huggingface dataset:
Complicated Mandible Fracture Treatment with Xenogenic Bone Graft
Authors: Diachkova Ekaterina Yu , Popova Sofia V. , Arazashvili Liana D. , Petruk Pavel Sergeevich , Cherkesov Igor V.
Published: February 2022

The problem of filling bone cavities remains relevant in maxillofacial and oral surgery. There is a large selection of osteotropic materials, of various natures, for filling bone defects of different etiologies. The aim of our research was to improve the outcome of surgical treatment in a patient with a complicated mandibular fracture, with the use of a collagenic xenograft during osteosynthesis. In this article, we share our experience of the treatment of a patient with a complicated mandibular angle fracture, in combination with a follicular cyst. The obligate steps of treatment included stabilization of the bone fragments, decreasing the risk of fracture line malposition, using titan mini-plates, and shortening the time of bone regeneration, by filling the bone defect with osteotropic material. This approach allowed us to reduce the rehabilitation period and further prosthetic treatment after 4–5 months, without additional bone grafting manipulations. Thus, the use of collagen osteotropic materials, possessing osteoconductive properties, can improve the treatment of patients with mandibular fractures.
A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
Authors: Mendez Milna Maria , Hsu Min-Chia , Yuan Jenq-Tay , Lynn Ke-Shiuan
Published: February 2022

Atrial fibrillation (AF) is characterized by totally disorganized atrial depolarizations without effective atrial contraction. It is the most common form of cardiac arrhythmia, affecting more than 46.3 million people worldwide and its incidence rate remains increasing. Although AF itself is not life-threatening, its complications, such as strokes and heart failure, are lethal. About 25% of paroxysmal AF (PAF) patients become chronic for an observation period of more than one year. For long-term and real-time monitoring, a PAF prediction system was developed with four objectives: (1) high prediction accuracy, (2) fast computation, (3) small data storage, and (4) easy medical interpretations. The system takes a 400-point heart rate variability (HRV) sequence containing no AF episodes as the input and outputs whether the corresponding subject will experience AF episodes in the near future (i.e., 30 min). It first converts an input HRV sequence into four image matrices via extended Poincaré plots to capture inter- and intra-person features. Then, the system employs a convolutional neural network (CNN) to perform feature selection and classification based on the input image matrices. Some design issues of the system, including feature conversion and classifier structure, were formulated as a binary optimization problem, which was then solved via a genetic algorithm (GA). A numerical study involving 6085 400-point HRV sequences excerpted from three PhysioNet databases showed that the developed PAF prediction system achieved 87.9% and 87.2% accuracy on the validation and the testing datasets, respectively. The performance is competitive with that of the leading PAF prediction system in the literature, yet our system is much faster and more intensively tested. Furthermore, from the designed inter-person features, we found that PAF patients often possess lower (~60 beats/min) or higher (~100 beats/min) heart rates than non-PAF subjects. On the other hand, from the intra-person features, we observed that PAF patients often exhibit smaller variations (≤5 beats/min) in heart rate than non-PAF subjects, but they may experience short bursts of large heart rate changes sometimes, probably due to abnormal beats, such as premature atrial beats. The other findings warrant further investigations for their medical implications about the onset of PAF.
A New Stochastic Process of Prestack Inversion for Rock Property Estimation
Authors: Yin Long , Zhang Sheng , Xiang Kun , Ma Yongqiang , Ji Yongzhen , Chen Ke , Zheng Dongyu
Published: February 2022

In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm optimization is used to find the global optimal value in the finite model space. The method overcomes the problem of over-fitting deterministic inversion and improves the efficiency of stochastic inversion. Meanwhile, the fusion of multiple sources of information can reduce the non-uniqueness of solutions and improve the inversion accuracy. We derive the APSO algorithm in detail, give the specific workflow of prestack stochastic inversion, and verify the validity of the inversion theory through the inversion test of two-dimensional prestack data in real areas.
A Survey on Design and Control of Lower Extremity Exoskeletons for Bipedal Walking
Authors: Tijjani Ibrahim , Kumar Shivesh , Boukheddimi Melya
Published: February 2022

Exoskeleton robots are electrically, pneumatically, or hydraulically actuated devices that externally support the bones and cartilage of the human body while trying to mimic the human movement capabilities and augment muscle power. The lower extremity exoskeleton device may support specific human joints such as hip, knee, and ankle, or provide support to carry and balance the weight of the full upper body. Their assistive functionality for physically-abled and disabled humans is demanded in medical, industrial, military, safety applications, and other related fields. The vision of humans walking with an exoskeleton without external support is the prospect of the robotics and artificial intelligence working groups. This paper presents a survey on the design and control of lower extremity exoskeletons for bipedal walking. First, a historical view on the development of walking exoskeletons is presented and various lower body exoskeleton designs are categorized in different application areas. Then, these designs are studied from design, modeling, and control viewpoints. Finally, a discussion on future research directions is provided.
The Simulated Effect of Adding Solder Layers on Reactive Multilayer Films Used for Joining Processes
Authors: Yuile Adam , Schulz Alexander , Wiss Erik , Müller Jens , Wiese Steffen
Published: February 2022

