bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Study on Handheld, Modular, and Laboratory Raman Instruments for the Analysis of Colon Tissues and Colorectal Polyps
Authors: Synytsya Alla , Kováčová Zuzana , Janstová Daniela , Vočka Michal , Petrtýl Jaromír , Petruželka Luboš
Published: January 2024

Portable Raman spectrometers may offer advantages for clinical medical diagnostics over laboratory instruments by allowing for quick measurements in the field and provision of data suitable for screening analyses. This work evaluates the potential of using available handheld, modular, and laboratory Raman spectrometers for screening normal colon tissues and benign and malignant colon polyps. The Raman spectra of tissue samples and reference biological macromolecules were measured with these instruments and analyzed using curve fitting and multivariate statistics. The spectra of calf thymus DNA measured with portable devices showed suitable signal-to-noise levels and half-widths of the prominent bands. Band positions, resolution, and relative intensities in the Raman spectra of colon tissues and reference compounds varied for the instruments, and the laboratory device demonstrated the best spectral feature. The principal component analysis (PCA) of the spectra obtained with all Raman devices showed well discrimination of normal colon tissue, adenomatous polyp, and adenocarcinoma. Dendrograms of similarity obtained using hierarchy cluster analysis (HCA) for the Raman spectra of all three devices also showed good separation of these samples. The soft independent modeling of class analogy (SIMCA) and support vector machine (SVM) models efficiently classified normal colon tissues and benign/malignant colorectal polyps based on the Raman data from all three devices. Despite its less pronounced spectral characteristics, the handheld Raman spectrometer can be used in early diagnosis of colorectal carcinoma, comparable to the modular and laboratory instruments.
Mod2VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
Authors: Aboumrad Willie , Widdows Dominic
Published: January 2024

This paper presents a system for solving binary-valued linear equations using quantum computers. The system is called Mod2VQLS, which stands for Modulo 2 Variational Quantum Linear Solver. As far as we know, this is the first such proposal. The design is a classical–quantum hybrid. The quantum components are a new circuit design for implementing matrix multiplication modulo 2, and a variational circuit to be optimized. The classical components are the optimizer, which measures the cost function and updates the quantum parameters for each iteration, and the controller that runs the quantum job and classical optimizer iterations. We propose two alternative ansatze or templates for the variational circuit and present results showing that the rotation ansatz designed specifically for this problem provides the most direct path to a valid solution. Numerical experiments in low dimensions indicate that Mod2VQLS, using the custom rotations ansatz, is on par with the block Wiedemann algorithm, which is the best-known to date solution for this problem.
Shear-Wave Velocity Model from Site Amplification Using Microtremors on Jeju Island
Authors: Kim Junkyoung , Park Dongkeuk , Nam Gitae , Jung Haiyoung
Published: January 2024

This study examines shear-wave velocity structures in the Jeju region utilizing horizontal-to-vertical spectral ratios (HVSRs) of environmental noise, focusing on identifying significant low-velocity layers (LVLs). Although conventional methodologies predominantly involve borehole and active seismic exploration, recent advancements in the diffuse-field theory of seismic waves have offered a theoretical foundation for this approach. In the volcanic region of Jeju Island characterized by unique geological features, a pervasive LVL composed of quaternary marine sediments and the Seoguipo sedimentary layer has been observed. These components are crucial for site amplification and attenuation in seismic microzonation. The present study introduces a novel discovery of a distinct LVL, specifically at the UDO site, suggesting that its origin may be attributable to a local magmatic intrusion event. Advanced algorithms and HVSR curve analysis have enabled reliable inversion processes, enhancing the comprehension of the subsurface geology of Jeju. These insights are essential for seismic microzonation practices and contribute significantly to the development of seismic design standards in the Jeju region.
The Development and Analysis of a Multistage Spraying Method for Liquids in an Ultrasonic Field
Authors: Shalunov Andrey , Khmelev Vladimir , Terentiev Sergey , Nesterov Viktor , Genne Dmitry
Published: January 2024

Spraying various liquids (primarily aqueous solutions of various substances) is widely used in various technological processes. For most of them, high dispersibility and a narrow droplet size distribution are essential. Ultrasonic spraying allows these parameters to be achieved. However, the higher the dispersity of droplets produced by ultrasonic spraying, the lower the spray productivity. To solve this problem, we propose a method of multistage spraying, consisting of the generation of a large number of particles of large initial size (to ensure high productivity) and their subsequent destruction by propagation in a periodic ultrasonic field (to ensure small droplet sizes at high productivity). To experimentally determine the capabilities of this technique, a tubular emitter in the shape of a cylinder with a stepped-variable cross-section was designed. The configuration of the ultrasonic field inside the emitter (22.2 kHz; 182 dB) implements three-stage spraying (the number of sputtering stages corresponds to the number of anti-nodes along the emitter axis). The effectiveness of the proposed and developed technique, suitable for the generation of droplets smaller than 40 µm, with performance exceeding the known ultrasonic spray techniques by at least 10-fold, was confirmed in the course of the conducted research.
Agile Attitude Maneuver Control of Micro-Satellites for Multi-Target Observation Based on Piecewise Power Reaching Law and Variable-Structure Sliding Mode Control
Authors: Yang Xinyan , Liao Yurong , Li Lei , Li Zhaoming
Published: January 2024

