bluuebunny/crossref_metadata_2025 huggingface dataset:
Amygdalar involvement in respiratory dysfunction
Authors: Trevizan-Baú Pedro , Hayes John A. , Bolser Donald C. , Reznikov Leah R.
Published: August 2024

The brainstem has long been recognized as the major respiratory control center, but it has become increasingly appreciated that areas upstream of the brainstem modulate respiration and airway defensive behaviors. This review aims to define the role of the amygdala, a key temporal brain region essential for limbic function, in respiration and airway defenses. We summarize literature describing roles for the amygdala in control of respiration, swallow, cough, airway smooth muscle contraction, and mucus secretion. We emphasize the need to understand how the amygdala regulates these functions both at a local scale and network scale and identify knowledge gaps for current and future investigations. Lastly, we highlight literature suggesting that amygdala dysfunction may contribute to respiratory dysfunction.
Comparative transcriptome analysis of whiteflies raised on cotton leaf curl Multan virus-infected cotton plants
Authors: Chen Ting , Jia Yanbo , Chen Jie , Qi Guojun
Published: August 2024

Cotton leaf curl Multan virus (CLCuMuV), a serious viral disease causative agent in cotton plants in South Asia, is transmitted by the Bemisia tabaci cryptic species complex in a persistent circulative manner. A previous study indicated that Asia II-7 whiteflies could transmit CLCuMuV, while Mediterranean (MED) whiteflies failed to transmit CLCuMuV. However, little is known about the genes involved in this process. In this study, Asia II-7 and MED B. tabaci were utilized to determine transcriptomic responses after 48 h of acquisition access periods (AAPs). Result of Illumina sequencing revealed that, 14,213 and 8,986 differentially expressed genes (DEGs) were identified. Furthermore, DEGs related to the immune system and metabolism of Asia II-7 and MED in response to CLCuMuV-infected plants were identified and analyzed using Gene Ontologies (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the number of related DEGs in MED was lower than that of Asia II-7. The most abundant groups of DEGs between both viruliferous and aviruliferous whitefly species were the zf-C2H2 family of transcription factors (TFs). Notably, in comparison to viruliferous MED, Asia II-7 exhibited more DEGs related to cathepsin biosynthesis. Overall, this study provides the basic information for investigating the molecular mechanism of how begomoviruses affect B. tabaci metabolism and immune response either as vector cryptic species or non-vector species.
Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace
Authors: Gao Jianwei , Pan Weijun
Published: August 2024

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.
Enhancing Leafy Greens’ Production: Nutrient Film Technique Systems and Automation in Container-Based Vertical Farming
Authors: Carrasco Gilda , Fuentes-Peñailillo Fernando , Manríquez Paula , Rebolledo Pabla , Vega Ricardo , Gutter Karen , Urrestarazu Miguel
Published: August 2024

Urban agriculture has emerged as a crucial strategy to address food security and sustainability challenges, particularly in densely populated areas. This study focused on enhancing leafy greens’ production, specifically lettuce (Lactuca sativa L.) and arugula or rocket (Eruca sativa L.), using Nutrient Film Technique (NFT) systems and automation in container-based vertical farming. The study utilized a 20-foot shipping container retrofitted to create a thermally insulated and automated growth environment equipped with energy-efficient LED lighting and precise climate control systems. The results demonstrated significant improvements in crop yields, with the NFT systems achieving productivity up to 11 times higher than traditional methods in protected horticulture. These systems enabled continuous cultivation cycles, responding to the high market demand for fresh local produce. Moreover, the integration of low-cost sensors and automation technologies, each costing under USD 300, ensured that the environmental conditions were consistently optimal, highlighting this approach’s economic feasibility and scalability. This low-cost framework aligns with industry standards for affordable technology, making it accessible for small- to medium-sized urban agriculture enterprises. This study underscores the potential of vertical farming as a sustainable solution for urban food production. It provides a model that can be replicated and scaled to meet the growing demand for healthy, locally grown vegetables.
HCFormer: A Lightweight Pest Detection Model Combining CNN and ViT
Authors: Zeng Meiqi , Chen Shaonan , Liu Hongshan , Wang Weixing , Xie Jiaxing
Published: August 2024