In order to introduce new bonding methods in the area of electronic packaging a theoretical analysis was conducted, which should give substantial information about the potential of reactive multilayer systems (rms) to create sufficient local heat for joining processes between silicon chips and ceramic substrates. For this purpose, thermal CFD (computational fluid dynamics) simulations have been carried out to simulate the temperature profile of the bonding zone during and after the reaction of the rms. This thermal analysis considers two different configurations. The first configuration consists of a silicon chip that is bonded to an LTCC-substrate (Low Temperature Co-fired Ceramics) using a bonding layer that contains an rms and a solder preform. The reaction propagation speed of the reactive multilayer was set to a value of 1 m/s, in order to partially melt a solder preform underneath a silicon chip. The second configuration, which consists only of the LTCC-substrate and the rms, was chosen to study the differences between the thermal outputs of the two arrangements. The analysis of the CFD simulations was particularly focused on interpretations of the temperature and liquid fraction contours. The CFD thermal simulation analysis conducted contains a melting/solidification model which can track the molten/solid state of the solder in addition to modelling the influence of latent heat. To provide information for the design of a test-substrate for experimental investigations, the real behaviour of Pt-100 temperature probes on the LTCC-substrate was simulated, in order to monitor an actual bonding in the experiment. All simulations were carried out using the ANSYS Fluent software.
A Novel Pathological Voice Identification Technique through Simulated Cochlear Implant Processing Systems
Authors: Islam Rumana , Abdel-Raheem Esam , Tarique Mohammed
Published: February 2022

This paper presents a pathological voice identification system employing signal processing techniques through cochlear implant models. The fundamentals of the biological process for speech perception are investigated to develop this technique. Two cochlear implant models are considered in this work: one uses a conventional bank of bandpass filters, and the other one uses a bank of optimized gammatone filters. The critical center frequencies of those filters are selected to mimic the human cochlear vibration patterns caused by audio signals. The proposed system processes the speech samples and applies a CNN for final pathological voice identification. The results show that the two proposed models adopting bandpass and gammatone filterbanks can discriminate the pathological voices from healthy ones, resulting in F1 scores of 77.6% and 78.7%, respectively, with speech samples. The obtained results of this work are also compared with those of other related published works.
Applications of Forward Stratigraphic Modelling in Modern Siliciclastic Settings: A Case Study from the Fraser River Delta, Canada
Authors: Ayranci Korhan
Published: February 2022

Forward stratigraphic modelling (FSM) is a relatively new approach that is used to test the importance of parameters that control stratigraphic stacking patterns and to reveal uncertainties such as sedimentation rate and accommodation space. Although FSM is commonly employed in the study of ancient systems, it is rarely applied to modern settings. The Fraser River Delta in Canada provides an opportunity to test applications of FSM in recently deposited sediments in an active sedimentary basin. Because it is un-dammed, the river enables comparison of the modern and ancient systems. It is also a well-studied river system, with sufficient data to generate a realistic model for predicting future scenarios. In this study, Dionisos software is used, and the evolution of the delta over the past 10,000 years is successfully simulated in two steps (5000 years each) using both realistic and real-time data. The main controlling parameters are observed to be the sediment supply and water discharge values, and to a lesser extent, sea level variation. Several possible future scenarios are tested, changing the main parameters to understand and to predict future morphological changes and stacking patterns. Increasing the main parameter values resulted in progradation, while reducing resulted in erosion, particularly in the subaqueous section of the delta. The results of this study can be used to calibrate numerical modelling applications in both modern and ancient deltaic settings.
Pulse-Shape Discrimination of SiPM Array-Coupled CLYC Detector Using Convolutional Neural Network
Authors: Lu Jing , Tuo Xianguo , Yang Hongchao , Luo Yushi , Liu Haolin , Deng Chao , Wang Qibiao
Published: February 2022