This paper addresses the issue of agile attitude maneuver control for low-Earth-orbit satellites during short arc segments for multi-target observations. Specifically, a configuration design for Control Moment Gyroscopes (CMGs) and a hybrid control law are provided. The control law is adept at avoiding singularities and escaping singular planes. Subsequently, an optimal time-based attitude maneuver path-planning method is presented, rooted in the relationship between Euler angles/axis and quaternions. Furthermore, a novel satellite attitude maneuver controller is developed based on a piecewise power-reaching law for variable structure sliding mode control. The paper theoretically demonstrates that the proposed piecewise power reaching law possesses two favorable properties regarding convergence time. On the other hand, the designed reaching law maintains continuity at all stages, theoretically eliminating buffeting. The simulation results demonstrate that the proposed controller achieves an Euler angle control precision of ±0.03° and angular velocity accuracy of ±0.15°/s, fulfilling the demands of multi-objective observational tasks. Compared to conventional power reaching law controllers, the convergence time is reduced by 3 s, and Euler angle accuracy is improved by 70%. This underscores the effectiveness of the proposed algorithm.
Improving Deep Learning Anomaly Diagnostics with a Physics-Based Simulation Model
Authors: Mäkiaho Teemu , Koskinen Kari T. , Laitinen Jouko
Published: January 2024

Deep learning algorithms often struggle to accurately distinguish between healthy and anomalous states due to the scarcity of high-quality data in real-world applications. However, these data can be obtained through a physics-based simulation model. In this research, the model serves a dual purpose: detecting anomalies in industrial processes and replicating the machine’s operational behavior with high fidelity in terms of a simulated torque signal. When anomalous behaviors are detected, their patterns are utilized to generate anomalous events, contributing to the enhancement of deep neural network model training. This research proposes a method, named Simulation-Enhanced Anomaly Diagnostics (SEAD), to detect anomalies and further create high-quality data related to the diagnosed faults in the machine’s operation. The findings of this study suggest that employing a physics-based simulation model as a synthetic-anomaly signal generator can significantly improve the classification accuracy of identified anomalous states, thereby enhancing the deep learning model’s ability to recognize deviating behavior at an earlier stage when more high-quality data of the identified anomaly has been available for the learning process. This research measures the classification capability of a Long Short-Term Memory (LSTM) autoencoder to classify anomalous behavior in different SEAD stages. The validated results clearly demonstrate that simulated data can contribute to the LSTM autoencoder’s ability to classify anomalies in a peripheral milling machine. The SEAD method is employed to test its effectiveness in detecting and replicating a failure in the support element of the peripheral milling machine.
Ensuring the Energy Efficiency of Buildings through the Simulation of Structural, Organizational, and Technological Solutions for Facade Insulation
Authors: Kos Zeljko , Babii Ihor , Grynyova Iryna , Nikiforov Oleksii
Published: January 2024

The article presents ways of selecting effective designs and technological and organizational solutions for the bonded thermal insulation systems of complex-shaped facades based on thermal field and flow modeling using the SolidWorks Simulation Xpress 2021 software and experimental–statistical modeling using the Compex program. Determining optimal insulation parameters at the design stage will help eliminate the negative effects of thermal bridges at balcony junctions and reduce the cost of implementing bonded thermal insulation systems for facades with complex shapes. It has been established that the most effective approach is to insulate not the entire perimeter of the balcony slab, as required by normative documentation, but rather to insulate a sufficient portion of the exterior wall, which is equal to 750 mm, with a 30 mm insulation thickness on top of the slab and 50 mm beneath it. This insulation technology is economically feasible for modern multistory buildings with nonstandard volumetric and architectural solutions, constructed using frame–brick, frame–monolithic, or monolithic schemes without thermal breaks between the balcony slab and the monolithic floor slab, with open-type balconies, bays, or uncovered loggias.
Development of a Performance-Based Design Technique for an Axial-Flow Fan Unit Using Airfoil Cascades Based on the Blade Strip Theory
Authors: Ryu Seo-Yoon , Cheong Cheolung , Kim Jong Wook , Park Byung Il
Published: January 2024

