bluuebunny/crossref_metadata_2025 huggingface dataset:
In Silico and In Vitro Study of Isoquercitrin against Kidney Cancer and Inflammation by Triggering Potential Gene Targets
Authors: Iqbal Safia , Karim Md. Rezaul , Mohammad Shahnawaz , Ahn Jong Chan , Kariyarath Valappil Anjali , Mathiyalagan Ramya , Yang Deok-Chun , Jung Dae-Hyo , Bae Hyocheol , Yang Dong Uk
Published: April 2024

Kidney cancer has emerged as a major medical problem in recent times. Multiple compounds are used to treat kidney cancer by triggering cancer-causing gene targets. For instance, isoquercitrin (quercetin-3-O-β-d-glucopyranoside) is frequently present in fruits, vegetables, medicinal herbs, and foods and drinks made from plants. Our previous study predicted using protein-protein interaction (PPI) and molecular docking analysis that the isoquercitrin compound can control kidney cancer and inflammation by triggering potential gene targets of IGF1R, PIK3CA, IL6, and PTGS2. So, the present study is about further in silico and in vitro validation. We performed molecular dynamic (MD) simulation, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, cytotoxicity assay, and RT-PCR and qRT-PCR validation. According to the MD simulation (250 ns), we found that IGF1R, PIK3CA, and PTGS2, except for IL6 gene targets, show stable binding energy with a stable complex with isoquercitrin. We also performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the final targets to determine their regulatory functions and signaling pathways. Furthermore, we checked the cytotoxicity effect of isoquercitrin (IQ) and found that 5 μg/mL and 10 μg/mL doses showed higher cell viability in a normal kidney cell line (HEK 293) and also inversely showed an inhibition of cell growth at 35% and 45%, respectively, in the kidney cancer cell line (A498). Lastly, the RT-PCR and qRT-PCR findings showed a significant decrease in PTGS2, PIK3CA, and IGF1R gene expression, except for IL6 expression, following dose-dependent treatments with IQ. Thus, we can conclude that isoquercitrin inhibits the expression of PTGS2, PIK3CA, and IGF1R gene targets, which in turn controls kidney cancer and inflammation.
Isolation and Identification of Salinity-Tolerant Rhizobia and Nodulation Phenotype Analysis in Different Soybean Germplasms
Authors: Yu Tong , Wu Xiaodong , Song Yunshan , Lv Hao , Zhang Guoqing , Tang Weinan , Zheng Zefeng , Wang Xiaohan , Gu Yumeng , Zhou Xin , Li Jianlin , Tian Siyi , Hou Xiuming , Chen Qingshan , Xin Dawei , Ni Hejia
Published: April 2024

Increasing the soybean-planting area and increasing the soybean yield per unit area are two effective solutions to improve the overall soybean yield. Northeast China has a large saline soil area, and if soybeans could be grown there with the help of isolated saline-tolerant rhizobia, the soybean cultivation area in China could be effectively expanded. In this study, soybeans were planted in soils at different latitudes in China, and four strains of rhizobia were isolated and identified from the soybean nodules. According to the latitudes of the soil-sampling sites from high to low, the four isolated strains were identified as HLNEAU1, HLNEAU2, HLNEAU3, and HLNEAU4. In this study, the isolated strains were identified for their resistances, and their acid and saline tolerances and nitrogen fixation capacities were preliminarily identified. Ten representative soybean germplasm resources in Northeast China were inoculated with these four strains, and the compatibilities of these four rhizobium strains with the soybean germplasm resources were analyzed. All four isolates were able to establish different extents of compatibility with 10 soybean resources. Hefeng 50 had good compatibility with the four isolated strains, while Suinong 14 showed the best compatibility with HLNEAU2. The isolated rhizobacteria could successfully establish symbiosis with the soybeans, but host specificity was also present. This study was a preliminary exploration of the use of salinity-tolerant rhizobacteria to help the soybean nitrogen fixation in saline soils in order to increase the soybean acreage, and it provides a valuable theoretical basis for the application of saline-tolerant rhizobia.
Effects of Trehalose Preconditioning on H9C2 Cell Viability and Autophagy Activation in a Model of Donation after Circulatory Death for Heart Transplantation
Authors: Gao Jingwen , Shingu Yasushige , Wakasa Satoru
Published: April 2024