Cs2LiYCl6: Ce3+ (CLYC) is a dual-mode gamma-neutron scintillator with a medium gamma-ray resolution and pulse-shape discrimination (PSD) capability. The PSD performance of CLYC is greatly weakened when coupled with silicon photomultipliers (SiPMs) because of SiPMs’ low detection efficiency for the ultrafast Core-Valence-Luminescence (CVL) component under gamma excitation. In our previous work, the PSD Figure-of-Merit (FoM) value was optimized to 2.45 at the gamma-equivalent energy region of the thermal neutron by using the charge comparison method. However, this value was reduced to 1.37 at the lower gamma-equivalent energy region of more than 325 keV, and neutrons were difficult to distinguish from gamma rays. Hence, new algorithms should be studied to improve the PSD performance at low gamma-equivalent energy regions. Convolutional Neural Networks (CNNs) have excellent image recognition capabilities, and thus, neutron and gamma-ray waveforms can be discriminated by their characteristics through a known training set. In this study, neutron and gamma-ray waveforms were measured with a 137Cs source and moderated 252Cf source via an SiPM array-coupled CLYC detector and divided into two groups: training and PSD testing. The CNN training set comprised 137Cs characteristic gamma-ray waveforms and thermal neutron waveforms that were discriminated by the charge comparison method from the training group. A CNN with two convolution-pooling layers was designed to accomplish PSD with the test group. The PSD FoM value of the CNN method was calculated to be 37.20 at the gamma-equivalent energy region of more than 325 keV. This result was much higher than that of the charge comparison method, indicating that neutrons and gamma rays could be better distinguished with the CNN method, especially at low gamma-equivalent energy regions.
Releasing Differentially Private Trajectories with Optimized Data Utility
Authors: Li Bing , Zhu Hong , Xie Meiyi
Published: February 2022

The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Furthermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy.
One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
Authors: Kilic Kursat , Toriya Hisatoshi , Kosugi Yoshino , Adachi Tsuyoshi , Kawamura Youhei
Published: February 2022

An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% R2, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s.
Smart System to Detect Painting Defects in Shipyards: Vision AI and a Deep-Learning Approach
Authors: Ma Hanseok , Lee Sunggeun
Published: February 2022

The shipbuilding industry has recently had to address several problems, such as improving productivity and overcoming the limitations of existing worker-dependent defect-inspection systems for painting on large steel plates while meeting the demands for information and smart-factory systems for quality management. The target shipyard previously used human visual inspection and there was no system to manage defect frequency, type, or history. This is challenging because these defects can have different sizes, shapes, and locations. In addition, the shipyard environment is variable and limits the options for camera placements. To solve these problems, we developed a new Vision AI deep-learning system for detecting painting defects in an actual shipyard production line and conducted experiments to optimize and evaluate the performance. We then configured and installed the Vision AI system to control the actual shipyard production line through a programmable logic controller interface. The installed system analyzes images in real-time and is expected to improve productivity by 11% and reduce quality incidents by 2%. This is the first practical application of AI operating in conjunction with the control unit of the actual shipyard production line. The lessons learned here can be applied to other industrial systems.
An Analysis of the Factors Influencing the Retroreflectivity Performance of In-Service Road Traffic Signs
Authors: Saleh Roxan , Fleyeh Hasan , Alam Moudud
Published: February 2022

The road traffic signs in Sweden have no inventory system and it is unknown when a sign has reached the end of its service life and needs to be replaced. As a result, the road authorities do not have a systematic maintenance program for road traffic signs, and many signs which are not in compliance with the minimum retroreflectivity performance requirements are still found on the roads. Therefore, it is very important to find an inexpensive, safe, easy, and highly accurate method to judge the retroreflectivity performance of road signs. This will enable maintenance staff to determine the retroreflectivity of road signs without requiring measuring instruments for retroreflectivity or colors performance. As a first step toward the above goal, this paper aims to identify factors affecting the retroreflectivity of road signs. Two different datasets were used, namely, the VTI dataset from Sweden and NMF dataset from Denmark. After testing different models, two logarithmic regression models were found to be the best-fitting models, with R2 values of 0.50 and 0.95 for the VTI and NMF datasets, respectively. The first model identified the age, direction, GPS positions, color, and class of road signs as significant predictors, while the second model used age, color, and the class of road signs.
Effect of Three Chlorhexidine-Based Mouthwashes on Human Gingival Fibroblasts: An In Vitro Study
Authors: Brunello Giulia , Becker Kathrin , Scotti Luisa , Drescher Dieter , Becker Jürgen , John Gordon
Published: February 2022

