bluuebunny/crossref_metadata_2025 huggingface dataset:
Flow Governed by Generalised Brinkman’s Equation through an Inclined Porous Channel
Authors: Roach Dale C. , Hamdan Mohammad H.
Published: October 2023

The unidirectional flow of a fluid with pressure-dependent viscosity through a porous structure is considered when the viscosity–pressure relationship is an exponential function of a pressure power function in order to investigate effects of the viscosity–pressure relation on the flow characteristics. The flow is governed by the generalized Brinkman’s equation with constant permeability, and a model flow domain of flow down an inclined porous channel is chosen for the sake of studying flow behaviour. Although the current work considers flow in a constant permeability porous structure, it does represent the first step in studying the more general flow through a variable permeability porous channel. The arising governing equations are solved numerically using MATLAB (version R2022a) and the flow is simulated to illustrate the effects of fluid properties, as well as flow and medium parameters, on the velocity profiles and shear stress. The results obtained should represent a baseline and a benchmark with which future experimental and theoretical work can be compared.
Special Issue on Modern Biomaterials: Latest Advances and Prospects
Authors: Baltatu Madalina Simona , Vizureanu Petrica , Sandu Andrei Victor
Published: October 2023

Biomaterials have quickly progressed from being passive objects created for tissue replacement to dynamic systems that can aid in tissue regeneration, transport medications, and even direct cellular behavior [...]
A Multi-Fidelity Successive Response Surface Method for Crashworthiness Optimization Problems
Authors: Lualdi Pietro , Sturm Ralf , Siefkes Tjark
Published: October 2023

Due to the high computational burden and the high non-linearity of the responses, crashworthiness optimizations are notoriously hard-to-solve challenges. Among various approaches, methods like the Successive Response Surface Method (SRSM) have stood out for their efficiency in enhancing baseline designs within a few iterations. However, these methods have limitations that restrict their application. Their minimum iterative resampling required is often computationally prohibitive. Furthermore, surrogate models are conventionally constructed using Polynomial Response Surface (PRS), a method that is poorly versatile, prone to overfitting, and incapable of quantifying uncertainty. Furthermore, the lack of continuity between successive response surfaces results in suboptimal predictions. This paper introduces the Multi-Fidelity Successive Response Surface (MF-SRS), a Gaussian process-based method, which leverages a non-linear multi-fidelity approach for more accurate and efficient predictions compared to SRSM. After initial testing on synthetic problems, this method is applied to a real-world crashworthiness task: optimizing a bumper cross member and crash box system. The results, benchmarked against SRSM and the Gaussian Process Successive Response Surface (GP-SRS)—a single-fidelity Gaussian process-driven extension of SRSM—show that MF-SRS offers distinct advantages. Specifically, it improves upon the specific energy absorbed optimum value achieved by SRSM by 14%, revealing its potential for future applications.
Prediction of Wind Power with Machine Learning Models
Authors: Karaman Ömer Ali
Published: October 2023

Wind power is a vital power grid component, and wind power forecasting represents a challenging task. In this study, a series of multiobjective predictive models were created utilising a range of cutting-edge machine learning (ML) methodologies, namely, artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks, and long short-term memory (LSTM) networks. In this study, two independent data sets were combined and used to predict wind power. The first data set contained internal values such as wind speed (m/s), wind direction (°), theoretical power (kW), and active power (kW). The second data set was external values that contained the meteorological data set, which can affect the wind power forecast. The k-nearest neighbours (kNN) algorithm completed the missing data in the data set. The results showed that the LSTM, RNN, CNN, and ANN algorithms were powerful in forecasting wind power. Furthermore, the performance of these models was evaluated by incorporating statistical indicators of performance deviation to demonstrate the efficacy of the employed methodology effectively. Moreover, the performance of these models was evaluated by incorporating statistical indicators of performance deviation, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) metrics to effectively demonstrate the efficacy of the employed methodology. When the metrics are examined, it can be said that ANN, RNN, CNN, and LSTM methods effectively forecast wind power. However, it can be said that the LSTM model is more successful in estimating the wind power with an R2 value of 0.9574, MAE of 0.0209, MSE of 0.0038, and RMSE of 0.0614.
Evaluation of Structural Stresses of Mountain-Embedded Railway Systems
Authors: Lee Hochan , Han Sang-yun , Lim Jeonghyeon , Eum Kiyoung , Kim Seungjun , Kang Young Jong
Published: October 2023

