bluuebunny/crossref_metadata_2025 huggingface dataset:
Stress-Induced Premature Senescence Related to Oxidative Stress in the Developmental Programming of Nonalcoholic Fatty Liver Disease in a Rat Model of Intrauterine Growth Restriction
Authors: Keshavjee Basile , Lambelet Valentine , Coppola Hanna , Viertl David , Prior John O. , Kappeler Laurent , Armengaud Jean-Baptiste , Chouraqui Jean-Pierre , Chehade Hassib , Vanderriele Paul-Emmanuel , Allouche Manon , Balsiger Anne , Sarre Alexandre , Peyter Anne-Christine , Simeoni Umberto , Yzydorczyk Catherine
Published: August 2022

Metabolic syndrome (MetS) refers to cardiometabolic risk factors, such as visceral obesity, dyslipidemia, hyperglycemia/insulin resistance, arterial hypertension and non-alcoholic fatty liver disease (NAFLD). Individuals born after intrauterine growth restriction (IUGR) are particularly at risk of developing metabolic/hepatic disorders later in life. Oxidative stress and cellular senescence have been associated with MetS and are observed in infants born following IUGR. However, whether these mechanisms could be particularly associated with the development of NAFLD in these individuals is still unknown. IUGR was induced in rats by a maternal low-protein diet during gestation versus. a control (CTRL) diet. In six-month-old offspring, we observed an increased visceral fat mass, glucose intolerance, and hepatic alterations (increased transaminase levels, triglyceride and neutral lipid deposit) in male rats with induced IUGR compared with the CTRL males; no differences were found in females. In IUGR male livers, we identified some markers of stress-induced premature senescence (SIPS) (lipofuscin deposit, increased protein expression of p21WAF, p16INK4a and Acp53, but decreased pRb/Rb ratio, foxo-1 and sirtuin-1 protein and mRNA expression) associated with oxidative stress (higher superoxide anion levels, DNA damages, decreased Cu/Zn SOD, increased catalase protein expression, increased nfe2 and decreased keap1 mRNA expression). Impaired lipogenesis pathways (decreased pAMPK/AMPK ratio, increased pAKT/AKT ratio, SREBP1 and PPARγ protein expression) were also observed in IUGR male livers. At birth, no differences were observed in liver histology, markers of SIPS and oxidative stress between CTRL and IUGR males. These data demonstrate that the livers of IUGR males at adulthood display SIPS and impaired liver structure and function related to oxidative stress and allow the identification of specific therapeutic strategies to limit or prevent adverse consequences of IUGR, particularly metabolic and hepatic disorders.
Targeting Redox Regulation as a Therapeutic Opportunity against Acute Leukemia: Pro-Oxidant Strategy or Antioxidant Approach?
Authors: Allegra Alessandro , Tonacci Alessandro , Giordano Laura , Musolino Caterina , Gangemi Sebastiano
Published: August 2022

Redox adaptation is essential for human health, as the physiological quantities of non-radical reactive oxygen species operate as the main second messengers to regulate normal redox reactions by controlling several sensors. An abnormal increase reactive oxygen species, called oxidative stress, induces biological injury. For this reason, variations in oxidative stress continue to receive consideration as a possible approach to treat leukemic diseases. However, the intricacy of redox reactions and their effects might be a relevant obstacle; consequently, and alongside approaches aimed at increasing oxidative stress in neoplastic cells, antioxidant strategies have also been suggested for the same purpose. The present review focuses on the molecular processes of anomalous oxidative stress in acute myeloid and acute lymphoblastic leukemias as well as on the oxidative stress-determined pathways implicated in leukemogenic development. Furthermore, we review the effect of chemotherapies on oxidative stress and the possibility that their pharmacological effects might be increased by modifying the intracellular redox equilibrium through a pro-oxidant approach or an antioxidant strategy. Finally, we evaluated the prospect of varying oxidative stress as an efficacious modality to destroy chemoresistant cells using new methodologies. Altering redox conditions may be advantageous for inhibiting genomic variability and the eradication of leukemic clones will promote the treatment of leukemic disease.
Olanzapine Ameliorates Ischemic Stroke-like Pathology in Gerbils and H2O2-Induced Neurotoxicity in SH-SY5Y Cells via Inhibiting the MAPK Signaling Pathway
Authors: Islam Md Sadikul , Shin Ha-Young , Yoo Yeo-Jin , Kim Ryunhee , Jang Young-Jin , Akanda Md Rashedunnabi , Tae Hyun-Jin , Kim In-Shik , Ahn Dongchoon , Park Byung-Yong
Published: August 2022

