bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Intake of Extra Virgin Olive Oil on Mortality in a South Italian Cohort with and without NAFLD
Authors: Bonfiglio Caterina , Cuccaro Francesco , Campanella Angelo , Rosso Natalia , Tatoli Rossella , Giannelli Gianluigi , Donghia Rossella
Published: October 2023

Background: Extra-virgin olive oil (EVOO) is the main source of seasoning fat in the Mediterranean diet and it is one of the components with known protective factors on chronic-degenerative disease. We aimed to evaluate the effect of a medium-high level of oil consumption on mortality in a cohort with good adherence to the Mediterranean diet. Methods: A total of 2754 subjects who had completed the food questionnaire in the Multicenter Italian study on Cholelithiasis (MICOL) cohort were included in the study. EVOO consumption was categorized in four levels (<20 g/die, 21–30 g/die, 31–40 g/die, >40 g/die). We performed a flexible parametric survival model to assess mortality by EVOO consumption level adjusted for some covariates. We also performed the analysis on subjects with and without non-alcoholic fatty liver disease (NAFLD) to evaluate the effects of oil in this more fragile sub-cohort. Results: We found a statistically significant negative effect on mortality for the whole sample when EVOO consumption was used, both as a continuous variable and when categorized. The protective effect was stronger in the sub-cohort with NAFLD, especially for the highest levels of EVOO consumption (HR = 0.58 with p < 0.05). Conclusions: Our study has shown a protective effect of EVOO consumption towards all causes of mortality. Despite the higher caloric intake, the protective power is greater for a consumption >40 g/day in both the overall cohort and the sub-cohorts with and without NAFLD.
The Relationship between Breakfast and Sleep and Cardiovascular Risk Factors
Authors: Kim Yejin , An Hye-Ji , Seo Young-Gyun
Published: October 2023

Despite extensive research on the individual effects of breakfast and sleep on health outcomes, there has been limited investigation into their combined effects. We aimed to evaluate the relationship between breakfast-eating behavior and sleep timing on cardiovascular disease (CVD) risk factors. A total of 16,121 participants (6744 men and 9377 women) aged 19 years or older were selected from the Korea National Health and Nutrition Examination Surveys (2016–2018, 2021). We classified participants into four groups: early sleep + regular breakfast eaters (group 1), late sleep + regular breakfast eaters (group 2), early sleep + infrequent breakfast eaters (group 3), and late sleep + infrequent breakfast eaters (group 4). In men, group 4 had a lower prevalence of obesity than group 1 (OR 0.78, 95%CI 0.62–0.97), and groups 2, 3, and 4 had a higher prevalence of metabolic syndrome (MetS) than group 1 (OR 1.43, 1.62, and 1.47, respectively). In women, group 4 had a lower prevalence of dyslipidemia than group 1 (OR 0.59, 95%CI 0.44–0.80), and group 2 had a higher prevalence of MetS than group 1 (OR 1.24, 95%CI 1.03–1.50). The combination of skipping breakfast and late sleep timing was associated with the higher prevalence of MetS particularly in men. Moreover, the relationship between breakfast and sleep timing on CVD risk factors differed by sex and age group.
Chitosan Nanoparticles Loaded with Capparis cartilaginea Decne Extract: Insights into Characterization and Antigenotoxicity In Vivo
Authors: Salman Asmaa S. , Alkhatib Shaza N. , Ahmed Fatimah M. , Hamouda Ragaa A.
Published: October 2023

Plant-based foods may enhance the prevention of cancer. The present investigation aimed to assess the antigenotoxic effects of chitosan nanoparticles (CNPs) when loaded with the ethanol extract of C. cartilaginea (CNPs/Cc). Synthesis of CNPs and CNPs/Cc and their characterization were carried out using TEM, EDS, DSC, and Zeta potential. For in vivo experiments, animal groups were treated in the following groups: negative control, ethyl methanesulfonate (EMS) (240 mg/kg), CNPs (350 mg/kg), high and low doses of CNPs/Cc, CNPs plus EMS, high dose of CNPs/Cc plus EMS, and low dose of CNPs/Cc plus EMS. Bone marrow chromosomal aberrations and sperm shape abnormalities were examined. TEM results showed that CNPs and CNPs/Cc are spherical particles. CNPs’ physical stability was observed to be lower than that of CNPs/Cc due to the presence of more positive charges on CNPs/Cc. EMS significantly enhanced chromosomal abnormalities and sperm shape abnormalities. CNPs showed powerful antigenotoxic properties. For the first time, it could be concluded that loading chitosan nanoparticles with C. cartilaginea extract significantly promotes its protective properties.
Beyond Choice: Reading Sigmund Freud at the End of Roe
Authors: McFadyen Karen
Published: October 2023

