bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection of Algorithmically Generated Malicious Domain Names with Feature Fusion of Meaningful Word Segmentation and N-Gram Sequences
Authors: Chen Shaojie , Lang Bo , Chen Yikai , Xie Chong
Published: March 2023

Domain generation algorithms (DGAs) play an important role in network attacks and can be mainly divided into two types: dictionary-based and character-based. Dictionary-based algorithmically generated domains (AGDs) are similar in composition to normal domains and are harder to detect. Although methods based on meaningful word segmentation and n-gram sequence features exhibit good detection performance for AGDs, they are inadequate for mining meaningful word features of domain names, and the performance of hybrid detection of character-based and dictionary-based AGDs needs to be further improved. Therefore, in this paper, we first describe the composition of dictionary-based AGDs using meaningful word segmentation, introduce the standard deviation to better measure the word distribution features, and construct additional 11-dimensional statistical features for word segmentation results as a supplement. Then, by combining 3-gram and 1-gram sequence features, we improve the detection performance for both character-based and dictionary-based AGDs. Finally, we perform feature fusion of the above four kinds of features to achieve an end-to-end detection method for both kinds of AGDs. Experimental results showed that our method achieved an accuracy of 97.24% on the full dataset and better accuracy and F1 values than existing methods on both dictionary-based and character-based AGD datasets.
Analysis of Explicit Model Predictive Control for Track-Following Servo Control of Lunar Gravity Compensation Facility
Authors: Zheng Yonggui , Liu Meng , Wu Hao , Wang Jun
Published: March 2023

The Lunar Gravity Compensation Facility (LGCF) is a critical component in ground tests for a crewed lunar roving vehicle (CLRV). The track-following servo subsystem’s performance is of critical importance in the LGCF, as it needs to achieve high-precision tracking of the CLRV’s fast, wide range of motion in the horizontal direction. The subsystem must also operate within various constraints, including those related to speed, acceleration, and position. These requirements introduce new challenges to both the design and control of the subsystem. To tackle these challenges, this paper employs a Permanent-Magnet Synchronous Motor (PMSM) vector control method based on Space Vector Pulse Width Modulation (SVPWM) to achieve accurate speed tracking. Additionally, this paper presents an Explicit Model Predictive Control (EMPC) strategy for precise position servo control of the track-following system under multi-parameter constraints. The simulation model of the track-following servo subsystem is established based on the above methods. The simulation results demonstrate that the position tracking error of the gravity compensation system, constructed using the above method combined with EMPC control, is less than 0.2 m. The control performance of the EMPC is significantly better than those of the PI and LQI controllers. The influence of errors on the drawbar pull is within 12.5%, and its effect on the compensation force is negligible. These results provide theoretical support for the design of a track-following servo subsystem.
Needle Fracture in Pterygomandibular Space during Loco-Regional Dental Anesthesia: Systematic Review
Authors: Cirulli Nunzio , Patano Assunta , Palmieri Giulia , Di Pede Chiara , Settanni Vito , Piras Fabio , Inchingolo Alessio Danilo , Mancini Antonio , Rapone Biagio , Malcangi Giuseppina , Inchingolo Francesco , Di Venere Daniela , Dipalma Gianna , Inchingolo Angelo Michele
Published: March 2023

Background: Dentists administer hundreds of thousands of injections every day without particular concern, but the administration of local anesthetics can cause problems. One event, fortunately uncommon, that is discussed accurately but can cause significant concern is needle breakage. The purpose of this article is to review what has been reported in the literature on this topic to learn about patient symptomatology, management and possible complications. In addition, the case of a 34-year-old patient with needle persistence in the pterygoid space due to accidental rupture during inferior alveolar nerve block (IAN) was reported, for whom non-removal and monitoring over time was chosen. Materials and Methods: A literature search of the PubMed, Scopus, Web of Science and ScienceDirect databases was conducted analyzing anesthetic needle rupture during dental surgery. Results: At the end of the selection process, 17 articles resulted. Asymptomatic subjects were found, as well as those who had several symptoms. No issues were recorded by any of the authors, whether they withdrew the needle or simply monitored the patient. Conclusions: The literature on needle rupture during dental local anesthesia is scarce, and studies report conflicting results on treatment options. Most authors reported removing the fragment; however, others preferred conservative management limited to patient monitoring. No complications were reported in any study.
Robustness of Deep Learning Models for Vision Tasks
Authors: Lee Youngseok , Kim Jongweon
Published: March 2023

In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks. Thus, the importance of improving robustness against adversarial attacks has emerged as an urgent issue in vision tasks. This article aims to provide a historical summary of the evolution of adversarial attacks and defense methods on CNN-based models and also introduces studies focusing on brain-inspired models that mimic the visual cortex, which is resistant to adversarial attacks. As the origination of CNN models was in the application of physiological findings related to the visual cortex of the time, new physiological studies related to the visual cortex provide an opportunity to create more robust models against adversarial attacks. The authors hope this review will promote interest and progress in artificially intelligent security by improving the robustness of deep learning models for vision tasks.
Liquid Organic Hydrogen Carriers Applied on Methane–Hydrogen-Fueled Internal Combustion Engines: A Preliminary Analysis of Process Heat Balance
Authors: Pedrazzi Simone , Zucchi Manuel , Muscio Alberto , Kaya Ahmet Fatih
Published: March 2023