Pests are widely distributed in nature, characterized by their small size, which, along with environmental factors such as lighting conditions, makes their identification challenging. A lightweight pest detection network, HCFormer, combining convolutional neural networks (CNNs) and a vision transformer (ViT) is proposed in this study. Data preprocessing is conducted using a bottleneck-structured convolutional network and a Stem module to reduce computational latency. CNNs with various kernel sizes capture local information at different scales, while the ViT network’s attention mechanism and global feature extraction enhance pest feature representation. A down-sampling method reduces the input image size, decreasing computational load and preventing overfitting while enhancing model robustness. Improved attention mechanisms effectively capture feature relationships, balancing detection accuracy and speed. The experimental results show that HCFormer achieves 98.17% accuracy, 91.98% recall, and a mean average precision (mAP) of 90.57%. Compared with SENet, CrossViT, and YOLOv8, HCFormer improves the average accuracy by 7.85%, 2.01%, and 3.55%, respectively, outperforming the overall mainstream detection models. Ablation experiments indicate that the model’s parameter count is 26.5 M, demonstrating advantages in lightweight design and detection accuracy. HCFormer’s efficiency and flexibility in deployment, combined with its high detection accuracy and precise classification, make it a valuable tool for identifying and classifying crop pests in complex environments, providing essential guidance for future pest monitoring and control.
Impact of Bio-Organic Fertilizer Incorporation on Soil Nutrients, Enzymatic Activity, and Microbial Community in Wheat–Maize Rotation System
Authors: Ali Aamir , Liu Xiaoli , Yang Wenping , Li Wenguang , Chen Jie , Qiao Yuejing , Gao Zhiqiang , Yang Zhenping
Published: August 2024

Excessive use of inorganic fertilizers disrupts soil nutrient balance and leads to soil degradation and a decrease in biodiversity. In contrast, bio-fertilizers enhance soil structure and fertility and promote plant growth and sustainable agriculture development. Therefore, this study focused on a rotation system of winter wheat and summer maize and aimed to explore the effects of applying chemical fertilizer (NPK) and bio-fertilizer (BF) in the winter wheat season on the sustainable soil development of current wheat and subsequent maize. Before sowing winter wheat four fertilization treatments were, respectively CK (100% NPK at 750 kg ha−1), A (60% NPK at 450 + 20% BF at 150 kg ha−1), B (60% NPK at 450 + 40% BF at 300 kg ha−1), and C (60% NPK at 450 + 60% BF at 450 kg ha−1), conducted. The results showed that treatment A (60% NPK + 20% BF) replacing the NPK at 300 kg ha−1 with BF at 150 kg ha−1 significantly soil nutrient contents, enzyme activity, and microbial metabolic activity. The study also found a positive correlation between soil parameters (total nitrogen, alkaline nitrogen, available phosphorus, organic matter, urease, and alkaline phosphatase in the winter wheat and maize cropping season). Furthermore, the soil microbial composition showed significant enrichment of Proteobacteria, Acidobacteria, Actinobacteria, and Firmicutes, and variations among treatments. Moreover, the application of biofertilizer enhanced the diversity of soil fungi species, particularly during the winter wheat season. This study highlights the importance of integrating biofertilizers with NPK fertilizer for agricultural system conversion and promoting agricultural production and sustainability.
Mitomycin C as an Anti-Persister Strategy against Klebsiella pneumoniae: Toxicity and Synergy Studies
Authors: Pacios Olga , Herrera-Espejo Soraya , Armán Lucía , Ibarguren-Quiles Clara , Blasco Lucía , Bleriot Inés , Fernández-García Laura , Ortiz-Cartagena Concha , Paniagua María , Barrio-Pujante Antonio , Aracil Belén , Cisneros José Miguel , Pachón-Ibáñez María Eugenia , Tomás María
Published: August 2024

The combination of several therapeutic strategies is often seen as a good way to decrease resistance rates, since bacteria can more easily overcome single-drug treatments than multi-drug ones. This strategy is especially attractive when several targets and subpopulations are affected, as it is the case of Klebsiella pneumoniae persister cells, a subpopulation of bacteria able to transiently survive antibiotic exposures. This work aims to evaluate the potential of a repurposed anticancer drug, mitomycin C, combined with the K. pneumoniae lytic phage vB_KpnM-VAC13 in vitro and its safety in an in vivo murine model against two clinical isolates of this pathogen, one of them exhibiting an imipenem-persister phenotype. At the same time, we verified the absence of toxicity of mitomycin C at the concentration using the human chondrocyte cell line T/C28a2. The viability of these human cells was checked using both cytotoxicity assays and flow cytometry.
Characteristics of Metallic Nanoparticles (Especially Silver Nanoparticles) as Anti-Biofilm Agents
Authors: Li Hongze , Yang Zhihe , Khan Sadaf Aiman , Walsh Laurence J. , Seneviratne Chaminda Jayampath , Ziora Zyta M.
Published: August 2024

