bluuebunny/crossref_metadata_2025 huggingface dataset:
Effective Framework for Pulmonary Nodule Classification from CT Images Using the Modified Gradient Boosting Method
Authors: Donga Harsha Vardhan , Karlapati Jaya Sai Aditya Nandan , Desineedi Harsha Sri Sumanth , Periasamy Prakasam , TR Sureshkumar
Published: August 2022

Lung carcinoma, which is commonly known as lung cancer, is one of the most common cancers throughout the world. Mostly, it is not diagnosed until it has spread, and it is very difficult to treat. Hence, early diagnosis of benign and malignant pulmonary nodules can help in the risk assessment of lung cancer for patients, and with proper treatment can save their lives. In this study, a framework for the classification of pulmonary nodules from Computerized Tomography (CT) images using the machine learning-based modified gradient boosting method is proposed. Initially, the obtained CT scan images are preprocessed for better image quality. Next, a random walker method is used to segment the lung nodule boundaries based on seeds provided by the user. After that, the intensity and texture features are extracted using the Local Binary Pattern (LBP) filter and the coefficients of the Riesz wavelet transform. Finally, the proposed modified gradient boost classifier model is trained and tested using the extracted features to classify nodules as either benign or malignant. The proposed framework is verified and validated using the Lung Image Database Consortium (LIDC-IDRI) dataset. From the performance analysis, it was observed that the proposed method achieves a precision, recall, F1 score, and validation accuracy of 0.957, 0.91, 0.941, and 95.67%, respectively. The performance of the proposed method is compared with existing models and is found to be superior. It was found that the proposed classifier is able to efficiently classify pulmonary nodules as either benign or malignant.
Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
Authors: Akinpelu Samson , Viriri Serestina
Published: August 2022

Speech Emotion Classification (SEC) relies heavily on the quality of feature extraction and selection from the speech signal. Improvement on this to enhance the classification of emotion had attracted significant attention from researchers. Many primitives and algorithmic solutions for efficient SEC with minimum cost have been proposed; however, the accuracy and performance of these methods have not yet attained a satisfactory point. In this work, we proposed a novel deep transfer learning approach with distinctive emotional rich feature selection techniques for speech emotion classification. We adopt mel-spectrogram extracted from speech signal as the input to our deep convolutional neural network for efficient feature extraction. We froze 19 layers of our pretrained convolutional neural network from re-training to increase efficiency and minimize computational cost. One flattened layer and two dense layers were used. A ReLu activation function was used at the last layer of our feature extraction segment. To prevent misclassification and reduce feature dimensionality, we employed the Neighborhood Component Analysis (NCA) feature selection algorithm for picking out the most relevant features before the actual classification of emotion. Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers were utilized at the topmost layer of our model. Two popular datasets for speech emotion classification tasks were used, which are: Berling Emotional Speech Database (EMO-DB), and Toronto English Speech Set (TESS), and a combination of EMO-DB with TESS was used in our experiment. We obtained a state-of-the-art result with an accuracy rate of 94.3%, 100% specificity on EMO-DB, and 97.2%, 99.80% on TESS datasets, respectively. The performance of our proposed method outperformed some recent work in SEC after assessment on the three datasets.
Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
Authors: Hayat Ahatsham , Morgado-Dias Fernando
Published: August 2022

Worker safety at construction sites is a growing concern for many construction industries. Wearing safety helmets can reduce injuries to workers at construction sites, but due to various reasons, safety helmets are not always worn properly. Hence, a computer vision-based automatic safety helmet detection system is extremely important. Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites. This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site. YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection. A benchmark dataset containing 5000 images of hard hats was used in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively. The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of 92.44%, thereby showing excellent results in detecting safety helmets even in low-light conditions.
Rolling Shutter OFDM Scheme for Optical Camera Communication Considering Mobility Environment Based on Deep Learning
Authors: Nguyen Huy , Nguyen Van Linh , Tran Duc Hoang , Jang Yeong Min
Published: August 2022

This paper presents a rolling shutter orthogonal frequency-division multiplexing (RS-OFDM) optical camera communication higher rate longer range proposed in IEEE 802.15.7a Task Group (TG7a) using an image sensor as a receiver. OFDM is a digital multi-carrier modulation scheme deployed for broadband wireless communication to resolve the inter-symbol interference (ISI) effect caused by the multipath channel. In optical wireless communication systems, OFDM was applied widely for indoor applications: internet of things, e-health, vehicular, and localization systems. The mobility scenario is a big problem for OWC systems, which reduces the system performance due to the optical channel variation in the processing time. In addition to that, signal detection should be considered in the mobility environment to improve the signal-to-noise ratio of OWC systems. In this paper, we proposed the convolution neural network (CNN) for LED detection in the RS-OFDM system, considering the mobility effect. In addition to that, the deep neural network was applied to detect the start of OFDM frame instead of conventional technology (Van De Beek algorithm). By applying our approach, the RS-OFDM system can achieve long communication (18 m distance) with a low error rate in the 2 m/s velocity environment.
5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model
Authors: Samidi Faris Syahmi , Mohamed Radzi Nurul Asyikin , Mohd Azmi Kaiyisah Hanis , Mohd Aripin Norazizah , Azhar Nayli Adriana
Published: August 2022

Resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Therefore, system improvisation techniques, such as machine learning (ML) and deep learning, must be applied to make the model customisable. Moreover, improvisation allows the prediction system to generate the most accurate outcomes and valuable insights from data whilst enabling effective decisions. In this study, we first provide a literature study on the applications of ML and a summary of the hyperparameters influencing the prediction capabilities of the ML models for the communication system. We demonstrate the behaviour of four ML models: k nearest neighbour, classification and regression trees, random forest and support vector machine. Then, we observe and elaborate on the suitable hyperparameter values for each model based on the accuracy in prediction performance. Based on our observation, the optimal hyperparameter setting for ML models is essential because it directly impacts the model’s performance. Therefore, understanding how the ML models are expected to respond to the system utilised is critical.
Recurrent Acute Otitis Media Environmental Risk Factors: A Literature Review from the Microbiota Point of View
Authors: Folino Francesco , Bosi Pietro , Torretta Sara , Gaffuri Michele , Marchisio Paola
Published: August 2022

Acute otitis media (AOM) constitutes a multifactorial disease, as several host and environmental factors contribute to its occurrence. Prevention of AOM represents one of the most important goals in pediatrics, both in developing countries, in which complications, mortality, and deafness remain possible consequences of the disease, compared to in developed countries, in which this condition has an important burden in terms of medical, social, and economical implications. The strategies for AOM prevention are based on reducing the burden of risk factors, through the application of behavioral, environmental, and therapeutic interventions. The introduction of culture-independent techniques has allowed high-throughput investigation of entire bacterial communities, providing novel insights into the pathogenesis of middle ear diseases through the identification of potential protective bacteria. The upper respiratory tract (URT) is a pivotal region in AOM pathogenesis, as it could act as a source of pathogens than of protective microorganisms for the middle ear (ME). Due to its direct connection with the external ambient, the URT is particularly exposed to the influence of environmental agents. The aim of this review was to evaluate AOM environmental risk factors and their impact on URT microbial communities, and to investigate AOM pathogenesis from the microbiota perspective.
Effectiveness of the Human Oral Microbe Identification Microarray in Identifying Periodontal Pathogens: A Systematic Review
Authors: Udoh Stephanie , Adukwu Emmanuel , Varadi Aniko , Saad Saliha
Published: August 2022

The aim of this review was to investigate the effectiveness of the Human Oral Microbe Identification Microarray (HOMIM) in identifying and quantifying bacterial species of the oral microbiome in periodontal disease. The search for articles was conducted in CENTRAL, CINAHL, MEDLINE and EMBASE by two reviewers, and included articles published in English between January 1990 and December 2021. The selected articles were human observational studies in adults between 18 and 65 years, presenting specific predefined keywords. Articles were initially selected by title and abstract; articles that met the inclusion criteria were analyzed for methodological quality using a detailed checklist for quality assessment. Data were extracted and reported using the PRISMA tool. The study design, sample, follow-up period, collection and microbial analysis methods, statistical treatment, results and discussion were quality assessed and risk of bias was evaluated using the Cochrane Risk-of-Bias tool. A narrative synthesis approach was used to synthesize and interpret the extracted data. From the initial search, 2931 articles were retrieved; 51 of these were then selected after screening by title and abstract. Subsequently, 8 articles met the inclusion after full-text reading and were classed according to methodological quality as high (2), moderate (3) or low (3). Studies included in this review were of high and medium quality. Data from the Human Oral Microbe Identification Microarray (HOMIM) provide much more robust results, showing major shifts between periodontal health and periodontal disease. Compared to earlier techniques such as Denaturing Gradient Gel Electrophoresis (DGGE), HOMIM represents a more effective approach for quantification due to its high sensitivity; thus, it is able to identify a high prevalence of periodontal pathogens and novel species in low abundance. The literature provides moderate evidence that the Human Oral Microbe Identification Microarray (HOMIM) is more effective in identifying and quantifying bacterial species of the oral microbiome in periodontal disease, compared to earlier molecular and non-molecular methods such as Denaturing Gradient Gel Electrophoresis (DGGE) and a culture-based approach with phenotypic tests.
Evaluating the Atmospheric Loss of H2 by NO3 Radicals: A Theoretical Study
Authors: Romanias Manolis N. , Nguyen Thanh Lam
Published: August 2022