Mountain railways are constructed on terrains with steep gradients and operate under unique conditions as they climb slopes at low speeds using rack rails. Recently, mountain railway systems in the form of embedded rail systems (ERSs) have been developed to minimize environmental damage caused by the construction of new roads. However, the use of ERS in mountain railway tracks is still limited, and research on their safety and behavior is lacking. Therefore, this study was conducted to evaluate the safety profile and behavioral responses of mountain-ERS under a steep gradient condition of 180‰. The effects of using high-strength concrete materials for precast concrete panels were investigated in this study, and the safety profile and behavioral response of mountain-ERS based on changes in the gradient and after adjusting the thickness of the subgrade of mountain-ERS was assessed. Under load conditions, the maximum tensile stress in the concrete elements did not exceed the tensile strength of the concrete. In the structural behavioral analysis, the patterns of stress variation were analyzed by applying stress to the concrete elements. The safety assessment and behavioral analysis results obtained in this study are considered valuable foundational research data for analytical studies on mountain-ERSs.
Effective Connectivity Changes among Brain Hierarchical Architecture of Pre-Supplementary Motor Area in Taxi Drivers
Authors: Wei Huilin , Wang Lubin , Peng Limin , Li Chenming , Ma Tian , Hu Dewen
Published: October 2023

Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain’s intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain’s intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM.
A Time-Optimal Continuous Jerk Trajectory Planning Algorithm for Manipulators
Authors: Zhou Yaosheng , Han Guirong , Wei Ziang , Huang Zixin , Chen Xubing
Published: October 2023

In this paper, we propose a new optimal trajectory planning method for the manipulator to optimize its operating efficiency and ensure the smoothness of the motion process. The position sequences in joint space corresponding to a specified trajectory in task space are obtained by using the inverse kinematic algorithm, and the seventh-degree B-spline curve interpolation method is used to construct the joint trajectory with controllable start–stop kinematic parameters, and continuous velocity, acceleration and jerk. The kinematic constraints of the manipulator are transformed into the control point constraints of the B-spline curve, the sequential quadratic programming method is used to solve the optimal motion time node, and then the time-optimal continuous jerk trajectory satisfying the nonlinear kinematic constraints is planned. Simulation results show that the proposed trajectory planning method provides the ideal trajectory for the joint controller, ensuring the manipulator can smoothly track any specified trajectory in the task space in the shortest time.
Dynamic Analysis of a Timoshenko–Ehrenfest Single-Walled Carbon Nanotube in the Presence of Surface Effects: The Truncated Theory
Authors: De Rosa Maria Anna , Elishakoff Isaac , Onorato Antonella , Lippiello Maria
Published: October 2023

The main objective of this paper is to study the free vibration of a Timoshenko–Ehrenfest single-walled carbon nanotube based on the nonlocal theory and taking surface effects into account. To model these effects on frequency response of nanotubes, we use Eringen’s nonlocal elastic theory and surface elastic theory proposed by Gurtin and Murdoch to modify the governing equation. A modified version of Timoshenko nonlocal elasticity theory—known as the nonlocal truncated Timoshenko beam theory—is put forth to investigate the free vibration behavior of single-walled carbon nanotubes (SWCNTs). Using Hamilton’s principle, the governing equations and the corresponding boundary conditions are derived. Finally, to check the accuracy and validity of the proposed method, some numerical examples are carried out. The impacts of the nonlocal coefficient, surface effects, and nanotube length on the free vibration of single-walled carbon nanotubes (SWCNTs) are evaluated, and the results are compared with those found in the literature. The findings indicate that the length of the nanotube, the nonlocal parameter, and the surface effect all play important roles and should not be disregarded in the vibrational analysis of nanotubes. Finally, the results show how effective and successful the current formulation is at explaining the behavior of nanobeams.
Colonial Carpenters: Construction, Race, and Agency in the Viceroyalty of Peru during the 16th and 17th Centuries
Authors: Mamani Fuentes Francisco
Published: October 2023

This article examines colonial documents to shed light on the presence of non-white carpenters in the carpentry trade during the first two centuries of Spanish colonial rule in Peru. It first offers a general definition of carpentry work during the sixteenth and seventeenth centuries and then explores the specific environments in which Indigenous, black, and mixed-race carpenters carried out their activities. Through this analysis, it becomes evident that the agency of non-white individuals and groups in the carpentry trade was shaped by the diverse labor systems that predominated in colonial society.
Can Climate Change Increase the Spread of Animal Diseases? Evidence from 278 Villages in China
Authors: Chang Qian , Zhou Hui , Khan Nawab , Ma Jiliang
Published: October 2023