After Roe v. Wade was overturned by the Supreme Court, pregnant people lost their Constitutional protection of abortion. The new, visible politics of susceptibility have invited a revisitation to the psychoanalytic work of Sigmund Freud. This article examines the trauma narrative of Freud’s Beyond the Pleasure Principle and the theory of the death drive in elaborating the enduring cultural investment in protecting fetal life while examining its implications for pregnant subjects.
Quantitative Proteomic Analysis Deciphers the Molecular Mechanism for Endosperm Nuclear Division in Early Rice Seed Development
Authors: Yoon Jinmi , Min Cheol Woo , Kim Jiyoung , Baek Gibeom , Kim Dohyeon , Jang Jeong Woo , Gupta Ravi , Kim Sun Tae , Cho Lae-Hyeon
Published: October 2023

Understanding the molecular mechanisms underlying early seed development is important in improving the grain yield and quality of crop plants. We performed a comparative label-free quantitative proteomic analysis of developing rice seeds for the WT and osctps1-2 mutant, encoding a cytidine triphosphate synthase previously reported as the endospermless 2 (enl2) mutant in rice, harvested at 0 and 1 d after pollination (DAP) to understand the molecular mechanism of early seed development. In total, 5231 proteins were identified, of which 902 changed in abundance between 0 and 1 DAP seeds. Proteins that preferentially accumulated at 1 DAP were involved in DNA replication and pyrimidine biosynthetic pathways. Notably, an increased abundance of OsCTPS1 was observed at 1 DAP; however, no such changes were observed at the transcriptional level. We further observed that the inhibition of phosphorylation increased the stability of this protein. Furthermore, in osctps1-2, minichromosome maintenance (MCM) proteins were significantly reduced compared with those in the WT at 1 DAP, and mutations in OsMCM5 caused defects in seed development. These results highlight the molecular mechanisms underlying early seed development in rice at the post-transcriptional level.
Phenotypic Diversity in Leaf Cuticular Waxes in Brassica carinata Accessions
Authors: Tomasi Pernell , Abdel-Haleem Hussein
Published: October 2023

Brassica carinata has received considerable attention as a renewable biofuel crop for semi-arid zones due to its high oil content and polyunsaturated fatty acids contents. It is important to develop new drought-resistant cultivars of B. carinata production to expand its areas into more arid regions. The accumulation of leaf cuticular wax on plant surfaces is one mechanism that reduces non-stomatal water loss, thus increasing drought resistance in plants. To explore phenotypic variations in cuticular wax in B. carinata, leaf waxes were extracted and quantified from a diversity panel consisting of 315 accessions. The results indicate that the accessions have a wide range of total leaf wax content (289–1356 µg dm−2), wax classes, and their components. The C29 and C31 homologues of alkanes, C29 ketone homologue, C29 secondary alcohol, and C30 aldehyde were the most abundant leaf waxes extracted from B. carinata accessions. The high heritability values of these waxes point to the positive selection for high wax content during early generations of future B. carinata breeding programs. Positive correlation coefficients, combined with the effects of these waxes on leaf wax content accumulation, suggest that modifying specific wax content could increase the total wax content and enhance cuticle composition. The identified leaf wax content and compositions in B. carinata will lead to the future discovery of wax biosynthetic pathways, the dissection of its genetic regulatory networks, the identification of candidate genes controlling production of these waxes, and thus, develop and release new B. carinata drought-tolerant cultivars.
Obtaining Cellulose Nanocrystals from Olive Tree Pruning Waste and Evaluation of Their Influence as a Reinforcement on Biocomposites
Authors: Jurado-Contreras Sofía , Navas-Martos Francisco J. , García-Ruiz Ángeles , Rodríguez-Liébana José A. , La Rubia M. Dolores
Published: October 2023