Axial-flow fans are widely used as cooling fans in the outdoor units of split-type air conditioners. The design of an axial-flow fan blade involves stacking several airfoils that can be differently designed for each spanwise section. However, the complex flow field around the fan blade, including circumferential and axial flows, presents challenges when applying the single airfoil theory. This study proposed a systematic performance-based design method for axial-flow fans using a cascade of airfoils based on the blade strip theory. The theory characterized the complex three-dimensional flow field driven by an axial-flow fan in terms of a two-dimensional cascade of airfoil flows. Computational fluid dynamics based on finite volume methods were used to predict the flow field and aerodynamic sound sources of an existing low-pressure axial-flow fan partially covered by a fan shroud, and the results were validated against experimental measurements. Three radial locations in the spanwise region from the hub to the blade tip that have a significant impact on aerodynamic performance were selected, and the two-dimensional flow field on a cylindrical surface with a constant radius was extracted from the three-dimensional flow field to characterize the performance of an axial fan. Then, the airfoils at the targeted span locations were optimized for a higher flow rate and greater efficiency via two-dimensional simulations using the cascades of the airfoil, and the selected optimized airfoils were applied to existing fan blades. The effectiveness of the proposed performance-based design method for low-pressure axial-flow fans was validated by the results, which showed that the redesigned fan blades with cascades of airfoils performed as predicted, increasing the intended higher flow rate by about 1%, improving power consumption by 8%, and lowering the overall sound pressure level by 1.5 dBA.
Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks
Authors: Wang Sicheng , Yan Bin , Xu Xinyue , Wang Weidong , Peng Jun , Zhang Yuzhe , Wei Xiao , Hu Wenbo
Published: January 2024

The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes. This paper adopted state-of-the-art deep learning algorithms for novel B-scan images for the automatic identification and localization of rail internal tracks. First, through image pre-processing of classification, denoising, and augmentation, four categories of defect image datasets were established, namely crescent-shaped fatigue cracks, fishbolt hole cracks, rail web cracks, and rail base transverse cracks; then, four representatives of deep learning object detection networks, YOLOv8, YOLOv5, DETR, and Faster R-CNN, were trained with the defects dataset and further applied to the testing dataset for defect identification; finally, the performances of the three detection networks were compared and evaluated at the data level, the network structure level, and the interference adaptability level, respectively. The results show that the YOLOv8 network can effectively classify and localize four categories of internal rail defects in B-scan images with a 93.3% mean average precision at three images per second, and the detection time is 58.9%, 376.8%, and 123.2% faster than YOLO v5, DETR, and Faster R-CNN, respectively. The proposed approach could ensure the real-time, accurate, and efficient detection and analysis of internal defects to a rail.
Thangka Image—Text Matching Based on Adaptive Pooling Layer and Improved Transformer
Authors: Wang Kaijie , Wang Tiejun , Guo Xiaoran , Xu Kui , Wu Jiao
Published: January 2024

Image–text matching is a research hotspot in the multimodal task of integrating image and text processing. In order to solve the difficult problem of associating image and text data in the multimodal knowledge graph of Thangka, we propose an image and text matching method based on the Visual Semantic Embedding (VSE) model. The method introduces an adaptive pooling layer to improve the feature extraction capability of semantic associations between Thangka images and texts. We also improved the traditional Transformer architecture by combining bidirectional residual concatenation and mask attention mechanisms to improve the stability of the matching process and the ability to extract semantic information. In addition, we designed a multi-granularity tag alignment module that maps global and local features of images and text into a common coding space, leveraging inter- and intra-modal semantic associations to improve image and text accuracy. Comparative experiments on the Thangka dataset show that our method achieves significant improvements compared to the VSE baseline method. Specifically, our method improves the recall by 9.4% and 10.5% for image-matching text and text-matching images, respectively. Furthermore, without any large-scale corpus pre-training, our method outperforms all models without pre-training and outperforms two out of four pre-trained models on the Flickr30k public dataset. Also, the execution efficiency of our model is an order of magnitude higher than that of the pre-trained models, which highlights the superior performance and efficiency of our model in the image–text matching task.
Combination of Artificial Neural Networks and Principal Component Analysis for the Simultaneous Quantification of Dyes in Multi-Component Aqueous Mixtures
Authors: Estrada-Moreno Julio Cesar , Rendon-Lara Eréndira , Jiménez-Núñez María de la Luz
Published: January 2024

Dyes are organic compounds capable of transmitting their color to materials, which is why they are widely used, for example, in textile fibers, leather, paper, plastic, and the food industry. In the dying process, measuring the dye’s content is extremely important to evaluate the process efficiency and minimize the dye’s discharge in wastewater, but most of the time, dyes are present in multi-component mixtures; hence, quantification by spectrophotometric methods presents a great challenge because the signal obtained in the measurement overlaps the components in the mixture. In order to overcome this issue, the use of the high-performance liquid chromatography (HPLC) method is recommended; however, it has the disadvantage of being an expensive technique, complex, and requiring excessive sample preparation. In recent years, some direct spectrophotometric methods based on multivariate regression algorithms for the quantification of dyes in bicomponent mixtures have been reported. This study presents a new framework that uses a combined ANN and principal component analysis (PCA) model for the determination of the concentration of three dyes in aqueous mixtures: Tartrazine (TZ), Amaranth Red (AR), and Blue 1 CFC (B1) dyes. The PCA–ANN model was trained and validated with ternary mixture samples of TZ, AR, and B1, and with known different compositions, spectra absorbance samples were measured in a UV-Vis spectrophotometer at wavelengths between 350–700 nm with intervals of 1 nm. The PCA–ANN model showed a mean absolute prediction error and correlation coefficient (r2) of less than 1% and greater than 0.99, respectively. The results demonstrate that the PCA–ANN model is a quick and highly accurate alternative in the simultaneous determination of dyes in ternary aqueous mixtures.
A Finite Element Study of Wire Arc Additive Manufacturing of Aluminum Alloy
Authors: Han Yousung
Published: January 2024