Several countries are currently evaluating the potential health impacts of climate change (CC), particularly in relation to the complex connections between CC-induced weather fluctuations. China, heavily affected by CC, provides clear evidence of its effects. Previous research in animal sciences indicates that factors like temperature, humidity, precipitation, and wind speed can affect animal epidemics. In China, a major global hub for animal husbandry, these factors pose significant challenges, warranting further investigation into their quantitative relationship with disease outbreaks. This study investigates the influence of these climatic conditions on epizootic diseases in China. In the current study, using data from 278 village-level surveys and daily meteorological data spanning 2012 to 2018, we used a fixed-effect model for analysis. The findings reveal that increasing temperatures and wind speeds exacerbate disease development, while the precipitation anomaly index negatively impacts animal epidemics, with humidity showing minimal influence. Addressing CC’s potential impact on animal disease, governments, organizations, and farmers need to pay more attention to the impacts of climate change on animal diseases and work together to better cope with the impacts through policies, measures, and research.
Cerebrospinal Fluid and Serum Biomarker Insights in Aneurysmal Subarachnoid Haemorrhage: Navigating the Brain–Heart Interrelationship for Improved Patient Outcomes
Authors: Burzyńska Małgorzata , Uryga Agnieszka , Załuski Rafał , Goździk Anna , Adamik Barbara , Robba Chiara , Goździk Waldemar
Published: October 2023

The pathophysiological mechanisms underlying severe cardiac dysfunction after aneurysmal subarachnoid haemorrhage (aSAH) remain poorly understood. In the present study, we focused on two categories of contributing factors describing the brain–heart relationship. The first group includes brain-specific cerebrospinal fluid (CSF) and serum biomarkers, as well as cardiac-specific biomarkers. The secondary category encompasses parameters associated with cerebral autoregulation and the autonomic nervous system. A group of 15 aSAH patients were included in the analysis. Severe cardiac complications were diagnosed in seven (47%) of patients. In the whole population, a significant correlation was observed between CSF S100 calcium-binding protein B (S100B) and brain natriuretic peptide (BNP) (rS = 0.62; p = 0.040). Additionally, we identified a significant correlation between CSF neuron-specific enolase (NSE) with cardiac troponin I (rS = 0.57; p = 0.025) and BNP (rS = 0.66; p = 0.029), as well as between CSF tau protein and BNP (rS = 0.78; p = 0.039). Patients experiencing severe cardiac complications exhibited notably higher levels of serum tau protein at day 1 (0.21 ± 0.23 [ng/mL]) compared to those without severe cardiac complications (0.03 ± 0.04 [ng/mL]); p = 0.009. Impaired cerebral autoregulation was noted in patients both with and without severe cardiac complications. Elevated serum NSE at day 1 was related to impaired cerebral autoregulation (rS = 0.90; p = 0.037). On the first day, a substantial, reciprocal correlation between heart rate variability low-to-high frequency ratio (HRV LF/HF) and both GFAP (rS = −0.83; p = 0.004) and S100B (rS = −0.83; p = 0.004) was observed. Cardiac and brain-specific biomarkers hold the potential to assist clinicians in providing timely insights into cardiac complications, and therefore they contribute to the prognosis of outcomes.
Development of Anti-OSCAR Antibodies for the Treatment of Osteoarthritis
Authors: Kim Gyeong Min , Park Doo Ri , Nguyen Thi Thu Ha , Kim Jiseon , Kim Jihee , Sohn Myung-Ho , Lee Won-Kyu , Lee Soo Young , Shim Hyunbo
Published: October 2023

Osteoarthritis (OA) is the most common joint disease that causes local inflammation and pain, significantly reducing the quality of life and normal social activities of patients. Currently, there are no disease-modifying OA drugs (DMOADs) available, and treatment relies on pain relief agents or arthroplasty. To address this significant unmet medical need, we aimed to develop monoclonal antibodies that can block the osteoclast-associated receptor (OSCAR). Our recent study has revealed the importance of OSCAR in OA pathogenesis as a novel catabolic regulator that induces chondrocyte apoptosis and accelerates articular cartilage destruction. It was also shown that blocking OSCAR with a soluble OSCAR decoy receptor ameliorated OA in animal models. In this study, OSCAR-neutralizing monoclonal antibodies were isolated and optimized by phage display. These antibodies bind to and directly neutralize OSCAR, unlike the decoy receptor, which binds to the ubiquitously expressed collagen and may result in reduced efficacy or deleterious off-target effects. The DMOAD potential of the anti-OSCAR antibodies was assessed with in vitro cell-based assays and an in vivo OA model. The results demonstrated that the anti-OSCAR antibodies significantly reduced cartilage destruction and other OA signs, such as subchondral bone plate sclerosis and loss of hyaline cartilage. Hence, blocking OSCAR with a monoclonal antibody could be a promising treatment strategy for OA.
Overview of Ursolic Acid Potential for the Treatment of Metabolic Disorders, Autoimmune Diseases, and Cancers via Nuclear Receptor Pathways
Authors: Kadasah Sultan F. , Radwan Mohamed O.
Published: October 2023