The objective of this work is to improve the mechanical properties of polylactic acid (PLA) by incorporating cellulose nanocrystals (CNC) previously obtained from a cellulose pulp extracted from olive tree pruning (OTP) waste. Composites were manufactured by melt processing and injection moulding to evaluate the effect of the introduction of CNC with conventional manufacturing methods. This OTP-cellulose pulp was subjected to a further purification process by bleaching, thus bringing the cellulose content up to 86.1%wt. This highly purified cellulose was hydrolysed with sulfuric acid to obtain CNCs with an average length of 267 nm and a degradation temperature of 300 °C. The CNCs obtained were used in different percentages (1, 3, and 5%wt.) as reinforcement in the manufacture of PLA-based composites. The effect of incorporating CNC into PLA matrix on the mechanical, water absorption, thermal, structural, and morphological properties was studied. Maximum tensile stress and Young’s modulus improved by 87 and 58%, respectively, by incorporating 3 and 5%wt. CNC. Charpy impact strength increased by 21% with 3%wt. These results were attributed to the good dispersion of CNCs in the matrix, which was corroborated by SEM images. Crystallinity index, glass transition, and melting temperatures were maintained.
Electrospun Nanofibers for Biomedical, Sensing, and Energy Harvesting Functions
Authors: Demir Didem , Bolgen Nimet , Vaseashta Ashok
Published: October 2023

The process of electrospinning is over a century old, yet novel material and method achievements, and later the addition of nanomaterials in polymeric solutions, have spurred a significant increase in research innovations with several unique applications. Significant improvements have been achieved in the development of electrospun nanofibrous matrices, which include tailoring compositions of polymers with active agents, surface functionalization with nanoparticles, and encapsulation of functional materials within the nanofibers. Recently, sequentially combining fabrication of nanofibers with 3D printing was reported by our group and the synergistic process offers fiber membrane functionalities having the mechanical strength offered by 3D printed scaffolds. Recent developments in electrospun nanofibers are enumerated here with special emphasis on biomedical technologies, chemical and biological sensing, and energy harvesting aspects in the context of e-textile and tactile sensing. Energy harvesting offers significant advantages in many applications, such as biomedical technologies and critical infrastructure protection by using the concept of finite state machines and edge computing. Many other uses of devices using electrospun nanofibers, either as standalone or conjoined with 3D printed materials, are envisaged. The focus of this review is to highlight selected novel applications in biomedical technologies, chem.-bio sensing, and broadly in energy harvesting for use in internet of things (IoT) devices. The article concludes with a brief projection of the future direction of electrospun nanofibers, limitations, and how synergetic combination of the two processes will open pathways for future discoveries.
Artificial Neural Networks for Predicting Mechanical Properties of Crystalline Polyamide12 via Molecular Dynamics Simulations
Authors: Tamur Caglar , Li Shaofan , Zeng Danielle
Published: October 2023

Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of such products is fundamentally different from the ones obtained by using conventional manufacturing methods, which makes the task even more difficult. As the first step of a systematic multiscale approach, in this work, we have developed an artificial neural network (ANN) to predict the mechanical properties of the crystalline form of Polyamide12 (PA12) based on data collected from molecular dynamics (MD) simulations. Using the machine learning approach, we are able to predict the stress–strain relations of PA12 once the macroscale deformation gradient is provided as an input to the ANN. We have shown that this is an efficient and accurate approach, which can provide a three-dimensional molecular-level anisotropic stress–strain relation of PA12 for any macroscale mechanics model, such as finite element modeling at arbitrary quadrature points. This work lays the foundation for a multiscale finite element method for simulating semicrystalline polymers, which will be published as a separate study.
Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis
Authors: Istrate Daniela , Crisan Luminita
Published: October 2023