Analyses in the present work focus on understanding the influence of the WAAM (wire arc additive manufacturing) deposition pattern and travel speed on residual stress and warpage in aluminum alloy. The thermal profiles are analyzed using thermomechanical FE simulations. Analysis shows that the out–in deposition pattern leads to the highest level of residual stress and warpage. It is also found that an increase in the travel speed decreases the peak temperature and thermal gradient during the AM deposition, which results in a lower level of residual stress generation. A comparison of results for the line-type patterns (raster and alternate) suggests that the deposition interval between each deposition has little influence on thermal profiles, residual stress generation, and warpage. However, the contour-type patterns significantly affect the heat transfer, thermal gradient, and cooling rate during the AM deposition.
Breathing Pattern Response after 6 Weeks of Inspiratory Muscle Training during Exercise
Authors: Salazar-Martínez Eduardo
Published: January 2024

(1) Background: The breathing pattern is defined as the relationship between the tidal volume (VT) and breathing frequency (BF) for a given VE. The aim of this study was to evaluate whether inspiratory muscle training influenced the response of the breathing pattern during an incremental effort in amateur cyclists. (2) Methods: Eighteen amateur cyclists completed an incremental test to exhaustion, and a gas analysis on a cycle ergometer and spirometry were conducted. Cyclists were randomly assigned to two groups (IMTG = 9; CON = 9). The IMTG completed 6 weeks of inspiratory muscle training (IMT) using a PowerBreathe K3® device at 50% of the maximum inspiratory pressure (Pimax). The workload was adjusted weekly. The CON did not carry out any inspiratory training during the experimental period. After the 6-week intervention, the cyclists repeated the incremental exercise test, and the gas analysis and spirometry were conducted. The response of the breathing pattern was evaluated during the incremental exercise test. (3) Results: The Pimax increased in the IMTG (p < 0.05; d = 3.1; +19.62%). Variables related to the breathing pattern response showed no differences between groups after the intervention (EXPvsCON; p > 0.05). Likewise, no differences in breathing pattern were found in the IMTG after training (PREvsPOST; p > 0.05). (4) Conclusions: IMT improved the strength of inspiratory muscles and sport performance in amateur cyclists. These changes were not attributed to alterations in the response of the breathing pattern.
Advancements and Challenges in Solid-State Battery Technology: An In-Depth Review of Solid Electrolytes and Anode Innovations
Authors: Machín Abniel , Morant Carmen , Márquez Francisco
Published: January 2024

The primary goal of this review is to provide a comprehensive overview of the state-of-the-art in solid-state batteries (SSBs), with a focus on recent advancements in solid electrolytes and anodes. The paper begins with a background on the evolution from liquid electrolyte lithium-ion batteries to advanced SSBs, highlighting their enhanced safety and energy density. It addresses the increasing demand for efficient, safe energy storage in applications like electric vehicles and portable electronics. A major part of the paper analyzes solid electrolytes, key to SSB technology. It classifies solid electrolytes as polymer-based, oxide-based, and sulfide-based, discussing their distinct properties and application suitability. The review also covers advancements in anode materials for SSBs, exploring materials like lithium metal, silicon, and intermetallic compounds, focusing on their capacity, durability, and compatibility with solid electrolytes. It addresses challenges in integrating these anode materials, like the interface stability and lithium dendrite growth. This review includes a discussion on the latest analytical techniques, experimental studies, and computational models to understand and improve the anode–solid electrolyte interface. These are crucial for tackling interfacial resistance and ensuring SSBs’ long-term stability and efficiency. Concluding, the paper suggests future research and development directions, highlighting SSBs’ potential in revolutionizing energy storage technologies. This review serves as a vital resource for academics, researchers, and industry professionals in advanced battery technology development. It offers a detailed overview of materials and technologies shaping SSBs’ future, providing insights into current challenges and potential solutions in this rapidly evolving field.
Hidden Markov Model for Parkinson’s Disease Patients Using Balance Control Data
Authors: Safi Khaled , Aly Wael Hosny Fouad , Kanj Hassan , Khalifa Tarek , Ghedira Mouna , Hutin Emilie
Published: January 2024

