bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Oxidative Stress on the Epigenetics of Fetal Alcohol Spectrum Disorders
Authors: Terracina Sergio , Tarani Luigi , Ceccanti Mauro , Vitali Mario , Francati Silvia , Lucarelli Marco , Venditti Sabrina , Verdone Loredana , Ferraguti Giampiero , Fiore Marco
Published: March 2024

Fetal alcohol spectrum disorders (FASD) represent a continuum of lifelong impairments resulting from prenatal exposure to alcohol, with significant global impact. The “spectrum” of disorders includes a continuum of physical, cognitive, behavioral, and developmental impairments which can have profound and lasting effects on individuals throughout their lives, impacting their health, social interactions, psychological well-being, and every aspect of their lives. This narrative paper explores the intricate relationship between oxidative stress and epigenetics in FASD pathogenesis and its therapeutic implications. Oxidative stress, induced by alcohol metabolism, disrupts cellular components, particularly in the vulnerable fetal brain, leading to aberrant development. Furthermore, oxidative stress is implicated in epigenetic changes, including alterations in DNA methylation, histone modifications, and microRNA expression, which influence gene regulation in FASD patients. Moreover, mitochondrial dysfunction and neuroinflammation contribute to epigenetic changes associated with FASD. Understanding these mechanisms holds promise for targeted therapeutic interventions. This includes antioxidant supplementation and lifestyle modifications to mitigate FASD-related impairments. While preclinical studies show promise, further clinical trials are needed to validate these interventions’ efficacy in improving clinical outcomes for individuals affected by FASD. This comprehensive understanding of the role of oxidative stress in epigenetics in FASD underscores the importance of multidisciplinary approaches for diagnosis, management, and prevention strategies. Continued research in this field is crucial for advancing our knowledge and developing effective interventions to address this significant public health concern.
Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
Authors: Zabihi Mehdi , Bhawya  , Pandya Parikshit , Shepley Brooke R. , Lester Nicholas J. , Anees Syed , Bain Anthony R. , Rondeau-Gagné Simon , Ahamed Mohammed Jalal
Published: March 2024

This paper proposes a novel data fusion technique for a wearable multi-sensory patch that integrates an accelerometer and a flexible resistive pressure sensor to accurately capture breathing patterns. It utilizes an accelerometer to detect breathing-related diaphragmatic motion and other body movements, and a flex sensor for muscle stretch detection. The proposed sensor data fusion technique combines inertial and pressure sensors to eliminate nonbreathing body motion-related artifacts, ensuring that the filtered signal exclusively conveys information pertaining to breathing. The fusion technique mitigates the limitations of relying solely on one sensor’s data, providing a more robust and reliable solution for continuous breath monitoring in clinical and home environments. The sensing system was tested against gold-standard spirometry data from multiple participants for various breathing patterns. Experimental results demonstrate the effectiveness of the proposed approach in accurately monitoring breathing rates, even in the presence of nonbreathing-related body motion. The results also demonstrate that the multi-sensor patch presented in this paper can accurately distinguish between varying breathing patterns both at rest and during body movements.
Diverse Feature-Level Guidance Adjustments for Unsupervised Domain Adaptative Object Detection
Authors: Zhu Yuhe , Liu Chang , Bai Yunfei , Wang Caiju , Wei Chengwei , Li Zhenglin , Zhou Yang
Published: March 2024

Unsupervised Domain Adaptative Object Detection (UDAOD) aims to alleviate the gap between the source domain and the target domain. Previous methods sought to plainly align global and local features across domains but adapted numerous pooled features and overlooked contextual information, which caused incorrect perceptions of foreground information. To tackle these problems, we propose Diverse Feature-level Guidance Adjustments (DFGAs) for two-stage object detection frameworks, including Pixel-wise Multi-scale Alignment (PMA) and Adaptative Threshold Confidence Adjustment (ATCA). Specifically, PMA adapts features within diverse hierarchical levels to capture sufficient contextual information. Through a customized PMA loss, features from different stages of a network facilitate information interaction across domains. Training with this loss function contributes to the generation of more domain-agnostic features. To better recognize foreground and background samples, ATCA employs adaptative thresholds to divide the foreground and background samples. This strategy flexibly instructs the classifier to perceive the significance of box candidates. Comprehensive experiments are conducted on Cityscapes, Foggy Cityscapes, KITTI, and Sim10k datasets to further demonstrate the superior performance of our method compared to the baseline method.
Correction: Goo, S.; Yang, H.-D. Restoring Raindrops Using Attentive Generative Adversarial Networks. Appl. Sci. 2021, 11, 7034
Authors: Goo Suhan , Yang Hee-Deok
Published: March 2024