Biofilm-associated infections account for a large proportion of chronic diseases and pose a major health challenge. Metal nanoparticles offer a new way to address this problem, by impairing microbial growth and biofilm formation and by causing degradation of existing biofilms. This review of metal nanoparticles with antimicrobial actions included an analysis of 20 years of journal papers and patent applications, highlighting the progress over that time. A network analysis of relevant publications showed a major focus on the eradication of single-species biofilms formed under laboratory conditions, while a bibliometric analysis showed growing interest in combining different types of metal nanoparticles with one another or with antibiotics. The analysis of patent applications showed considerable growth over time, but with relatively few patents progressing to be granted. Overall, this profile shows that intense interest in metal nanoparticles as anti-biofilm agents is progressing beyond the confines of simple laboratory biofilm models and coming closer to clinical application. Looking to the future, metal nanoparticles may provide a sustainable approach to combatting biofilms of drug-resistant bacteria.
Crude Blueberry Phenolic Extracts Improve Gut Barrier Integrity and Exert Anti-Inflammatory and Antimicrobial Activity in an In Vitro Weaning Stress Model
Authors: Nathan Vignesh B. , Eckrote Sarah , Li Shiyu , Reddivari Lavanya
Published: August 2024

Piglet weaning is accompanied by gastrointestinal tract (GIT) dysfunction, resulting in post-weaning diarrhea (PWD). The treatment involves antibiotics due to the susceptibility of the weaned GIT to pathogens. However, antibiotic resistance has shifted attitudes toward a nutraceutical approach by enriching feed with functional compounds. Polyphenols are touted for their antimicrobial activity and ability to improve GIT function. Thus, we investigated the protective effects of crude blueberry phenolic extracts (BPE) in vitro using porcine cells challenged with lipopolysaccharide (LPS) as a weaning model. Cells were pretreated with 1 µg/mL and 2.5 µg/mL BPE for 24 h, followed by 10 µg/mL LPS stimulation for 6 h. Antioxidant status, paracellular permeability, the gene expression of proinflammatory cytokines, and tight junction proteins were measured. The antimicrobial activity of the extract was evaluated against porcine pathogens. The pretreatment of cells with 1 µg/mL BPE preserved catalase (CAT) activity. Reduced paracellular permeability was observed in a dose-dependent manner. The BPE preserved the relative mRNA abundance of tight junctions and reduced inflammatory cytokine expression. Pretreatment with the BPE was able to preserve occludin (OCLN) protein levels. The minimum inhibitory concentration of the BPE against Enterotoxigenic E. coli (ETEC) and Salmonella typhimurium (ST) was 62.50 µg/mL. These findings indicate that blueberry polyphenols hold potential as feed additives in swine weaning.
Enhancing Wound Healing and Anti-Inflammatory Effects by Combination of CIGB-258 and Apolipoprotein A-I against Carboxymethyllysine Toxicity in Zebrafish: Insights into Structural Stabilization and Antioxidant Properties
Authors: Cho Kyung-Hyun , Lee Yunki , Lee Sang Hyuk , Kim Ji-Eun , Bahuguna Ashutosh , Dominguez-Horta Maria del Carmen , Martinez-Donato Gillian
Published: August 2024

CIGB-258 is known to exert anti-inflammatory activity via structural stabilization of apolipoprotein A-I (apoA-I) and functional enhancement of high-density lipoproteins (HDL) against acute toxicity of carboxymethyllysine (CML). The co-presence of CIGB-258 in reconstituted HDL (rHDL) formed larger rHDL particles and enhanced anti-inflammatory activity in a dose-dependent manner of apoA-I:CIGB-258, 1:0, 1:0.1, 1:0.5, and 1:1 of molar ratio, in the synthesis of the rHDL. However, no study has evaluated the enhancement of HDL functionality by the co-presence of lipid-free apoA-I and CIGB-258. The present study was therefore designed to compare the structural stabilization and functional improvement of HDL in the presence of lipid-free apoA-I and CIGB-258 in molar ratios of 1:0, 1:0.1, 1:0.5, and 1:1 within both HDL2 and HDL3. As the concentration of CIGB-258 increased, it effectively inhibited the cupric-ion-induced oxidation of HDL, thereby safeguarding apoA-I from proteolytic degradation. Additionally, the wound-healing activity of zebrafish was significantly (p < 0.01) enhanced by the co-addition of apoA-I:CIGB-258 (1:1) up to 1.6-fold higher than apoA-I alone (1:0) under the presence of CML. ApoA-I:CIGB-258 (1:1) treatment exhibited the lowest apoptosis and production of reactive oxygen species against CML-induced damage in the wound site. Also, an increase in wounded tissue granulation and epidermis thickness was observed with increasing concentration of CIGB-258 during 48 h post-treatment via the healing process. Intraperitoneal injection of apoA-I:CIGB-258 mixture remarkably ameliorated the acute paralysis and restored zebrafish swimming ability impaired by the acute toxicity of CML. The increase of CIGB-258 content, especially co-injection of apoA-I:CIGB-258 (1:1), leads to a significant 2.3-fold (p < 0.001) and 4.1-fold (p < 0.001) higher zebrafish survivability and recovery of swimming ability, respectively, than those of CML-control. In the apoA-I:CIGB-258 (1:1) group, neutrophil infiltration and interleukin (IL)-6 production was lowest in the hepatic tissue with the least cellular damage and apoptosis. Additionally, the group treated with apoA-I:CIGB-258 (1:1) demonstrated the lowest plasma levels of total cholesterol (TC) and triglycerides (TG), along with minimal damage to the kidney, ovary, and testicular cells. Conclusively, co-treatment of CIGB-258 with apoA-I effectively mitigated acute inflammation in zebrafish, safeguarded vital organs, structurally stabilized apoA-I, and enhanced HDL functionality.
Improved Functions of Fermented Coffee by Lactic Acid Bacteria
Authors: Kim Seon-Gyu , Abbas Aoun , Moon Gi-Seong
Published: August 2024