One of the main issues that has limited the use of hydrogen as an energy vector for a long time is its low energy density per unit of volume. Alternative chemical storage methods have been developed in recent years to overcome the limitations associated with compressed or liquified hydrogen storage. One of these is the Liquid Organic Hydrogen Carrier (LOHC), which utilizes organic hydrocarbons that can capture hydrogen (through an exothermic hydrogenation reaction) and release hydrogen (through an endothermic dehydrogenation reaction). In this paper, a 0D model of an internal combustion engine fueled with a mixture of hydrogen and methane was used to investigate whether the enthalpy of the exhaust gases can balance the heat rate required to self-sustain the dehydrogenation stage. Two LOHC+ compounds were considered, namely, Perhydro-dibenzyltoluene and Perhydro-N-Ethylcarbazole. Four different hydrogen-to-methane ratios were considered, assuming an engine maximum brake power ranging from 500 to 6000 RPM. An energy balance was performed, balancing the dehydrogenation heat rate and the exhaust gas cooling heat rate, in order to establish the minimum temperatures of the exhaust gases required to self-sustain the LOHC+ dehydrogenation. We demonstrated that the minimum exhaust temperatures required to self-sustain the process in different running regimes and at different hydrogen-to-methane ratios are lower than literature and experimental exhaust temperatures.
TS-CGANet: A Two-Stage Complex and Real Dual-Path Sub-Band Fusion Network for Full-Band Speech Enhancement
Authors: Chen Haozhe , Zhang Xiaojuan
Published: March 2023

Speech enhancement based on deep neural networks faces difficulties, as modeling more frequency bands can lead to a decrease in the resolution of low-frequency bands and increase the computational complexity. Previously, we proposed a convolution-augmented gated attention unit (CGAU), which captured local and global correlation in speech signals through the fusion of the convolution and gated attention unit. In this paper, we further improved the CGAU, and proposed a two-stage complex and real dual-path sub-band fusion network for full-band speech enhancement called TS-CGANet. Specifically, we proposed a dual-path CGA network to enhance low-band (0–8 kHz) speech signals. In the medium band (8–16 kHz) and high band (16–24 kHz), noise suppression is only performed in the magnitude domain. The Voice Bank+DEMAND dataset was used to conduct experiments on the proposed TS-CGANet, which consistently outperformed state-of-the-art full-band baselines, as evidenced by the results.
FAS-Res2net: An Improved Res2net-Based Script Identification Method for Natural Scenes
Authors: Zhang Zhiyun , Mamat Hornisa , Xu Xuebin , Aysa Alimjan , Ubul Kurban
Published: March 2023

Problems such as complex image backgrounds, low image quality, diverse text forms, and similar or common character layouts in different script categories in natural scenes pose great challenges to scene script identification. This paper proposes a new Res2Net-based improved script identification method, namely FAS-Res2Net. In the feature extraction part, the feature pyramid network (FPN) module is introduced, which is beneficial to aggregate the geometric feature information extracted by the shallow network and the semantic feature information extracted by the deep network. Integrating the Adaptive Spatial Feature Fusion (ASFF) module is beneficial to obtain local feature information for optimal weight fusion. In addition, the global feature information of the image is extracted by introducing the swin transformer coding block, which makes the extracted feature information more abundant. In the classification part, the convolutional classifier is used to replace the traditional Linear classification, and the classification confidence of each category is output, which improves the identification efficiency. The improved algorithm achieved identification rates of 94.7% and 96.0% on public script identification datasets SIW-13 and CVSI-2015, respectively, which verified the superiority of the method.
Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
Authors: Zhang Mengzhao , Tang Qian , Kim Jeong-Geun , Burgstaller Bernd , Kim Shin-Dug
Published: March 2023

This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models.
Indirect Thermographic Temperature Measurement of a Power Rectifying Diode Die under Forced Convection Conditions
Authors: Dziarski Krzysztof , Hulewicz Arkadiusz , Drużyński Łukasz , Dombek Grzegorz
Published: March 2023

The supply of energy with the correct parameters to electrical appliances is possible with the use of energy converters. When a direct current is required, rectifier bridges are needed. These can be made using rectifier diodes. The problem of excessive junction temperatures in power diodes, which are used to build rectifier bridges and power converters, was recognized. For this reason, research work was carried out to create a model of a rectifier diode placed on a heat sink and to analyze the heat dissipation from the junction of this diode under forced convection conditions. The results obtained from the simulation work were compared with the results of thermographic temperature measurements. The boundary conditions chosen for the simulation work are presented. A method is also presented that determined the convection coefficient under forced convection conditions. The difference between the simulation results and the results of the thermographic measurements was found to be 0.1 °C, depending on the power dissipated at the junction and the air velocity around the diode.
Readability Metrics for Machine Translation in Dutch: Google vs. Azure & IBM
Authors: van Toledo Chaïm , Schraagen Marijn , van Dijk Friso , Brinkhuis Matthieu , Spruit Marco
Published: March 2023