Molecular hydrogen (H2) is now considered among the most prominent substitute for fossil fuels. The environmental impacts of a hydrogen economy have received more attention in the last years, but still, the knowledge is relatively poor. In this work, the reaction of H2 with NO3 radical (the dominant night-time detergent of the atmosphere) is studied for the first time using high-level composite G3B3 and modification of high accuracy extrapolated ab initio thermochemistry (mHEAT) methods in combination with statistical kinetics analysis using non-separable semi-classical transition state theory (SCTST). The reaction mechanism is characterized, and it is found to proceed as a direct H-abstraction process to yield HNO3 plus H atom. The reaction enthalpy is calculated to be 12.8 kJ mol−1, in excellent agreement with a benchmark active thermochemical tables (ATcT) value of 12.2 ± 0.3 kJ mol−1. The energy barrier of the title reaction was calculated to be 74.6 and 76.7 kJ mol−1 with G3B3 and mHEAT methods, respectively. The kinetics calculations with the non-separable SCTST theory give a modified-Arrhenius expression of k(T) = 10−15 × T0.7 × exp(−6120/T) (cm3 s−1) for T = 200–400 K and provide an upper limit value of 10−22 cm3 s−1 at 298 K for the reaction rate coefficient. Therefore, as compared to the main consumption pathway of H2 by OH radicals, the title reaction plays an unimportant role in H2 loss in the Earth’s atmosphere and is a negligible source of HNO3.
Effects of Horizontal Transport and Vertical Mixing on Nocturnal Ozone Pollution in the Pearl River Delta
Authors: Yang Honglong , Lu Chao , Hu Yuanyuan , Chan Pak-Wai , Li Lei , Zhang Li
Published: August 2022

Based on the meteorological, ozone (O3), and vertical observation data of 2020, this study sought to evaluate the daily variation in O3, particularly the characteristics of nocturnal ozone pollution. We also discuss the effect of local and mesoscale horizontal transport and vertical mixing on the formation of nocturnal O3 pollution. Distinct seasonal characteristics of the daily O3 variation in Shenzhen were identified. In particular, significant nocturnal peaks were found to regularly occur in the winter and spring (November–December and January–April). The monthly average of daily variation had a clear bimodal distribution. During the period, O3 pollution frequently occurred at night, with the maximum hourly O3 concentration reaching 203.5 μg/m3. Nocturnal O3 pollution was closely associated with horizontal transport and vertical mixing. During the study period, the O3 maximum values were recorded on 68 nights, primarily between 23:00 and 03:00, with occasional observation of two peaks. The impact of horizontal transport and vertical mixing on the nocturnal secondary O3 maximum values was elaborated in two case studies, where vertical mixing was mainly associated with low-level jets, with strong wind shear enhancing turbulent mixing and transporting O3 from the upper layers to the surface.
A 3D Monte Carlo Simulation of Convective Diffusional Deposition of Ultrafine Particles on Fiber Surfaces
Authors: Wu Shixian , Chen Yongping , Qi Can , Liu Chunyu , Li Gang , Zhu Hui
Published: August 2022

The microscale simulation of ultrafine particle transport and deposition in fibrous filtration media was achieved with a novel particle tracking model using a 3D Monte Carlo model. The particle deposition process is governed by the convection–diffusion field. Simulations were performed by considering the fibrous filtration media as an array of identical parallel fibers, in which the flow field was accurately described by an analytical solution. The model of particle movement was described by the random probability distribution characterized by a dimensionless factor, the Peclet number (Pe), based on a convection–diffusive equation of particle transport in fluid. The influence of the particle Peclet number (Pe) on the particle deposition process and the resulting deposition morphology was investigated. The results were analyzed in terms of dust layer growth, particles’ trajectories and dust layer porosity for a vast range of Peclet numbers. The development of distinct deposition morphologies was found by varying the Peclet number (Pe). With a small Peclet number, diffusion dominated deposition and led to the formation of a more open and looser dust layer structure, while with larger Peclet numbers, convection dominated deposition and was found to form compact deposits. According to the change in the location of the packing densities along the dust layer height direction, the dust layer structure could be divided into three typical parts: the substructure, main profile and surface layer. In addition, the deposit morphologies observed for a high Pe were in good agreement with the experimental results found in the literature.
Non-Monotonic dc Stark Shifts in the Rapidly Ionizing Orbitals of the Water Molecule
Authors: Pirkola Patrik , Horbatsch Marko
Published: August 2022