Coffee is one of the most popular beverages in the world, and at present, specialty coffees are developing for better tastes, flavors, or functions. Fermented coffees also reflect this trend and some brands are commercialized. Unlike general fermented coffees, we tried to ferment coffee extract with several lactic acid bacteria. Finally, we selected three strains that persisted in the coffee extract, and show strong antimicrobial activity, for the fermentation starters. The strains were identified as Pediococcus pentosaceus (KNUT 0384), Lacticaseibacillus paracasei (CJNU 1840), and Lactiplantibacillus plantarum (CJNU 0441) based on 16S rRNA gene sequences. During the fermentation process, the total acidities (p < 0.05 vs. the control, non-fermented coffee extract) and cell masses increased, which indicates that the bacteria metabolized properly in the extract. Furthermore, the fermented coffee extracts showed increased antimicrobial activities against Listeria monocytogenes (p < 0.05 vs. the control) and Streptococcus mutans (p < 0.05 vs. the control), increased anti-oxidative activities (p < 0.05 vs. the control, except for the KNUT 0384 sample), decreased caffeine content (p < 0.05 vs. the control, except for the KNUT 0384 sample), and increased chlorogenic acid content (p < 0.05 vs. the control). Taken together, the fermented coffee extracts with the selected lactic acid bacteria could be specialty coffees where several functions are improved when compared with a control coffee extract.
A Study of an Integrated Analysis Model with Secondary Flow for Assessing the Performance of a Micro Turbojet Engine
Authors: Lee DongEun , Chung Heeyoon , Kang Young Seok , Rhee Dong-Ho
Published: August 2024

The objective of this study is to implement a more realistic integrated analysis model for micro gas turbines by incorporating secondary flow and combustion efficiency into the existing model, which includes main engine components such as the compressor and turbine, and to validate this model by comparing it with test results. The study was based on the JetCat P300-RX, which has a maximum thrust level of 300 N. Simulations were performed using ANSYS CFX, employing the κ-ω SST turbulence model and a mixing plane interface between individual components. The eddy dissipation model (EDM), with a combustion efficiency of 90%, was used as the combustion model. A user subroutine was also applied for the power matching of the compressor and turbine to calculate the fuel flow rate in each iteration. For secondary flow, it was assumed that 3% of the total air flow rate would flow through the secondary path and be applied to the compressor and turbine. Simulations were conducted over a range of 30,000 to 104,000 RPM, with ground conditions evaluated, including altitude-simulated conditions. To validate the analysis model, engine performance metrics such as pressure ratio, air flow rate, fuel flow rate, and exhaust gas temperature (EGT) were compared with test results. The results demonstrated that errors were less than 5% for most engine performance metrics, except for EGT and fuel flow. The discrepancy in EGT was attributed to differences in the sensing methods, while the variation in fuel flow was found to be due to the lubrication system and losses due to the secondary air flow. Consequently, this study confirmed that the integrated simulation model accurately predicts engine performance. The results indicate that the integrated simulation model provides a more realistic prediction of overall engine performance compared to previous studies. Therefore, it can evaluate detailed thermo-fluid properties without the need for component performance maps, enhancing performance evaluation and analysis.
Optimized Prescreen Survey Tool for Predicting Sleep Apnea Based on Deep Neural Network: Pilot Study
Authors: Kim Jungyoon , Park Jaehyun , Park Jangwoon , Surani Salim
Published: August 2024