This paper introduces a novel method to predict when a Google translation is better than other machine translations (MT) in Dutch. Instead of considering fidelity, this approach considers fluency and readability indicators for when Google ranked best. This research explores an alternative approach in the field of quality estimation. The paper contributes by publishing a dataset with sentences from English to Dutch, with human-made classifications on a best-worst scale. Logistic regression shows a correlation between T-Scan output, such as readability measurements like lemma frequencies, and when Google translation was better than Azure and IBM. The last part of the results section shows the prediction possibilities. First by logistic regression and second by a generated automated machine learning model. Respectively, they have an accuracy of 0.59 and 0.61.
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
Authors: Solano Alberto , Dietrich Kevin N. , Martínez-Sober Marcelino , Barranquero-Cardeñosa Regino , Vila-Tomás Jorge , Hernández-Cámara Pablo
Published: March 2023

For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net. The arrival of self-attention-based networks to the field of computer vision through ViTs seems to have changed the trend of using standard convolutions. Throughout this work, we apply different architectures such as U-Net, ViTs and ConvMixer, to compare their performance on a medical semantic segmentation problem. All the models have been trained from scratch on the DRIVE dataset and evaluated on their private counterparts to assess which of the models performed better in the segmentation problem. Our major contribution is showing that the best-performing model (ConvMixer) is the one that shares the approach from the ViT (processing images as patches) while maintaining the foundational blocks (convolutions) from the U-Net. This mixture does not only produce better results (DICE=0.83) than both ViTs (0.80/0.077 for UNETR/SWIN-Unet) and the U-Net (0.82) on their own but reduces considerably the number of parameters (2.97M against 104M/27M and 31M, respectively), showing that there is no need to systematically use large models for solving image problems where smaller architectures with the optimal pieces can get better results.
In Silico Modeling the Impact of Cartilage Stiffness on Bone Tissue Stress
Authors: Alekna Vidmantas , Ardatov Oleg , Selivonec Jelena , Chabarova Olga
Published: March 2023

The knee joint is a complex biomechanical subsystem, modeling of which can reveal a deeper understanding of the processes occurring within it. The purpose of this study is to examine the stress alteration in bone based on mechanical properties of cartilage. To achieve this, a numerical model of the knee joint was developed and tested under different displacement values. The mechanical behavior of the model was represented by considering the hyperelastic properties of soft tissues, along with the verification of trabecular structure of bones, resulting in a more realistic mechanical depiction of the biological subsystem. The results showed that as the stiffness of the cartilage increased; the distribution of stresses in the bone became uneven; and stress concentrators dispersed over articular surface, while in the case of mild cartilage no stress concentrators were expressed. The proposed modeling approach allows the adaptation of patient-specific data in order to predict the outcomes of tissue diseases. The obtained results allow us to state that taking into account the non-linear properties of soft tissues is extremely important for assessing the stress state of the entire biological subsystem. The main difficulty, however, is the lack of data regarding the mechanical behavior of tissues in certain diseases.
Affective-Knowledge-Enhanced Graph Convolutional Networks for Aspect-Based Sentiment Analysis with Multi-Head Attention
Authors: Cui Xiaodong , Tao Wenbiao , Cui Xiaohui
Published: March 2023

Aspect-based sentiment analysis (ABSA) is a task in natural language processing (NLP) that involves predicting the sentiment polarity towards a specific aspect in text. Graph neural networks (GNNs) have been shown to be effective tools for sentiment analysis tasks, but current research often overlooks affective information in the text, leading to irrelevant information being learned for specific aspects. To address this issue, we propose a novel GNN model, MHAKE-GCN, which is based on the graph convolutional neural network (GCN) and multi-head attention (MHA). Our model incorporates external sentiment knowledge into the GCN and fully extracts semantic and syntactic information from a sentence using MHA. By adding weights to sentiment words associated with aspect words, our model can better learn sentiment expressions related to specific aspects. Our model was evaluated on four publicly benchmark datasets and compared against twelve other methods. The results of the experiments demonstrate the effectiveness of the proposed model for the task of aspect-based sentiment analysis.
Design and Test of a Levelling System for a Mobile Safflower Picking Platform
Authors: Guo Hui , Lu Hao , Gao Guomin , Wu Tianlun , Chen Haiyang , Qiu Zhaoxin
Published: March 2023