Understanding the behavior of the human postural system has become a very attractive topic for many researchers. This system plays a crucial role in maintaining balance during both stationary and moving states. Parkinson’s disease (PD) is a prevalent degenerative movement disorder that significantly impacts human stability, leading to falls and injuries. This research introduces an innovative approach that utilizes a hidden Markov model (HMM) to distinguish healthy individuals and those with PD. Interestingly, this methodology employs raw data obtained from stabilometric signals without any preprocessing. The dataset used for this study comprises 60 subjects divided into healthy and PD patients. Impressively, the proposed method achieves an accuracy rate of up to 98% in effectively differentiating healthy subjects from those with PD.
ECG Forecasting System Based on Long Short-Term Memory
Authors: Zacarias Henriques , Marques João Alexandre Lôbo , Felizardo Virginie , Pourvahab Mehran , Garcia Nuno M.
Published: January 2024

Worldwide, cardiovascular diseases are some of the primary causes of death; yet the early detection and diagnosis of such diseases have the potential to save many lives. Technological means of detection are becoming increasingly essential and numerous techniques have been created for this purpose, such as forecasting. Of these techniques, the time series forecasting technique seeks to predict future events. The long-term time series forecasting of physiological data could assist medical professionals in predicting and treating patients based on very early diagnosis. This article presents a model that utilizes a deep learning technique to predict long-term ECG signals. The forecasting model can learn signals’ nonlinearity, nonstationarity, and complexity based on a long short-term memory architecture. However, this is not a trivial task as the correct forecasting of a signal that closely resembles the original complex signal’s structure and behavior while minimizing any differences in amplitude continues to pose challenges. To achieve this goal, we used a dataset available on the Physio net database, called MIT-BIH, with 48 ECG recordings of 30 min each. The developed model starts with pre-processing to reduce interference in the original signals, then applies a deep learning algorithm, based on a long short-term memory (LTSM) neural network with two hidden layers. Next, we applied the root mean square error (RMSE) and mean absolute error (MAE) metrics to evaluate the performance of the model and obtained an average RMSE of 0.0070±0.0028 and an average MAE of 0.0522±0.0098 across all simulations. The results indicate that the proposed LSTM model is a promising technique for ECG forecasting, considering the trends of the changes in the original data series, most notably in R-peak amplitude. Given the model’s accuracy and the features of the physiological signals, the system could be used to improve existing predictive healthcare systems for cardiovascular monitoring.
Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares
Authors: Usategui Iciar , Arroyo Yoel , Torres Ana María , Barbado Julia , Mateo Jorge
Published: January 2024

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.
Validation of the Intermolecular Disulfide Bond in Caspase-2
Authors: Amason Megan E. , Li Lupeng , Harvest Carissa K. , Lacey Carolyn A. , Miao Edward A.
Published: January 2024

Caspases are a family of proteins involved in cell death. Although several caspase members have been well characterized, caspase-2 remains enigmatic. Caspase-2 has been implicated in several phenotypes, but there has been no consensus in the field about its upstream activating signals or its downstream protein targets. In addition, the unique ability of caspase-2 to form a disulfide-bonded dimer has not been studied in depth. Herein, we investigate the disulfide bond in the context of inducible dimerization, showing that disulfide bond formation is dimerization dependent. We also explore and review several stimuli published in the caspase-2 field, test ferroptosis-inducing stimuli, and study in vivo infection models. We hypothesize that the disulfide bond will ultimately prove to be essential for the evolved function of caspase-2. Proving this will require the discovery of cell death phenotypes where caspase-2 is definitively essential.
Reduced form of Galectin-1 Suppresses Osteoclastic Differentiation of Human Peripheral Blood Mononuclear Cells and Murine RAW264 Cells In Vitro
Authors: Takeuchi Tomoharu , Oyama Midori , Tamura Mayumi , Arata Yoichiro , Hatanaka Tomomi
Published: January 2024

Galectin-1 (Gal-1) is an evolutionarily conserved sugar-binding protein found in intra- and extracellular spaces. Extracellularly, it binds to glycoconjugates with β-galactoside(s) and functions in various biological phenomena, including immunity, cancer, and differentiation. Under extracellular oxidative conditions, Gal-1 undergoes oxidative inactivation, losing its sugar-binding ability, although it exhibits sugar-independent functions. An age-related decrease in serum Gal-1 levels correlates with decreasing bone mass, and Gal-1 knockout promotes osteoclastic bone resorption and suppresses bone formation. However, the effect of extracellular Gal-1 on osteoclast differentiation remains unclear. Herein, we investigated the effects of extracellular Gal-1 on osteoclastogenesis in human peripheral blood mononuclear cells (PBMCs) and mouse macrophage RAW264 cells. Recombinant Gal-1 suppressed the macrophage colony-stimulating factor and receptor activator of nuclear factor-κB ligand-dependent osteoclast formation, actin ring formation, and bone-resorption activity of human PBMCs. Similar results were obtained for RAW264 cells. Gal-1 knockdown increased osteoclast-like cell formation, suggesting that it affected differentiation in an autocrine-like manner. Oxidized Gal-1 slightly affected differentiation, and in the presence of lactose, the differentiation inhibitory effect of galectin-1 was not observed. These findings suggest that extracellular Gal-1 inhibits osteoclast differentiation in a β-galactoside-dependent manner, and an age-related decrease in serum Gal-1 levels may contribute to reduced osteoclast activity and decreasing bone mass.
Real-World Safety and Effectiveness of Voretigene Neparvovec: Results up to 2 Years from the Prospective, Registry-Based PERCEIVE Study
Authors: Fischer M. Dominik , Simonelli Francesca , Sahni Jayashree , Holz Frank G. , Maier Rainer , Fasser Christina , Suhner Andrea , Stiehl Daniel P. , Chen Bee , Audo Isabelle , Leroy Bart P.
Published: January 2024