Obstructive sleep apnea (OSA) is one of the common sleep disorders related to breathing. It is important to identify an optimal set of questions among the existing questionnaires, using a data-driven approach, that can prescreen OSA with high sensitivity and specificity. The current study proposes reliable models that are based on machine learning techniques to predict the severity of OSA. A total of 66 participants consisted of 45 males and 21 females (average age = 52.4 years old; standard deviation ± 14.6). Participants were asked to fill out the questionnaire items. If the value of the Respiratory Disturbance Index (RDI) was more than 30, the participant was diagnosed with severe OSA. Several different modeling techniques were applied, including deep neural networks with a scaled principal component analysis (DNN-PCA), random forest (RF), Adaptive Boosting Classifier (ABC), Decision Tree Classifier (DTC), K-nearest neighbors classifier (KNC), and support vector machine classifier (SVMC). Among the participants, 27 participants were diagnosed with severe OSA (RDI > 30). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the developed models. As a result, the AUROC values of DNN-PCA, RF, ABC, DTC, KNC, and SVMC models were 0.95, 0.62, 0.53, 0.53, 0.51, and 0.78, respectively. The highest AUROC value was found in the DNN-PCA model with a sensitivity of 0.95, a specificity of 0.75, a positive predictivity of 0.95, an F1 score of 0.95, and an accuracy of 0.95. The DNN-PCA model outperforms the existing screening questionnaires, scores, and other models.
Selected Soluble Dietary Fibres as Replacers of Octenyl Succinic Anhydride (OSA) Starch in Spray-Drying Production of Linseed Oil Powders Applied to Apple Juice
Authors: Ogrodowska Dorota , Tańska Małgorzata , Banaszczyk Paweł , Czaplicki Sylwester , Piłat Beata , Konopka Iwona
Published: August 2024

The aim of the study was to compare two kinds of soluble dietary fibres in a mixture with OSA-starch as wall components of linseed oil capsules. Comparison was made based on emulsion (droplet size, polydispersity index, and viscosity) and powder properties (outer structure, colour, surface oil content, and encapsulation efficiency). Additionally, linseed oil powders were applied to the food model (apple juice) and the colour, physical stability, and volatile compound profile of fortified juice were determined. Although the obtained linseed oil emulsions with different compositions of polysaccharide components showed some variation in droplet size, polydisperse index and viscosity, their encapsulation efficiency by spray-drying was very high (>98%). The powders produced had a similar structure and low surface oil content, and their 2% addition to apple juice did not change its stability and only slightly decreased its colour lightness and yellowness. However, greater differences in the volatile compounds of obtained juices were observed. Overall, the added powders reduced the volatility of aroma compounds typical of apple juice but introduced propanal and hexanal, especially the powders with the highest OSA-starch share.
Aerodynamic Optimization Design of a Supergravity Centrifuge: A Low-Resistance Strategy
Authors: Guo Yi-Nan , Yang Yi , Lin Wei-An , Jiang Jian-Qun , Ding De
Published: August 2024

Wind resistance optimization is crucial for enhancing the rotational speed of supergravity centrifuges. We conducted a study using computational fluid dynamics on the Centrifugal Hypergravity and Interdisciplinary Experiment Facility (CHIEF) under construction at Zhejiang University and validated it experimentally using a ZJU400gt centrifuge. Our findings indicate significant reductions in wind resistance through structural modifications of the CHIEF. Reducing the outer radius from 4650 to 4150 mm decreased wind resistance by 16%, primarily due to reduced effective viscosity in the wake region’s gases. More substantial reductions were achieved by lowering the height of the outer wall from 2200 to 1400 mm, which cut wind resistance by 25%. This height reduction suppressed vortex shedding and Kármán vortex street development via the Venturi effect. Adjustments to the roughness height of wall surfaces further decreased wind resistance, with minimal impact from arm roughness. A critical roughness height was identified, below which no further reductions in wind resistance could be attained. Notably, using disc-shaped arms reduced wind resistance by approximately 73% because of their minimal pressure–resistance components and predominant frictional resistance, highlighting their potential in future high-speed centrifuge designs.
Smart Internet of Things Power Meter for Industrial and Domestic Applications
Authors: Pălăcean Alexandru-Viorel , Trancă Dumitru-Cristian , Rughiniș Răzvan-Victor , Rosner Daniel
Published: August 2024

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device’s continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%.
Multidisciplinary Applications of AI in Dentistry: Bibliometric Review
Authors: Allani Hela , Santos Ana Teresa , Ribeiro-Vidal Honorato
Published: August 2024

This review explores the impact of Artificial Intelligence (AI) in dentistry, reflecting on its potential to reshape traditional practices and meet the increasing demands for high-quality dental care. The aim of this research is to examine how AI has evolved in dentistry over the past two decades, driven by two pivotal questions: “What are the current emerging trends and developments in AI in dentistry?” and “What implications do these trends have for the future of AI in the dental field?”. Utilizing the Scopus database, a bibliometric analysis of the literature from 2000 to 2023 was conducted to address these inquiries. The findings reveal a significant increase in AI-related publications, especially between 2018 and 2023, underscoring a rapid expansion in AI applications that enhance diagnostic precision and treatment planning. Techniques such as Deep Learning (DL) and Neural Networks (NN) have transformed dental practices by enhancing diagnostic precision and reducing workload. AI technologies, particularly Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs), have improved the accuracy of radiographic analysis, from detecting dental pathologies to automating cephalometric evaluations, thereby optimizing treatment outcomes. This advocacy is underpinned by the need for AI applications in dentistry to be both efficacious and ethically sound, ensuring that they not only improve clinical outcomes but also adhere to the highest standards of patient care.
Integrating Multi-Organ Imaging-Derived Phenotypes and Genomic Information for Predicting the Occurrence of Common Diseases
Authors: Liu Meng , Li Yan , Sun Longyu , Sun Mengting , Hu Xumei , Li Qing , Yu Mengyao , Wang Chengyan , Ren Xinping , Ma Jinlian
Published: August 2024