Donation after circulatory death (DCD) is a promising strategy for alleviating donor shortage in heart transplantation. Trehalose, an autophagy inducer, has been shown to be cardioprotective in an ischemia-reperfusion (IR) model; however, its role in IR injury in DCD remains unknown. In the present study, we evaluated the effects of trehalose on cardiomyocyte viability and autophagy activation in a DCD model. In the DCD model, cardiomyocytes (H9C2) were exposed to 1 h warm ischemia, 1 h cold ischemia, and 1 h reperfusion. Trehalose was administered before cold ischemia (preconditioning), during cold ischemia, or during reperfusion. Cell viability was measured using the Cell Counting Kit-8 after treatment with trehalose. Autophagy activation was evaluated by measuring autophagy flux using an autophagy inhibitor, chloroquine, and microtubule-associated protein 1A/1B light chain 3 B (LC3)-II by western blotting. Trehalose administered before the ischemic period (trehalose preconditioning) increased cell viability. The protective effects of trehalose preconditioning on cell viability were negated by chloroquine treatment. Furthermore, trehalose preconditioning increased autophagy flux. Trehalose preconditioning increased cardiomyocyte viability through the activation of autophagy in a DCD model, which could be a promising strategy for the prevention of cardiomyocyte damage in DCD transplantation.
Preparation of Durable Superhydrophobic Coatings Based on Discrete Adhesives
Authors: Liu Xuejuan , Zhou Zhiguo , Chen Ming , Liu Zheng , Jiang Shuhui , Wang Lei
Published: April 2024

Due to the low adhesion observed at the interface between solid and liquid, superhydrophobic coatings hold significant promise for various applications, such as self-cleaning, anti-corrosion, anti-icing, and drag reduction. However, a notable challenge hindering their widespread adoption in these domains lies in their delicate durability. In this study, we propose a straightforward method for preparation. The fluorosilicone resin is initially discretized through a gradual introduction of nonsolvent into its solution, followed by thorough mixing and stirring with silica nanoparticles. The resulting mixture is then sprayed onto the substrate surface after drying, forming a self-similar, porous, and rough structure extending from top to bottom. This process yields a coating exhibiting excellent chemical and mechanical durability simultaneously. Using this approach, we achieved a superhydrophobic coating with a contact angle of 156° and a roll angle of 2.2°, with water droplet adhesion of only 10.8 ± 0.4 µN. Remarkably, the coating maintained excellent superhydrophobicity even after undergoing sandpaper abrasion (10 m), tape peeling (30 times), and prolonged water impact (60 min), showing its robust mechanical stability. Furthermore, following exposure to acid, alkali, and aqueous solutions (7 days), UV irradiation (10 days), and extreme temperature variations (–20 °C to 80 °C), the coatings retained their superhydrophobic properties and exhibited good chemical durability. This method offers a novel approach to enhance the durability and practicality of superhydrophobic coatings.
A Zero-Shot Image Classification Method of Ship Coating Defects Based on IDATLWGAN
Authors: Bu Henan , Yang Teng , Hu Changzhou , Zhu Xianpeng , Ge Zikang , Yan Zhuwen , Tang Yingxin
Published: April 2024

In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However, in real applications, due to the complexity and uncertainty of ship painting conditions, it is very likely that there are unknown classes of painting defects, and the traditional deep learning model cannot identify a few classes, which leads to model overfitting and reduces its generalization ability. In this paper, a zero-shot Image classification method for ship painting defects based on IDATLWGAN is proposed to identify new unknown classes of defects in the target domain. The method is based on a deep convolutional neural network combined with adversarial transfer learning. First, a preprocessed ship painting defect dataset is used as input for the domain-invariant feature extractor. Then, the domain invariant feature extractor takes domain invariant features from the source and target domains. Finally, Defect discriminators and domain alignment discriminators are employed to classify the known categories of unlabeled defects and unknown categories of unlabeled defects in the target domain and to further reduce the distance between the edge distributions of the source and target domains. The experimental results show that the proposed model in this paper extracts a better distribution of invariant features in the source and target domains compared to other existing transfer learning models. It can successfully complete the migration task and accurately recognize the painting defects of known categories and new unknown categories, which is a perfect combination of intelligent algorithms and engineering practice.
The Role of Rare Earths on Steel and Rare Earth Steel Corrosion Mechanism of Research Progress
Authors: Bai Yuzhen , Zheng Shujia , Liu Na , Liu Yang , Wang Xiaoning , Qiu Lina , Gong Aijun
Published: April 2024