We extend a previously developed model for the Stark resonances of the water molecule. The method employs a partial-wave expansion of the single-particle orbitals using spherical harmonics. To find the resonance positions and decay rates, we use the exterior complex scaling approach which involves the analytic continuation of the radial variable into the complex plane and yields a non-hermitian Hamiltonian matrix. The real part of the eigenvalues provides the resonance positions (and thus the Stark shifts), while the imaginary parts −Γ/2 are related to the decay rates Γ, i.e., the full-widths at half-maximum of the Breit–Wigner resonances. We focus on the three outermost (valence) orbitals, as they dominate the ionization process. We find that for forces directed along the three Cartesian co-ordinates, the fastest ionizing orbital always displays a non-monotonic Stark shift. For the case of fields along the molecular axis we show results as a function of the number of spherical harmonics included (ℓmax=3,4). Comparison is made with total molecule resonance parameters from the literature obtained with Hartree–Fock and coupled cluster methods.
The Performance of Topic Evolution Based on a Feature Maximization Measurement for the Linguistics Domain
Authors: Feng Junchao , Miao Jianjun , Tang Yue , Li Yuechen , Feng Jundong
Published: August 2022

Understanding the performance of the data mining approach and topic evolution in a certain scientific domain is imperative to capturing key domain developments and facilitating knowledge transfer within and across domains. Our research selects linguistics as an exploratory domain and exploits the feature maximization (FM) measurement for feature selection, combined with the contrast ratio to conduct the diachronic analysis for the linguistics domain’s topics. To accurately mine the linguistics domain’s topics and obtain the optimal clustering model selection, we exploit an integrated method associated with the deep embedding for clustering (DEC) algorithm based on the keywords-based Text Representation Matrix (KTRM) and Lamirel’s EC index and test the performance of this method. The results show that the FM measurement is applicable in the linguistics domain for topic mining, and the combinatory method has the advantage of an unbiased clustering optimization model and applies to the design of non-parameter clustering and algorithms from the low dimension to the high dimension of datasets. The findings suggest that this approach could be suitable for a diachronic analysis of topic evolution and facilitate the performance of topic detection. In addition, these findings of text detection can rise to knowledge fusion cognition with the factor of language as an available research objective in interdisciplinary research.
Fixed Point of Modified F-Contraction with an Application
Authors: Wang Min , Saleem Naeem , Bashir Shahid , Zhou Mi
Published: August 2022

In this paper, we prove the existence of fixed points for every 2-rotative continuous mapping in Banach spaces to answer an open question raised by Goebel and Koter. Further, we modify F-contraction by developing F-rotative mapping and establish some fixed-point theorems. Finally, we apply our results to prove the existence of a solution of a non-linear fractional differential equation.
A Fuzzy Random Boundary Value Problem
Authors: Srivastava Hari M. , Chaharpashlou Reza , Saadati Reza , Li Chenkuan
Published: August 2022

A system of generalized fuzzy random differential equations with boundary conditions is investigated, which is a fuzzy version of a system of general random differential equations. We first present random fixed point (RFP) theorems in fuzzy metric space (FM). In the sequel, we define the operators that are of integral type. Furthermore, these operators are related to a part of random differential equations (RDE). For the desired system with boundary conditions, we study the suitable integral operators associated with a large family of random differential equations. Finally, we prove the existence of a unique random solution (EURS).
Echelon Utilization of Retired Power Lithium-Ion Batteries: Challenges and Prospects
Authors: Wang Ningbo , Garg Akhil , Su Shaosen , Mou Jianhui , Gao Liang , Li Wei
Published: August 2022

The explosion of electric vehicles (EVs) has triggered massive growth in power lithium-ion batteries (LIBs). The primary issue that follows is how to dispose of such large-scale retired LIBs. The echelon utilization of retired LIBs is gradually occupying a research hotspot. Solving the issue of echelon utilization of large-scale retired power LIBs brings not only huge economic but also produces rich environmental benefits. This study systematically examines the current challenges of the cascade utilization of retired power LIBs and prospectively points out broad prospects. Firstly, the treatments of retired power LIBs are introduced, and the performance evaluation methods and sorting and regrouping methods of retired power LIBs are comprehensively reviewed for echelon utilization. Then, the problems faced by the scenario planning and economic research of the echelon utilization of retired power LIBs are analyzed, and value propositions are put forward. Secondly, this study summarizes the technical challenges faced by echelon utilization in terms of security, performance evaluation methods, supply and demand chain construction, regulations, and certifications. Finally, the future research prospects of echelon utilization are discussed. In the foreseeable future, technologies such as standardization, cloud technology, and blockchain are urgently needed to maximize the industrialization of the echelon utilization of retired power LIBs.
Computer-Aided Design and 3D Printing of Hemipelvic Endoprosthesis for Personalized Limb-Salvage Reconstruction after Periacetabular Tumor Resection
Authors: Hu Xianglin , Chen Yong , Cai Weiluo , Cheng Mo , Yan Wangjun , Huang Wending
Published: August 2022