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.
Comparative Study of Bacillus-Based Plant Biofertilizers: A Proposed Index
Authors: Barros-Rodríguez Adoración , Pacheco Pamela , Peñas-Corte María , Fernández-González Antonio J. , Cobo-Díaz José F. , Enrique-Cruz Yasmira , Manzanera Maximino
Published: August 2024

The market for bacteria as agricultural biofertilizers is growing rapidly, offering plant-growth stimulants; biofungicides; and, more recently, protectors against extreme environmental factors, such as drought. This abundance makes it challenging for the end user to decide on the product to use. In this work, we describe the isolation of a strain of Bacillus velezensis (belonging to the operational group Bacillus amyloliquefaciens) for use as a plant-growth-promoting rhizobacterium, a biofungicide, and a protector against drought. To compare its effectiveness with other commercial strains of the same operational group, Bacillus amyloliquefaciens, we analyzed its ability to promote the growth of pepper plants and protect them against drought, as well as its fungicidal activity through antibiosis and antagonism tests, its ability to solubilize potassium and phosphates, and its ability to produce siderophores. Finally, we used a probit function, a type of regression analysis used to model the outcomes of analyses, to quantify the biostimulatory effectiveness of the different plant-growth-promoting rhizobacteria, developing what we have called the Agricultural Protection Against Stress Index, which allowed us to numerically compare the four commercial strains of the operational group Bacillus amyloliquefaciens, based on a Delphi method—a type of regression analysis that can be used to model a cumulative normal distribution—and integrate the results from our panel of tests into a single value.
Unmodulated 40 Hz Stimulation as a Therapeutic Strategy for Aging: Improvements in Metabolism, Frailty, and Cognitive Function in Senescence-Accelerated Prone 10 Mice
Authors: Shimizu Tatsunori , Ota Hidetaka , Kodama Ayuto , Suzuki Yasuhiro , Ohnuma Takako , Suzuki Rieko , Sugawara Kaoru , Sato Yasushi , Kodama Hiroyuki
Published: August 2024

With aging populations in many countries, including Japan, efforts to mitigate the aging-related decline in physical function have gained importance not only for improving individual quality of life but also for mitigating the effects of this loss of function on society. Impaired glucose tolerance, muscle weakness, and cognitive decline are well-known effects of aging. These interrelated factors can create a vicious cycle because impaired glucose tolerance can accelerate muscle weakness and cognitive decline. Unmodulated 40 Hz (u40Hz) stimulation is imperceptible to the human ear and has been reported to improve cognitive function in humans and mice. However, research on the effects of u40Hz stimulation is still limited. This study aimed to report the effects of u40Hz stimulation on glucose tolerance and muscle strength in senescence-accelerated prone (SAMP)-10 mice, a model of accelerated aging. SAMP-10 mice underwent five weeks of u40Hz stimulation followed by glucose-tolerance tests, cognitive and behavioral assessments, and frailty evaluations. In comparison with the control group, the u40Hz-stimulation group showed mitigation of age-related decline in glucose tolerance, a better frailty index (FI), and notably preserved muscle strength. Microarray analysis of stimulated muscle tissue revealed significant upregulation of β-oxidation genes and genes functioning downstream of peroxisome proliferator-activated receptor gamma, and significant downregulation of clock genes. These findings indicate the beneficial effects of u40Hz stimulation on glucose tolerance, muscle strength, and cognitive function, warranting further research in this area.
Management of Microcomplications of Diabetes Mellitus: Challenges, Current Trends, and Future Perspectives in Treatment
Authors: Yapislar Hande , Gurler Esra Bihter
Published: August 2024

Diabetes mellitus is a chronic metabolic disorder characterized by high blood sugar levels, which can lead to severe health issues if not managed effectively. Recent statistics indicate a significant global impact, with 463 million adults diagnosed worldwide and this projected to rise to 700 million by 2045. Type 1 diabetes is an autoimmune disorder where the immune system attacks pancreatic beta cells, reducing insulin production. Type 2 diabetes is primarily due to insulin resistance. Both types of diabetes are linked to severe microvascular and macrovascular complications if unmanaged. Microvascular complications, such as diabetic retinopathy, nephropathy, and neuropathy, result from damage to small blood vessels and can lead to organ and tissue dysfunction. Chronic hyperglycemia plays a central role in the onset of these complications, with prolonged high blood sugar levels causing extensive vascular damage. The emerging treatments and current research focus on various aspects, from insulin resistance to the intricate cellular damage induced by glucose toxicity. Understanding and intervening in these pathways are critical for developing effective treatments and managing diabetes long term. Furthermore, ongoing health initiatives, such as increasing awareness, encouraging early detection, and improving treatments, are in place to manage diabetes globally and mitigate its impact on health and society. These initiatives are a testament to the collective effort to combat this global health challenge.
The Role of Bone and Root Resorption on the Biomechanical Behavior of Mandibular Anterior Teeth Subjected to Orthodontic Forces: A Finite Element Approach
Authors: Flatten Jana , Gedrange Thomasz , Bourauel Christoph , Keilig Ludger , Konermann Anna
Published: August 2024