Corrosion has always been an important factor affecting the life of steel, which causes huge economic losses every year. How to improve the corrosion resistance of steel has always been a research focus. Adding rare earth elements into steel is an important method to improve the corrosion resistance of steel. In this paper, the effects of rare earth elements on steel are summarized, including the purification of molten steel, modification and modification of inclusions, improvement of grain boundaries by solid solution strengthening, the influence of phase transformation and the refinement of microstructure, and reduction in C and N desolubilization. On this basis, the progress of research on the corrosion resistance mechanisms of rare earth steel is summarized, focusing on rare earth-modified inclusions. This includes the changes in composition and sizes of inclusions by rare earth addition, promoting the transformation of MnS and Al2O3 in rare earth inclusions with regular shapes, smaller sizes and better performance, or composite rare earth inclusions. The corrosion pits that form in the early stages of corrosion are shallow in depth, fewer in number and light in corrosion degree. The effects of rare earth materials on the rust layer include: rare earth promotes the formation of a more stable corrosion product α-FeOOH, and rare earth promotes the formation of a dense rust layer, which covers the surface of the matrix and hinders the transmission of corrosion ions. The protective effect of the rare earth atomic layer on the substrate and the corrosion inhibition effect of rare earth ions are formed by the segregation of rare earth at the interface. In the end, the existing problems in the research into rare earth steel and future research directions are briefly put forward, including improving the addition process of rare earth steel, theoretical guidance on enhancing the corrosion resistance mechanism of rare earth steel, and extending the research from La, Ce, and Y steel to more rare earth steels.
Rapid Detection and Elimination of Subsurface Mechanical Damage for Improving Laser-Induced Damage Performance of Fused Silica
Authors: Li Qingzhi , Zhang Yubin , Shao Ting , Shi Zhaohua , Huang Jin , Ye Xin , Yang Liming , Zheng Wanguo
Published: April 2024

The fabrication of SSD-free fused silica optics is a crucial objective for high-power laser applications. To treat the surface of polished fused silica, a combination of RIE/RIBE and deep-controlled etch (DCE) techniques are typically employed. Currently, it is important to consider and study the ideal etching depth and precision while using combined etching techniques to remove the identified SSD. Herein, we present a novel approach to identify the distribution of SSD in fused silica, which corresponds to a specific grinding/polishing process condition. Our method involves using a mobile RIBE to perform cone cutting and remove material from the polished fused silica surface. Afterward, we etch the optical element’s surface with HF to visualize the subsurface cracks and understand their relationship with the RIBE depth. Through a systematic investigation of the combined etching technique, we establish a correlation between the depth of RIBE and DCE and the performance of laser damage. The combined etching technique can be implemented as a dependable approach to treat the surface/subsurface defects in fused silica and has the potential to improve laser damage resistance significantly.
Application of Secondary Ion Mass Spectrometry for Analysis of Decorative Coatings on Fancy Goods Accessories
Authors: Abudouwufu Tushagu , Lan Yueqiang , Han Bin , Fu Dejun , Tolstoguzov Alexander
Published: April 2024

The results of a secondary ion mass spectrometry (SIMS) study on Ag and ZrN decorative coatings on nickel and white bronze substrates for fancy goods accessories are presented. It was found that for Ag coatings, an intense diffusion of Cr from the adhesion layer between the coating and the substrate is observed, and corrosion testing in an acetic salt (CH3COOH+NaCl) atmosphere leads to the almost complete degradation of such coatings. ZrN coatings on white bronze turned out to be the most resistant to Cr diffusion and corrosion processes.
Approach to Heterogeneous Surface Roughness Evaluation for Surface Coating Preparation
Authors: Vrbová Hana , Kubišová Milena , Pata Vladimír , Knedlová Jana , Javořík Jakub , Bočáková Barbora
Published: April 2024

This paper focuses on evaluating the roughness of heterogeneous surfaces, aiming to interpret data effectively for thorough assessment. Previous research highlights the significant impact of surface roughness on final coatings. Beam-cutting machining generates surfaces with position-dependent roughness parameter changes. However, there is inconsistency in the methods for investigating roughness in such surfaces, leading to the loss of crucial information and potentially inaccurate results. This could result in flawed coating preparation and subsequent defects. This paper proposes a suitable evaluation method involving an optical 3D profilometer and a stabilizing support system for reliable measurements. It provides a detailed description of the materials and methods used. The objective is to establish a more consistent and accurate approach to assessing roughness for coating preparation. Technical applications demonstrate up-to-fivefold fluctuations in surface topography parameters, as illustrated in this manuscript. Overall, this paper seeks to address these challenges and provide a robust framework for evaluating roughness in heterogeneous surfaces, thereby enhancing surface coating preparation processes.
Evaluation of the Bond Quality of Metal-Clad Plates Using Laser Ultrasonic Local Resonance
Authors: Ji Baoping , Cao Jianshu , Zhang Qingdong
Published: April 2024

The effective detection of delamination defects, especially sub-millimeter delamination defects, in metal-clad plates is of great significance in improving product quality. In this work, the laser ultrasonic (LU) local resonance method is used to locate and characterize the sub-millimeter defects in stainless/carbon steel-clad plates. The influence of the delamination radius on the amplitude and resonant frequency of the laser ultrasound was investigated using 2D axisymmetric finite element (FE) simulations. The simulation results show that both the amplitude and the first resonance frequency (FRF) are effective features for detecting large-scale delamination defects, but the FRF is a better feature for detecting tiny delamination defects. A 304/Q235/304-clad plate specimen was made through a hot rolling bonding process, which contained a large number of self-forming delamination defects. The laser ultrasonic signals of different composite states collected in the experiment have good consistency with the simulated waveforms. The experimental results show that the laser ultrasonic local resonance method is a high-resolution imaging method, which can locate and characterize the sub-millimeter delamination defects in stainless/carbon steel-clad plates.
The Dynamical Properties of Three Different Variants of the Orange Carotenoid Protein: A Quasielastic Neutron Scattering Study
Authors: Hajizadeh Mina , Golub Maksym , Moldenhauer Marcus , Lohstroh Wiebke , Friedrich Thomas , Pieper Jörg
Published: April 2024