Olanzapine (OLNZ) is used to treat psychotic disorders. To look into the neurological basis of this phenomenon, we investigated the neuroprotective effects of OLNZ in gerbils and SH-SY5Y cells. Gerbils were subjected to transient global cerebral ischemia (TGCI) by blocking both common carotid arteries, and OLNZ (10 mg/kg) was injected intraperitoneally. Hydrogen peroxide (H2O2) was used to induce oxidative-stress-mediated damage in the SH-SY5Y cells. The results indicated that OLNZ administration markedly reduced neuron damage and glial cell triggering within CA1 zone of the hippocampus. We used RNA sequencing to assess the numbers of up-and downregulated genes involved in TGCI. We found that OLNZ treatment downregulated the expression of complement-component-related genes and the expression of mitogen-activated protein kinases (MAPKs) in the hippocampus. In cells, OLNZ co-treatment significantly improved cell viability and reduced lactate dehydrogenase (LDH), and reactive oxygen species (ROS) generation. Expression of antioxidant superoxide dismutase-1,2 enzymes (SOD-1, SOD-2) was also intensely upregulated by OLNZ, while the expression of MAPKs and NF-κB were reduced. Co-incubation with OLNZ also regulated apoptosis-related proteins Bax/Bcl-2 expression. Finally, the results demonstrated that treatment with OLNZ showed neuroprotective effects and that the MAPK pathway could involve in the protective effects.
Blast Hole Pressure Measurement and a Full-Scale Blasting Experiment in Hard Rock Quarry Mine Using Shock-Reactive Stemming Materials
Authors: Ko Younghun , Shin Chanhwi , Jeong Youngjun , Cho Sangho
Published: August 2022

By increasing the effectiveness of the energy generated by the explosive charge inserted into a blast hole, stemming increases rock fragmentation. Missing or improper stemming, which can lead to the detonation gas escaping from the blast hole in advance, results not only in the waste of explosive energy and poor fragmentation but also in environmental problems, such as ground vibration, noise, flying rocks, back breaks, and air blasts. In this study, a stemming material based on a shear thickening fluid (STF) that reacts to dynamic pressure was developed. Two blasting experiments were conducted to verify the performance of the STF-based stemming material. In the first experiment, the pressure inside the blast hole was directly measured based on the application of the stemming material. In the second experiment, full-scale bench blasting was performed, and the blasting results of sand stemming and the STF-based stemming cases were compared. The measurement results of the pressure in the blast hole showed that when the STF-based stemming material was applied, the pressure at the top of the blast hole was lower than in the sand stemming case, and the stemming ejection was also lower. Full-scale bench blasting was conducted to compare the two types of stemming materials by evaluating the size of the rock fragments using image processing. The results of the two blasting experiments helped to verify that the blockage performance of the STF-based stemming material in the blast hole was superior to that of the sand stemming material.
New Analytical Free Vibration Solutions of Thin Plates Using the Fourier Series Method
Authors: Leng Bing , Ullah Salamat , Yu Tianlai , Li Kexin
Published: August 2022

This article aims at analytically solving the free vibration problem of rectangular thin plates with one corner free and its opposite two adjacent edges rotationally-restrained, which is difficult to handle by conventional semi-inverse approaches such as the Levy solution and Naiver solution, etc. Based on the classical Fourier series theory, this work presents a first endeavor to treat the two-dimensional half-sinusoidal Fourier series, which is quite similar to the Navier’s form solution, as the solution form of plate deflection. By utilizing the orthogonality of the present trial function and the Stoke’s transformation technique, the present solution procedure converts the complicated plate problem into solving sets of linear algebra equations, which heavily decreases the difficulties. Therefore, the present approach enables one to solve the title problem in a unified, simple and straightforward way, which is very easily implemented by researchers. Another advantage of the present method over other analytical approaches is that it has general applicability to various boundary conditions through utilizing different types of Fourier series and it can be extended for further dynamic/static analysis of plates under different shear deformation theories. Moreover, without any extra derivation processes, new, precise analytical free vibration solutions for plates under three non-Levy-type boundary conditions are also obtained by choosing different rotating fixed coefficients. Consequently, we present more than 400 comprehensive free vibration results for plates with classical/non-classical boundaries, all the present results are confirmed by FEM/analytical solutions and can be used as benchmark data for further research.
Computational Linguistics with Deep-Learning-Based Intent Detection for Natural Language Understanding
Authors: Alshahrani Hala J. , Tarmissi Khaled , Alshahrani Hussain , Ahmed Elfaki Mohamed , Yafoz Ayman , Alsini Raed , Alghushairy Omar , Ahmed Hamza Manar
Published: August 2022

Computational linguistics explores how human language is interpreted automatically and then processed. Research in this area takes the logical and mathematical features of natural language and advances methods and statistical procedures for automated language processing. Slot filling and intent detection are significant modules in task-based dialogue systems. Intent detection is a critical task in any natural language understanding (NLU) system and constitutes the base of a task-based dialogue system. In order to build high-quality, real-time conversational solutions for edge gadgets, there is a demand for deploying intent-detection methods on devices. This mandates an accurate, lightweight, and fast method that effectively operates in a resource-limited environment. Earlier works have explored the usage of several machine-learning (ML) techniques for detecting intent in user queries. In this article, we propose Computational Linguistics with Deep-Learning-Based Intent Detection and Classification (CL-DLBIDC) for natural language understanding. The presented CL-DLBIDC technique receives word embedding as input and learned meaningful features to determine the probable intention of the user query. In addition, the presented CL-DLBIDC technique uses the GloVe approach. In addition, the CL-DLBIDC technique makes use of the deep learning modified neural network (DLMNN) model for intent detection and classification. For the hyperparameter tuning process, the mayfly optimization (MFO) algorithm was used in this study. The experimental analysis of the CL-DLBIDC method took place under a set of simulations, and the results were scrutinized for distinct aspects. The simulation outcomes demonstrate the significant performance of the CL-DLBIDC algorithm over other DL models.
Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
Authors: Cespedes Adolfo Javier Jara , Pangestu Bramandika Holy Bagas , Hanazawa Akitoshi , Cho Mengu
Published: August 2022

CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s experience. Therefore, telemetry data analysis is less reliable, considering the data’s complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions.
Absolute Distance Measurement Based on Self-Mixing Interferometry Using Compressed Sensing
Authors: Li Li , Zhang Yue , Zhu Ye , Dai Ya , Zhang Xuan , Liang Xuwen
Published: August 2022

An absolute distance measurement sensor based on self-mixing interferometry (SMI) is suitable for application in aerospace due to its small size and light weight. However, an SMI signal with a high sampling rate places a burden on sampling devices and other onboard sources. SMI distance measurement using compressed sensing (CS) is proposed in this work to relieve this burden. The SMI signal was sampled via a measurement matrix at a sampling rate lower than Nyquist’s law and then recovered by the greedy pursuit algorithm. The recovery algorithm was improved to increase its robustness and iteration speed. On a distance measuring system with a measurement error of 60 µm, the difference between raw data with 1800 points and CS recovered data with 300 points was within 0.15 µm, demonstrating the feasibility of SMI distance measurement using CS.
Indoor Air Quality in the Uffizi Gallery of Florence: Sampling, Assessment and Improvement Strategies
Authors: Sciurpi Fabio , Carletti Cristina , Cellai Gianfranco , Piselli Cristina
Published: August 2022

The assessment of indoor air quality (IAQ) in museums is a complex issue. In this study, a comprehensive investigation methodology was defined and applied to a museum to be validated. This methodology includes the analysis of exposed objects, the optimal conditions for conservation, the building features and the HVAC systems, and the indoor thermo-hygrometric and air quality conditions. In 2019, a survey in the Uffizi Gallery of Florence, one of the most important museums in the world, was carried out to assess the IAQ conditions in the museum, and the workers and visitors’ well-being, by focusing on some representative rooms (nine) of the museum complex in terms of visitor turnout and HVAC systems, including rooms closed to the public. Since IAQ is related to the possible presence and concentration of chemical and biological pollutants, these indicators, as well as thermo-hygrometric parameters, were monitored. The monitoring results were analyzed, evaluated, and compared with those suggested by the literature, guidelines and legislative documents dealing with IAQ in museums. Monitoring approaches for deepening investigations, as well as guidelines aimed at improving IAQ in the Uffizi Gallery and similar buildings are proposed.
A Comparison of Pooling Methods for Convolutional Neural Networks
Authors: Zafar Afia , Aamir Muhammad , Mohd Nawi Nazri , Arshad Ali , Riaz Saman , Alruban Abdulrahman , Dutta Ashit Kumar , Almotairi Sultan
Published: August 2022

One of the most promising techniques used in various sciences is deep neural networks (DNNs). A special type of DNN called a convolutional neural network (CNN) consists of several convolutional layers, each preceded by an activation function and a pooling layer. The feature map of the previous layer is sampled by the pooling layer (that seems to be an important layer) to create a new feature map with condensed resolution. This layer significantly reduces the spatial dimension of the input. It always accomplished two main goals. As a first step, it reduces the number of parameters or weights to minimize computational costs. The second step is to prevent the overfitting of the network. In addition, pooling techniques can significantly reduce model training time and computational costs. This paper provides a critical understanding of traditional and modern pooling techniques and highlights the strengths and weaknesses for readers. Moreover, the performance of pooling techniques on different datasets is qualitatively evaluated and reviewed. This study is expected to contribute to a comprehensive understanding of the importance of CNNs and pooling techniques in computer vision challenges.
Adjustable Capacity Evaluation Method Based on Step-by-Step Power Mapping of Offshore Wind Farms
Authors: Zhao Jingtao , Lv Zhiyong , Dong Xiaofeng , Zheng Shu , Zhu Junpeng , Wu Zhi
Published: August 2022

Offshore wind power has developed rapidly in recent years, but its scale still lags far behind onshore wind power. Offshore wind power still has great development potential. One of the key factors restricting the development of offshore wind power is the unsatisfactory control effect of offshore wind farms, and the reason is that the adjustable capacity of the wind farm cannot be obtained accurately and quickly. Aims to meet the high precision requirements for adjustable capacity evaluation of offshore wind farms, this paper establishes a step-by-step power mapping framework based on the division of power transmission processes in offshore wind farms, considering the loss of each transmission process in detail. By establishing a step-by-step mapping from the wind turbine power to the injected power at the grid connection point of the offshore wind farm, the adjustable capacity of the offshore wind farm can be estimated based on the maximum theoretical power of the wind turbines. The performance of proposed method has been demonstrated in a real offshore wind farm.
sEMG-Upper Limb Interaction Force Estimation Framework Based on Residual Network and Bidirectional Long Short-Term Memory Network
Authors: Lu Wei , Gao Lifu , Cao Huibin , Li Zebin
Published: August 2022