Aims: This study was conducted to systematically evaluate the biomechanical impact of varying degrees of root and bone resorption resulting from periodontitis and orthodontic tooth movement (OTM) on the mandibular anterior teeth. The objective was to determine whether these distinct resorption patterns exert a specific influence on tooth displacement and strain patterns. Methods: A finite element (FE) model of an idealized anterior mandible from the first premolar in the third to the fourth quadrant was developed without bone or root resorption and a constant periodontal ligament (PDL) thickness of 0.2 mm. Variations included three root resorption levels (0%, 20%, 50%) and three bone resorption types (circular 50%, circular 80%, vestibular 80%). Models ranged from 200,000 to 440,000 elements and 55,000 to 130,000 nodes. Orthodontic forces, namely root torque (5 Nmm), intrusion (0.2 N), and distalization (0.5 N) were applied for subsequent crown displacement and PDL strain analysis. Results: A total of 180 simulations were performed. Simulations showed that displacement was similar across different bone resorption conditions, irrespective of modeled root resorptions. Circumferential bone resorption increased tooth displacement, regardless of root resorption status. Vestibular bone resorption exhibited less increase in tooth displacement. However, when accompanied by root resorption, the combination exacerbated tooth displacement. Strains in the PDL clearly increased with a circumferential bone resorption of 80%. Conclusions: This study highlights the critical role of bone resorption in tooth displacement during OTM, particularly the challenges associated with circumferential resorption. Clinicians must consider both bone and root resorption for personalized medicine treatment of patients with severe periodontitis, in favor of low-force application strategies to optimize outcomes and minimize complications linked to excessive tooth displacement.
Electrocatalysis in MOF Films for Flexible Electrochemical Sensing: A Comprehensive Review
Authors: Zhang Suyuan , Wang Min , Wang Xusheng , Song Jun , Yang Xue
Published: August 2024

Flexible electrochemical sensors can adhere to any bendable surface with conformal contact, enabling continuous data monitoring without compromising the surface’s dynamics. Among various materials that have been explored for flexible electronics, metal–organic frameworks (MOFs) exhibit dynamic responses to physical and chemical signals, offering new opportunities for flexible electrochemical sensing technologies. This review aims to explore the role of electrocatalysis in MOF films specifically designed for flexible electrochemical sensing applications, with a focus on their design, fabrication techniques, and applications. We systematically categorize the design and fabrication techniques used in preparing MOF films, including in situ growth, layer-by-layer assembly, and polymer-assisted strategies. The implications of MOF-based flexible electrochemical sensors are examined in the context of wearable devices, environmental monitoring, and healthcare diagnostics. Future research is anticipated to shift from traditional microcrystalline powder synthesis to MOF thin-film deposition, which is expected to not only enhance the performance of MOFs in flexible electronics but also improve sensing efficiency and reliability, paving the way for more robust and versatile sensor technologies.
Centrifugal Model Test of Multi-Level Slope under Combined Support of Pile-Anchor and Frame-Anchor
Authors: Yang Kuibin , Zhu Yanpeng , Guo Hong , Xie Yongbin , Wang Zhiqiang
Published: August 2024

The combination support structure of pile-anchor and frame-anchor is widely used in multi-level slope engineering, but the relevant theoretical research lags behind engineering practice. To make up for this deficiency, this paper takes the multi-level slope under combined support of pile-anchor and frame-anchor as the research object, conducts a model test using the geotechnical centrifuge, analyzes the most relevant structural force and slope deformation in engineering, and provides some reinforcement suggestions. The test results show that the combination support structure of pile-anchor and frame-anchor has a good reinforcement effect on multi-level soil slopes, but the deformation range of the slope is greatly affected by the anchoring structure and the anchor rods located within the deformation zone. The structural force condition is significantly different from traditional structures that place the anchor rod anchoring section in a stable soil layer. According to the structural force characteristics, it is recommended that the support design fully consider the positional relationship between the anchor rods and the sliding surface. The design of the frame-anchor support section should strictly control the anchoring depth, so that the potential sliding surface of the slope can be moved backward, thereby increasing the difficulty of sliding. The design of pile-anchor support should ensure that the anchor rods have sufficient length to provide greater anchoring force, thereby improving the bearing capacity of the pile. These research results can enrich and improve the support design theory of multi-level soil slopes, providing guidance for engineering practice.
On the Assessment of Reinforced Concrete (RC) Walls under Contact/Near-Contact Explosive Charges: A Deep Neural Network Approach
Authors: Holgado David , Mourão Rodrigo , Montalva Arturo , Florek Jason
Published: August 2024