Besides a well-adapted structure, proteins often require a specific dynamical flexibility to undergo conformational changes in order to carry out their function. The latter dynamics can be directly measured by quasielastic neutron scattering as demonstrated here for three variants of the orange carotenoid protein (OCP), which plays a pivotal role in the protection of the cyanobacterial photosynthetic apparatus against photodamage. We investigate the dynamics of the structurally compact, dark-adapted wild type of OCP (OCPwt) in comparison with that of two mutant forms. The latter two mutants differ preferentially in their structures. The orange mutant OCP-W288A is assumed to have a compact structure and to preferentially bind the pigment echinenone, while the pink mutant OCP-W288A appears to represent the more elongated structure of the red active state of OCP binding the carotenoid canthaxanthin, respectively. The study reveals three major findings: (a) the dynamics of the red active state of OCP is significantly enhanced due to a larger number of protein residues being exposed to the solvent at the surface of the protein; (b) the dynamics of all OCP forms appear to be suppressed upon the freezing of the solvent, which is most likely due to an ice-induced aggregation of the proteins; and (c) the wild type and the compact mutant exhibit different dynamics attributed to a missing H-bond between the pigment and protein, resulting a destabilization of the surrounding protein.
Analysing the Photo-Physical Properties of Liquid Crystals
Authors: Hobbs Jordan , Mattsson Johan , Nagaraj Mamatha
Published: April 2024

Intrinsically fluorescent liquid crystals are highly sought after for a variety of applications. Most of the measurements of photo-physical properties of liquid crystals are made in dilute solutions, which is mainly due to the relative ease of both these measurements and the interpretation of data. The fluorescence spectra depend on a number of parameters including the concentration in liquid crystal solutions, the device geometry, and the mesophase in which the spectra have been measured. Working with neat, or concentrated, liquid crystal samples adds experimental complexities such as the inner filter effect (IFE), which affects the collection of data, interpretation of the results, and accuracy of the conclusions. In this paper, we present a systematic study of the photo-physical properties of both a model reference material, Nile red, and a nematic liquid crystal, 4-cyano-4′-pentylbiphenyl (5CB). The influence on the emission spectra of an increasing solute concentration is investigated and discussed. Moreover, a detailed investigation of the influence of the used device geometry, as well as the choice of appropriate data fitting methodologies, are presented.
Active Surveillance in Non-Muscle Invasive Bladder Cancer, the Potential Role of Biomarkers: A Systematic Review
Authors: Parrao Diego , Lizana Nemecio , Saavedra Catalina , Larrañaga Matías , Lindsay Carolina B. , San Francisco Ignacio F. , Bravo Juan Cristóbal
Published: April 2024

Bladder cancer (BC) is the tenth most common cause of cancer worldwide and is the thirteenth leading cause of cancer mortality. The non-muscle invasive (NMI) variant represents 75% of cases and has a mortality rate of less than 1%; however, it has a high recurrence rate. The gold standard of management is transurethral resection in the case of new lesions. However, this is associated with significant morbidity and costs, so the reduction of these procedures would contribute to reducing complications, morbidity, and the burden to the health system associated with therapy. In this clinical scenario, strategies such as active surveillance have emerged that propose to manage low-risk BC with follow-up; however, due to the low evidence available, this is a strategy that is underutilized by clinicians. On the other hand, in the era of biomarkers, it is increasingly known how to use them as a tool in BC. Therefore, the aim of this review is to provide to clinical practitioners the evidence available to date on AS and the potential role of biomarkers in this therapeutic strategy in patients with low-grade/risk NMIBC. This is the first review linking use of biomarkers and active surveillance, including 29 articles.
The Effects of Infectious Diseases on the Consequences of Interspecific Competition in Grassland Communities
Authors: Chen Yanli , Liu Yanping , Liu Xiaoni , Zhang Zhengzhong , Zhang Feng
Published: April 2024