Nuclear receptors (NRs) form a family of druggable transcription factors that are regulated by ligand binding to orchestrate multifaceted physiological functions, including reproduction, immunity, metabolism, and growth. NRs represent attractive and valid targets for the management and treatment of a vast array of ailments. Pentacyclic triterpenes (PTs) are ubiquitously distributed natural products in medicinal and aromatic plants, of which ursolic acid (UA) is an extensively studied member, due to its diverse bio-pertinent activities against different cancers, inflammation, aging, obesity, diabetes, dyslipidemia, and liver injury. In fact, PTs share a common lipophilic structure that resembles NRs’ endogenous ligands. Herein, we present a review of the literature on UA’s effect on NRs, showcasing the resulting health benefits and potential therapeutic outcomes. De facto, UA exhibited numerous pharmacodynamic effects on PPAR, LXR, FXR, and PXR, resulting in remarkable anti-inflammatory, anti-hyperlipidemic, and hepatoprotective properties, by lowering lipid accumulation in hepatocytes and mitigating non-alcoholic steatohepatitis (NASH) and its subsequent liver fibrosis. Furthermore, UA reversed valproate and rifampicin-induced hepatic lipid accumulation. Additionally, UA showed great promise for the treatment of autoimmune inflammatory diseases such as multiple sclerosis and autoimmune arthritis by antagonizing RORγ. UA exhibited antiproliferative effects against skin, prostate, and breast cancers, partially via PPARα and RORγ pathways. Herein, for the first time, we explore and provide insights into UA bioactivity with respect to NR modulation.
Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy
Authors: Jiang Joanna , Chao Wei-Lun , Cao Troy , Culp Stacey , Napoléon Bertrand , El-Dika Samer , Machicado Jorge D. , Pannala Rahul , Mok Shaffer , Luthra Anjuli K. , Akshintala Venkata S. , Muniraj Thiruvengadam , Krishna Somashekar G.
Published: October 2023

Despite the increasing rate of detection of incidental pancreatic cystic lesions (PCLs), current standard-of-care methods for their diagnosis and risk stratification remain inadequate. Intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent PCLs. The existing modalities, including endoscopic ultrasound and cyst fluid analysis, only achieve accuracy rates of 65–75% in identifying carcinoma or high-grade dysplasia in IPMNs. Furthermore, surgical resection of PCLs reveals that up to half exhibit only low-grade dysplastic changes or benign neoplasms. To reduce unnecessary and high-risk pancreatic surgeries, more precise diagnostic techniques are necessary. A promising approach involves integrating existing data, such as clinical features, cyst morphology, and data from cyst fluid analysis, with confocal endomicroscopy and radiomics to enhance the prediction of advanced neoplasms in PCLs. Artificial intelligence and machine learning modalities can play a crucial role in achieving this goal. In this review, we explore current and future techniques to leverage these advanced technologies to improve diagnostic accuracy in the context of PCLs.
Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs
Authors: Badawy Mahmoud , Balaha Hossam Magdy , Maklad Ahmed S. , Almars Abdulqader M. , Elhosseini Mostafa A.
Published: October 2023

The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with ’ImageNet’ weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a ’normal’ class with 2494 images and an ’OSCC’ (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.
Prefrontal Glutathione Levels in Major Depressive Disorder Are Linked to a Lack of Positive Affect
Authors: Tuura Ruth O’Gorman , Buchmann Andreas , Ritter Christopher , Hase Adrian , Haynes Melanie , Noeske Ralph , Hasler Gregor
Published: October 2023