At this stage, safflower picking is mostly performed manually or semi-manually, the picking method is antiquated and the picking precision is low. In this experimental study, a new attitude tilt levelling system was designed for a safflower-picking robot, which has created a solid foundation for the realization of future safflower-picking machine automation. The mobile platform was simplified as a four-point support, and an automatic levelling control system was designed based on the multi-sensor data collected by a multi-inclination sensor, a multi-pressure sensor, and a displacement sensor. The error range of the levelling of the mobile platform was obtained by MATLAB simulation analysis, the relationship between the inclination of the mobile platform and the displacement of the levelling mechanism was analyzed by coordinate transformation, and the maximum levelling range of the levelling mechanism was analyzed. On this basis, an automatic levelling control system was designed. Finally, the safflower-picking mobile platform was tested, and we concluded that the levelling control system can adjust the inclination angle of the mobile platform to within 0.2° and the levelling time to within 7 s. The design of the automatic levelling control system fills the gap in the field of safflower picking and adopts multi-sensor fusion. Compared with other methods, the collected inclination data is more accurate, the levelling accuracy higher, and the levelling time shorter. The final results show that this experimental study provides a strong basis for the realization of the full-mechanical automation of safflower picking.
Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection
Authors: Zemla Katarzyna , Wojcik Grzegorz M. , Postepski Filip , Wróbel Krzysztof , Kawiak Andrzej , Sedek Grzegorz
Published: March 2023

Coronavirus disease 2019 (COVID-19) has caused everything from daily hassles, relationship issues, and work pressures to health concerns and debilitating phobias. Relaxation techniques are one example of the many methods used to address stress, and they have been investigated for decades. In this study, we aimed to check whether there are differences in the brain cortical activity of participants during relaxation or mental workload tasks, as observed using dense array electroencephalography, and whether these differences can be modeled and then classified using a machine learning classifier. In this study, guided imagery as a relaxation technique was used in a randomized trial design. Two groups of thirty randomly selected participants underwent a guided imagery session; other randomly selected participants performed a mental task. Participants were recruited among male computer science students. During the guided imagery session, the electroencephalographic activity of each student’s brain was recorded using a dense array amplifier. This activity was compared with that of a group of another 30 computer science students who performed a mental task. Power activity maps were generated for each participant, and examples are presented and discussed to some extent. These types of maps cannot be easily interpreted by therapists due to their complexity and the fact that they vary over time. However, the recorded signal can be classified using general linear models. The classification results as well as a discussion of prospective applications are presented.
Optimization of Probiotic Lactobacilli Production for In-Feed Supplementation to Feedlot Cattle
Authors: Aristimuño Ficoseco Cecilia , Mansilla Flavia I. , Vignolo Graciela M. , Nader-Macías María E. Fátima
Published: March 2023

The selection of probiotic bacteria based on their beneficial characteristics does not necessarily mean they can be later scaled up and used for technological applications and formula design. Three probiotic strains—Lactobacillus acidophilus CRL2074, Limosilactobacillus fermentum CRL2085, and Limosolactobacillus mucosae CRL2069, originally isolated from feedlot cattle feces—have demonstrated beneficial characteristics when used as in-feed probiotics. Therefore, the current study was conducted to develop a low-cost culture medium to optimize growth conditions to enhance biomass production. The study also sought to identify appropriate cryoprotective agents to sustain high functional cell numbers after freeze drying. A central composite design was applied to determine the optimal medium composition. This yielded a simplified, low-cost effective medium containing 3% molasses and industrial yeast extracts (0.5 to 2.5%) as carbon and nitrogen sources, which were added to a basal medium for each strain. Established production conditions at 37 °C, without agitation, and pH-controlled for the CRL2085 and CRL2069 strains, and free pH for the CRL2074 strain, allowed us to obtain biomass yields of 12.95, 18.20, and 12.25 g, respectively, at 24-h incubation, compared with the MRS medium. In addition, the cryoprotective effect of the selected agents was demonstrated to be strain-dependent. Thus, the highest viability (109–1010 CFU/g), stability during 30-d storage, and survival rate (88–99%) were achieved when 10% MSG (monosodium glutamate), sucrose + fructose + trehalose + WPC (whey protein concentrate) + 10% MSG, and 1.2% WPC + 10% trehalose, were used for freeze drying CRL2074, CRL2085, and CRL2069, respectively. Moreover, the probiotic strains retained their probiotic functionality when hydrophobic characteristics were evaluated. These results highlight the need to perform strain-specific evaluation of the critical factors involved in the large-scale production of probiotic lactobacilli to sustain viability and stability after the freeze drying and storage processes.
Pathogenic Role and Antibiotic Resistance of Methicillin-Resistant Staphylococcus aureus (MRSA) Strains Causing Severe Community-Acquired Pneumonia in Vietnamese Children
Authors: Tran Khai Quang , Nguyen Thuy Thi Dieu , Pham Van Hung , Pham Quan Minh , Tran Hung Do
Published: March 2023