As infectious diseases have a severe impact on the individual survival and competitive ability of host species in grassland communities, competition between relevant species may have different consequences, potentially influencing the species composition of grassland communities and the functioning of grassland ecosystems. Understanding the impact of infectious diseases on competition is thus crucial for the health management of grassland ecosystems. How do infectious diseases affect the competitive coexistence of grassland plant species? In this study, by drawing on Tilman’s multispecies competition model and incorporating the spread characteristics of grassland plant diseases, we built three spatial competition models of two grass species subject to infectious diseases, and we analyzed the invasion conditions of infectious diseases and how they impact the population dynamics and competitive consequences of the species. Our model revealed the following: (1) Diseases with high transmission rates and low virulence are more likely to invade populations, while the presence of non-host species is detrimental to disease invasion. (2) Disease promotes the coexistence of competing species, breaking down the original competition–compromise trade-off mechanism for species coexistence and greatly expanding the range of parameters within which the two species can coexist. (3) Pathogen infections indirectly weaken the suppression of inferior species by dominant species, which is similar to the cascade effect seen in trophic interactions. Our findings highlight the importance of disease in species coexistence and grassland biodiversity maintenance.
Usefulness of Diffusion-Weighted Imaging in Evaluating Acute Cellular Rejection and Monitoring Treatment Response in Liver Transplant Recipients
Authors: Chiang Hsien-Jen , Chuang Yi-Hsuan , Li Chun-Wei , Lin Chih-Che , Eng Hock-Liew , Chen Chao-Long , Cheng Yu-Fan , Chou Ming-Chung
Published: April 2024

Acute cellular rejection (ACR) is a significant immune issue among recipients following liver transplantation. Although diffusion-weighted magnetic resonance imaging (DWI) is widely used for diagnosing liver disease, it has not yet been utilized for monitoring ACR in patients after liver transplantation. Therefore, the aim of this study was to evaluate the efficacy of DWI in monitoring treatment response among recipients with ACR. This study enrolled 25 recipients with highly suspected ACR rejection, and all subjects underwent both biochemistry and DWI scans before and after treatment. A pathological biopsy was performed 4 to 24 h after the first MRI examination to confirm ACR and degree of rejection. All patients were followed up and underwent a repeated MRI scan when their liver function returned to the normal range. After data acquisition, the DWI data were post-processed to obtain the apparent diffusion coefficient (ADC) map on a voxel-by-voxel basis. Five regions of interest were identified on the liver parenchyma to measure the mean ADC values from each patient. Finally, the mean ADC values and biochemical markers were statistically compared between ACR and non-ACR groups. A receiver operating characteristic (ROC) curve was constructed to evaluate the performance of the ADC and biochemical data in detecting ACR, and correlation analysis was used to understand the relationship between the ADC values, biochemical markers, and the degree of rejection. The histopathologic results revealed that 20 recipients had ACR, including 10 mild, 9 moderate, and 1 severe rejection. The results demonstrated that the ACR patients had significantly lower hepatic ADC values than those in patients without ACR. After treatment, the hepatic ADC values in ACR patients significantly increased to levels similar to those in non-ACR patients with treatment. The ROC analysis showed that the sensitivity and specificity for detecting ACR were 80% and 95%, respectively. Furthermore, the correlation analysis revealed that the mean ADC value and alanine aminotransferase level had strong and moderate negative correlation with the degree of rejection, respectively (r = −0.72 and −0.47). The ADC values were useful for detecting hepatic ACR and monitoring treatment response after immunosuppressive therapy.
B-Type Natriuretic Peptide—A Paradox in the Diagnosis of Acute Heart Failure with Preserved Ejection Fraction in Obese Patients
Authors: Rus Marius , Banszki Loredana Ioana , Andronie-Cioara Felicia Liana , Pobirci Oana Liliana , Huplea Veronica , Osiceanu Alina Stanca , Osiceanu Gheorghe Adrian , Crisan Simina , Pobirci Decebal Dumitru , Guler Madalina Ioana , Marian Paula
Published: April 2024

Background and objectives: B-type natriuretic peptide (BNP) represents a clinical tool for the diagnosis and prognostic evaluation of acute and chronic heart failure patients. The purpose of this retrospective study was to evaluate BNP values in obese and non-obese patients with acute heart failure with preserved ejection fraction. Materials and methods: In this study, we enrolled 240 patients who presented to the emergency department complaining of acute shortness of breath and fatigue. The patients were divided into two groups according to their body mass index (BMI) values. The BMI was calculated as weight (kilograms) divided by height (square meters). The BNP testing was carried out in the emergency department. Results: Group I included patients with a BMI of <30 kg/m2 and group II included patients with a BMI of ≥30 kg/m2. The average age of the patients was 60.05 ± 5.02 years. The patients in group II were significantly younger compared with those included in group I. Group II included a higher number of women compared to group I. Group I had fewer patients classified within New York Heart Association (NYHA) functional classes III and IV compared with group II. Echocardiography revealed an ejection fraction of ≥50% in all participants. Lower BNP levels were observed in patients from group II (median = 56, IQR = 53–67) in comparison to group I (median = 108.5, IQR = 106–112) (p < 0.001). Conclusions: Obesity and heart failure are continuously rising worldwide. In this retrospective study, we have highlighted the necessity to lower the threshold of BNP levels in obese patients with acute heart failure and preserved ejection fraction.
Radiation-Related Fractures after Radical Radiotherapy for Cervical and Endometrial Cancers: Are There Any Differences?
Authors: Malikova Hana , Nadova Katarina , Reginacova Klaudia , Kremenova Karin , Rob Lukas
Published: April 2024