Voretigene neparvovec (VN) is the first available gene therapy for patients with biallelic RPE65-mediated inherited retinal dystrophy who have sufficient viable retinal cells. PERCEIVE is an ongoing, post-authorization, prospective, multicenter, registry-based observational study and is the largest study assessing the real-world, long-term safety and effectiveness of VN. Here, we present the outcomes of 103 patients treated with VN according to local prescribing information. The mean (SD) age was 19.5 (10.85) years, 52 (50.5%) were female, and the mean (SD) duration of the follow up was 0.8 (0.64) years (maximum: 2.3 years). Thirty-five patients (34%) experienced ocular treatment-emergent adverse events (TEAEs), most frequently related to chorioretinal atrophy (n = 13 [12.6%]). Eighteen patients (17.5%; 24 eyes [13.1%]) experienced ocular TEAEs of special interest, including intraocular inflammation and/or infection related to the procedure (n = 7). The mean (SD) changes from baseline in full-field light-sensitivity threshold testing (white light) at month 1, month 6, year 1, and year 2 were −16.59 (13.48) dB (51 eyes), −18.24 (14.62) dB (42 eyes), −15.84 (14.10) dB (10 eyes), and −13.67 (22.62) dB (13 eyes), respectively. The change in visual acuity from baseline was not clinically significant. Overall, the outcomes of the PERCEIVE study are consistent with the findings of VN pivotal clinical trials.
Answering the Cell Stress Call: Satellite Non-Coding Transcription as a Response Mechanism
Authors: Fonseca-Carvalho Marisa , Veríssimo Gabriela , Lopes Mariana , Ferreira Daniela , Louzada Sandra , Chaves Raquel
Published: January 2024

Organisms are often subjected to conditions that promote cellular stress. Cell responses to stress include the activation of pathways to defend against and recover from the stress, or the initiation of programmed cell death to eliminate the damaged cells. One of the processes that can be triggered under stress is the transcription and variation in the number of copies of satellite DNA sequences (satDNA), which are involved in response mechanisms. Satellite DNAs are highly repetitive tandem sequences, mainly located in the centromeric and pericentromeric regions of eukaryotic chromosomes, where they form the constitutive heterochromatin. Satellite non-coding RNAs (satncRNAs) are important regulators of cell processes, and their deregulation has been associated with disease. Also, these transcripts have been associated with stress-response mechanisms in varied eukaryotic species. This review intends to explore the role of satncRNAs when cells are subjected to adverse conditions. Studying satDNA transcription under various stress conditions and deepening our understanding of where and how these sequences are involved could be a key factor in uncovering important facts about the functions of these sequences.
Early Post-Natal Immune Activation Leads to Object Memory Deficits in Female Tsc2+/− Mice: The Importance of Including Both Sexes in Neuroscience Research
Authors: López-Aranda Manuel F. , Bach Karen , Bui Raymond , Phan Miranda , Lu Odilia , Thadani Chirag , Luchetti Alessandro , Mandanas Rochelle , Herrera Isaiah , López-Ávalos María Dolores , Silva Alcino J.
Published: January 2024

There is evidence that viral infections during pre-natal development constitute a risk factor for neuropsychiatric disorders and lead to learning and memory deficits. However, little is known about why viral infections during early post-natal development have a different impact on learning and memory depending on the sex of the subject. We previously showed that early post-natal immune activation induces hippocampal-dependent social memory deficits in a male, but not in a female, mouse model of tuberous sclerosis complex (TSC; Tsc2+/− mice). Here, we explored the impact of a viral-like immune challenge in object memory. We demonstrate that early post-natal immune activation (during the first 2 weeks of life) leads to object memory deficits in female, but not male, mice that are heterozygous for a gene responsible for tuberous sclerosis complex (Tsc2+/− mice), while no effect was observed in wild type (WT) mice. Moreover, we found that the same immune activation in Tsc2+/− adult mice was not able to cause object memory deficits in females, which suggests that the early post-natal development stage constitutes a critical window for the effects of immune challenge on adult memory. Also, our results suggest that mTOR plays a critical role in the observed deficit in object memory in female Tsc2+/− mice. These results, together with previous results published by our laboratory, showing sex-specific memory deficits due to early post-natal immune activation, reinforce the necessity of using both males and females for research studies. This is especially true for studies related to immune activation, since the higher levels of estrogens in females are known to affect inflammation and to provide neuroprotection.
Structural and Biomechanical Properties of Supramolecular Nanofiber-Based Hydrogels in Biomedicine
Authors: Pugliese Raffaele
Published: January 2024