In recent years, the pathogenic role and antibiotic resistance of methicillin-resistant Staphylococcus aureus (MRSA) strains causing severe community-acquired pneumonia (CAP) have received increasing attention in clinical practice. The aim of this study was to determine the rate of isolates of MRSA strains causing severe CAP in children and to assess their level of antibiotic resistance. The study design was cross-sectional. Children with severe CAP were sampled by nasopharyngeal aspiration for the culture, isolation, and identification of MRSA. Antimicrobial susceptibility testing was performed using the gradient diffusion method to determine the minimum inhibitory concentration (MIC) of antibiotics. Results: MRSA was identified as the second leading cause of severe CAP in Vietnamese children. The rate of isolates of S. aureus was 41/239 (17.5%), of which most were MRSA, at 32/41 (78.0%). MRSA strains were completely non-susceptible to penicillin (100%), more resistant to clindamycin and erythromycin, less sensitive to ciprofloxacin and levofloxacin, and fully susceptible to vancomycin and linezolid, with a 32-fold decreased MIC90 for vancomycin (0.5 mg/L) and a 2-fold decreased MIC90 for linezolid (4 mg/L). Therefore, vancomycin and linezolid may be appropriate options for severe CAP identified by MRSA.
Comparing the Evolutionary Trajectories of Industry 4.0 and 5.0: A Management Fashion Perspective
Authors: Madsen Dag Øivind , Slåtten Kåre
Published: March 2023

The concept of Industry 4.0 has received enormous levels of attention since it was introduced in 2011 and continues to be a dominant management trend. However, during the last few years, Industry 5.0 has emerged as an alternative visionary management concept. Although Industry 5.0 builds on and is related to its predecessor, it provides a different vision of the future of industry. Therefore, the aim of this paper is to carry out an in-depth examination of these two related management concepts, using management fashion theory as an interpretive framework and lens. Viewing Industry 4.0 and 5.0 from this vantage point involves analyzing and tracing the involvement activities of the suppliers of the two concepts as well as gauging the concepts’ demand-side impact. Generally, the comparative analysis of the cases of Industry 4.0 and 5.0 indicates that, while the growth and popularity of Industry 4.0 appear to have plateaued, it remains at a high level. Although Industry 5.0 is growing in popularity and is gaining followers and supporters, it is still a much more marginal concept and movement. This study contributes to the management fashion literature by showing how two highly related but competing management concept movements coevolve. Furthermore, the two cases provide novel insights into the trajectories and lifecycles of fashionable management concepts, in particular, the early phase where actors are actively attempting to generate credibility and legitimacy and drum up support and enthusiasm for their preferred ideas.
Machine Learning-Aided Remote Monitoring of NOx Emissions from Heavy-Duty Diesel Vehicles Based on OBD Data Streams
Authors: Ge Yang , Hou Pan , Lyu Tao , Lai Yitu , Su Sheng , Luo Wanyou , He Miao , Xiao Lin
Published: March 2023

Most of the current, popular approaches to monitoring real driving NOx emissions are based on direct measurement. However, due to the uncertainty of sensor-based measurements, such methods cannot always be used to accurately screen out the malfunctions of an emission control system. In this paper, a random forest (RF) model which extracts information from on-board diagnostics (OBD) data streams transmitted by a remote emission management vehicle terminal (REMVT) is proposed to provide a specific emission method for the online screening of high NOx emissions. First, two particular forms of modeling, random forest and logistic regression (LR), are laid out as representatives of nonparametric models and specified linear models. These two models were trained, validated and compared using OBD data collected from three China-VI heavy-duty diesel vehicles (HDDVs). The results show that as a data-driven, highly adaptive and robust learning method, the RF model can more accurately identify an abnormal emission state. Finally, a further validation was conducted, in which another China-VI HDDV was tested in two typical states, including a fault state and a normal state. The results indicated that the RF model could clearly distinguish the out-of-control emission condition from the normal operation state. The outcome of this research verifies the feasibility of using a machine learning model to process remote OBD data on HD vehicles and to identify high emissions in the case of an in-use fleet. On this basis, more sophisticated combined models and multi-stage models could be developed.
Study of the Freeze–Thaw Weathering’s Influence on Thermal Properties of Stone Artifacts
Authors: Zhang Jia’nan , Yang Xue , Chen Beichen , Wang Xiangyu , Hu Gangbo , Tao Ning , Sun Jiangang
Published: March 2023

A study of the weathering process of three types of stones, sandstone, marble, and granite, which are commonly found in artifacts worldwide, is presented. Freeze–thaw cycles are used to accelerate changes in the stone’s physical properties, such as the surface morphology, mass, and porosity. Pulsed infrared thermography, as an in situ non-destructive testing method, is adopted to measure the stone’s thermal properties, such as the diffusivity, effusivity, and conductivity. These thermal parameters influence the natural weathering process, and in turn can change with the decay of the stone materials. Preliminary experimental results show that all three types of stone experience a process of mass loss and porosity increase after the freeze–thaw cycles, and their thermal properties change differently depending on their types. Since the thermal effusivity can be obtained non-destructively by reflectance thermography, we propose for the first time the use of thermal effusivity to characterize the influence of the weathering process and as an indicator of the aging degree of stone artifacts.
Unification Theories: Rings, Boolean Algebras and Yang–Baxter Systems
Authors: Nichita Florin F.
Published: March 2023

This paper continues a series of papers on unification constructions. After a short discussion on the Euler’s relation, we introduce a matrix version of the Euler’s relation, E I π+U=O. We refer to a related equation, the Yang–Baxter equation, and to Yang–Baxter systems. The most consistent part of the paper is on the unification of rings and Boolean algebras. These new structures are related to the Yang–Baxter equation and to Yang–Baxter systems.
A Study of the Monotonic Properties of Solutions of Neutral Differential Equations and Their Applications
Authors: Moaaz Osama , Alhgilan Abtehal E.
Published: March 2023