Mouthwashes containing chlorhexidine (CHX) are deemed to be associated with dose-dependent side effects, including burning sensation and taste alteration. To overcome these drawbacks, mouthwashes with CHX at lower concentrations with or without adjunctive agents are proposed. The aim of this in vitro study was to investigate the effects of three CHX-based mouthwashes on human gingival fibroblasts (HGFs). After 3 days of cell culture, groups were randomly treated for 30 s, 60 s or 120 s with (a) CHX 0.05% in combination with cetylpyridnium chloride (CPC) 0.05%; (b) CHX 0.1%; (c) CHX 0.2%; or (d) NaCl as control. Cell viability, cytotoxicity and apoptosis were evaluated at 2 h, 3 days and 6 days after the exposure to the different solutions. Similar cell viability values were found among the test groups at all time points. At day 0, higher cytotoxicity was measured in the group treated with CHX 0.2%, in particular after long application time (120 s), while no significant difference was found between CHX + CPC and the control group. All the investigated mouthwashes were well tolerated by HGF cells for the tested application times. The highest cytotoxic effect was observed for CHX 0.2%; therefore, clinicians should consider limiting its usage to carefully selected clinical situations.
Analytical Study on the Low-Frequency Vibrations Isolation System for Vehicle’s Seats Using Quasi-Zero-Stiffness Isolator
Authors: Abuabiah Mohammad , Dabbas Yazan , Herzallah Luqman , Alsurakji Ihab H. , Assad Mahmoud , Plapper Peter
Published: February 2022

Improving the vibration isolation for the seat of small vehicles under low excitation frequencies is important for providing good comfort for the driver and passengers. Thus, in this study, a compact, low-dynamic, and high-static stiffness vibration isolation system has been designed. A theoretical analysis of the proposed quasi-zero stiffness (QZS) isolator system for vehicle seats is presented. The isolator consists of two oblique springs and a vertical spring to support the load and to achieve quasi-zero stiffness at the equilibrium position. To support any additional load above the supported weight, a sleeve air spring is used. Furthermore, the two oblique springs are equipped with a horizontal adjustment mechanism that is aimed to reach higher frequencies with the existed stroke when a heavy load is applied. The proposed system can be fitted for small vehicles, especially for B-segment and C-segment cars. Finally, the simulation results reveal that the proposed system has a large isolation frequency range compared to that of the linear isolator.
Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization
Authors: Lee Gihwan , Choe Yoonsik
Published: February 2022

Sparse orthonormal transform is based on orthogonal sparse coding, which is relatively fast and suitable in image compression such as analytic transforms with better performance. However, because of the constraints on its dictionary, it has performance limitations. This paper proposes an extension of a sparse orthonormal transform based on unions of orthonormal dictionaries for image compression. Unlike unions of orthonormal bases (UONB), which implement an overcomplete dictionary with several orthonormal dictionaries, the proposed method allocates patches to an orthonormal dictionary based on their directions. The dictionaries are constructed into a discrete cosine transform and an orthonormal matrix. To determine a trade-off parameter between the reconstruction error and sparsity, which hinders efficient implementation, the proposed method adapts Bayesian optimization. The framework exhibits an improved performance with fast implementation to determine the optimal parameter. It is verified that the proposed method performs similar to an overcomplete dictionary with a faster speed via experiments.
Application of Genetic Algorithm to Optimize Location of BRB for Reinforced Concrete Frame with Curtailed Shear Wall
Authors: Maulana Taufiq Ilham , Fonseca Patricia Angelica de Fatima , Saito Taiki
Published: February 2022

The shear walls are essential seismic elements to increase buildings bearing capacity against earthquakes. In mid- and high-rise buildings, shear walls are subjected to predominant bending deformation under earthquakes, and the responses in upper floors increase. In order to utilize the shear walls appropriately, previous studies proposed to install shear walls until a certain building level, referred to as the curtailed wall. However, the upper frame structure without shear walls suffered significant deformation during earthquakes compared to the lower stories. Therefore, the objective of this study is to present structural configuration for buildings with curtailed shear walls by installing buckling-restrained braces (BRBs) in the upper frame to reduce its deformation under earthquakes. Firstly, the analysis accuracy was verified by simulating the experimental results of four sets of scaled frames with curtailed walls tested on a shaking table. Then, ten- and twenty-story plane frames with the different heights of curtailed walls were created, and their nonlinear responses to earthquake ground motions were evaluated. The genetic algorithm was applied to establish the optimum BRB locations to satisfy the design criteria. It was proved that using BRBs at specific locations in upper frames can significantly improve the seismic response of buildings with curtailed walls.
Management of Aggression in Young Male Adults Using the Virtual Reality-Based Communication Modification Program
Authors: Kim Junhyung , Jung Young Hoon , Baek Ki-Dong , Hong Yeon-Ju , Jeong Hyu Seok , Kim Jae-Jin
Published: February 2022