3D-printed hemipelvic endoprosthesis is an emerging solution for personalized limb-salvage reconstruction after periacetabular tumor resection. Further clinical studies are still required to report its surgical characteristics, outcomes, benefits and drawbacks. Sixteen consecutive patients underwent periacetabular tumor wide resection and pelvic reconstruction with a 3D-printed hemipelvic endoprosthesis from 2018 to 2021. The surgical characteristics and outcomes are described. The mean follow-up duration was 17.75 months (range, 6 to 46 months). Five patients underwent surgery for type I + II resection and reconstruction, seven for type II + III resection and reconstruction, three for type II resection and reconstruction, and one for type I + II + IV resection and reconstruction. The incidence of postoperative complication was 12.5% (2/16) for deep venous thrombosis (DVT), 12.5% (2/16) for pneumonia, and 12.5% (2/16) for would deep or superficial infection. During follow-up, two patients (12.5%) suffered hip dislocation and underwent revision surgery. CT demonstrated an obvious prosthetic porous structure–bone fusion after follow-up of at least 6 months. At the final follow-up, 12 lived with no evidence of disease while four lived with disease; no patients experienced pain; and 15 had independent ambulation, with a mean Musculoskeletal Tumor Society (MSTS) score of 85.8% (range, 26.7% to 100%). 3D-printed hemipelvic endoprosthesis facilitates wide resection of periacetabular tumor and limb-salvage reconstruction, thus resulting in good oncological and functional outcomes. The custom-made nature is able to well mimic the skeletal anatomy and microstructure and promote osseointegration. Perioperative complications and rehabilitation exercise still need to be stressed for this engineering technology-assisted major orthopedic surgery.
Mechanisms and Biomarker Potential of Extracellular Vesicles in Stroke
Authors: Ollen-Bittle Nikita , Roseborough Austyn D. , Wang Wenxuan , Wu Jeng-liang D. , Whitehead Shawn N.
Published: August 2022

Stoke is a prevalent and devastating neurologic condition with limited options for therapeutic management. Since brain tissue is rarely accessible clinically, peripheral biomarkers for the central nervous system’s (CNS’s) cellular response to stroke may prove critical for increasing our understanding of stroke pathology and elucidating novel therapeutic targets. Extracellular vesicles (EVs) are cell-derived, membrane-enclosed vesicles secreted by all cell types within the CNS that can freely pass the blood-brain barrier (BBB) and contain unique markers and content linked to their cell of origin. These unique qualities make brain-derived EVs novel candidates for non-invasive blood-based biomarkers of both cell specificity and cell physiological state during the progression of stroke and recovery. While studies are continuously emerging that are assessing the therapeutic potential of EVs and profiling EV cargo, a vast minority of these studies link EV content to specific cell types. A better understanding of cell-specific EV release during the acute, subacute, and chronic stages of stroke is needed to further elucidate the cellular processes responsible for stroke pathophysiology. Herein, we outline what is known about EV release from distinct cell types of the CNS during stroke and the potential of these EVs as peripheral biomarkers for cellular function in the CNS during stroke.
Highly Efficient Use of Infrared Spectroscopy (ATR-FTIR) to Identify Aphid Species
Authors: Durak Roma , Ciak Beata , Durak Tomasz
Published: August 2022

Aphids are commonly considered to be serious pests for trees, herbaceous and cultivated plants. Recognition and identification of individual species is very difficult and is based mainly on morphological features. The aims of the study were to suggest the possibility of identifying aphids through the use of Fourier-transform infrared (FTIR) spectroscopy, and to determine which absorption peaks are the most useful to separate aphid species. Using FTIR spectroscopy, based on the chemical composition of the body, we were able to distinguish 12 species of aphid. We have shown that using nine distinct peaks corresponding to the molecular vibrations from carbohydrates, lipids, amides I and II, it is possible to accurately identify aphid species with an efficiency of 98%.
Attack and Defense Performance in Goalball: A Proposal for Throwing, Balance and Acoustic Reaction Evaluation
Authors: Bataller-Cervero A. Vanessa , Bascuas Pablo J. , Rabal-Pelay Juan , Gutiérrez Héctor , Piedrafita Eduardo , Berzosa César
Published: August 2022