In this paper, we aim to study the monotonic properties of the solutions of a class of neutral delay differential equations. The importance of this study lies in the fact that the monotonic properties largely control the study of the oscillation and asymptotic behaviour of the solutions to delay differential equations. Then, by using the new properties, we create improved criteria for testing the oscillation of solutions to the studied equation. We also find new criteria that can be applied more than once. Moreover, we discuss the importance and novelty of the results through the application to a special case of the studied equation.
Lipid Aldehydes 4-Hydroxynonenal and 4-Hydroxyhexenal Exposure Differentially Impact Lipogenic Pathways in Human Placenta
Authors: Rasool Aisha , Mahmoud Taysir , O’Tierney-Ginn Perrie
Published: March 2023

Long chain polyunsaturated fatty acids (LCPUFAs), such as the omega-6 (n-6) arachidonic acid (AA) and n-3 docosahexanoic acid (DHA), have a vital role in normal fetal development and placental function. Optimal supply of these LCPUFAs to the fetus is critical for improving birth outcomes and preventing programming of metabolic diseases in later life. Although not explicitly required/recommended, many pregnant women take n-3 LCPUFA supplements. Oxidative stress can cause these LCPUFAs to undergo lipid peroxidation, creating toxic compounds called lipid aldehydes. These by-products can lead to an inflammatory state and negatively impact tissue function, though little is known about their effects on the placenta. Placental exposure to two major lipid aldehydes, 4-hydroxynonenal (4-HNE) and 4-hydroxyhexenal (4-HHE), caused by peroxidation of the AA and DHA, respectively, was examined in the context of lipid metabolism. We assessed the impact of exposure to 25 μM, 50 μM and 100 μM of 4-HNE or 4-HHE on 40 lipid metabolism genes in full-term human placenta. 4-HNE increased gene expression associated with lipogenesis and lipid uptake (ACC, FASN, ACAT1, FATP4), and 4-HHE decreased gene expression associated with lipogenesis and lipid uptake (SREBP1, SREBP2, LDLR, SCD1, MFSD2a). These results demonstrate that these lipid aldehydes differentially affect expression of placental FA metabolism genes in the human placenta and may have implications for the impact of LCPUFA supplementation in environments of oxidative stress.
Sepsis-like Energy Deficit Is Not Sufficient to Induce Early Muscle Fiber Atrophy and Mitochondrial Dysfunction in a Murine Sepsis Model
Authors: Pierre Alexandre , Bourel Claire , Favory Raphael , Brassart Benoit , Wallet Frederic , Daussin Frederic N. , Normandin Sylvain , Howsam Michael , Romien Raphael , Lemaire Jeremy , Grolaux Gaelle , Durand Arthur , Frimat Marie , Bastide Bruno , Amouyel Philippe , Boulanger Eric , Preau Sebastien , Lancel Steve
Published: March 2023

Sepsis-induced myopathy is characterized by muscle fiber atrophy, mitochondrial dysfunction, and worsened outcomes. Whether whole-body energy deficit participates in the early alteration of skeletal muscle metabolism has never been investigated. Three groups were studied: “Sepsis” mice, fed ad libitum with a spontaneous decrease in caloric intake (n = 17), and “Sham” mice fed ad libitum (Sham fed (SF), n = 13) or subjected to pair-feeding (Sham pair fed (SPF), n = 12). Sepsis was induced by the intraperitoneal injection of cecal slurry in resuscitated C57BL6/J mice. The feeding of the SPF mice was restricted according to the food intake of the Sepsis mice. Energy balance was evaluated by indirect calorimetry over 24 h. The tibialis anterior cross-sectional area (TA CSA), mitochondrial function (high-resolution respirometry), and mitochondrial quality control pathways (RTqPCR and Western blot) were assessed 24 h after sepsis induction. The energy balance was positive in the SF group and negative in both the SPF and Sepsis groups. The TA CSA did not differ between the SF and SPF groups, but was reduced by 17% in the Sepsis group compared with the SPF group (p < 0.05). The complex-I-linked respiration in permeabilized soleus fibers was higher in the SPF group than the SF group (p < 0.05) and lower in the Sepsis group than the SPF group (p < 0.01). Pgc1α protein expression increased 3.9-fold in the SPF mice compared with the SF mice (p < 0.05) and remained unchanged in the Sepsis mice compared with the SPF mice; the Pgc1α mRNA expression decreased in the Sepsis compared with the SPF mice (p < 0.05). Thus, the sepsis-like energy deficit did not explain the early sepsis-induced muscle fiber atrophy and mitochondrial dysfunction, but led to specific metabolic adaptations not observed in sepsis.
Population Structure and Selection Signatures of Domestication in Geese
Authors: Chen Li , Cao Yongqing , Li Guoqin , Tian Yong , Zeng Tao , Gu Tiantian , Xu Wenwu , Konoval Oksana , Lu Lizhi
Published: March 2023