It is of great significance to estimate the interaction force of upper limbs accurately for improving the control performance of human–computer interaction. However, due to the randomness of the input biological signals and the influence of environmental interference, the interaction force is difficult to estimate using the current methods. Therefore, based on the advantages of the Residual Network (ResNet) and Bidirectional Long Short-Term Memory Network (BiLSTM) model, this paper proposes an end-to-end regression model that integrates ResNet and BiLSTM with an attention mechanism. This model is more suitable for time series sEMG signals. Moreover, it improves the feature extraction ability of the signal and improves the accuracy of interaction force estimation. Experimental results show that this method can automatically extract effective features without professional knowledge. In addition, our method is superior to existing methods in estimation accuracy and generalization ability.
Time-Domain Electromagnetics as a Geophysical Tool in Hydrogeological Exploitation Projects in Mesozoic Formations
Authors: Carrasco-García Javier , Porras-Sanchiz Daniel , Carrasco-García Pedro , Herrero-Pacheco José Luis , Martín-Nieto Ignacio , Benito-Herrero Juan Manuel , Huerta-Hurtado Pedro
Published: August 2022

The need to use groundwater resources for agricultural and livestock farms is a constantly growing issue, as these resources condition the socio-economic development of many areas with limited climatic regimes and surface water resources. Decisions on the location and characteristics of groundwater extraction works are based on hydrogeological studies, in which geophysical techniques are a very useful tool. In our study, the Time-Domain Electromagnetics technique (TDEM) was used, which significantly improves the precision and safety when planning to drill for water extraction in Mesozoic formations thanks to the significant resistivity contrast among its different hydrogeological units. The performance of a research campaign using TDEM allows for obtaining 2D geoelectrical profiles which define the hydrogeological structure of the study area and the materials of which it is composed, enabling the selection of a site where drilling can be carried out with the best prospects of success. This article shows the usefulness of this study methodology, applied to Mesozoic formations in a specific area located in the municipality of Liceras (Soria, Spain).
Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
Authors: Liu Jui-Hung , Corbita Nelson T. , Lee Rong-Mao , Wang Chun-Chieh
Published: August 2022

Wind energy is becoming a common source of renewable energy in the world. Wind turbines are increasing in number, both for onshore and offshore applications. One challenge with wind turbines is in detecting anomalies that cause their breakdown. Due to the complex nature of the wind turbine assembly, it is quite an extensive process to detect causes of malfunctions in the system. This study uses the Mahalanobis distance (MD) to detect anomalies in wind turbine operation, using SCADA alarm data as a comparison. Different predictive models were generated as the bases for analyses in MD computations. Using the SCADA alarm data as a reference, trend patterns that deviated from the threshold value were compared. Results showed that the MD could be used to detect anomalies within a group of data sets, with behaviors learned based on the model used. A large portion of those data sets deviated from the threshold level, corresponding to serious alarms in the SCADA data. We concluded that the MD can detect anomalies in different wind turbine components, based on this study. MD analysis of models can be used in conditions monitoring systems of wind turbines.
Experimental Study on the Heat Transfer Performance of Various Magnet Arrangements in a Closed Space Filled with Ferrofluid
Authors: Kang Hyun-Su , Choi Yun-Seok , Seo Hyeon-Seok , Kim Youn-Jea
Published: August 2022

In this study, experiments, simulations, and optimization were performed to evaluate heat transfer performance of ferrofluids. Ferrofluids are colloidal suspensions containing magnetic-nano particles with a diameter of 5 to 15 nm in a base fluid such as oil or water. Recently, as many devices are miniaturized, the design of heat dissipation systems are being diversified to consider cost and safety, and it is becoming important to separate an ancillary device for cooling from main unit. In ferrofluids, the behavior and vortex of magnetic-nano particles are actively generated by an external magnetic field, and the cooling system can be designed in a simplified manner by using this characteristic. The main design parameter is the arrangement of permanent magnets, and the output variable is the temperature inside the magnetic nanofluid. The permanent magnet can be moved up and down, and the temperature inside the magnetic nanofluid was measured at various locations. A predictive model was created using a design of experiments (DOE) and response surface method (RSM) using selected design and temperature variables. Based on the generated regression model, an optimization was applied to find a permanent magnet arrangement that maximizes heat transfer performance. Through the optimization technique used in this study, economic efficiency in terms of time and cost was obtained by reducing the number of experiments.
Walking in the Absence of Anterior Cruciate Ligament: The Role of the Quadriceps and Hamstrings
Authors: Donno Lucia , Sansone Valerio , Galluzzo Alessandro , Frigo Carlo Albino
Published: August 2022