High aggression is common and costly for mental health problems in young adults. Because communication is a universal part of social relationships, including conflicts with others, it could be a possible target for mediating aggression. This study aimed to evaluate whether the virtual reality (VR)-based communication modification program can be utilized for aggression management. Fifty-eight individuals with high aggression (n = 30) and with low aggression (n = 28) completed psychological assessments associated with aggression and functional communication, and they participated in the program, consisting of the three tasks: exploring the communication style, practicing functional communication, and expressing empathy. The participants’ selections and their visual analog scale scores, in response to questions in the tasks, were collected as behavioral data. Results indicated that the high aggression group selected blaming dysfunctional communication style more frequently than the low aggression group. VR-based parameters, expected to reflect dysfunctional communication-related characteristics, showed significantly different correlations with aggression-related traits between the two groups. These findings show that our program may accurately represent an individual’s aggressive traits and elicit the appropriate reaction.
Synthesis of Carbonaceous Hydrophobic Layers through a Flame Deposition Process
Authors: Merchan-Breuer Duncan A. , Murphy Ethan , Berka Benjamin , Nova Luis Carlos Mendoza , Liu Yingtao , Merchan-Merchan Wilson
Published: February 2022

In this study we report the effect of fuel type (biodiesel vs. methane), flame structure and flame height (inner-cone vs. outer-cone), and the percent of oxygen content in the oxidizer stream for the formation of hydrophobic carbon layers using co-flow diffusion flames. It was found that a flame formed using a gaseous fuel (methane) over a vaporized liquid fuel, Canola Methyl Ester (CME), has significant structural differences that enable vastly different deposition behavior of soot layers on the surface of solid substrates. Due to its larger pyrolysis zone (taller inner-cone), the CH4/air flame has a smaller region that supports uniform soot deposition of hydrophobic carbon layers (C-layers) compared to the CME/air flame. When a solid substrate is placed within the pyrolysis zone (inner-cone) of a flame the resulting layer is non-uniform, hydrophilic, and consists of undeveloped soot. However, when outside the pyrolysis zone, the deposited soot tends to be uniform and mature, ultimately creating a hydrophobic C-layer consisting of the typical microscale interconnected weblike structures formed of spherical soot nanoparticles. The effect of oxygen content (35% and 50% O2) in the oxidizer stream for the formation of hydrophobic C-layers was also studied in this work. It was found that oxygen enrichment within the CME flame alters the structure of the flame, hence affecting the morphology of the formed C-layer. Under oxygen enrichment the central region of the deposited C-layer is composed of a weblike structure similar to those seen in the air flames; however, this central region is bordered by a region of densely compacted soot that shows signs of significant thermal stress. At 35% O2 the thermal stress is expressed as multiple microscale cracks while at 50% O2 this border region shows much larger cracks and macroscale layer peeling. The formed C-layers under the different flame conditions were tested for hydrophobicity by measuring the contact angle of a water droplet. The morphology of the C-layers was analyzed using scanning electron microscopy.
Hull Form Optimization Study Based on Multiple Parametric Modification Curves and Free Surface Reynolds-Averaged Navier–Stokes (RANS) Solver
Authors: Park Sung-Woo , Kim Seung-Hyeon , Kim Yang-Ik , Lee Inwon
Published: February 2022

In this study, the hull form optimization process to minimize resistance of KCS (KRISO containership) at Fn=0.26 is described. The bow hull form of KCS was modified by varying such design parameters as sectional area curve (SAC), section shape, bulb breadth, and bulb height using multiple parametric modification curves devised by the authors. The resistance performances of modified hull forms were analysed by the viscous flow Reynolds-Averaged Navier–Stokes (RANS) solver of WAVIS ver.2.2. With a view to saving computational time during iterative analyses in the optimization process, the sinkage and trim were set to the fixed values which had been obtained for the original hull form with free condition. The validity of such constant sinkage/trim was then verified by conducting analysis for the optimal hull form with free condition. Optimization to minimize the cost function of the total resistance coefficient of model CTM was performed by sequential quadratic programming (SQP), which is one of the gradient-based local optimization methods. Utilization of parallel computing led to the simultaneous calculation of the gradient, thereby speeding up the whole optimization process. At the design speed of 24 knots, the optimal hull yielded CTM reduction by 1.8%, which is extrapolated to 3.1% reduction of effective power PE in full scale.
A New TPACK Training Model for Tackling the Ongoing Challenges of COVID-19
Authors: Cheng Ping-Han , Molina José , Lin Mei-Chun , Liu Hsiang-Hu , Chang Chun-Yen
Published: February 2022