Major depressive disorder (MDD) is one of the most common neuropsychiatric disorders, with symptoms including persistent sadness and loss of interest. MDD is associated with neurochemical alterations in GABA, glutamate, and glutamine levels but, to date, few studies have examined changes in glutathione (GSH) in MDD. This study investigated changes in GSH in an unmedicated group of young adults, including 46 participants with current (n = 12) or past MDD (n = 34) and 20 healthy controls. Glutathione levels were assessed from GSH-edited magnetic resonance (MR) spectra, acquired from a voxel in the left prefrontal cortex, and depressive symptoms were evaluated with validated questionnaires and clinical assessments. Cortisol levels were also assessed as a marker for acute stress. Participants with current MDD demonstrated elevated GSH in comparison to participants with past MDD and controls, although the results could be influenced by differences in tissue composition within the MRS voxel. In addition, participants with both current and past MDD showed elevated cortisol levels in comparison to controls. No significant association was observed between GSH and cortisol levels, but elevated GSH levels were associated with a decrease in positive affect. These results demonstrate for the first time that elevated GSH in current but not past depression may reflect a state rather than a trait neurobiological change, related to a loss of positive affect.
Remembering Sets: Capacity Limit and Time Limit of Ensemble Representations in Working Memory
Authors: Xu Chaoer , Qian Yingzhu , Chen Hui , Shen Mowei , Zhou Jifan
Published: October 2023

In a constantly changing visual environment, the ability to extract and store ensemble representations plays a crucial role in efficiently processing and remembering complex visual information. However, how working memory maintains these ensemble representations remains unclear. Therefore, the present study aimed to investigate the limits and characteristics of ensemble representations in working memory using a change detection paradigm. Participants were presented with multiple sets of circles grouped by spatial proximity and were asked to memorize the mean diameter of the circles in each set. Results showed that working memory could stably maintain mean sizes of approximately two sets for at least four seconds. Moreover, the memory performance of ensembles was not affected by the number of circles within a set, suggesting that individual details were not stored in working memory. These results suggest that the visual system can effectively store ensembles in working memory without preserving detailed individual information.
Identifying Façade Orientations with Closely Similar Thermal Performance for Unifying Façade Design Features in Hot Arid Climate
Authors: Sherif Ahmed , Tarabieh Khaled , Mashaly Islam , Amer Mariam
Published: October 2023

Large-scale residential projects require architects to balance energy efficiency, thermal comfort, and cost-effectiveness in designing building units distributed in different orientations. Applying a single design to all orientations may not result in comfort conditions and energy efficiency; meanwhile, creating a unique design solution for each orientation can be time-consuming and costly to implement. Despite numerous studies recommending design alternatives for particular orientations, the ranges where orientation changes do not demand a change in the façade design still need to be determined. This research aims to identify ranges for which changes in building orientation do not necessitate changes in the façade design. It involves conducting parametric energy and thermal comfort simulations in a test room and testing the findings using real-life datasets from a large-scale residential project. Glass type, window-to-wall ratio (WWR), and utilization of shading devices were among the design parameters investigated. Results show that for all-year-round occupancy projects, a common solution can be applied for orientation changes between 0° to 10° from the north, −10° from the south, and ±5° from east and west for WWR of less than 50% in hot arid climates. These ranges can increase for small WWRs and when reflective glazing or shading devices are applied. This helps streamline design and construction processes, lower costs, and save energy in large-scale residential projects.
Dissecting the Role of Autophagy-Related Proteins in Cancer Metabolism and Plasticity
Authors: Torres-López Liliana , Dobrovinskaya Oxana
Published: October 2023

Modulation of autophagy as an anticancer strategy has been widely studied and evaluated in several cell models. However, little attention has been paid to the metabolic changes that occur in a cancer cell when autophagy is inhibited or induced. In this review, we describe how the expression and regulation of various autophagy-related (ATGs) genes and proteins are associated with cancer progression and cancer plasticity. We present a comprehensive review of how deregulation of ATGs affects cancer cell metabolism, where inhibition of autophagy is mainly reflected in the enhancement of the Warburg effect. The importance of metabolic changes, which largely depend on the cancer type and form part of a cancer cell’s escape strategy after autophagy modulation, is emphasized. Consequently, pharmacological strategies based on a dual inhibition of metabolic and autophagy pathways emerged and are reviewed critically here.
Inducing Angiogenesis in the Nucleus Pulposus
Authors: Damle Sheela R. , Krzyzanowska Agata K. , Korsun Maximilian K. , Morse Kyle W. , Gilbert Susannah , Kim Han Jo , Boachie-Adjei Oheneba , Rawlins Bernard A. , van der Meulen Marjolein C. H. , Greenblatt Matthew B. , Hidaka Chisa , Cunningham Matthew E.
Published: October 2023