The actual need for surgical reconstruction of an injured anterior cruciate ligament (ACL) is still a matter of debate. The aim of this study was to quantify and analyze the knee kinematics and the ligament force redistribution resulting from the ACL sacrifice and the consequent effects of changing the quadriceps and hamstring activity during walking. A three-dimensional musculoskeletal model was used to simulate the behavior of the knee joint during the gait cycle, in different testing conditions: an ACL-deficient knee, an ACL-deficient knee with hamstrings inactivated and an ACL-deficient knee with quadriceps inactivated. Our results showed that in the ACL-deficient knee, the medial collateral ligament (MCL) was the main passive stabilizer of the anterior translation of the tibia. Furthermore, the compensation effects produced by the hamstrings and quadriceps in the ACL-deficient knee during walking were quantified and analyzed. In the case of ACL sacrifice, reducing the quadriceps activity would allow for relieving all ligaments in general, except for the posterior cruciate ligament, which required more consistent recruitment in the support phase. Meanwhile, hamstring activity was crucial not only to compensate for the absence of ACL in limiting the anterior tibial displacement but also to reduce the risk of MCL injury.
Electrodegradation of Acid Mixture Dye through the Employment Electrooxidation and Lemnoideae in Na2SO4 Synthetic Wastewater
Authors: Bęś Agnieszka , Sikorski Łukasz , Mikołajczyk Tomasz , Kuczyński Mateusz , Łuba Mateusz , Pierożyński Bogusław , Jasiecka-Mikołajczyk Agnieszka
Published: August 2022

In this study, we report on the effectiveness of electrochemical and biological wastewater treatment for artificially prepared industrial wastewater, comprising small amounts of technologically important dyes, namely Acid Mixture composed of Acid Violet 90 (AV90) and Acid Red 357 (AR357) in Na2SO4 (ESS—electrolyte supporting solution), as well as their impact on the environment, using Lemna minor as a bioindicator. Our study revealed that among the tested dyes, the raw ones (AM in ESS+OM) and those subjected to electrooxidation with the use of an iron anode and a copper cathode [AMFe/Cuox in ESS+OM (OECD medium is a medium recommended by the Organization for Economic Co-operation and Development for Lemna sp. Growth Inhibition Test)] were the most phytotoxic for L. minor. No phytotoxicity was detected for the tested plants in solution after electrooxidation with graphite anode and cathode (AMCox in ESS+OM). Quantitative identification of acid mixture removal was carried out by supplementary UPLC/MS-MS (Ultra-Performance Liquid Chromatography/tandem Mass Spectrometry) and UV-VIS (UltraViolet-Visible spectroscopy) instrumental analysis. The final removal after electrochemical and biological treatment of AV90 and AR357 dye components was 98 and over 99%, respectively. The results suggest that it may be a suitable replacement/addition for the generally used wastewater treatment methods.
The Effect of Aggregated Alpha Synuclein on Synaptic and Axonal Proteins in Parkinson’s Disease—A Systematic Review
Authors: Murphy Jennifer , McKernan Declan P.
Published: August 2022

α-synuclein is a core component of Lewy bodies, one of the pathological hallmarks of Parkinson’s disease. Aggregated α-synuclein can impair both synaptic functioning and axonal transport. However, understanding the pathological role that α-synuclein plays at a cellular level is complicated as existing findings are multifaceted and dependent on the mutation, the species, and the quantity of the protein that is involved. This systematic review aims to stratify the research findings to develop a more comprehensive understanding of the role of aggregated α-synuclein on synaptic and axonal proteins in Parkinson’s disease models. A literature search of the PubMed, Scopus, and Web of Science databases was conducted and a total of 39 studies were included for analysis. The review provides evidence for the dysregulation or redistribution of synaptic and axonal proteins due to α-synuclein toxicity. However, due to the high quantity of variables that were used in the research investigations, it was challenging to ascertain exactly what effect α-synuclein has on the expression of the proteins. A more standardized experimental approach regarding the variables that are employed in future studies is crucial so that existing literature can be consolidated. New research involving aggregated α-synuclein at the synapse and regarding axonal transport could be advantageous in guiding new treatment solutions.
Possible Association of Nucleobindin-1 Protein with Depressive Disorder in Patients with HIV Infection
Authors: Yang Yun , Zhang Qian , Yang Jing , Wang Yun , Zhuang Ke , Zhao Changcheng
Published: August 2022

Background: Mental disorders linked with dysfunction in the temporal cortex, such as anxiety and depression, can increase the morbidity and mortality of people living with HIV (PLWHA). Expressions of both nucleobindin 1 (NUCB1) and cannabinoid receptor 1 (CNR1) in the neurons have been found to alter in patients with depressive disorder, but whether it is involved in the development of depression in the context of HIV infection is unknown. Objectives To investigate the effects of NUCB1 on depressive disorder among PLWHA and preliminarily explore the underlying molecular mechanisms. Methods: Individuals who were newly HIV diagnosed were assessed on the Hospital Anxiety and Depression scale (HADS). Then SHIV-infected rhesus monkeys were used to investigate the possible involvement of the NUCB1 and the CNR1 protein in depression-like behavior. Results: The prevalence rate of depression among PLWHA was 27.33% (41/150). The mechanism results showing elevated NUCB1 levels in cerebrospinal fluid from HIV-infected patients suffering from depression were confirmed compared to those of HIV-infected patients. Moreover, the immunohistochemical analysis indicated the expression of NUCB1 in the temporal cortex neurons of SHIV-infected monkeys was higher than that of the healthy control. Conversely, CNR1 expression was down-regulated at protein levels. Conclusions: Depression symptoms are common among PLWHA and associate with NUCB1 expression increases, and NUCB1 may be a potential target for depression.
Examining the Longitudinal Associations between Adjustment Disorder Symptoms and Boredom during COVID-19
Authors: Bambrah Veerpal , Wyman Amanda , Friedman Eva , Eastwood John D.
Published: August 2022