This study investigated the effects of integrating the “CloudClassRoom” (CCR) and the DEmo-CO-design/teach-feedback-DEbriefing (DECODE) model to improve pre-service teachers’ online technological pedagogical and content knowledge (TPACK). The DECODE model includes four stages: Teacher’s DEmonstrations, Students CO-train in using CloudClassRoom, Students CO-design a CloudClassRoom-integrated course, Students CO-teach, and finally DE-brief what they have learned through the stages mentioned above. This model integrates teacher-student experiences, teaching-learning processes, and technology-embedded systems to promote collaborative and active learning, information and resources sharing, and creative communication. A self-evaluating questionnaire with open-ended questions evaluated participants’ technological pedagogical and content knowledge outcomes. CloudClassRoom significantly increases technology-related knowledge considering the current social distancing measures provoked by COVID-19. The findings show that DECODE with CloudClassRoom provides an integrated process for improving pre-service teachers’ technological pedagogical and content knowledge, assisting pre-service teachers in designing educational technology-integrated courses.
Climate Trends and Extremes in the Indus River Basin, Pakistan: Implications for Agricultural Production
Authors: Heureux Ana Magali Carrera , Alvar-Beltrán Jorge , Manzanas Rodrigo , Ali Mehwish , Wahaj Robina , Dowlatchahi Mina , Afzaal Muhammad , Kazmi Dildar , Ahmed Burhan , Salehnia Nasrin , Fujisawa Mariko , Vuolo Maria Raffaella , Kanamaru Hideki , Gutiérrez Jose Manuel
Published: February 2022

Historical and future projected changes in climatic patterns over the largest irrigated basin in the world, the Indus River Basin (IRB), threaten agricultural production and food security in Pakistan, in particular for vulnerable farming communities. To build a more detailed understanding of the impacts of climate change on agriculture s in the IRB, the present study analyzes (1) observed trends in average temperature, precipitation and related extreme indicators, as well as seasonal shifts over a recent historical period (1997–2016); and (2) statistically downscaled future projections (up to 2100) from a set of climate models in conjunction with crop-specific information for the four main crops of the IRB: wheat, cotton, rice and sugarcane. Key findings show an increasing trend of about over 0.1 °C/year in observed minimum temperature across the study area over the historical period, but no significant trend in maximum temperature. Historical precipitation shows a positive annual increase driven mainly by changes in August and September. Future projections highlight continued warming resulting in critical heat thresholds for the four crops analyzed being increasingly exceeded into the future, in particular in the Kharif season. Concurrently, inter-annual rainfall variability is projected to increase up to 10–20% by the end of the 21st century, augmenting uncertainty of water availability in the basin. These findings provide insight into the nature of recent climatic shifts in the IRB and emphasize the importance of using climate impact assessments to develop targeted investments and efficient adaptation measures to ensure resilience of agriculture in Pakistan into the future.
Attribution Analysis of Annual Precipitation Simulation Differences and Its Correction of CMIP5 Climate Models on the Chinese Mainland
Authors: Sun Xinyu , Wang Yongdi
Published: February 2022

Using the self-organizing maps (SOM) method, we ranked and compared the simulation results of atmospheric circulation and precipitation for 32 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) over China, and found that the ranking of the GCM’s ability to simulate the frequency of sea level pressure (SLP) weather patterns (WPs) was not correlated with the ranking of its ability to simulate annual precipitation WPs. Then, we attributed the precipitation simulation differences and identified three main components for the differences in the multi-model simulation results: internal variability, frequency differences, and the combined term of the two, with internal variability being the largest of the three components. These three deviations depend ultimately on two factors: the ability to simulate the frequency of WPs and the ability to simulate the corresponding average daily precipitation generated by these WPs, with the second factor playing a decisive role. Then, to address the drawback that the model ensemble results cannot be effectively improved when each single model that makes up the ensemble model is dry or wet, a solution was proposed to correct for the simulation differences: the nodal precipitation differences of each WP were corrected. After the correction of the simulation differences, the simulation capability of all the individual models was greatly improved, which increases our confidence in using the CMIP5 models for future weather patterns and precipitation simulation and forecasting.
Subtotal Petrosectomy (SP) in Cochlear Implantation (CI): A Report of 92 Cases
Authors: Arístegui Ignacio , Aranguez Gracia , Casqueiro José Carlos , Gutiérrez-Triguero Manuel , Pozo Almudena del , Arístegui Miguel
Published: February 2022

In most cases, cochlear implantation is a straightforward procedure. Nevertheless, there are clinical situations in which the presence of the middle ear may compromise access and/or the outcome in terms of complications. This article includes a series of patients for whom we eliminated the middle ear to facilitate placement of the electrode array of the implant and/or reduce potential complications. A total of 92 cases in 83 patients, managed by the senior author, are included in this series. Different indications are outlined that justify associating a subtotal petrosectomy technique with cochlear implantation. The steps of the technique are described. We include complications from this series that compare favorably with standard techniques.
K-Nearest Neighbor Estimation of Functional Nonparametric Regression Model under NA Samples
Authors: Hu Xueping , Wang Jingya , Wang Liuliu , Yu Keming
Published: February 2022