Bone morphogenetic protein (BMP) gene delivery to Lewis rat lumbar intervertebral discs (IVDs) drives bone formation anterior and external to the IVD, suggesting the IVD is inhospitable to osteogenesis. This study was designed to determine if IVD destruction with a proteoglycanase, and/or generating an IVD blood supply by gene delivery of an angiogenic growth factor, could render the IVD permissive to intra-discal BMP-driven osteogenesis and fusion. Surgical intra-discal delivery of naïve or gene-programmed cells (BMP2/BMP7 co-expressing or VEGF165 expressing) +/- purified chondroitinase-ABC (chABC) in all permutations was performed between lumbar 4/5 and L5/6 vertebrae, and radiographic, histology, and biomechanics endpoints were collected. Follow-up anti-sFlt Western blotting was performed. BMP and VEGF/BMP treatments had the highest stiffness, bone production and fusion. Bone was induced anterior to the IVD, and was not intra-discal from any treatment. chABC impaired BMP-driven osteogenesis, decreased histological staining for IVD proteoglycans, and made the IVD permissive to angiogenesis. A soluble fragment of VEGF Receptor-1 (sFlt) was liberated from the IVD matrix by incubation with chABC, suggesting dysregulation of the sFlt matrix attachment is a possible mechanism for the chABC-mediated IVD angiogenesis we observed. Based on these results, the IVD can be manipulated to foster vascular invasion, and by extension, possibly osteogenesis.
Synthesis and Characterization of Dithiooxamidate-Bridged Polynuclear Ni Complexes
Authors: Hamaguchi Tomohiko , Kuraoka Ryo , Yamamoto Takumi , Takagi Naoya , Ando Isao , Kawata Satoshi
Published: October 2023

Mixed-valence complexes contain two metals with different formal oxidation numbers and, therefore, show mixed properties that are influenced by the electronic coupling between the two metals, which is, in turn, regulated by a bridging ligand. This is an attractive point for many researchers. Oxalate is widely used as a bridging ligand for preparing polynuclear complexes. More than 1000 complexes have been reported until now. However, dithiooxamidate, which is an oxalate analog, is less popular as a bridging ligand. Here, a new dithiooxamidate-bridged Ni-diphosphine dinuclear complex with the formula [(μ2-toxa){Ni(dppe)}2](BF4)2 (toxa = dithiooxamidate; dppe = 1,2-bis(diphenylphosphino)ethane) was prepared and characterized via single-crystal X-ray diffraction. When using 1,3-bis(diphenylphosphino)propane (dppp) instead of dppe, dinuclear, trinuclear, and tetranuclear complexes were obtained, i.e., [(μ2-toxa){Ni(dppp)}2](BF4)2, [{μ2-Ni(toxa)2}{Ni(dppp)}2](BF4)2, and [{μ3-Ni(toxa)3}{Ni(dppp)}3](BF4)2, respectively. Bidentate toxa ligands in dinuclear complexes coordinate each Ni atom as κ(S,N). However, the trinuclear and the tetranuclear complexes have the toxa ligands with κ(N,N) and κ(S,S) coordination. The [(μ2-toxa){Ni(dppe)}2](BF4)2 complex undergoes four reversible redox processes, whose analysis via a controlled-potential absorption spectrum reveals the presence of a Ni(II)-Ni(I) mixed-valence state at ∆E1/2 = 0.22 V with a comproportionation constant of 6.1 × 103.
Does Parenting Perfectionism Ironically Increase Violent Behaviors from Parent towards Children?
Authors: Schittek Alice , Roskam Isabelle , Mikolajczak Moira
Published: October 2023

Background: Past research has shown that perfectionistic strivings (PS) and perfectionistic concerns (PC) in the parenting domain are associated with an increase in parental burnout (PB), and that PB causally increases violence towards one’s offspring. One may therefore wonder whether parenting perfectionism may ironically increase violence towards one’s offspring. Objective: To the best of our knowledge, no study has ever investigated whether perfectionism (PS and PC) predicts violence towards one’s offspring, or whether PB could explain this link. In the current pre-registered cross-lagged study, we hypothesized that an increase in PS and PC would lead to an increase in violence via an increase in PB. Method: 228 participants responded to a longitudinal online survey, with three measurement occasions spaced 2 months apart. Results: Contrary to expectations, cross-lagged path models revealed that violence towards the offspring prospectively predicted an increase in PS and PC. Mediation models showed that PB was not a significant mediator. Results of all models did not change when controlling for social desirability. Conclusion: The present study shows that violence towards the offspring increases the risk of PS and PC in parents. Results are discussed in light of the feeling of guilt experienced by parents. Implications: Current worries that parenting perfectionism may paradoxically increase violence appear to be unwarranted at this stage. Moreover, correlation is not causation; thus, emphasizing caution before coming to clinically and societally relevant conclusions in cross-sectional studies. Thus, the PB and child maltreatment literature should slowly shift to using more longitudinal and causal designs.
Does the Modality of Dental Treatment Affect the Treatment Prognosis and the Necessity of Re-Treatments?
Authors: Bağ İrem , Çalışkan Seçil , Erenel Alaz Oya , Sevimli Kübra Nur , Candan Merve
Published: October 2023