Suhan Goo was not included as an author in the original publication [...]
On the Use of the Effective Width for Simply Supported Generally Loaded Box-Girders
Authors: Pancella Francesca , Luongo Angelo
Published: March 2024

A new method for evaluating the effective flange width of simply supported rectangular box-girders in bending is illustrated. It aims to overcome the lack in the literature of a general method able to supply the effective flange width for a general distribution of loads along the beam axis, not restricted, i.e., to the few cases reported in standard codes. The method consists of finding an analytical expression for the effective width under sinusoidal loads of arbitrary wavelength and combining the results in the framework of a Fourier analysis. The model allows investigating the shear-lag effects on displacements and stresses. The analytical formulation proposed is validated against numerical results supplied by refined Finite Element analyses.
A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm
Authors: Zhao Wenshuo , Ning Yipeng , Jia Xiang , Chai Dashuai , Su Fei , Wang Shengli
Published: March 2024

Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms.
A Numerical Bio-Geotechnical Model of Pressure-Responsive Microbially Induced Calcium Carbonate Precipitation
Authors: Wang Jianye , Mitrani Helen , Wipat Anil , Moreland Polly , Haystead Jamie , Zhang Meng , Robertson Martyn Dade
Published: March 2024

The employment of Microbially Induced Calcium Carbonate Precipitation (MICP) is of increasing interest as a technique for environmentally sustainable soil stabilisation. Recent advancements in synthetic biology have allowed for the conception of a pressure-responsive MICP process, wherein bacteria are engineered to sense environmental loads, thereby offering the potential to stabilise specific soil regions selectively. In this study, a 2D smart bio-geotechnical model is proposed based on a pressure-responsive MICP system. Experimentally obtained pressure-responsive genes and hypothetical genes with different pressure responses were applied in the model and two soil profiles were evaluated. The resulting model bridges scales from gene expression within bacteria cells to geotechnical simulations. The results show that both strata and gene expression–pressure relationships have a significant influence on the distribution pattern of calcium carbonate precipitation within the soil matrix. Among the evaluated experimental genes, Gene A demonstrates the best performance in both of the two soil profiles due to the effective stabilisation in the centre area beneath the load, while Genes B and C are more effective in reinforcing peripheral regions. Furthermore, when the hypothetical genes are utilised, there is an increasing stabilisation area with a decreased threshold value. The results show that the technique can be used for soil reinforcement in specific areas.
How Pseudomonas nitroreducens Passivates Cadmium to Inhibit Plant Uptake
Authors: Chen Yakui , Yu Yongquan , Fang Xiaoyu , Zhou Yinhuan , Lu Diannan
Published: March 2024

Cadmium (Cd) has been widely used in industry applications, leading to water and soil contamination. This study investigated the potential ability of Pseudomonas nitroreducens (11830) to perform the biosorption of cadmium from aqueous solution and soil. The biosorption characteristics were described using equilibrium isotherm and kinetic studies. The Langmuir adsorption isotherm indicated a better fit with the experimental data (R2 = 0.980), with a maximum capacity of 160.51 mg/g at 30 °C in an initial aqueous solution of 300 mg/L Cd2+. The experiments followed a pseudo-second-order kinetics model (R2 > 0.99), especially at a low initial concentration. The biosorption mechanisms involved were determined through scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive spectroscopy (EDS) and protein analysis. The SEM and TEM figures showed that the morphology of cells changed before and after the adsorption of Cd, and the EDS confirmed that Cd was absorbed on the surface of the cell. The analysis of proteins indicated that the protein species increased after the stimulation of Cd, which further confirmed the biosorption mechanism. A pot experiment confirmed that 11830 could passivate the cadmium in soil and reduce its uptake and utilization by Houttuynia cordata Thunb (H. cordata). This work demonstrates the potential application of microorganisms in inhibiting the accumulation of Cd in crops.
Time of Flight Distance Sensor–Based Construction Equipment Activity Detection Method
Authors: Park Young-Jun , Yi Chang-Yong
Published: March 2024

In this study, we delve into a novel approach by employing a sensor-based pattern recognition model to address the automation of construction equipment activity analysis. The model integrates time of flight (ToF) sensors with deep convolutional neural networks (DCNNs) to accurately classify the operational activities of construction equipment, focusing on piston movements. The research utilized a one-twelfth-scale excavator model, processing the displacement ratios of its pistons into a unified dataset for analysis. Methodologically, the study outlines the setup of the sensor modules and their integration with a controller, emphasizing the precision in capturing equipment dynamics. The DCNN model, characterized by its four-layered convolutional blocks, was meticulously tuned within the MATLAB environment, demonstrating the model’s learning capabilities through hyperparameter optimization. An analysis of 2070 samples representing six distinct excavator activities yielded an impressive average precision of 95.51% and a recall of 95.31%, with an overall model accuracy of 95.19%. When compared against other vision-based and accelerometer-based methods, the proposed model showcases enhanced performance and reliability under controlled experimental conditions. This substantiates its potential for practical application in real-world construction scenarios, marking a significant advancement in the field of construction equipment monitoring.
Field Reconnaissance and Earthquake Vulnerability of the RC Buildings in Adıyaman during 2023 Türkiye Earthquakes
Authors: Işık Ercan , Avcil Fatih , İzol Rabia , Büyüksaraç Aydın , Bilgin Hüseyin , Harirchian Ehsan , Arkan Enes
Published: March 2024