The goose is an economically important poultry species and was one of the first to be domesticated. However, studies on population genetic structures and domestication in goose are very limited. Here, we performed whole genome resequencing of geese from two wild ancestral populations, five Chinese domestic breeds, and four European domestic breeds. We found that Chinese domestic geese except Yili geese originated from a common ancestor and exhibited strong geographical distribution patterns and trait differentiation patterns, while the origin of European domestic geese was more complex, with two modern breeds having Chinese admixture. In both Chinese and European domestic geese, the identified selection signatures during domestication primarily involved the nervous system, immunity, and metabolism. Interestingly, genes related to vision, skeleton, and blood-O2 transport were also found to be under selection, indicating genetic adaptation to the captive environment. A forehead knob characterized by thickened skin and protruding bone is a unique trait of Chinese domestic geese. Interestingly, our population differentiation analysis followed by an extended genotype analysis in an additional population suggested that two intronic SNPs in EXT1, an osteochondroma-related gene, may plausibly be sites responsible for knob. Moreover, CSMD1 and LHCGR genes were found to be significantly associated with broodiness in Chinese domestic geese and European domestic geese, respectively. Our results have important implications for understanding the population structure and domestication of geese, and the selection signatures and variants identified in this study might be useful in genetic breeding for forehead knob and reproduction traits.
Adjustable Compression Wraps (ACW) vs. Compression Bandaging (CB) in the Acute Phase of Breast Cancer-Related Arm Lymphedema Management—A Prospective Randomized Study
Authors: Ochalek Katarzyna , Kurpiewska Joanna , Gradalski Tomasz
Published: March 2023

The objective of this study is to compare the effectiveness, comfort and possibilities of the self-application of adjustable compression wraps (ACW) with compression bandaging (CB) in the acute phase of treatment in advanced upper-limb lymphedema. In total, 36 patients who fulfilled the admission criteria were randomly assigned into ACW-Group (18 patients), or CB-Group (18 patients). Treatment in both groups lasted for two weeks. In the first, all patients were educated in applying adjustable compression wraps (ACW-Group) or self-bandaging (CB-Group) and treated by experienced physiotherapists. In the second week, the use of ACW and CB was continued by the patients themselves at home. In both groups, a clinically significant reduction in the affected limb volume was found after the first week (p < 0.001). A further decrease in the affected limb volume within the second week was noted only in the CB-Group (p = 0.02). A parallel trend was found in the percentage reduction in the excess volume after one and two weeks of compression therapy. Within two weeks, both groups achieved a significant improvement in decreasing lymphedema-related symptoms, but women from the ACW-Group reported complications related to carrying out compression more frequently (p = 0.002). ACW can reduce lymphedema and disease-related symptoms, but based on the results it is difficult to recommend this method as an alternative option in the acute phase of CPT among women with advanced arm lymphedema.
Microtubule Regulation in Plants: From Morphological Development to Stress Adaptation
Authors: Hsiao An-Shan , Huang Ji-Ying
Published: March 2023

Microtubules (MTs) are essential elements of the eukaryotic cytoskeleton and are critical for various cell functions. During cell division, plant MTs form highly ordered structures, and cortical MTs guide the cell wall cellulose patterns and thus control cell size and shape. Both are important for morphological development and for adjusting plant growth and plasticity under environmental challenges for stress adaptation. Various MT regulators control the dynamics and organization of MTs in diverse cellular processes and response to developmental and environmental cues. This article summarizes the recent progress in plant MT studies from morphological development to stress responses, discusses the latest techniques applied, and encourages more research into plant MT regulation.
Correction: Luedemann et al. Prostate Cancer-Associated miRNAs in Saliva: First Steps to an Easily Accessible and Reliable Screening Tool. Biomolecules 2022, 12, 1366
Authors: Luedemann Christoph , Reinersmann Jan-Ludwig , Klinger Claudia , Degener Stephan , Dreger Nici Markus , Roth Stephan , Kaufmann Michael , Savelsbergh Andreas
Published: March 2023

The authors wish to make the following corrections to their paper [...]
The Impact of Cholecaciferol Supplementation on Bone Mineral Density in Long-Term Kidney Transplant Recipients
Authors: Battaglia Yuri , Bellasi Antonio , Esposito Pasquale , Bortoluzzi Alessandra , Rotondi Silverio , Andreucci Michele , Fiorini Fulvio , Russo Domenico , Storari Alda
Published: March 2023