The COVID-19 pandemic has led to a myriad of stressors, underscoring the relevance of adjustment disorder during these extraordinary times. Boredom—as a feeling and as a dispositional characteristic—is an equally pertinent experience during the pandemic that has been cross-sectionally linked to various mental health difficulties. The current longitudinal study expanded on this work, examining the associations between adjustment disorder symptoms and boredom (both as a feeling and as a trait) over time during the COVID-19 pandemic. Community participants completed questionnaires three times, rating their trait boredom at Time 1 and their feelings of boredom and adjustment disorder symptoms (preoccupation with a pandemic stressor and failure to adapt) over the past week at Times 1–3. Latent growth curve analyses found that an increase in feelings of boredom was significantly associated with increased preoccupation with a pandemic stressor and increased difficulties with adapting over time. Additionally, trait boredom significantly predicted changes in preoccupation and the failure to adapt, such that participants high in trait boredom increasingly struggled with these symptoms over time. Our results suggest that increased feelings of boredom and a trait disposition towards boredom can be detrimental to people’s ability to adjust over time to the stressors associated with the pandemic. Boredom, as an aversive state and as a chronic difficulty, may be important to address in treatment approaches for adjustment disorder symptoms during COVID-19.
Media Bias and Factors Affecting the Impartiality of News Agencies during COVID-19
Authors: Xu Minghua , Luo Ziling , Xu Han , Wang Bang
Published: August 2022

When COVID-19 was raging around the world, people were more fearful and anxious. In this context, the media should uphold impartiality and shoulder the responsibility of eliminating misinformation. Therefore, our research adopted sentiment analysis technologies to analyze the impartiality of news agencies and analyzed the factors that affect the impartiality of COVID-19-related articles about various countries. The SentiWordNet3.0 and bidirectional encoder representations from transformers (BERT) models were employed to analyze the articles and visualize the data. The following conclusions were redrawn in our research. During the pandemic, articles of some news agencies were not objective; the impartiality of news agencies was related to the reliability of news agencies instead of the bias of news agencies; there were obvious differences in the coverage and positivity of international news agencies to report the performance of COVID-19 prevention and control in different countries.
A Study on Multi-Objective Optimization of Large Deformable Elastic Plates
Authors: Sawada Kiichiro , Kajitani Keigo , Uno Tatsuya , Teramoto Junpei , Komatsu Shingo
Published: August 2022

Metallic yielding dampers are damaged early before the main frame during earthquakes and take over the damage of the main frame. Under the assumption of the main frame elasticity, both maximum and residual deformation are expected to be reduced. However, the main frame may be damaged, and the assumption of the main frame elasticity may not hold under major earthquakes. However, large deformable elastic plates have larger yielding deformation than standard steel core plates of the damper and can be used as braces to reduce the seismic response of buildings. This study presents a simple definition of design variables for a topology optimization problem involving large deformable elastic plates with rectangular shapes. As bi-objective functions, yielding deformation and yielding force capacity maximization were used. The optimization results were compared with the findings of a previous study. Finally, the efficacy of the braces was verified by experimental tensile tests. The obtained results are as follows. (1) A distinct trade-off relationship was obtained between tensile yielding deformation and tensile yielding load through muti-objective optimizations using the proposed formulation. (2) The Pareto fronts using the proposed formulation were almost identical to the findings of the previous study. (3) While the experimental test results of yielding tensile load are overestimated by the analysis results by the 10 mm rough grid elements, the test results almost correspond to reanalysis results with the 2.5 mm fine grid elements.
Structural Damage Identification Based on Convolutional Neural Networks and Improved Hunter–Prey Optimization Algorithm
Authors: Xiang Chunyan , Gu Jianfeng , Luo Jin , Qu Hao , Sun Chang , Jia Wenkun , Wang Feng
Published: August 2022