The 6th February 2023 Pazarcık and Elbistan earthquakes (Mw = 7.7 and Mw = 7.6) caused great destruction in many cities and were the disaster of the century for Türkiye. The greatest destruction was caused in the provinces of Hatay, Kahramanmaraş, and Adıyaman during these earthquakes, which were independent of each other and occurred on the same day. Information about earthquakes and strong ground motion records is given within the scope of this study. Reinforced concrete (RC) structures which constitute a large part of the urban building stock in the earthquake region were exposed to structural damage at different levels. The structural damage in the RC structures in the city center, Gölbaşı, and Kahta districts of the province of Adıyaman was evaluated within the scope of earthquake and civil engineering after field investigations. Insufficient RC, low-strength concrete reinforcement problems, RC frame failure, heavy overhang, short columns, soft story, and pounding effect are the main causes of the earthquake damage. The presence of these factors that reduce the earthquake resistance of RC structures increased the damage level. In addition, the fact that the earthquakes occurred nine hours apart and the continuation of aftershocks during that period negatively affected the damage levels. It has been observed that structures that receive the necessary engineering services during the construction and project phases ensure the safety of life and property, even if the structure is slightly damaged. In this study, we also tried to reveal whether the target displacements were satisfactorily represented by numerical analysis for a sample RC structure.
The Efficacy of a Cosmetic Preparation Containing Sheep Colostrum on Mature Skin: A Randomized Placebo-Controlled Double-Blind Study
Authors: Kazimierska Kinga , Erkiert-Polguj Anna , Kalinowska-Lis Urszula
Published: March 2024

Colostrum, the first milk produced by mammals, is rich in various bioactive components that provide numerous health benefits to newborns, such as growth factors, hormones, immunoglobulins, cytokines, and enzymes. Topical application of bovine or equine colostrum has been found to improve regeneration, accelerate cutaneous wound healing, and have moisturizing, protective, and anti-aging properties. The aim of this study was to examine the effect of a cosmetic preparation containing sheep colostrum on skin with signs of aging in mature women. Fifty-two women, aged 40–70, were randomized into two groups to receive either colostrum or placebo cream. The participants applied the cream for eight weeks. Skin hydration, TEWL, sebum, erythema, and tone were measured using a standardized Courage + Khazaka electronic GmbH Multi Probe Adapter; skin elasticity was measured with a cutometer, and images were taken by FotoMedicus. The treatment increased skin moisture, reduced TEWL, and improved skin firmness. These findings were confirmed by the subjective survey. The participants reported, inter alia, improved skin softness and less redness and hypersensitivity. Sheep colostrum cream was more effective at improving skin conditions than placebo cream. Colostrum creams can improve certain aspects of skin quality, especially the hydrolipid barrier, and overall rejuvenation.
Hybrid-Margin Softmax for the Detection of Trademark Image Similarity
Authors: Wang Chenyang , Zheng Guangyuan , Shan Hongtao
Published: March 2024

The detection of image similarity is critical to trademark (TM) legal registration and court judgment on infringement cases. Meanwhile, there are great challenges regarding the annotation of similar pairs and model generalization on rapidly growing data when deep learning is introduced into the task. The research idea of metric learning is naturally suited for the task where similarity of input is given instead of classification, but current methods are not targeted at the task and should be upgraded. To address these issues, loss-driven model training is introduced, and a hybrid-margin softmax (HMS) is proposed exactly based on the peculiarity of TM images. Two additive penalty margins are attached to the softmax to expand the decision boundary and develop greater tolerance for slight differences between similar TM images. With the HMS, a Siamese neural network (SNN) as the feature extractor is further penalized and the discrimination ability is improved. Experiments demonstrate that the detection model trained on HMS can make full use of small numbers of training data and has great discrimination ability on bigger quantities of test data. Meanwhile, the model can reach high performance with less depth of SNN. Extensive experiments indicate that the HMS-driven model trained completely on TM data generalized well on the face recognition (FR) task, which involves another type of image data.
A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
Authors: Zhang Yanhua , Yang Lei , Cheng Yuehua , Ying Kaixin
Published: March 2024