Goalball is a sport for visually impaired athletes, where the roles of attack and defense change continuously during the game. Performance evaluation should consider the variables that determine the throwing and the stop and clearance of the ball. The aim of this study is to evaluate the precision and velocity of the ball throwing in goalball, besides core stability and balance as variables that determine an optimal throwing. Moreover, a novel acoustic reaction time is applied to analyze the defense performance. Eight goalball players (33 ± 9 years old; 77.8 ± 22.7 kg; 174 ± 13 cm; 10 ± 5 years of experience) were recruited to assess ball velocity, with a radar gun, and throwing accuracy. Anthropometry, static balance, and core stability were assessed using a computerized pressure platform. Acoustic reaction time was measured with a photoelectric system. A significant positive correlation was found between throwing speed and the years of experience (Ƿ = 0.714, p = 0.047), height (Ƿ = 0.786, p = 0.021), dominant leg surface area of the stabilogram (Ƿ = 0.738, p = 0.037), and non-dominant leg center of pressure mean velocity (Ƿ = 0.714, p = 0.017). In the present pilot study, height and years of experience are correlated to throwing velocity. This is also the first test proposal to assess throwing precision and complex acoustic reaction in goalball players, which could be used to assess the level of performance in future studies.
(3α,5α)3-Hydroxypregnan-20-one (3α,5α-THP) Regulation of the HPA Axis in the Context of Different Stressors and Sex
Authors: Boero Giorgia , Tyler Ryan E. , O’Buckley Todd K. , Balan Irina , Besheer Joyce , Morrow A. Leslie
Published: August 2022

Corticotropin-releasing factor (CRF) regulates the stress response in the hypothalamus and modulates neurotransmission across the brain through CRF receptors. Acute stress increases hypothalamic CRF and the GABAergic neurosteroid (3α,5α)3-hydroxypregnan-20-one (3α,5α-THP). We previously showed that 3α,5α-THP regulation of CRF is sex and brain region dependent. In this study, we investigated 3α,5α-THP regulation of stress-induced hypothalamic CRF, CRF receptor type 1 (CRFR1), CRF binding protein (CRFBP), pro-opiomelanocortin (POMC), and glucocorticoid receptor (GR) by western blot and circulating corticosterone (CORT) by enzyme-linked immunosorbent assay (ELISA) in male and female Sprague Dawley rats. Tissue was collected after rats were injected with 3α,5α-THP (15 mg/kg, IP) or vehicle 15 min prior to 30 min of restraint stress (RS), or 10 min of forced swim stress (FSS) and 20 min recovery. The initial exposure to a stress stimulus increased circulating CORT levels in both males and females, but 3α,5α-THP attenuated the CORT response only in females after RS. 3α,5α-THP reduced GR levels in male and females, but differently between stressors. 3α,5α-THP decreased the CRF stress response after FSS in males and females, but after RS, only in female rats. 3α,5α-THP reduced the CRFR1, CRFBP, and POMC increases after RS and FSS in males, but in females only after FSS. Our results showed different stress responses following different types of stressors: 3α,5α-THP regulated the HPA axis at different levels, depending on sex.
Limb Remote Ischemic Conditioning Promotes Neurogenesis after Cerebral Ischemia by Modulating miR-449b/Notch1 Pathway in Mice
Authors: Li Sijie , Yang Yong , Li Ning , Li Haiyan , Xu Jiali , Zhao Wenbo , Wang Xiaojie , Ma Linqing , Gao Chen , Ding Yuchuan , Ji Xunming , Ren Changhong
Published: August 2022

Neurogenesis plays an important role in the prognosis of stroke patients and is known to be promoted by the activation of the Notch1 signaling pathway. Studies on the airway epithelium have shown that miR-449b represses the Notch pathway. The study aimed to investigate whether limb remote ischemic conditioning (LRIC) was able to promote neurogenesis in cerebral ischemic mice, and to investigate the role of the miR-449b/Notch1 pathway in LRIC-induced neuroprotection. Male C57BL/6 mice (22–25 g) were subjected to transient middle cerebral artery occlusion (MCAO), and LRIC was performed in the bilateral lower limbs immediately after MCA occlusion. Immunofluorescence staining was performed to assess neurogenesis. The cell line NE-4C was used to elucidate the proliferation of neuronal stem cells in 8% O2. After LRIC treatment on day 28, mice recovered neurological function. Neuronal precursor proliferation was enhanced in the SVZ, and neuronal precursor migration was enhanced in the basal ganglia on day 7. LRIC promoted the improvement of neurological function in mice on day 28, promoted neuronal precursor proliferation in the SVZ, and enhanced neuronal precursor migration in the basal ganglia on day 7. The neurological function score was negatively correlated with the number of BrdU-positive/DCX-positive cells in the SVZ and striatum. LRIC promoted activated Notch1 protein expression in the SVZ and substantially downregulated miR-449b levels in the SVZ and plasma. In vitro, miR-449b was found to target Notch1. Lentivirus-mediated miR-449b knockdown increased Notch1 levels in NE-4C cells and increased proliferation in the cells. The effects of miR-449b inhibition on neurogenesis were ablated by the application of Notch1 shRNA. Our study showed that LRIC promoted the proliferation and migration of neural stem cells after MCAO, and these effects were modulated by the miR-449b/Notch1 pathway.
In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model
Authors: Mazaya Maulida , Kwon Yung-Keun
Published: August 2022