Dipeptidyl Peptidase 4 (DPP-4) expressed on the surface of many different cells is a promising target to develop new candidates for Type 2 diabetes mellitus (T2DM) management. In this light, we performed a computer-aided simulation involving 3-D pharmacophore screening, molecular docking, and drug-likeness assessment to identify novel potential DPP-4 inhibitors with an improved physicochemical profile to treat T2DM. In addition, global reactivity descriptors, including HOMO and LUMO energies, HOMO-LUMO gaps, and Fukui indices, were computed to confirm the essential structural features to achieve DPP-4 activity. The gathered outcomes recommend that eight out of 240 million compounds collected from eight pre-built databases (Molport, Chembl30, ChemDiv, ChemSpace, Mcule, Mcule-ultimate, LabNetwork, and ZINC) are drug-like and nontoxic, and may serve as starting points for designing novel, selective, and potent DPP-4 inhibitors. Furthermore, the success of the current workflow to identify DPP-4-potential inhibitors strengthens its potential efficiency to also predict natural compounds as novel adjutants or main therapy for T2DM or discover hit compounds of other targets.
Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City
Authors: Tian Yuxin , Wang Zhenghai , Xiao Bei
Published: October 2023

Ground deformation poses an imminent threat to urban development. This study uses the multiscale geographically weighted regression (MGWR) model to investigate the spatial heterogeneity in factors influencing ground deformation, thereby elucidating the drivers behind regional variations in ground deformation patterns. To gain insights into the characteristics of ground deformation in Zhuhai, China, and its spatial relationship with natural and anthropogenic features, we initially utilized the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method to collect data on ground deformation and its distribution across the entire area. Concurrently, remote sensing imagery was used to identify the various mechanisms affecting ground deformation during the same period, including geotectonic conditions, geographic environment, and human activities. Subsequently, we used the MGWR model to quantitatively estimate the effects of these driving force factors on ground deformation in Zhuhai. Our findings reveal significant ground deformation in specific areas, including Baijiao Town (Doumen District), Hongqi Town (Jinwan District), the Gaolan Port Economic Zone, and the northern part of Hengqin Town, with peak deformation rates reaching 117 mm/y. Key drivers of ground deformation in Zhuhai include NDVI, groundwater extraction intensity, and soft soil thickness. The application of the MGWR model, with an R-sq value of 0.910, outperformed both the global regression model ordinary least squares (OLS), with an R-sq value of 0.722, and the local regression model geographically weighted regression (GWR), with an R-sq value of 0.770, in identifying driving forces. This study can provide valuable insights for government policies aimed at mitigating the disaster risks associated with urban ground deformation.
Generalized Zero-Shot Space Target Recognition Based on Global-Local Visual Feature Embedding Network
Authors: Zhang Yuanpeng , Guan Jingye , Wang Haobo , Li Kaiming , Luo Ying , Zhang Qun
Published: October 2023

Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we propose a space target recognition framework to simultaneously recognize space targets from both seen and unseen classes. First, we defined semantic attributes to describe the characteristics of different categories of space targets. Second, we constructed a dual-branch neural network, termed the global-local visual feature embedding network (GLVFENet), which jointly learns global and local visual features to obtain discriminative feature representations, thereby achieving GZSL for space targets with higher accuracy. Specifically, the global visual feature embedding subnetwork (GVFE-Subnet) calculates the compatibility score by measuring the cosine similarity between the projection of global visual features in the semantic space and various semantic vectors, thereby obtaining global visual embeddings. The local visual feature embedding subnetwork (LVFE-Subnet) introduces soft space attention, and an encoder discovers the semantic-guided local regions in the image to then generate local visual embeddings. Finally, the visual embeddings from both branches were combined and matched with semantics. The calibrated stacking method is introduced to achieve GZSL recognition of space targets. Extensive experiments were conducted on an electromagnetic simulation dataset of nine categories of space targets, and the effectiveness of our GLVFENet is confirmed.
Dynamic Monitoring of Surface Water Bodies and Their Influencing Factors in the Yellow River Basin
Authors: Zhao Zikun , Li Huanwei , Song Xiaoyan , Sun Wenyi
Published: October 2023