The study of satellite performance evaluation can reveal the ability of satellite systems to fulfil corresponding tasks in the space environment, and provide information support for the resource allocation and mission scheduling of in-orbit satellites. In this paper, we took the satellite attitude control system in attitude tracking mode as the research object. In accordance with the system’s mission requirements, the control performance evaluation indicator set, characterized by a generalized grey number, is constructed to tackle the uncertainty and inadequacy of information contained in flight status data resulting from the complex space operating environment and sensor measurement noise. An improved principal component analysis method based on generalized grey number is proposed to solve the weight amplification caused by the correlation between performance indicators and realize the weight allocation of the indicators. Finally, the grey-target decision model is established to determine the weights of the performance indicators, and the performance evaluation model is established under the tracking mode. The feasibility of the grey-target decision-evaluation model based on the improved principal component is confirmed through comparative experiments.
MaxEnt SeismoSense Model: Ionospheric Earthquake Anomaly Detection Based on the Maximum Entropy Principle
Authors: Wang Linyue , Li Zhitao , Chen Yifang , Wang Jianjun , Fu Jihua
Published: March 2024

In our exploration, we aimed at identifying seismic anomalies using limited ionospheric data for earthquake forecasting and we meticulously compiled datasets under conditions of minimal geomagnetic disturbance. Our systematic evaluation affirmed the ITransformer as a potent tool for the feature extraction of ionospheric data, standing out within the domain of transformer-based time series prediction models. We integrated the maximum entropy principle to fully leverage the available information, while minimizing the influence of presuppositions on our predictions. This led to the creation of the MaxEnt SeismoSense Model, a novel composite model that combines the strengths of the transformer architecture with the maximum entropy principle to improve prediction accuracy. The application of this model demonstrated a proficient capability to detect seismic disturbances in the ionosphere, showcasing an improvement in both recall rate and accuracy to 71% and 69%, respectively, when compared to conventional baseline models. This indicates that the combined use of transformer technology and the maximum entropy principle could allow pre-seismic anomalies in the ionosphere to be sensed more efficiently and could offer a more reliable and precise approach to earthquake prediction.
Weak Sharp Type Solutions for Some Variational Integral Inequalities
Authors: Treanţă Savin , Saeed Tareq
Published: March 2024

Weak sharp type solutions are analyzed for a variational integral inequality defined by a convex functional of the multiple integral type. A connection with the sufficiency property associated with the minimum principle is formulated, as well. Also, an illustrative numerical application is provided.
Digital Templating of Hip Arthroplasty Using Microsoft PowerPoint: A Pilot Study with Technical Details
Authors: Cha Yonghan , Chung Jun Young , Kim Jin-Woo , Yoo Jun-Il , Lee Woohyun , Kim Jung-Taek
Published: March 2024

Templating is essential in hip arthroplasty preparation, facilitating implant size prediction and surgical rehearsal. It ensures the selection of suitable implants according to patient anatomy and disease, aiming to minimize post-operative complications. Various templating methods exist, including traditional acetate templating on both analog and digital images, alongside digital templating on digital images, which is categorized into 2D and 3D approaches. Despite the popularity of acetate templating on digital images, challenges such as the requirement for physical templates and result preservation persist. To address these limitations, digital templating with software like OrthoSize and Orthoview has been suggested, although not universally accessible. This technical note advocates for Microsoft PowerPoint as an effective alternative for 2D digital templating, highlighting its user-friendly features for image manipulation without needing specialized software. The described method involves scanning acetate templates, adjusting the images in PowerPoint 365 for size, position, and calibration on patient radiographs, and demonstrating reliability through preliminary assessments, with intraclass correlation coefficient (ICC) values indicating a high level of agreement for cup and stem size (ICC = 0.860, 0.841, respectively) but moderate for neck length (ICC = 0.592). We have introduced a method for performing 2D digital templating in the clinical field without the need for specialized software dedicated to digital templating. We believe this method significantly improves the accessibility to 2D digital templating, which was previously limited by the need for digital templating software. Additionally, it enables surgeons to easily establish arthroplasty plans and share them, overcoming the limitations of acetate templates.
Investigation of Deconvolution Method with Adaptive Point Spread Function Based on Scintillator Thickness in Wavelet Domain
Authors: Kim Kyuseok , Cha Bo Kyung , Jeong Hyun-Woo , Lee Youngjin
Published: March 2024