Accurate damage identification is of great significance to maintain timely and prevent structural failure. To accurately and quickly identify the structural damage, a novel two-stage approach based on convolutional neural networks (CNN) and an improved hunter–prey optimization algorithm (IHPO) is proposed. In the first stage, the cross-correlation-based damage localization index (CCBLI) is formulated using acceleration and is input into the CNN to locate structural damage. In the second stage, the IHPO algorithm is applied to optimize the objective function, and then the damage severity is quantified. A numerical model of the American Society of Civil Engineers (ASCE) benchmark frame structure and a test structure of a three-storey frame are adopted to verify the effectiveness of the proposed method. The results demonstrate that the proposed approach is effective in locating and quantifying structural damage precisely regardless of noise perturbations. In addition, the reliability of the proposed approach is evaluated using a comparison between it and approaches based on CNN or the IHPO algorithm alone. The comparison results indicate that in single and multiple damage events, the proposed two-stage damage identification approach outperforms the other two approaches on the accuracy, and the average consumption time is 20% less than the method using the IHPO algorithm alone. Therefore, this paper provides a guideline for the study of high-accuracy and quick damage identification using both data-based and model-based hybrid methods.
An Evaluation of the Kinetic Properties Controlling the Combined Chemical and Biological Treatment of Toxic Recalcitrant Organic Compounds from Aqueous Solution
Authors: Makgato Seshibe , Nkhalambayausi-Chirwa Evans
Published: August 2022

Due to their high toxicity, propensity for cancer, teratogenicity, mutagenicity, and genotoxicity, hazardous water-soluble phenolic compounds must be controlled immediately. In this study, a model was created to simulate the degradation of harmful recalcitrant organic compounds in a combined chemical and biological treatment system. The parameter estimations with inhibition coefficient (Haldane model) and without inhibition coefficient (Michaelis-Menten model) were assessed over a wide range of initial concentrations using the Monod-like model. The kinetic parameters were optimized using AQUASIM 2.0 software. At a 50 mg·L−1 feed concentration of 4-chlorophenol, removal efficiencies of more than 98% were attained under these circumstances. The primary kinetic parameters were identified and their values models were validated using the fitted parameter values that reached a good degree of agreement (R2 = 0.998). We may better comprehend and make use of the complex phenolic compounds’ biodegradation processes, such as progress optimization and scale-up, by understanding the mechanisms of substrate interaction and the new kinetic models that have been provided in this work.
Improved Oxygen Reduction on GC-Supported Large-Sized Pt Nanoparticles by the Addition of Pd
Authors: Golubović Jelena , Rakočević Lazar , Vasiljević Radović Dana , Štrbac Svetlana
Published: August 2022

PdPt bimetallic nanoparticles on carbon-based supports functioning as advanced electrode materials have attracted attention due to their low content of noble metals and high catalytic activity for fuel cell reactions. Glassy carbon (GC)-supported Pt and PdPt nanoparticles, as promising catalysts for the oxygen reduction reaction (ORR), were prepared by the electrochemical deposition of Pt and the subsequent spontaneous deposition of Pd. The obtained electrodes were examined using X-ray Photoelectron Spectroscopy (XPS), Atomic Force Microscopy (AFM), and electroanalytical techniques. An XPS analysis of the PdPt/GC with the highest ORR performance revealed that the stoichiometric ratio of Pd: Pt was 1:2, and that both Pt and Pd were partially oxidized. AFM images of PdPt2/GC showed the full coverage of GC with PdPt nanoparticles with sizes from 100–300 nm. The ORR activity of PdPt2/GC in an acid solution approached that of polycrystalline Pt (E1/2 = 0.825 V vs. RHE), while exceeding it in an alkaline solution (E1/2 = 0.841 V vs. RHE). The origin of the improved ORR on PdPt2/GC in an alkaline solution is ascribed to the presence of a higher amount of adsorbed OH species originating from both PtOH and PdOH that facilitated the 4e-reaction pathway.
Tetraspanin 8 Subfamily Members Regulate Substrate-Specificity of a Disintegrin and Metalloprotease 17
Authors: Müller Miryam , Saunders Claire , Senftleben Anke , Heidbuechel Johannes P. W. , Halwachs Birgit , Bolik Julia , Hedemann Nina , Röder Christian , Bauerschlag Dirk , Rose-John Stefan , Schmidt-Arras Dirk
Published: August 2022

Ectodomain shedding is an irreversible process to regulate inter- and intracellular signaling. Members of the a disintegrin and metalloprotease (ADAM) family are major mediators of ectodomain shedding. ADAM17 is involved in the processing of multiple substrates including tumor necrosis factor (TNF) α and EGF receptor ligands. Substrates of ADAM17 are selectively processed depending on stimulus and cellular context. However, it still remains largely elusive how substrate selectivity of ADAM17 is regulated. Tetraspanins (Tspan) are multi-membrane-passing proteins that are involved in the organization of plasma membrane micro-domains and diverse biological processes. Closely related members of the Tspan8 subfamily, including CD9, CD81 and Tspan8, are associated with cancer and metastasis. Here, we show that Tspan8 subfamily members use different strategies to regulate ADAM17 substrate selectivity. We demonstrate that in particular Tspan8 associates with both ADAM17 and TNF α and promotes ADAM17-mediated TNF α release through recruitment of ADAM17 into Tspan-enriched micro-domains. Yet, processing of other ADAM17 substrates is not altered by Tspan8. We, therefore, propose that Tspan8 contributes to tumorigenesis through enhanced ADAM17-mediated TNF α release and a resulting increase in tissue inflammation.
Inflammation: A Target for Treatment in Spinal Cord Injury
Authors: Freyermuth-Trujillo Ximena , Segura-Uribe Julia J. , Salgado-Ceballos Hermelinda , Orozco-Barrios Carlos E. , Coyoy-Salgado Angélica
Published: August 2022