Surface water bodies exhibit dynamic characteristics, undergoing variations in size, shape, and flow patterns over time due to numerous natural and human factors. The monitoring of spatial-temporal changes in surface water bodies is crucial for the sustainable development and efficient utilization of water resources. In this study, Landsat series images on the Google Earth Engine (GEE) platform, along with the HydroLAKES and China Reservoir datasets, were utilized to establish an extraction process for surface water bodies from 1986 to 2021 in the Yellow River Basin (YRB). The study aims to investigate the dynamics of surface water bodies and the driving factors within the YRB. The findings reveal an overall expansion tendency of surface water bodies in the YRB between 1986 and 2021. In the YRB, the total area of surface water bodies, natural lakes, and artificial reservoirs increased by 2983.8 km2 (40.4%), 281.1 km2 (11.5%), and 1017.6 km2 (101.7%), respectively. A total of 102 natural lakes expanded, while 23 shrank. Regarding artificial reservoirs, 204 expanded, and 77 shrank. The factors that contributed most to the increase in the surface water bodies were increasing precipitation and reservoir construction, whose contribution rates could reach 47% and 32.6%, respectively. Additionally, the rising temperatures melted permafrost, ice, and snow, positively correlating with water expansion in the upper reaches of the YRB, particularly natural lakes.
Research on the Real-Time Ambiguity Resolution Algorithm of GPS/Galileo/BDS Based on CNES Real-Time Products
Authors: Gao Meng , Meng Ziheng , Zhu Huizhong , Xu Aigong , Cao Zhihua , Tan Chunbo
Published: October 2023

Real-Time (RT) Precise Point Positioning (PPP) uses precise satellite orbits and clock corrections, and employs a separate receiver for positioning. With the growing demand, RT PPP is becoming an increasingly popular research topic. The ambiguity resolution (AR) can significantly improve the positioning accuracy and convergence time of PPP, so it is essential to study PPPAR in RT mode. In this paper, 37 MGEX stations from around the world are chosen, and the RT orbit, clock, and phase biases products broadcast by the Centre National d’Etudes Spatiales (CNES) are applied to PPPAR. Additionally, the residuals of the RT phase biases products, convergence time, and positioning accuracy are investigated. The results indicate that GPS products have the best quality of AR, with wide-lane (WL) and narrow-lane (NL) residuals of 98.9% and 95.3%, respectively, within ±0.25 cycles. Within ±0.25 cycles, the WL and NL residuals of the Galileo are 98.2% and 94.3%, respectively. Within ±0.25 cycles, the (Beidou Navigation Satellite System) BDS has a poor quality of AR, with WL and NL residuals of 97.3% and 73.1%, respectively. Due to the poor quality of the BDS AR, the convergence time of the BDS is not calculated in this paper. The convergence time of other systems is significantly reduced after AR processing, and the convergence time of the GPS/Galileo combination is the fastest, being 17.14 min in kinematic mode and only 11.85 min in static mode. The positioning accuracy of the GPS, Galileo, GPS/Galileo, and GPS/Galileo/BDS in the E and U directions is significantly improved after PPPAR.
Empowering Climate Resilience: Leveraging Cloud Computing and Big Data for Community Climate Change Impact Service (C3IS)
Authors: Levin Eugene , Beisekenov Nail , Wilson Michael , Sadenova Marzhan , Nabaweesi Rosemary , Nguyen Long
Published: October 2023

The challenges associated with climate change are increasing, so there is an urgent need for modern tools to effectively assess, predict and minimise climate risks. This research paper presents the results of the development of the innovative Community Climate Change Impacts Service (C3IS) system, which represents a paradigm shift in climate risk analysis. C3IS is a module that includes a pioneering set of tools with an interactive application programming interface (API) fully integrated with the Google Earth Engine (GEE) platform. The C3IS module enables lightning-fast collection and visualisation (in real time) of critical climate risk data through flexible integration with GEE. The advantages of this integration are the ability to use the GEE platform to access an extensive petabyte-scale catalogue of geospatial data and an ever-expanding database of satellite imagery. The defining feature of the developed module is accessibility and usability due to the exclusion of operations such as the time-consuming preliminary processing of “big data”; complex modelling; and large-scale data storage. The study shows the promising application of the C3IS module for the operational decision making and development of sound strategies for effective climate change mitigation.
Coherent Accumulation for Measuring Maneuvering Weak Targets Based on Stepped Dechirp Generalized Radon–Fourier Transform
Authors: Sun Yuxian , Chang Shaoqiang , Cai Bowen , Wang Dewu , Liu Quanhua
Published: October 2023