In recent years, indirect digital radiography detectors have been actively studied to improve radiographic image performance with low radiation exposure. This study aimed to achieve low-dose radiation imaging with a thick scintillation detector while simultaneously obtaining the resolution of a thin scintillation detector. The proposed method was used to predict the optimal point spread function (PSF) between thin and thick scintillation detectors by considering image quality assessment (IQA). The process of identifying the optimal PSF was performed on each sub-band in the wavelet domain to improve restoration accuracy. In the experiments, the edge preservation index (EPI) values of the non-blind deblurred image with a blurring sigma of σ = 5.13 pixels and the image obtained with optimal parameters from the thick scintillator using the proposed method were approximately 0.62 and 0.76, respectively. The coefficient of variation (COV) values for the two images were approximately 1.02 and 0.63, respectively. The proposed method was validated through simulations and experimental results, and its viability is expected to be verified on various radiological imaging systems.
Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective
Authors: Bokelmann Carolin , Ehsani Alireza , Schaub Jochen , Stiefel Fabian
Published: March 2024

Due to their high specificity, monoclonal antibodies (mAbs) have garnered significant attention in recent decades, with advancements in production processes, such as high-seeding-density (HSD) strategies, contributing to improved titers. This study provides a thorough investigation of high seeding processes for mAb production in Chinese hamster ovary (CHO) cells, focused on identifying significant metabolites and their interactions. We observed high glycolytic fluxes, the depletion of asparagine, and a shift from lactate production to consumption. Using a metabolic network and flux analysis, we compared the standard fed-batch (STD FB) with HSD cultivations, exploring supplementary lactate and cysteine, and a bolus medium enriched with amino acids. We reconstructed a metabolic network and kinetic models based on the observations and explored the effects of different feeding strategies on CHO cell metabolism. Our findings revealed that the addition of a bolus medium (BM) containing asparagine improved final titers. However, increasing the asparagine concentration in the feed further prevented the lactate shift, indicating a need to find a balance between increased asparagine to counteract limitations and lower asparagine to preserve the shift in lactate metabolism.
Multiphotonic Ablation and Electro-Capacitive Effects Exhibited by Candida albicans Biofilms
Authors: Arano-Martinez Jose Alberto , Hernández-Benítez José Alejandro , Martines-Arano Hilario , Rodríguez-Tovar Aída Verónica , Trejo-Valdez Martin , García-Pérez Blanca Estela , Torres-Torres Carlos
Published: March 2024

This work reports the modification in the homogeneity of ablation effects with the assistance of nonlinear optical phenomena exhibited by C. albicans ATCC 10231, forming a biofilm. Equivalent optical energies with different levels of intensity were irradiated in comparative samples, and significant changes were observed. Nanosecond pulses provided by an Nd:YAG laser system at a 532 nm wavelength in a single-beam experiment were employed to explore the photodamage and the nonlinear optical transmittance. A nonlinear optical absorption coefficient −2 × 10−6 cm/W was measured in the samples studied. It is reported that multiphotonic interactions can promote more symmetric optical damage derived by faster changes in the evolution of fractional photoenergy transference. The electrochemical response of the sample was studied to further investigate the electronic dynamics dependent on electrical frequency, and an electro-capacitive behavior in the sample was identified. Fractional differential calculations were proposed to describe the thermal transport induced by nanosecond pulses in the fungi media. These results highlight the nonlinear optical effects to be considered as a base for developing photothermally activated phototechnology and high-precision photodamage in biological systems.
Chemical Inhibition of Sterol Biosynthesis
Authors: Peeples Eric , Mirnics Karoly , Korade Zeljka
Published: March 2024

Cholesterol is an essential molecule of life, and its synthesis can be inhibited by both genetic and nongenetic mechanisms. Hundreds of chemicals that we are exposed to in our daily lives can alter sterol biosynthesis. These also encompass various classes of FDA-approved medications, including (but not limited to) commonly used antipsychotic, antidepressant, antifungal, and cardiovascular medications. These medications can interfere with various enzymes of the post-lanosterol biosynthetic pathway, giving rise to complex biochemical changes throughout the body. The consequences of these short- and long-term homeostatic disruptions are mostly unknown. We performed a comprehensive review of the literature and built a catalogue of chemical agents capable of inhibiting post-lanosterol biosynthesis. This process identified significant gaps in existing knowledge, which fall into two main areas: mechanisms by which sterol biosynthesis is altered and consequences that arise from the inhibitions of the different steps in the sterol biosynthesis pathway. The outcome of our review also reinforced that sterol inhibition is an often-overlooked mechanism that can result in adverse consequences and that there is a need to develop new safety guidelines for the use of (novel and already approved) medications with sterol biosynthesis inhibiting side effects, especially during pregnancy.
Unlocking Potential: Low Bovine Serum Albumin Enhances the Chondrogenicity of Human Adipose-Derived Stromal Cells in Pellet Cultures
Authors: Casado-Losada Isabel , Acosta Melanie , Schädl Barbara , Priglinger Eleni , Wolbank Susanne , Nürnberger Sylvia
Published: March 2024