In this study, we reviewed CT/MRI scans and studied the rates of radiation-related fractures in subjects treated for cervical cancer (CC, 63 subjects) by radical radiotherapy (RT) and in subjects treated for endometrial cancer (EC, 64 subjects) by radical surgery and RT. The differences between bone density measured in L1 on pretreatment CT, age and body mass index (BMI) were evaluated. Despite significant differences in RT total dose, age, BMI, etc., between both groups, the rate of radiation-related fractures was similar: 28.6% of CC versus 26.6% of EC subjects. CC subjects with fractures were significantly older (62.4 ± 10.1 vs. 49.0 ± 12.4 years; p < 0.001), and their bone densities were significantly lower (106.3 ± 40.0 vs. 168.2 ± 49.5 HU; p < 0.001); no difference in BMI was found. EC subjects with fractures were without significant difference in age but had significantly lower bone densities (103.8 ± 29.0 vs. 133.8 ± 42.3 HU; p = 0.009) and BMIs (26.1 ± 4.9 vs. 31.8 ± 6.9 kg/m2; p = 0.003). Bone density strongly correlated with age (r = −0.755) only in CC subjects. Subjects with fractures from both groups had similarly low bone densities (106.3 ± 40.0 vs. 103.8 ± 29.0 HU; p = 0.829); however, no correlation between bone density and BMI was found. The rate of radiation-related fractures in both groups was clearly associated only with low pretreatment bone density, reflecting osteoporosis.
Novelties and Perspectives of Intestinal Ultrasound in the Personalised Management of Patients with Inflammatory Bowel Diseases—A Systematic Review
Authors: Mihai Vasile-Claudiu , Gheorghe Liliana , Rezuș Ioana-Irina , Jucan Alina Ecaterina , Andronic Mihaela-Cristiana , Gavrilescu Otilia , Dranga Mihaela , Andronic Andrei-Mihai , Prelipcean Cristina Cijevschi , Rezuș Ciprian , Mihai Cătălina
Published: April 2024

Inflammatory bowel diseases (IBDs) affect over 4.9 million individuals worldwide. Colonoscopy (CS) is the gold-standard technique for diagnosis. The remissive–recurrent pattern of evolution raises the need for non-invasive techniques to monitor disease activity. This review aims to present the advantages of intestinal ultrasound (IUS) in managing IBDs. Our search was conducted on the PubMed, Embase, and Cochrane (CENTRAL) databases, selecting original studies comparing IUS with other imaging and invasive monitoring methods. Our search yielded 8654 results, of which 107 met the inclusion criteria. Increased bowel wall thickness (BWT) and colour Doppler signal (CDS) are discriminative for disease activity. IUS can predict disease outcomes and detect response to treatment or postoperative recurrence. Contrast-enhanced ultrasound (CEUS) and elastography help differentiate fibrotic from inflammatory stenoses. The difficult rectal assessment limits the use of IUS in ulcerative colitis (UC). Transmural healing may develop as a therapeutic target as it is associated with better outcomes. Patients are compliant with this technique, and its results correlate well with CS and other imaging methods. In conclusion, IUS proves to be essential in assessing IBD activity and treatment response, predicting outcomes and detecting complications. CEUS and elastography are researched to improve the diagnostic values of IUS.
Unveiling Human Values: Analyzing Emotions behind Arguments
Authors: Jafari Amir Reza , Rajapaksha Praboda , Farahbakhsh Reza , Li Guanlin , Crespi Noel
Published: April 2024

Detecting the underlying human values within arguments is essential across various domains, ranging from social sciences to recent computational approaches. Identifying these values remains a significant challenge due to their vast numbers and implicit usage in discourse. This study explores the potential of emotion analysis as a key feature in improving the detection of human values and information extraction from this field. It aims to gain insights into human behavior by applying intensive analyses of different levels of human values. Additionally, we conduct experiments that integrate extracted emotion features to improve human value detection tasks. This approach holds the potential to provide fresh insights into the complex interactions between emotions and values within discussions, offering a deeper understanding of human behavior and decision making. Uncovering these emotions is crucial for comprehending the characteristics that underlie various values through data-driven analyses. Our experiment results show improvement in the performance of human value detection tasks in many categories.
Assessing Production and Marketing Efficiency of Organic Horticultural Commodities: A Stochastic Frontier Analysis
Authors: Lestari Etty Puji , Prajanti Sucihatiningsih Dian Wisika , Adzim Fauzul , Mubarok Faizul , Hakim Arif Rahman
Published: April 2024