The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this paper, we investigate the capabilities of dechirp-receiving stepped-frequency radar, a modern system using a linear frequency modulation signal for downconversion. This permits the radar to function at reduced sampling rates while maintaining the transmission of large-bandwidth signals and achieving synthetic imaging. Our primary contribution is introducing the stepped dechirp generalized Radon–Fourier transform (stepped DGRFT) algorithm. This novel approach allows the radar system to perform coherent accumulation, enhancing the accuracy of motion parameter estimates for low-RCS maneuvering targets. Results from our simulations and measured data analysis validate the effectiveness of our proposed algorithm, demonstrating its superiority over other methods.
Canopy Height Mapping for Plantations in Nigeria Using GEDI, Landsat, and Sentinel-2
Authors: Tsao Angela , Nzewi Ikenna , Jayeoba Ayodeji , Ayogu Uzoma , Lobell David B.
Published: October 2023

Canopy height data from the Global Ecosystem Dynamics Investigation (GEDI) mission has powered the development of global forest height products, but these data and products have not been validated in non-forest tree plantation settings. In this study, we collected field observations of the canopy heights throughout oil palm plantations in Nigeria and evaluated the performance of existing global canopy height map (CHM) products as well as a local model trained on the GEDI and various Landsat and Sentinel-2 feature combinations. We found that existing CHMs fared poorly in the region, with mean absolute errors (MAE) of 4.2–6.2 m. However, the locally trained models performed well (MAE = 2.5 m), indicating that using the GEDI and optical satellite data can still be effective, even in a region with relatively sparse GEDI coverage. In addition to improved overall performance, the local model was especially effective at reducing errors for short (<5 m) trees, where the global products struggle to capture the canopy height.
An AI-Based Workflow for Fast Registration of UAV-Produced 3D Point Clouds
Authors: Feng Yong , Leung Ka Lun , Li Yingkui , Wong Kwai Lam
Published: October 2023

The integration of structure from motion (SFM) and unmanned aerial vehicle (UAV) technologies has allowed for the generation of very high-resolution three-dimensional (3D) point cloud data (up to millimeters) to detect and monitor surface changes. However, a bottleneck still exists in accurately and rapidly registering the point clouds at different times. The existing point cloud registration algorithms, such as the Iterative Closest Point (ICP) and the Fast Global Registration (FGR) method, were mainly developed for the registration of small and static point cloud data, and do not perform well when dealing with large point cloud data with potential changes over time. In particular, registering large data is computationally expensive, and the inclusion of changing objects reduces the accuracy of the registration. In this paper, we develop an AI-based workflow to ensure high-quality registration of the point clouds generated using UAV-collected photos. We first detect stable objects from the ortho-photo produced by the same set of UAV-collected photos to segment the point clouds of these objects. Registration is then performed only on the partial data with these stable objects. The application of this workflow using the UAV data collected from three erosion plots at the East Tennessee Research and Education Center indicates that our workflow outperforms the existing algorithms in both computational speed and accuracy. This AI-based workflow significantly improves computational efficiency and avoids the impact of changing objects for the registration of large point cloud data.
Genomic and Proteomic Analysis of Pseudomonas aeruginosa Isolated from Industrial Wastewater to Assess Its Resistance to Antibiotics
Authors: Wang Zongwu , Tian Wantao , Sun Siyuan , Chen Xing , Wang Haifeng
Published: October 2023