In recent years, the use of machine learning has been expanded to several fields, with promising advances in structural engineering applications. Deep neural network models have been implemented to predict the structural response of systems under conventional loading. Some of those neural network models are based on datasets containing images, test data, and/or data produced by using finite element models developed for a specific environment. While the accuracy of these models relies on the size and quality of the dataset, their use for blast analysis is rather limited, as publicly available data are scarce or restricted. Reinforced concrete (RC) walls or slabs under blast loading are commonly evaluated for flexural and shear behaviour, for which performance guidelines are widely available. While such response mechanisms are typically associated with the far-field range, the target response is controlled by local failure modes when blast loads are generated by contact or near-contact detonations. This paper introduces the implementation of a neural network model for the response prediction of RC walls subjected to contact and near-contact explosions. The model predicts the damage category (i.e., no damage, spall, and breach) associated with a given explosion scenario. The model is trained using experimental data from multiple test programmes available in open-source literature. It considers several parameters associated with the explosive charge (e.g., type, geometry, charge weight, and standoff) and RC target (e.g., material properties, geometry, and reinforcement). The model is able to accurately predict 81% of the total breached specimens, 66% of the total spalled specimens, and 71% of the full set of non-damaged specimens, with an overall accuracy of 72%, with precision and recall ranging from 60 to 76% and 66 to 81%, respectively. The current model is shown to be a significantly better predictor of the damage category than the semi-empirical approach outlined in UFC 3-340-02, making it a promising tool that can be improved with the inclusion of more experimental data.
Accurate Prediction of Compression Index of Normally Consolidated Soils Using Artificial Neural Networks
Authors: Uzer Ali Ulvi
Published: August 2024

The compression index (Cc) serves as a crucial parameter in predicting consolidation settlement in fine-grained soils, representing the slope of the void ratio logarithmic effective stress curve obtained from oedometer tests. However, traditional consolidation testing methods are notably time-consuming, typically spanning a 15-day period for preparation, execution, and parameter calculation, leading to significant delays in civil engineering projects. Therefore, there is an urgent need for effective methodologies to determine consolidation parameters within a shorter timeframe. Although various empirical formulas have been proposed over the years to correlate compressibility with soil parameters, none have reliably predicted the Cc across different datasets. In this study, to overcome this challenge, an alternative approach using artificial neural network (ANN) methodology to predict the compression index of fine-grained soils based on index properties is proposed. For this purpose, an ANN was trained and validated using a dataset consisting of 560 high and low- plasticity soil samples obtained from construction sites in various regions of Turkey over the last forty years, as well as soil borings in Istanbul. The modeling of artificial neural networks was performed using the Regression Learner program, which integrates with the Matlab 2023a software package and offers a user-friendly graphical interface for AI model development without coding. The data set, which was structured as a matrix with dimensions of 458 × 6, included input parameters such as the natural water content, liquid limit, plastic limit, plastic index and initial void ratio, as well as information on the compression index, which was the output variable. The developed ANN model showed an outstanding predictive performance when predicting the output of the test data, achieving an outstanding R2 score of 0.81. This underlines the potential of ANN methodologies to efficiently extract important data with fewer experiments and in less time, and offers promising applications in the field of geotechnical engineering.
Understanding Structural Changes in Recycled Aggregate Concrete under Thermal Stress
Authors: Cao Shuwen , Cheng Xubin , Ran Hui
Published: August 2024

Objective: This study investigates the influence of high-temperature treatment on the deformation properties and structural deformation of recycled aggregate concrete (RAC) in response to potential fire hazards in the construction industry. Methods: Standard-cured 28-day RAC specimens were subjected to microwave heating at 300 °C and 600 °C, with subsequent uniaxial compression tests utilizing a WDW-2000 machine and a VIC 3D strain measurement system to analyze strain data through digital image correlation (DIC) technology. Results: After treatment at 300 °C, recycled aggregate concrete (RAC) demonstrated superior mechanical properties to fresh concrete aggregates. This enhancement may be attributed to the more robust siloxane bonds (Si-O-Si) in the recycled materials. Conversely, exposure to 600 °C intensified internal structural damage, notably lowering the material’s elastic modulus and peak stress. DIC analysis highlighted the correlation among temperature, volumetric strain, and crack development patterns, with more extensive cracking at 600 °C. Conclusions: Moderate-temperature treatment enhances RAC’s structure and deformation properties, while high-temperature treatment diminishes its performance. These findings provide valuable insights for assessing building safety post-fire and the application of RAC, emphasizing its suitability at moderate temperatures and risks at high temperatures.