Although reduced bone mineral density (BMD) is associated with a higher risk of fractures, morbidity, and mortality in kidney transplant patients (KTRs), there is no consensus on optimal treatment for the alterations of BMD in this population. This study aims at assessing the effect of cholecalciferol supplementation on BMD over a follow-up period of 2 years in a cohort of long-term KTRs. Patients with age ≥ 18 years were included and divided into two subgroups based on treatment with bisphosphonate and/or calcimimetics and/or active vitamin D sterols (KTRs-treated) or never treated with the above medications (KTRs-free). BMD was evaluated at lumbar vertebral bodies (LV) and right femoral neck (FN) with standard DEXA at the beginning and end of the study. According to World Health Organization (WHO) criteria, results were expressed as T-score and Z-score. Osteoporosis and osteopenia were defined as T score ≤ −2.5 SD and T score < −1 and >−2.5 SD, respectively. Cholecalciferol was supplemented at a dose of 25,000 IU/week over 12 weeks followed by 1500 IU/day. KTRs-free (n. 69) and KTRs-treated (n. 49) consecutive outpatients entered the study. KTRs-free were younger (p < 0.05), with a lower prevalence of diabetes (p < 0.05) and of osteopenia at FN (46.3 % vs. 61.2 %) compared to KTRs-treated. At the entry none of the study subjects had a sufficient level of cholecalciferol; Z-score and T-score at LV and FN were not different between groups. At the end of the study period, serum cholecalciferol concentration was significantly increased in both groups (p < 0.001); the KTRs-free group presented an improvement in both T-score and Z-score at LV (p < 0.05) as well as a lower prevalence of osteoporotic cases (21.7% vs. 15.9%); in contrast, no changes were recorded in KTR-treated individuals. In conclusion, supplementation with cholecalciferol ameliorated Z-score and T-score at LV in long-term KTRs who had been never treated with active or inactive vitamin D sterols, bisphosphonates, and calcimimetics. Future endeavours are needed to confirm these preliminary findings.
Anti-Citrullinated Protein Antibody Reactivity towards Neutrophil-Derived Antigens: Clonal Diversity and Inter-Individual Variation
Authors: Cîrciumaru Alexandra , Afonso Marcelo Gomes , Wähämaa Heidi , Krishnamurthy Akilan , Hansson Monika , Mathsson-Alm Linda , Keszei Márton , Stålesen Ragnhild , Ottosson Lars , de Vries Charlotte , Shelef Miriam A. , Malmström Vivianne , Klareskog Lars , Catrina Anca I. , Grönwall Caroline , Hensvold Aase , Réthi Bence
Published: March 2023

Background: Why the adaptive immune system turns against citrullinated antigens in rheumatoid arthritis (RA) and whether anti-citrullinated protein antibodies (ACPAs) contribute to pathogenesis are questions that have triggered intense research, but still are not fully answered. Neutrophils may be crucial in this context, both as sources of citrullinated antigens and also as targets of ACPAs. To better understand how ACPAs and neutrophils contribute to RA, we studied the reactivity of a broad spectrum of RA patient-derived ACPA clones to activated or resting neutrophils, and we also compared neutrophil binding using polyclonal ACPAs from different patients. Methods: Neutrophils were activated by Ca2+ ionophore, PMA, nigericin, zymosan or IL-8, and ACPA binding was studied using flow cytometry and confocal microscopy. The roles of PAD2 and PAD4 were studied using PAD-deficient mice or the PAD4 inhibitor BMS-P5. Results: ACPAs broadly targeted NET-like structures, but did not bind to intact cells or influence NETosis. We observed high clonal diversity in ACPA binding to neutrophil-derived antigens. PAD2 was dispensable, but most ACPA clones required PAD4 for neutrophil binding. Using ACPA preparations from different patients, we observed high patient-to-patient variability in targeting neutrophil-derived antigens and similarly in another cellular effect of ACPAs, the stimulation of osteoclast differentiation. Conclusions: Neutrophils can be important sources of citrullinated antigens under conditions that lead to PAD4 activation, NETosis and the extrusion of intracellular material. A substantial clonal diversity in targeting neutrophils and a high variability among individuals in neutrophil binding and osteoclast stimulation suggest that ACPAs may influence RA-related symptoms with high patient-to-patient variability.
Profiling the Spectrum of Headache Disorders on 440 Breast Cancer Patients: Highlights on Clinical and Pathological Mechanisms
Authors: Ilieva Mariya , Tiberio Paola , Torrisi Rosalba , Lanzone Jacopo , Di Piero Vittorio , Santoro Armando , Viganò Alessandro , De Sanctis Rita
Published: March 2023

Although widely studied, the association between migraines (M) and breast cancer (BC) risk remains evasive. In this prospective single-center study, 440 early or locally advanced BC patients were enrolled at IRCCS Humanitas Research Hospital. Clinical and demographical data were collected. Those who suffered from headaches were evaluated with the International Classification of Headache Disorders. M was found to be significantly more prevalent in BC patients: 56.1% versus an expected prevalence of 17% in the global population. M patients showed a higher risk of having stage II or III BC than stage I, which was more frequently found in the non-headache population. Interestingly, the frequency of headache attacks was positively correlated with estrogen (r = 0.11, p = 0.05) and progesterone (r = 0.15, p = 0.007) expression, especially in patients with migraine without aura. The higher the expression of hormone receptors in BC, the higher the headache frequency. Moreover, patients suffering from headaches showed an overall earlier onset of BC. Our findings challenge the idea of a net preventive role of M on BC, suggesting a rather complex interaction in which M mostly influences some BC subtypes and vice versa. Further multi-center studies with extended follow-up are needed.