Bovine serum albumin (BSA) plays a crucial role in cell culture media, influencing cellular processes such as proliferation and differentiation. Although it is commonly included in chondrogenic differentiation media, its specific function remains unclear. This study explores the effect of different BSA concentrations on the chondrogenic differentiation of human adipose-derived stromal/stem cells (hASCs). hASC pellets from six donors were cultured under chondrogenic conditions with three BSA concentrations. Surprisingly, a lower BSA concentration led to enhanced chondrogenesis. The degree of this effect was donor-dependent, classifying them into two groups: (1) high responders, forming at least 35% larger, differentiated pellets with low BSA in comparison to high BSA; (2) low responders, which benefitted only slightly from low BSA doses with a decrease in pellet size and marginal differentiation, indicative of low intrinsic differentiation potential. In all cases, increased chondrogenesis was accompanied by hypertrophy under low BSA concentrations. To the best of our knowledge, this is the first study showing improved chondrogenicity and the tendency for hypertrophy with low BSA concentration compared to standard levels. Once the tendency for hypertrophy is understood, the determination of BSA concentration might be used to tune hASC chondrogenic or osteogenic differentiation.
SCFFBXW11 Complex Targets Interleukin-17 Receptor A for Ubiquitin–Proteasome-Mediated Degradation
Authors: Jin Ben , Moududee Sayed Ala , Ge Dongxia , Zhou Pengbo , Wang Alun R. , Liu Yao-Zhong , You Zongbing
Published: March 2024

Interleukin-17 (IL-17) is a pro-inflammatory cytokine that participates in innate and adaptive immune responses and plays an important role in host defense, autoimmune diseases, tissue regeneration, metabolic regulation, and tumor progression. Post-translational modifications (PTMs) are crucial for protein function, stability, cellular localization, cellular transduction, and cell death. However, PTMs of IL-17 receptor A (IL-17RA) have not been investigated. Here, we show that human IL-17RA was targeted by F-box and WD repeat domain-containing 11 (FBXW11) for ubiquitination, followed by proteasome-mediated degradation. We used bioinformatics tools and biochemical techniques to determine that FBXW11 ubiquitinated IL-17RA through a lysine 27-linked polyubiquitin chain, targeting IL-17RA for proteasomal degradation. Domain 665-804 of IL-17RA was critical for interaction with FBXW11 and subsequent ubiquitination. Our study demonstrates that FBXW11 regulates IL-17 signaling pathways at the IL-17RA level.
Bipedal Robot Gait Generation Using Bessel Interpolation
Authors: Wang Zhen , Li Qingfeng , Kou Lei , Zheng Danni , Ke Wende , Lu Dongxin
Published: March 2024

This paper introduces a novel approach to bipedal robot gait generation by proposing a higher-order form through the parameter equation of first-order Bessel interpolation. The trajectory planning for the bipedal robot, specifically for stepping up or down stairs, is established based on a three-dimensional interpolation equation. The experimental prototype, Roban, is utilized for the study, and the structural sketch of a single leg is presented. The inverse kinematics expression for the leg is derived using kinematic methods. Employing a position control method, the angle information is transmitted to the robot’s joints, enabling the completion of both downstairs simulation experiments and physical experiments with the Roban prototype. The analysis of the experimental process reveals a noticeable phenomenon of hip and ankle joint tilting in the robot. This observation suggests that low-cost bipedal robots driven by servo motors exhibit low stiffness characteristics in their joints.
Differential Brain Activation for Four Emotions in VR-2D and VR-3D Modes
Authors: Zhang Chuanrui , Su Lei , Li Shuaicheng , Fu Yunfa
Published: March 2024

Similar to traditional imaging, virtual reality (VR) imagery encompasses nonstereoscopic (VR-2D) and stereoscopic (VR-3D) modes. Currently, Russell’s emotional model has been extensively studied in traditional 2D and VR-3D modes, but there is limited comparative research between VR-2D and VR-3D modes. In this study, we investigate whether Russell’s emotional model exhibits stronger brain activation states in VR-3D mode compared to VR-2D mode. By designing an experiment covering four emotional categories (high arousal–high pleasure (HAHV), high arousal–low pleasure (HALV), low arousal–low pleasure (LALV), and low arousal–high pleasure (LAHV)), EEG signals were collected from 30 healthy undergraduate and graduate students while watching videos in both VR modes. Initially, power spectral density (PSD) computations revealed distinct brain activation patterns in different emotional states across the two modes, with VR-3D videos inducing significantly higher brainwave energy, primarily in the frontal, temporal, and occipital regions. Subsequently, Differential entropy (DE) feature sets, selected via a dual ten-fold cross-validation Support Vector Machine (SVM) classifier, demonstrate satisfactory classification accuracy, particularly superior in the VR-3D mode. The paper subsequently presents a deep learning-based EEG emotion recognition framework, adeptly utilizing the frequency, spatial, and temporal information of EEG data to improve recognition accuracy. The contribution of each individual feature to the prediction probabilities is discussed through machine-learning interpretability based on Shapley values. The study reveals notable differences in brain activation states for identical emotions between the two modes, with VR-3D mode showing more pronounced activation.
Effect of Calcium Aluminate and Carbide Slag on Mechanical Property and Hydration Mechanism of Supersulfated Cement
Authors: Qi Guangzheng , Zhang Qiang , Sun Zhengning
Published: March 2024