Functional data, which provides information about curves, surfaces or anything else varying over a continuum, has become a commonly encountered type of data. The k-nearest neighbor (kNN) method, as a nonparametric method, has become one of the most popular supervised machine learning algorithms used to solve both classification and regression problems. This paper is devoted to the k-nearest neighbor (kNN) estimators of the nonparametric functional regression model when the observed variables take values from negatively associated (NA) sequences. The consistent and complete convergence rate for the proposed kNN estimator is first provided. Then, numerical assessments, including simulation study and real data analysis, are conducted to evaluate the performance of the proposed method and compare it with the standard nonparametric kernel approach.
Acute Inflammation Induces Neuroendocrine and Opioid Receptor Genes Responses in the Seabass Dicentrarchus labrax Brain
Authors: Azeredo Rita , Machado Marina , Pereiro Patricia , Barany Andre , Mancera Juan Miguel , Costas Benjamín
Published: February 2022

In fish, as observed in mammals, any stressful event affects the immune system to a larger or shorter extent. The neuroendocrine-immune axis is a bi-directional network of mobile compounds and their receptors that are shared between both systems (neuroendocrine and immune) and that regulate their respective responses. However, how and to what extent immunity modulates the neuroendocrine system is not yet fully elucidated. This study was carried out to understand better central gene expression response patterns in a high-valued farmed fish species to an acute peripheral inflammation, focusing on genes related to the hypothalamus-pituitary-interrenal axis and the opioid system. European seabass, Dicentrarchus labrax, were intra-peritoneally injected with either Freund’s Incomplete Adjuvant to induce a local inflammatory response or Hanks Balances Salt Solution to serve as the control. An undisturbed group was also included to take into account the effects due to handling procedures. To evaluate the outcomes of an acute immune response, fish were sampled at 4, 24, 48, and 72 h post-injection. The brain was sampled and dissected for isolation of different regions: telencephalon, optic tectum, hypothalamus, and pituitary gland. The expression of several genes related to the neuroendocrine response was measured by real-time PCR. Data were statistically analyzed by ANOVA and discriminant analyses to obtain these genes’ responsiveness for the different brain regions. Serotonergic receptors were upregulated in the telencephalon, whereas the optic tectum inhibited these transcription genes. The hypothalamus showed a somewhat delayed response in which serotonin and glucocorticoid receptors were concerned. Still, the hypothalamic corticotropin-releasing hormone played an important role in differentiating fish undergoing an inflammatory response from those not under such conditions. Opioid receptors gene expression increased in both the hypothalamus and the telencephalon, while in the optic tectum, most were downregulated. However, no changes in the pituitary gland were observed. The different brain regions under immune stimulation demonstrated clear, distinct responses regarding gene transcription rates as well as the time period needed for the effect to occur. Further, more integrative studies are required to associate functions to the evaluated genes more safely and better understand the triggering mechanisms.
Climate Change and Vector-Borne Diseases in China: A Review of Evidence and Implications for Risk Management
Authors: Wu Yurong , Huang Cunrui
Published: February 2022

Vector-borne diseases have posed a heavy threat to public health, especially in the context of climate change. Currently, there is no comprehensive review of the impact of meteorological factors on all types of vector-borne diseases in China. Through a systematic review of literature between 2000 and 2021, this study summarizes the relationship between climate factors and vector-borne diseases and potential mechanisms of climate change affecting vector-borne diseases. It further examines the regional differences of climate impact. A total of 131 studies in both Chinese and English on 10 vector-borne diseases were included. The number of publications on mosquito-borne diseases is the largest and is increasing, while the number of studies on rodent-borne diseases has been decreasing in the past two decades. Temperature, precipitation, and humidity are the main parameters contributing to the transmission of vector-borne diseases. Both the association and mechanism show vast differences between northern and southern China resulting from nature and social factors. We recommend that more future research should focus on the effect of meteorological factors on mosquito-borne diseases in the era of climate change. Such information will be crucial in facilitating a multi-sectorial response to climate-sensitive diseases in China.
Sulfide Oxidation Evidences the Immediate Cellular Response to a Decrease in the Mitochondrial ATP/O2 Ratio
Authors: Bouillaud Frédéric
Published: February 2022

The present article will not attempt to deal with sulfide per se as a signaling molecule but will aim to examine the consequences of sulfide oxidation by mitochondrial sulfide quinone reductase in mammalian cells. This oxidation appears first as a priority to avoid self-poisoning by endogenous sulfide and second to occur with the lowest ATP/O2 ratio when compared to other mitochondrial substrates. This is explained by the injection of electrons in the respiratory chain after complex I (as for succinate) and by a sulfur oxidation step implying a dioxygenase that consumes oxygen but does not contribute to mitochondrial bioenergetics. Both contribute to increase cellular oxygen consumption if sulfide is provided below its toxic level (low µM). Accordingly, if oxygen supply or respiratory chain activity becomes a limiting factor, small variations in sulfide release impact the cellular ATP/ADP ratio, a major metabolic sensor.
Sequencing Depth Has a Stronger Effect than DNA Extraction on Soil Bacterial Richness Discovery
Authors: Sanchez-Cid Concepcion , Tignat-Perrier Romie , Franqueville Laure , Delaurière Laurence , Schagat Trista , Vogel Timothy M.
Published: February 2022