Inefficiency is a problem in the production process, including in the organic farming sector. Over a long term period, this problem can disrupt the productivity of agricultural crops. This research aims to analyze the production and marketing efficiency of organic cabbage farming in the Kopeng agropolitan area, Indonesia. We utilized a Cobb–Douglas production efficiency analysis with the Stochastic Frontier Analysis (SFA) approach. The variables in this study include organic cabbage production, land area, seedlings, organic fertilizers, organic pesticides, and labor. We conducted in-depth interviews with 60 organic cabbage farmers in Kopeng, Indonesia, from January to August 2023. The research results showed that organic cabbage cultivation was economically inefficient in production, technical, and marketing. The use of organic fertilizers, the ability to diversify products on limited land, and the use of pesticides, have not been utilized optimally yet. The results of the marketing efficiency analysis showed that it was efficient. Organic plants were believed to have their market share and to have a higher selling value than non-organic ones. The implication was that the government needed to provide training in producing organic fertilizers and pesticides to reduce production costs so that organic farming could be technically and financially efficient. This research enriched the discussion regarding the need to analyze production and marketing efficiency to find strategies to increase organic cabbage productivity.
Assessing Numerical Analysis Performance with the Practi Mobile App
Authors: Cutumisu Maria , Garn Kristin , Spiteri Raymond J.
Published: April 2024

Numerical analysis is a unique combination of mathematical and computing skills. It facilitates a deeper understanding of data analytics and machine learning software libraries, which are exploding in use and importance. However, it is a topic that continues to challenge students because it requires a confluence of conceptual, procedural, and computational skills and associated pedagogies. Therefore, it is valuable to identify effective pedagogies and tools to enhance and assess student numerical analysis skills. Despite the proliferation of mobile technology in postsecondary education, its role in the context of numerical analysis is largely unknown. This quasi-experimental pilot study used Practi, an educational mobile app designed to assess numerical analysis performance and promote both retrieval practice and deliberate practice, which have been shown to help improve performance and develop expertise. Participants were 32 undergraduate students enrolled in a second-year introductory Numerical Analysis course at a large North American university. They were prompted to use Practi to solve quizzes on a regular basis throughout the course, before and after each lecture, to promote deliberate practice and spaced retrieval. Results of a paired t-test analysis showed that Practi was able to detect improvement in student quiz performance after the lectures compared to before the lectures. Additionally, performance on the Practi quizzes after the lectures was positively associated with the overall course performance. This suggests that mobile apps supporting deliberate and retrieval practice can complement more traditional means of instruction and assessment of numerical analysis in postsecondary mathematics education.
Fast Sparse Bayesian Learning Based on Beamformer Power Outputs to Solve Wideband DOA Estimation in Underwater Strong Interference Environment
Authors: Zhang Yahao , Liang Ningning , Yang Yixin , Yang Yunchuan
Published: April 2024

Wideband direction-of-arrival (DOA) estimation is an important task for passive sonar signal processing. Nowadays, sparse Bayesian learning (SBL) attracts much attention due to its good performance. However, performance degrades in the existence of strong interference. This problem can be solved by combining the beamformer and the SBL. The beamformer is a useful tool to suppress interference. Then, the SBL can easily estimate the DOA of the targets from the beamformer power outputs (BPO). Unfortunately, the latter step needs to compute the matrix inversion frequently, which brings some computational burden to the sonar system. In this paper, the BPO-based SBL is modified. A sequential solution is provided for the parameters in the BPO probabilistic model. In this manner, only one signal precision parameter involved in the probabilistic model is updated in each iteration and the matrix inversion is avoided during the iteration, thus reducing the computational burden. Simulation and experimental results show that the proposed method maintains high estimation precision in the interference environment. At the same time, its computational efficiency is almost three times higher in comparison with state-of-the-art methods.
Plato’s Shadows in the Digital Cave: Controlling Cultural Bias in Generative AI
Authors: Karpouzis Kostas
Published: April 2024

Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato’s Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies.
A PTM-Based Framework for Enhanced User Requirement Classification in Product Design
Authors: Zhang Zhiwei , Dou Yajie , Xu Xiangqian , Tan Yuejin
Published: April 2024

Accurately identifying and classifying customer requirements is crucial for successful product design. However, traditional methods for requirement classification, such as Kano models based on questionnaires, can be time-consuming and may not capture all requirements accurately. Analyzing large volumes of user reviews using simple natural language processing techniques can also result in accuracy issues. To address these challenges, we propose a framework that combines pre-trained models (PTMs), Kano models, and the sentiment analysis technique. Our approach integrates an LDA-K-Means model enhanced by PTM ERNIE for pinpointing product feature topics within user reviews. Then, a sentiment analysis is performed using the fine-tuned PTM SKEP to assess user satisfaction with features. Finally, the Kano model is applied to perform requirement classification. We evaluate our framework quantitatively, demonstrating its superior performance compared to the baseline models. Our sentiment analysis model also outperforms the other baseline models. Moreover, a case study on smartphones illustrates the effectiveness of our framework. This research results suggest that leveraging a suitable PTM can better address the problem of requirement classification in user review analyses, leading to improved product design.
Multifrequency Electrical Impedance Tomography Images for Swine Pneumonia Detection Using Conductivity Differences
Authors: Busarello Wolff Julia Grasiela , Kingeski Rafael , Santos Wellington Pinheiro dos , Cristani José , Traverso Sandra Davi , Paterno Aleksander Sade
Published: April 2024