Supersulfated cement (SSC), a low-carbon, energy-efficient, eco-friendly cementitious material, is mainly made from industrial byproducts. However, SSC’s slow early strength development leads to inadequate initial hardening and reduced durability, which restricts its practical application. This study investigated the potential enhancement of SSC by incorporating calcium aluminate (CA) and carbide slag (CS) alongside anhydrite as activators to address its slow early strength development. The effects of varying CA and CS proportions on the mechanical property and hydration mechanism of CA-CS-SSC were examined. Results indicate that employing 1% CA and 4% CS as alkaline activators effectively activates slag hydration in the 1CA-4CS-SSC, achieving a compressive strength of 9.7 MPa at 1 day. Despite the limited improvement in early compressive strength of other mixtures with higher CA and lower CS proportions in the CA-CS-SSC system, all mixtures exhibited enhanced compressive strength during long-term hydration. After 90 days, ettringite formation in the CA-CS-SSC system decelerated, whereas anhydrite remained. Concurrently, the formation of C-S-H continued to increase, promoting late compressive strength. The mechanism for enhancing the early compressive strength of the CA-CS-SSC system is attributed to the swift hydration of CA with anhydrite, dissolution of fine slag particles, and reaction with anhydrite under conditions with suitable alkali content to augment the ettringite production. This process also generates a C-S-H and OH-hydrotalcite to fill the void in the skeleton structure formed by ettringite, resulting in a dense microstructure that improves early compressive strength.
Generation of Construction Scheduling through Machine Learning and BIM: A Blueprint
Authors: Al-Sinan Mazen A. , Bubshait Abdulaziz A. , Aljaroudi Zainab
Published: March 2024

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity.
Planning Regulations and Modelled Constraints in BIM: A Dutch Case Study
Authors: Koutamanis Alexander
Published: March 2024

Planning regulations determine a substantial part of buildings, but their constraints are usually not included in the setup of a BIM model or used explicitly for design guidance, but only tested in compliance checks once a model has been made. This is symptomatic of wider tendencies and ingrained biases that emphasize tacit knowledge and assume that information in a project starts from scratch—an assumption that runs contrary to predesign information ordering practices, as well as to the findings of creativity studies. In terms of process control, it negates important possibilities for feedforward. The paper proposes that BIM and design computerization, in general, should avoid the generate-and-test view of design, the view of design knowledge as tacit, and the adherence to analogue workflows, but develop, instead, approaches and workflows that keep information explicit and utilize it to frame design problems. To demonstrate this, we describe an exercise in which the expectation that the geometric representation of planning regulations returns permissible building envelopes was tested on the basis of a large number of cases produced by students who each collected planning regulations for a particular plot of land in the Netherlands and modelled their constraints in BIM, using a workflow that can be accommodated within the scope of predesign information gathering in any project. The results confirm that, for a large part of Dutch housing, the representation of planning regulations in BIM returns the permissible building envelope, and, so, forms a clear frame for subsequent design actions. They also suggest that including such information in the setup of a model is constructive and feasible, even for novices, and produces a bandwidth view of project information that integrates pre-existing information in a BIM workflow through feedforward. By extension, they also indicate a potential for a closer relation between analysis and synthesis in BIM, characterized by transparency and simultaneity, as well as the thorough understanding of problem constraints required for both efficiency and creativity.
Autoimmune Atrophic Gastritis: A Clinical Review
Authors: Castellana Chiara , Eusebi Leonardo Henry , Dajti Elton , Iascone Veronica , Vestito Amanda , Fusaroli Pietro , Fuccio Lorenzo , D’Errico Antonietta , Zagari Rocco Maurizio
Published: March 2024