Background: The utilization of evidence-based approaches is crucial to achieving long-term positive outcomes for treatment performed chairside or under general anesthesia. The study aimed to evaluate if treatment modality (at the chairside or under general anesthesia) affects prognosis and the need for re-treatment. Methods: Oral-hygiene, gingival, and plaque indexes were recorded during the control appointment. The success of all treatments was evaluated according to the scoring of particular evaluation criteria. Results: A total of 1066 dental procedures were performed on 92 children. Plaque index scores were higher for patients treated under general anesthesia. The success rate of restorative procedures was 82.5% under general anesthesia and 80.6% at the chairside. There was no statistically significant difference between the mean number of restorative treatments and the need for re-treatment between general anesthesia or chairside (p = 0.649, p = 0.311). The mean number of unsuccessful endodontic treatments performed under general anesthesia was higher than performed chairside. Only two out of thirty stainless-steel crowns were decemented, all performed under general anesthesia. Conclusions: The high volume of restoration failure due to secondary caries has highlighted the need for alternative approaches to caries management, especially given the risks associated with repeat general anesthetic.
The Role of p16/Ki67 Dual Staining in Cervical Cancer Screening
Authors: Dovnik Andraž , Repše Fokter Alenka
Published: October 2023

Cervical cancer screening has enabled a decrease in the incidence and mortality of cervical cancer. Various screening modalities have been studied to date. In many countries, screening is still based on cervical cytology, where cervical cells obtained either on glass or in a liquid medium are examined under a microscope. However, the fact that the vast majority of cervical cancers are a result of persistent infection with high-risk human papillomaviruses (hr-HPV) has led to the implementation of primary HPV screening in many countries. Taking into consideration the fact that the majority of HPV infections are transient and do not cause cervical precancer, effective triage methods are needed to prevent an increase in colposcopy referrals. Among these, the most extensively investigated are HPV genotyping, HPV methylation, and p16/Ki67 dual staining. In this manuscript, we briefly summarize the current knowledge regarding different screening strategies for the prevention of cervical cancer, with a focus on p16/Ki67 dual staining. In addition, we provide an explanation regarding the rationale for the use of various screening modalities based on the molecular biology of cervical cancer and cervical precancerous lesions.
Presynaptic Purinergic Modulation of the Rat Neuro-Muscular Transmission
Authors: Khairullin Adel E. , Grishin Sergey N. , Ziganshin Ayrat U.
Published: October 2023

ATP, being a well-known universal high-energy compound, plays an important role as a signaling molecule and together with its metabolite adenosine they both attenuate the release of acetylcholine in the neuro-muscular synapse acting through membrane P2 and P1 receptors, respectively. In this work, using a mechanomyographic method, we analyzed the presynaptic mechanisms by which ATP and adenosine can modulate the transduction in the rat m. soleus and m. extensor digitorum longus. N-ethylmaleimide, a G-protein antagonist, prevents the modulating effects of both ATP and adenosine. The action of ATP is abolished by chelerythrin, a specific phospholipase C inhibitor, while the inhibitory effect of adenosine is slightly increased by Rp-cAMPS, an inhibitor of protein kinase A, and by nitrendipine, a blocker of L-type Ca2+ channels. The addition of DPCPX, an A1 receptor antagonist, fully prevents the inhibitory action of adenosine in both muscles. Our data indicate that the inhibitory action of ATP involves metabotropic P2Y receptors and is mediated by phospholipase C dependent processes in rat motor neuron terminals. We suggest that the presynaptic effect of adenosine consists of negative and positive actions. The negative action occurs by stimulation of adenosine A1 receptors while the positive action is associated with the stimulation of adenosine A2A receptors, activation of protein kinase A and opening of L-type calcium channels. The combined mechanism of the modulating action of ATP and adenosine provides fine tuning of the synapse to fast changing conditions in the skeletal muscles.
Optimization of Additively Manufactured and Lattice-Structured Hip Implants Using the Linear Regression Algorithm from the Scikit-Learn Library
Authors: Alkentar Rashwan , Mankovits Tamás
Published: October 2023