This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portability, and non-radiation. The system is capable of reconstructing electrical conductivity tomographic images at a rate of 30.624 frames per second, taking about 5 min for imaging. The current source employs a 0.5 mA adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. This article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians to obtain images of respiratory diseases. The modified reconstruction method GREIT (EIDORS) was evaluated with experimental data and was compared with the Gauss–Newton and Total Variation methods, where GREIT 2D proved to be superior.
Boundary Protection Based on S-Transform Considering Fault Factors
Authors: Guo Zhenwei , Huang Jiemei , Deng Yingcai , Huang Qian , Luo Yi , Huang Zebo
Published: April 2024

Boundary protection is a protection that takes advantage of the characteristic that signals will be attenuated when passing through the “line boundary”. The location of the traps and current transformers in the structure of extra-high voltage (EHV) transmission lines makes it difficult to apply current-based travelling wave protection in engineering practice. If the protection is put into use, it is necessary to carry out a large number of engineering modifications to the existing transmission lines, which greatly increases the economic cost. And after simulation, the protection will be misjudged under weak fault conditions, and it has low reliability. After analyzing the influence of fault factors, a boundary protection method using high-frequency voltage component energy is proposed. The fault signal is processed by S-transform, and the transient voltage energy is normalized with the initial fault phase and transition resistance. The reduced characteristic quantity is used to construct a criterion to judge the fault condition of the protection line. This protection eliminates the influence of fault factors on transient protection. The ATP-Draw 6.0 simulation results based on the proposed protection scheme show that the protection scheme can distinguish internal and external faults, and can work normally under weak faults with high reliability.
An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration
Authors: Chen Xintong , Xie Yuxin , Zhou Zihan , He Yingying , Wang Qianli , Chen Zhuming
Published: April 2024

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated errors in multi-floor scenes. The proposed calibration method involves two steps: motion pattern recognition and position matching-based calibration. The motion pattern recognition aims to detect different motion patterns, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion patterns and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR, achieving accurate 3D positioning. The average error is reduced from 6.60 m to 1.37 m.
Multi-Representation Joint Dynamic Domain Adaptation Network for Cross-Database Facial Expression Recognition
Authors: Yan Jingjie , Yue Yuebo , Yu Kai , Zhou Xiaoyang , Liu Ying , Wei Jinsheng , Yang Yuan
Published: April 2024

In order to obtain more fine-grained information from multiple sub-feature spaces for domain adaptation, this paper proposes a novel multi-representation joint dynamic domain adaptation network (MJDDAN) and applies it to achieve cross-database facial expression recognition. The MJDDAN uses a hybrid structure to extract multi-representation features and maps the original facial expression features into multiple sub-feature spaces, aligning the expression features of the source domain and target domain in multiple sub-feature spaces from different angles to extract features more comprehensively. Moreover, the MJDDAN proposes the Joint Dynamic Maximum Mean Difference (JD-MMD) model to reduce the difference in feature distribution between different subdomains by simultaneously minimizing the maximum mean difference and local maximum mean difference in each substructure. Three databases, including eNTERFACE, FABO, and RAVDESS, are used to design a large number of cross-database transfer learning facial expression recognition experiments. The accuracy of emotion recognition experiments with eNTERFACE, FABO, and RAVDESS as target domains reach 53.64%, 43.66%, and 35.87%, respectively. Compared to the best comparison method chosen in this article, the accuracy rates were improved by 1.79%, 0.85%, and 1.02%, respectively.
A Selective Multi-Branch Network for Edge-Oriented Object Localization and Classification
Authors: Su Kai , Tomioka Yoichi , Zhao Qiangfu , Liu Yong
Published: April 2024

This study introduces a novel selective multi-branch network architecture designed to speed up object localization and classification on low-performance edge devices. The concept builds upon the You Only Look at Interested Cells (YOLIC) method, which was proposed by us earlier. In this approach, we categorize cells of interest (CoIs) into distinct regions of interest (RoIs) based on their locations and urgency. We then employ some expert branch networks for detailed object detection in each of the RoIs. To steer these branches effectively, a selective attention unit is added into the detection process. This unit can locate RoIs that are likely to contain objects under concern and trigger corresponding expert branch networks. The inference can be more efficient because only part of the feature map is used to make decisions. Through extensive experiments on various datasets, the proposed network demonstrates its ability to reduce the inference time while still maintaining competitive performance levels compared to the current detection algorithms.