The field of supramolecular nanofiber-based hydrogels in biomedicine has witnessed remarkable growth over the past two decades [...]
Modulation of D3R Splicing, Signaling, and Expression by D1R through PKA→PTB Phosphorylation
Authors: Casados-Delgado Orlando , Avalos-Fuentes José Arturo , Lara-Lozano Manuel , Tovar-Medina Gisela , Florán-Hernández Carla Daniela , Martínez-Nolasco Karla Gisela , Cortes Hernán , Felix Ricardo , Segovia José , Florán Benjamín
Published: January 2024

The D1R and D3R receptors functionally and synergistically interact in striatonigral neurons. Dopaminergic denervation turns this interaction antagonistic, which is correlated with a decrement in D3nf isoform and an increment in D3R membranal expression. The mechanisms of such changes in D3R are attributed to the dysregulation of the expression of their isoforms. The cause and mechanism of this phenomenon remain unknown. Dopaminergic denervation produces a decrement in D1R and PKA activity; we propose that the lack of phosphorylation of PTB (regulator of alternative splicing) by PKA produces the dysregulation of D3R splicing and changes D3R functionality. By using in silico analysis, we found that D3R mRNA has motifs for PTB binding and, by RIP, co-precipitates with PTB. Moreover, D1R activation via PKA promotes PTB phosphorylation. Acute and 5-day D1R blockade decreases the expression of D3nf mRNA. The 5-day treatment reduces D3R, D3nf, and PTB protein in the cytoplasm and increases D3R in the membrane and PTB in the nucleus. Finally, the blockade of D1R mimics the effect of dopaminergic denervation in D1R and D3R signaling. Thus, our data indicate that through PKA→PTB, D1R modulates D3R splicing, expression, and signaling, which are altered during D1R blockade or the lack of stimulation in dopaminergic denervation.
Prevalence and Characteristics of Metabolic Hyperferritinemia in a Population-Based Central-European Cohort
Authors: Gensluckner Sophie , Wernly Bernhard , Koutny Florian , Strebinger Georg , Zandanell Stephan , Stechemesser Lars , Paulweber Bernhard , Iglseder Bernhard , Trinka Eugen , Frey Vanessa , Langthaler Patrick , Semmler Georg , Valenti Luca , Corradini Elena , Datz Christian , Aigner Elmar
Published: January 2024

Background: Hyperferritinemia (HF) is a common finding and can be considered as metabolic HF (MHF) in combination with metabolic diseases. The definition of MHF was heterogenous until a consensus statement was published recently. Our aim was to apply the definition of MHF to provide data on the prevalence and characteristics of MHF in a Central-European cohort. Methods: This study was a retrospective analysis of the Paracelsus 10,000 study, a population-based cohort study from the region of Salzburg, Austria. We included 8408 participants, aged 40–77. Participants with HF were divided into three categories according to their level of HF and evaluated for metabolic co-morbidities defined by the proposed criteria for MHF. Results: HF was present in 13% (n = 1111) with a clear male preponderance (n = 771, 69% of HF). Within the HF group, 81% (n = 901) of subjects fulfilled the metabolic criteria and were defined as MHF, of which 75% (n = 674) were characterized by a major criterion. In the remaining HF cohort, 52% (n = 227 of 437) of subjects were classified as MHF after application of the minor criteria. Conclusion: HF is a common finding in the general middle-aged population and the majority of cases are classified as MHF. The new classification provides useful criteria for defining MHF.
Supplementation with a Symbiotic Induced Neuroprotection and Improved Memory in Rats with Ischemic Stroke
Authors: Cruz-Martínez Yolanda , Aguilar-Ponce Leslie , Romo-Araiza Alejandra , Chávez-Guerra Almudena , Martiñón Susana , Ibarra-García Andrea P. , Arias-Santiago Stella , Gálvez-Susano Vanessa , Ibarra Antonio
Published: January 2024

After an ischemic stroke, various harmful mechanisms contribute to tissue damage, including the inflammatory response. The increase in pro-inflammatory cytokines has been related to greater damage to the neural tissue and the promotion of neurological alterations, including cognitive impairment. Recent research has shown that the use of prebiotics and/or probiotics counteracts inflammation and improves cognitive function through the production of growth factors, such as brain-derived neurotrophic factor (BDNF), by reducing inflammatory molecules. Therefore, in this study, the effect of the symbiotic inulin and Enterococcus faecium on neuroprotection and memory improvement was evaluated in a rat model of transient middle cerebral artery occlusion (tMCAO). In order to accomplish this, the animals were subjected to ischemia; the experimental group was supplemented with the symbiotic and the control group with the vehicle. The neurological deficit as well as spatial and working memory were evaluated using the Zea Longa scale, Morris water maze, and the eight-arm maze tests, respectively. Infarct size, the levels of BDNF, and tumor necrosis factor-alpha (TNF-α) were also assessed. The results show that supplementation with the symbiotic significantly diminished the neurological deficit and infarct size, improved memory and learning, increased BDNF expression, and reduced TNF-α production. These findings provide new evidence about the therapeutic use of symbiotics for ischemic stroke and open up the possibilities for the design of further studies.
The Impact of Microglia on Neurodevelopment and Brain Function in Autism
Authors: Luo Yuyi , Wang Zhengbo
Published: January 2024