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations.
Masticatory Myoelectric Side Modular Ratio Asymmetry during Maximal Biting in Women with and without Temporomandibular Disorders
Authors: de Paiva Felipe Acácio , Ferreira Kariny Realino , Barbosa Michelle Almeida , Barbosa Alexandre Carvalho
Published: August 2022

There is no consensus on the role of electromyographic analysis in detecting and characterizing the asymmetries of jaw muscle excitation in patients with temporomandibular disorders (TMD). To analyze the TMD patients (n = 72) in comparison with the healthy controls (n = 30), the surface electromyography (sEMG) of the temporalis anterior muscle (TA) and masseter muscle (M) was recorded while a maximal biting task was performed. The differences in the asymmetry of the relationship between the masseter muscles were assessed in a module to determine the sensitivity (Sn) of binomial logistic models, based on the dominance of the TA or the M muscle, in accurately predicting the presence of TMD. All assumptions were met, and comparisons between the groups showed significant differences for the TA muscle ratio (p = 0.007), but not for the M muscle ratio (p = 0.13). The left side was predominant over the right side in the TMD group for both the TA (p = 0.02) and M muscles (p = 0.001), while the non-TMD group had a higher frequency of the right side. Binary logistic regression showed a significant model (χ2 = 9.53; p = 0.002) for the TA muscle with Sn = 0.843. The model for the M muscle also showed significance (χ2 = 8.03; p = 0.005) with Sn = 0.837. The TMD patients showed an increased TA muscle ratio and asymmetry of left dominance, compared to the healthy subjects. Both of the binomial logistic models, based on muscle dominance TA or M, were moderately sensitive for predicting the presence of TMD.
Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth
Authors: Athaya Tasbiraha , Choi Sunwoong
Published: August 2022

Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.
Long-Term Effects of Repetitive Transcranial Magnetic Stimulation on Tinnitus in a Guinea Pig Model
Authors: Amat Farah , Zimdahl Jack W. , Barry Kristin M. , Rodger Jennifer , Mulders Wilhelmina H. A. M.
Published: August 2022

The auditory phantom sensation of tinnitus is associated with neural hyperactivity. Modulating this hyperactivity using repetitive transcranial magnetic stimulation (rTMS) has shown beneficial effects in human studies. Previously, we investigated rTMS in a tinnitus animal model and showed that rTMS over prefrontal cortex (PFC) attenuated tinnitus soon after treatment, likely via indirect effects on auditory pathways. Here, we explored the duration of these beneficial effects. Acoustic trauma was used to induce hearing loss and tinnitus in guinea pigs. Once tinnitus developed, high-frequency (20 Hz), high-intensity rTMS was applied over PFC for two weeks (weekdays only; 10 min/day). Behavioral signs of tinnitus were monitored for 6 weeks after treatment ended. Tinnitus developed in 77% of animals between 13 and 60 days post-trauma. rTMS treatment significantly reduced the signs of tinnitus at 1 week on a group level, but individual responses varied greatly at week 2 until week 6. Three (33%) of the animals showed the attenuation of tinnitus for the full 6 weeks, 45% for 1–4 weeks and 22% were non-responders. This study provides further support for the efficacy of high-frequency repetitive stimulation over the PFC as a therapeutic tool for tinnitus, but also highlights individual variation observed in human studies.
The Impact of Bilateral Anodal tDCS over Left and Right DLPFC on Executive Functions in Children with ADHD
Authors: Salehinejad Mohammad Ali , Vosough Younes , Nejati Vahid
Published: August 2022

Transcranial direct current stimulation (tDCS) is increasingly used for therapeutic purposes in attention-deficit hyperactivity disorder (ADHD). The dorsolateral prefrontal cortex (DLPFC) is the most targeted region of tDCS studies in ADHD. There is limited knowledge and mixed results about the relevance of left or right DLPFCs in ADHD’s cognitive deficits. No study so far has investigated the impact of the increased excitability of both left and right DLPFC with anodal tDCS on cognitive deficits in ADHD. Here, we explored the impact of online bilateral anodal left and right DLPFC tDCS on executive dysfunction in children with ADHD. Twenty-two children with ADHD (mean age ± SD =8.86 ± 1.80) received bilateral anodal online tDCS over the left and right DLPFC (1.5 mA, 15 min) in two separate sessions in active and sham states. They underwent a battery of four neuropsychological tasks of executive functions during stimulation that measured working memory, cognitive flexibility, response inhibition, and executive control. Bilateral anodal left and right DLPFC tDCS did not improve performance on working memory, cognitive flexibility, and response inhibition. Executive control was, however, partially improved for those who received active tDCS first. The upregulation of bilateral DLPFC tDCS with anodal polarity does not improve executive dysfunction in children with ADHD. The unilateral modulation of DLPFC with anodal tDCS may be more beneficial to cognitive deficits in ADHD in light of previous works targeting only left and/or right DLPFC.
Effect of Electroacupuncture at Fengchi on Facial Allodynia, Microglial Activation, and Microglia–Neuron Interaction in a Rat Model of Migraine
Authors: Pei Pei , Cui Shengwei , Zhang Shuaishuai , Hu Sheng , Wang Linpeng , Yang Wenming
Published: August 2022