Autoimmune atrophic gastritis (AAG) is a chronic condition characterized by the presence of atrophy in the oxyntic mucosa due to anti-parietal cell antibodies. This review provides a comprehensive and up-to-date overview of autoimmune atrophic gastritis, reporting recent evidence on epidemiology, pathogenesis, diagnosis, clinical presentation, risk of malignancies, and management. The prevalence of AAG has been estimated at between 0.3% and 2.7% in the general population. The diagnosis of AAG is based on a combination of the serologic profile and the histological examination of gastric biopsies. Patients with AAG are often asymptomatic but can also have dyspeptic or reflux symptoms. The atrophy of the oxyntic mucosa leads to iron and vitamin B12 malabsorption, which may result in anemia and neurological affections. Autoimmune atrophic gastritis is associated with an increased risk of type I neuroendocrine tumors (NETs) and gastric cancer, with an incidence rate of 2.8% and 0.5% per person/year, respectively. Management is directed to reinstate vitamins and iron and to prevent malignancies with endoscopic surveillance. In conclusion, atrophic autoimmune gastritis is an infrequent condition, often asymptomatic and misdiagnosed, that requires an early diagnosis for appropriate vitamin supplementation and endoscopic follow-up for the early diagnosis of NETs and gastric cancer.
Targeting Dysregulated Lipid Metabolism in Cancer with Pharmacological Inhibitors
Authors: Gupta Amogh , Das Dipanwita , Taneja Reshma
Published: March 2024

Metabolic plasticity is recognised as a hallmark of cancer cells, enabling adaptation to microenvironmental changes throughout tumour progression. A dysregulated lipid metabolism plays a pivotal role in promoting oncogenesis. Oncogenic signalling pathways, such as PI3K/AKT/mTOR, JAK/STAT, Hippo, and NF-kB, intersect with the lipid metabolism to drive tumour progression. Furthermore, altered lipid signalling in the tumour microenvironment contributes to immune dysfunction, exacerbating oncogenesis. This review examines the role of lipid metabolism in tumour initiation, invasion, metastasis, and cancer stem cell maintenance. We highlight cybernetic networks in lipid metabolism to uncover avenues for cancer diagnostics, prognostics, and therapeutics.
Lead-Time Corrected Effect on Breast Cancer Survival in Germany by Mode of Detection
Authors: Schumann Laura , Hadwiger Moritz , Eisemann Nora , Katalinic Alexander
Published: March 2024

(1) Background: Screen-detected breast cancer patients tend to have better survival than patients diagnosed with symptomatic cancer. The main driver of improved survival in screen-detected cancer is detection at earlier stage. An important bias is introduced by lead time, i.e., the time span by which the diagnosis has been advanced by screening. We examine whether there is a remaining survival difference that could be attributable to mode of detection, for example, because of higher quality of care. (2) Methods: Women with a breast cancer (BC) diagnosis in 2000–2022 were included from a population-based cancer registry from Schleswig-Holstein, Germany, which also registers the mode of cancer detection. Mammography screening was available from 2005 onwards. We compared the survival for BC detected by screening with symptomatic BC detection using Kaplan–Meier, unadjusted Cox regressions, and Cox regressions adjusted for age, grading, and UICC stage. Correction for lead time bias was carried out by assuming an exponential distribution of the period during which the tumor is asymptomatic but screen-detectable (sojourn time). We used a common estimate and two recently published estimates of sojourn times. (3) Results: The analysis included 32,169 women. Survival for symptomatic BC was lower than for screen-detected BC (hazard ratio (HR): 0.23, 95% confidence interval (CI): 0.21–0.25). Adjustment for prognostic factors and lead time bias with the commonly used sojourn time resulted in an HR of 0.84 (CI: 0.75–0.94). Using different sojourn times resulted in an HR of 0.73 to 0.90. (4) Conclusions: Survival for symptomatic BC was only one quarter of screen-detected tumors, which is obviously biased. After adjustment for lead-time bias and prognostic variables, including UICC stage, survival was 27% to 10% better for screen-detected BC, which might be attributed to BC screening. Although this result fits quite well with published results for other countries with BC screening, further sources for residual confounding (e.g., self-selection) cannot be ruled out.
Heterostructured S-TiO2/g-C3N4 Photocatalysts with High Visible Light Photocatalytic Activity
Authors: Alaya Yassine , Chouchene Bilel , Medjahdi Ghouti , Balan Lavinia , Bouguila Noureddine , Schneider Raphaël
Published: March 2024

Novel heterojunctions associating graphitic carbon nitride g-C3N4 and S-doped TiO2 nanoparticles were successfully designed and prepared via a hydrothermal method and used for photocatalytic degradations. The loading in S-TiO2 nanoparticles on g-C3N4 was varied (5, 10 and 20 wt%), and the photocatalysts were characterized by XRD, FT-IR, solid-state UV-visible diffuse reflectance, photoluminescence, XPS, TEM and SEM. The S-TiO2 (5%)/g-C3N4 catalyst exhibits the highest activity for the photocatalytic degradation of the methylene blue (MB) dye under visible light irradiation. The high photocatalytic performance originates from the enhanced separation and transfer of photogenerated charge carriers. The S-TiO2 (5%)/g-C3N4 photocatalyst is stable and can be reused five times without a sharp drop in activity, indicating its high potential for wastewater remediation.