Microglia, as one of the main types of glial cells in the central nervous system (CNS), are widely distributed throughout the brain and spinal cord. The normal number and function of microglia are very important for maintaining homeostasis in the CNS. In recent years, scientists have paid widespread attention to the role of microglia in the CNS. Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder, and patients with ASD have severe deficits in behavior, social skills, and communication. Most previous studies on ASD have focused on neuronal pathological changes, such as increased cell proliferation, accelerated neuronal differentiation, impaired synaptic development, and reduced neuronal spontaneous and synchronous activity. Currently, more and more research has found that microglia, as immune cells, can promote neurogenesis and synaptic pruning to maintain CNS homeostasis. They can usually reduce unnecessary synaptic connections early in life. Some researchers have proposed that many pathological phenotypes of ASD may be caused by microglial abnormalities. Based on this, we summarize recent research on microglia in ASD, focusing on the function of microglia and neurodevelopmental abnormalities. We aim to clarify the essential factors influenced by microglia in ASD and explore the possibility of microglia-related pathways as potential research targets for ASD.
Targeting the Metabolic Paradigms in Cancer and Diabetes
Authors: Bosso Mira , Haddad Dania , Al Madhoun Ashraf , Al-Mulla Fahd
Published: January 2024

Dysregulated metabolic dynamics are evident in both cancer and diabetes, with metabolic alterations representing a facet of the myriad changes observed in these conditions. This review delves into the commonalities in metabolism between cancer and type 2 diabetes (T2D), focusing specifically on the contrasting roles of oxidative phosphorylation (OXPHOS) and glycolysis as primary energy-generating pathways within cells. Building on earlier research, we explore how a shift towards one pathway over the other serves as a foundational aspect in the development of cancer and T2D. Unlike previous reviews, we posit that this shift may occur in seemingly opposing yet complementary directions, akin to the Yin and Yang concept. These metabolic fluctuations reveal an intricate network of underlying defective signaling pathways, orchestrating the pathogenesis and progression of each disease. The Warburg phenomenon, characterized by the prevalence of aerobic glycolysis over minimal to no OXPHOS, emerges as the predominant metabolic phenotype in cancer. Conversely, in T2D, the prevailing metabolic paradigm has traditionally been perceived in terms of discrete irregularities rather than an OXPHOS-to-glycolysis shift. Throughout T2D pathogenesis, OXPHOS remains consistently heightened due to chronic hyperglycemia or hyperinsulinemia. In advanced insulin resistance and T2D, the metabolic landscape becomes more complex, featuring differential tissue-specific alterations that affect OXPHOS. Recent findings suggest that addressing the metabolic imbalance in both cancer and diabetes could offer an effective treatment strategy. Numerous pharmaceutical and nutritional modalities exhibiting therapeutic effects in both conditions ultimately modulate the OXPHOS–glycolysis axis. Noteworthy nutritional adjuncts, such as alpha-lipoic acid, flavonoids, and glutamine, demonstrate the ability to reprogram metabolism, exerting anti-tumor and anti-diabetic effects. Similarly, pharmacological agents like metformin exhibit therapeutic efficacy in both T2D and cancer. This review discusses the molecular mechanisms underlying these metabolic shifts and explores promising therapeutic strategies aimed at reversing the metabolic imbalance in both disease scenarios.
Effect of Indentation Depth on Friction Coefficient in Adhesive Contacts: Experiment and Simulation
Authors: Lyashenko Iakov A. , Pham Thao H. , Popov Valentin L.
Published: January 2024

The quasi-static regime of friction between a rigid steel indenter and a soft elastomer with high adhesion is studied experimentally. An analysis of the formally calculated dependencies of a friction coefficient on an external load (normal force) shows that the friction coefficient monotonically decreases with an increase in the load, following a power law relationship. Over the entire range of contact loads, a friction mode is realized in which constant shear stresses are maintained in the tangential contact, which corresponds to the “adhesive” friction mode. In this mode, Amonton’s law is inapplicable, and the friction coefficient loses its original meaning. Some classical works, which show the existence of a transition between “adhesive” and “normal” friction, were analyzed. It is shown that, in fact, there is no such transition. A computer simulation of the indentation process was carried out within the framework of the boundary element method, which confirmed the experimental results.