As the name implies, patient-specific latticed hip implants vary in design depending on the properties required by the patient to serve as a valid suitable organ. Unit cells are typically built based on a 3D design of beams, and the properties of unit cells change depending on their geometries, which, in turn, are defined by two main parameters: beam length and beam thickness. Due to the continuous increase in the complexity of the unit cells’ designs and their reactions against different loads, the call for machine learning techniques is inevitable to help explore the parameters of the unit cells that can build lattice structures with specific desirable properties. In this study, a machine learning technique is used to predict the best defining parameters (length and thickness) to create a latticed design with a set of required properties (mainly porosity). The data (porosity, mass, and latticed area) from the properties of three unit-cell types, applied to the latticed part of a hip implant design, were collected based on the random length and thickness for three unit-cell types. Using the linear regression algorithm (a supervised machine learning method) from the scikit-learn library, a machine learning model was developed to predict the value of the porosity for the lattice structures based on the length and thickness as input data. The number of samples needed to generate an accurate result for each type of unit cell is also discussed.
Cybersecurity Risk Assessments within Critical Infrastructure Social Networks
Authors: Aktayeva Alimbubi , Makatov Yerkhan , Tulegenovna Akku Kubigenova , Dautov Aibek , Niyazova Rozamgul , Zhamankarin Maxud , Khan Sergey
Published: October 2023

Cybersecurity social networking is a new scientific and engineering discipline that was interdisciplinary in its early days, but is now transdisciplinary. The issues of reviewing and analyzing of principal tasks related to information collection, monitoring of social networks, assessment methods, and preventing and combating cybersecurity threats are, therefore, essential and pending. There is a need to design certain methods, models, and program complexes aimed at estimating risks related to the cyberspace of social networks and the support of their activities. This study considers a risk to be the combination of consequences of a given event (or incident) with a probable occurrence (likelihood of occurrence) involved, while risk assessment is a general issue of identification, estimation, and evaluation of risk. The findings of the study made it possible to elucidate that the technique of cognitive modeling for risk assessment is part of a comprehensive cybersecurity approach included in the requirements of basic IT standards, including IT security risk management. The study presents a comprehensive approach in the field of cybersecurity in social networks that allows for consideration of all the elements that constitute cybersecurity as a complex, interconnected system. The ultimate goal of this approach to cybersecurity is the organization of an uninterrupted scheme of protection against any impacts related to physical, hardware, software, network, and human objects or resources of the critical infrastructure of social networks, as well as the integration of various levels and means of protection.
PRF Lysates Enhance the Proliferation and Migration of Oral Squamous Carcinoma Cell Lines
Authors: Panahipour Layla , Croci Rebecca , Guarnieri Sara , Gruber Reinhard
Published: October 2023

Platelet-rich fibrin (PRF) is an autologous fibrin-rich matrix where activated platelets and leucocytes accumulate. PRF has a wide spectrum of clinical indications with the overall aim of supporting tissue regeneration which in dentistry includes the healing of healthy oral mucosa with epithelial cells. In oral squamous cell carcinoma lesions, however, epithelial cells undergo malignant transformation, indicated by their unrestricted proliferation and migration potential, which should not be further enhanced by a wound-healing formula. Yet, little is known about how oral squamous cell carcinomas respond to PRF lysates. The aim of the present study was, therefore, to test the capacity of PRF lysates to change the transcriptome of HSC2 oral squamous carcinoma cells and perform bioassays to support the findings. Based on the RNAseq analysis, PRF lysates caused an increase in the genes functionally linked to cell replication and migration. In support of this screening approach, PRF lysates enhanced the proliferation of HSC2 oral squamous carcinoma cells, as indicated by 3[H]-thymidine incorporation, cell counting, and the expression of proliferation-related genes. Moreover, PRF lysates sped up cell migration in a scratch assay requiring actin polymerization. Taken together, our data showing that PRF lysates are mitogenic and stimulate motility of oral squamous carcinoma cell lines could be an indication that treatment with PRF in cases of oral carcinoma should be carefully considered.
Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method
Authors: Wang Dongyu , Liu Yue , Yu Hongda , Hou Yanzhao
Published: October 2023

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.