Although Next-Generation Sequencing techniques have increased our access to the soil microbiome, each step of soil metagenomics presents inherent biases that prevent the accurate definition of the soil microbiome and its ecosystem function. In this study, we compared the effects of DNA extraction and sequencing depth on bacterial richness discovery from two soil samples. Four DNA extraction methods were used, and sequencing duplicates were generated for each DNA sample. The V3–V4 region of the 16S rRNA gene was sequenced to determine the taxonomical richness measured by each method at the amplicon sequence variant (ASV) level. Both the overall functional richness and antibiotic resistance gene (ARG) richness were evaluated by metagenomics sequencing. Despite variable DNA extraction methods, sequencing depth had a greater influence on bacterial richness discovery at both the taxonomical and functional levels. Sequencing duplicates from the same sample provided access to different portions of bacterial richness, and this was related to differences in the sequencing depth. Thus, the sequencing depth introduced biases in the comparison of DNA extraction methods. An optimisation of the soil metagenomics workflow is needed in order to sequence at a sufficient and equal depth. This would improve the accuracy of metagenomic comparisons and soil microbiome profiles.
Effects of Deferasirox in Alzheimer’s Disease and Tauopathy Animal Models
Authors: Kwan Ping , Ho Amy , Baum Larry
Published: February 2022

The accumulation of iron may contribute to Alzheimer’s disease (AD) and other tauopathies. The iron chelator desferrioxamine slows disease progression in AD patients. However, desferrioxamine requires injection, which is inconvenient and may hinder compliance. We therefore tested an oral iron chelator, desferasirox (Exjade), in transgenic animal models. Tg2576 mice overexpress the mutant human APP protein and produce the Aβ peptide. JNPL3 mice (Tau/Tau) overexpress the mutant human tau protein. Crossing these produced APP/Tau mice, overexpressing both APP and tau. Treating the three models with 1.6 mg deferasirox thrice weekly from age 8 to 14 months did not affect memory as measured by contextual fear conditioning or motor function as measured by rotarod, but tended to decrease hyperphosphorylated tau as measured by AT8 immunohistochemistry and immunoblotting. Deferasirox might act by decreasing iron, which aggregates tau, or directly binding tau to inhibit aggregation.
The Role of Epigenetic Modifications in Human Cancers and the Use of Natural Compounds as Epidrugs: Mechanistic Pathways and Pharmacodynamic Actions
Authors: Bouyahya Abdelhakim , Mechchate Hamza , Oumeslakht Loubna , Zeouk Ikrame , Aboulaghras Sara , Balahbib Abdelaali , Zengin Gokhan , Kamal Mohammad Amjad , Gallo Monica , Montesano Domenico , El Omari Nasreddine
Published: February 2022

Cancer is a complex disease resulting from the genetic and epigenetic disruption of normal cells. The mechanistic understanding of the pathways involved in tumor transformation has implicated a priori predominance of epigenetic perturbations and a posteriori genetic instability. In this work, we aimed to explain the mechanistic involvement of epigenetic pathways in the cancer process, as well as the abilities of natural bioactive compounds isolated from medicinal plants (flavonoids, phenolic acids, stilbenes, and ketones) to specifically target the epigenome of tumor cells. The molecular events leading to transformation, angiogenesis, and dissemination are often complex, stochastic, and take turns. On the other hand, the decisive advances in genomics, epigenomics, transcriptomics, and proteomics have allowed, in recent years, for the mechanistic decryption of the molecular pathways of the cancerization process. This could explain the possibility of specifically targeting this or that mechanism leading to cancerization. With the plasticity and flexibility of epigenetic modifications, some studies have started the pharmacological screening of natural substances against different epigenetic pathways (DNA methylation, histone acetylation, histone methylation, and chromatin remodeling) to restore the cellular memory lost during tumor transformation. These substances can inhibit DNMTs, modify chromatin remodeling, and adjust histone modifications in favor of pre-established cell identity by the differentiation program. Epidrugs are molecules that target the epigenome program and can therefore restore cell memory in cancerous diseases. Natural products isolated from medicinal plants such as flavonoids and phenolic acids have shown their ability to exhibit several actions on epigenetic modifiers, such as the inhibition of DNMT, HMT, and HAT. The mechanisms of these substances are specific and pleiotropic and can sometimes be stochastic, and their use as anticancer epidrugs is currently a remarkable avenue in the fight against human cancers.