Spinal cord injury (SCI) is a significant cause of disability, and treatment alternatives that generate beneficial outcomes and have no side effects are urgently needed. SCI may be treatable if intervention is initiated promptly. Therefore, several treatment proposals are currently being evaluated. Inflammation is part of a complex physiological response to injury or harmful stimuli induced by mechanical, chemical, or immunological agents. Neuroinflammation is one of the principal secondary changes following SCI and plays a crucial role in modulating the pathological progression of acute and chronic SCI. This review describes the main inflammatory events occurring after SCI and discusses recently proposed potential treatments and therapeutic agents that regulate inflammation after insult in animal models.
Anthropometric Indicators of the Cardiometabolic Risk, Muscle Strength, and Functional Capacity of Schoolchildren with Intellectual Disabilities during Lockdown in Chile
Authors: Farías-Valenzuela Claudio , Ferrari Gerson , Espoz-Lazo Sebastián , Ferrero-Hernández Paloma , Jofré-Saldia Emilio , Álvarez-Arangua Sebastián , Poblete-Aro Carlos , Godoy-Cumillaf Andrés , Cofre-Bolados Cristian , Valdivia-Moral Pedro
Published: August 2022

Lockdown due to the COVID-19 pandemic has negatively impacted the social, psychological, and physical well-being of the world population. In the case of people with intellectual disabilities, the impact of lockdown on their physical condition and functionality is not completely clear. This study aimed to determine the effects of COVID-19 lockdown on the anthropometric indicators of cardiometabolic risk, muscle strength, and functionality on schoolchildren with intellectual disabilities. The sample was composed of 132 students of both sexes (n = 74 pre-lockdown; n = 58 lockdown) belonging to two special education centers from the Metropolitan Region of Santiago, Chile. Our results showed significant reductions (p ≤ 0.05) in absolute and relative handgrip strength, as well as in functionality, when comparing pre-lockdown and lockdown measurements, with a greater loss in girls than boys. The design and implementation of physical exercise programs centered on strength training are necessary for the physical and functional reconditioning of this population. These programs need to be implemented in special education centers considering the general well-being, quality of life and work needs of people with intellectual disabilities.
Upscaling Gross Primary Production from Leaf to Canopy for Potato Crop (Solanum tuberosum L.)
Authors: Martínez-Maldonado Fabio Ernesto , Castaño-Marín Angela María , Góez-Vinasco Gerardo Antonio , Marin Fabio Ricardo
Published: August 2022

Estimating gross primary production (GPP) is important to understand the land–atmosphere CO2 exchange for major agroecosystems. Eddy covariance (EC) measurements provide accurate and reliable information about GPP, but flux measurements are often not available. Upscaling strategies gain importance as an alternative to the limitations of the use of the EC. Although the potato provides an important agroecosystem for worldwide carbon balance, there are currently no studies on potato GPP upscaling processes. This study reports two GPP scaling-up approaches from the detailed leaf-level characterization of gas exchange of potatoes. Multilayer and big leaf approaches were applied for extrapolating chamber and biometric measurements from leaf to canopy. Measurements of leaf area index and photosynthesis were performed from planting to the end of the canopy life cycle using an LP-80 ceptometer and an IRGA Li-Cor 6800, respectively. The results were compared to concurrent measurements of surface–atmosphere GPP from the EC measurements. Big-leaf models were able to simulate the general trend of GPP during the growth cycle, but they overestimated the GPP during the maximum LAI phase. Multilayer models correctly reproduced the behavior of potato GPP and closely predicted both: the daily magnitude and half-hourly variation in GPP when compared to EC measurements. Upscaling is a reliable alternative, but a good treatment of LAI and the photosynthetic light-response curves are decisive factors to achieve better GPP estimates. The results improved the knowledge of the biophysical control in the carbon fluxes of the potato crop.
Severe Stenosis of Mitral Bioprosthetic Valve Thrombosis in a Patient with HCV-Related Cirrhosis and Duodenal Variceal Bleeding: The Deadly Triad
Authors: Cocchia Rosangela , Chianese Salvatore , Lombardi Giovanni , Romano Luigia , Capone Valentina , Amitrano Lucio , Bennato Raffaele , Ranieri Brigida , Russo Giuseppe , Mauro Ciro , Bossone Eduardo
Published: August 2022

Bioprosthetic valve thrombosis (BPVT) is considered a relatively rare but life-threatening clinical entity. Thus, there is the need of high clinical suspicion in order to make a timely diagnosis and related appropriate therapeutic interventions. In this regard, the management of BPVT is high risk, whatever the option taken (surgery and/or systemic fibrinolysis). The presence of severe comorbidities—as decompensated cirrhosis—further complicates the clinical decision-making process, calling for a patient-tailored integrated multidisciplinary approach. We report a challenging case of a 45-year-old patient with mitral bioprosthetic valve thrombosis and hepatitis C virus (HCV)-related cirrhosis complicated by active duodenal variceal bleeding.