The purpose of the work was to investigate whether electroacupuncture (EA) could ameliorate migraine central sensitization by modulating microglial activation and the subsequent release of inflammatory cytokines in the trigeminal nucleus caudalis (TNC) in a rat model. Establishment of a rat model of recurrent migraine was achieved through repeated dural electrical stimulation (DES). After nine sessions of acupuncture treatment at Fengchi (GB20), facial mechanical thresholds were measured by electronic von Frey measurements. Microglial activation and cytokine receptors of TNC were evaluated by immunofluorescence staining. The expression of microglial biological marker Ibal-1, proinflammatory cytokines, and cytokine receptors in the TNC were evaluated by Western blot and/or real-time polymerase chain reaction. In addition, the effects of inhibition of microglial activation on facial thresholds and neuronal activation (i.e., expression of c-Fos in the TNC) induced by DES were observed. After consecutive EA-GB20 treatments, the facial withdrawal threshold was significantly higher than in the model group at different time points (p < 0.05). The hyperreactivity of microglia induced by DES was significantly inhibited, and the expressions of Ibal-1, interleukin-1β, tumor necrosis factor-α, and their receptors in the TNC were also significantly decreased (p < 0.05). Inhibition of microglia by minocycline demonstrated an acupuncture-like role, which was manifested by ameliorated mechanical hyperalgesia and decreased neuronal expression of c-Fos, Iba-1, and inflammatory factors. EA at GB20 could ameliorate migraine facial allodynia by inhibiting microglial activation and the subsequent release of inflammatory cytokines and their receptors in the TNC.
Investigating Students’ Answering Behaviors in a Computer-Based Mathematics Algebra Test: A Cognitive-Load Perspective
Authors: Wang Jing-Fong , Wang Tzu-Hua , Huang Chao-Hsien
Published: August 2022

Computer-based testing is an emerging method to evaluate students’ mathematics learning outcomes. However, algebra problems impose a high cognitive load due to requiring multiple calculation steps, which might reduce students’ performance in computer-based testing. In order to understand students’ cognitive load when answering algebra questions in a computer-based testing environment, three perspectives, element interactivity, practice effect, and individual differences, were investigated in this study. Seven levels of algebra exam questions were created using unary and simultaneous linear equations, and the inverse efficiency scores were employed as a measure of cognitive load in the study. Forty undergraduate and graduate students were tested. There were four findings: (1) As the element interactivity of test materials increased, the cognitive load increased rapidly. (2) The high-efficiency group had a lower cognitive load than the low-efficiency group, suggesting that the high-efficiency group had an advantage in a computer-based testing environment. (3) “Practice” has a considerable effect on reducing cognitive load, particularly in level 6 and 7 test items. (4) The low-efficiency group can reduce but not eliminate the gap with the high-efficiency group; they may require additional experience in a computer-based testing environment in order to improve reducing their cognitive load.
From Trees to Skyscrapers: Holistic Review of the Advances and Limitations of Multi-Storey Timber Buildings
Authors: González-Retamal Marcelo , Forcael Eric , Saelzer-Fuica Gerardo , Vargas-Mosqueda Mauricio
Published: August 2022

Products derived from trees have been used by mankind for thousands of years, where timber has a long tradition as an ecological construction material. There is currently an increasing trend in multi-storey timber buildings, because of the projected growth in the demand for housing in urban areas between now and 2050, along with the urgent need for a more sustainable and productive construction industry. The construction of these buildings is now possible thanks to the new advances in architecture, engineering, and construction (AEC) and the new technological developments around timber construction. Its industrialization requirements imply a paradigm shift for the construction industry, which requires, among other aspects, the early and collaborative integration of stakeholders in its design and construction process. According to this, the objective of this review article is to determine the main advances and limitations related to the design and construction of multi-storey timber buildings, categorizing them in aspects such as sustainability, engineering and construction sciences, and collaborative design. The methodology of this article was based on the review of 266 articles published in Web of Science (WoS), as indexed scientific journals, between 2017 and mid-2022, performing a comparative and cooccurrence analysis of the contents. The results evidenced that 73% of the articles showed advances and limitations corresponding to the engineering and construction sciences category, 23% to sustainability, and the remaining 4% to collaborative design. The main advances in the development of multi-storey timber buildings are related to seismic analysis, connections design, fire performance, and fire design. While the main limitations are related to social sustainability, the results are not conclusive due to the low number of publications that support them.