Industrial wastewater usually contains a large amount of organic and inorganic pollutants, and many microorganisms. However, the types of microorganism present in industrial wastewater are still unclear. The aim of this study was to analyze the physicochemical properties and drug resistance of Pseudomonas aeruginosa isolated from industrial wastewater containing high concentrations of sulfate compounds. Pseudomonas aeruginosa was isolated from industrial wastewater from industrial produce with high concentrations of sulfate and phosphate, and mass spectrometry identification, gene identification, biochemical analysis and genomic and proteomic property identification were carried out. According to the results of matrix-assisted flight mass spectrometry and 16S rDNA sequencing, the isolated bacterium was identified as Pseudomonas aeruginosa, and was positive for reactions of ONPG, ACE, GLU, MNE, etc. Through growth experiments, it can be seen that Pseudomonas aeruginosa had a significant growth rate in the LB medium. Antibiotic sensitivity tests showed that Pseudomonas aeruginosa was susceptible to most antibiotics and moderately resistant to Polymyxin B and Polymyxin E. The drug resistance gene experiment showed that Pseudomonas aeruginosa had the gyrB gene related to antibiotic resistance. Proteomic analysis revealed that six proteins were involved in antibiotic resistance. This experiment isolated Pseudomonas aeruginosa from industrial produce wastewater containing high concentrations of sulfate and phosphate ions, providing a new perspective for further research on the characteristics and drug resistance of microorganisms in industrial wastewater and their potential functions when using them to deal with environmental pollution.
Assessing the Socioeconomic Impacts of Transitioning from Plutocracy to Meritocracy in University Admissions
Authors: Labadze Lasha
Published: October 2023

This paper examines the effects of transitioning from a plutocratic to a meritocratic university admission system on students and society. We develop a theoretical model to predict the socioeconomic impacts of this transition and validate our predictions using simulations and empirical data from Georgia, where education reform shifted university admissions from a plutocratic system to a meritocratic one, providing a natural experiment that enables us to validate predictions of our theoretical model. The findings demonstrate positive outcomes for individuals from low socioeconomic backgrounds, including improved educational attainment and increased labor income.
Designing for Intergenerational Communication among Older Adults: A Systematic Inquiry in Old Residential Communities of China’s Yangtze River Delta
Authors: Li Cun , Cao Ming
Published: October 2023

Presently, a substantial majority of older individuals in urban regions of China prefer to inhabit older residential communities over newer counterparts. Within these aging communities, the intricate matter of intergenerational communication among older adults presents a complex and multifaceted issue that warrants comprehensive investigation from a systematic perspective. This paper first employs the observational method to study multiple old residential communities in a city in the Yangtze River Delta region of China. The POEMS framework and the AEIOU framework are applied, focusing on the analysis of individuals and the interaction between individuals and objects, respectively. Semistructured interviews are then conducted with three groups of people, emphasizing community participation by older adults, intergenerational interaction from the perspective of older adults, and intergenerational interaction from the perspective of young people. Finally, the paper categorizes the types and characteristics of individuals in the old communities, identifying the intersections between these groups. The current social situation of older adults and young people is summarized, including behavioral and psychological characteristics and social interaction challenges. Based on these findings, ten system design directions to enhance intergenerational interaction in old communities are proposed, and three of these system design directions are further developed.
Energy Efficient CH Selection Scheme Based on ABC and Q-Learning Approaches for IoUT Applications
Authors: Sayed Ali Elmustafa , Saeed Rashid A. , Eltahir Ibrahim Khider , Abdelhaq Maha , Alsaqour Raed , Mokhtar Rania A.
Published: October 2023

Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.
Effects of Sesquiterpene Lactones on Primary Cilia Formation (Ciliogenesis)
Authors: Murillo-Pineda Marina , Coto-Cid Juan M. , Romero María , Zorrilla Jesús G. , Chinchilla Nuria , Medina-Calzada Zahara , Varela Rosa M. , Juárez-Soto Álvaro , Macías Francisco A. , Reales Elena
Published: October 2023

Sesquiterpene lactones (SLs), plant-derived metabolites with broad spectra of biological effects, including anti-tumor and anti-inflammatory, hold promise for drug development. Primary cilia, organelles extending from cell surfaces, are crucial for sensing and transducing extracellular signals essential for cell differentiation and proliferation. Their life cycle is linked to the cell cycle, as cilia assemble in non-dividing cells of G0/G1 phases and disassemble before entering mitosis. Abnormalities in both primary cilia (non-motile cilia) and motile cilia structure or function are associated with developmental disorders (ciliopathies), heart disease, and cancer. However, the impact of SLs on primary cilia remains unknown. This study evaluated the effects of selected SLs (grosheimin, costunolide, and three cyclocostunolides) on primary cilia biogenesis and stability in human retinal pigment epithelial (RPE) cells. Confocal fluorescence microscopy was employed to analyze the effects on primary cilia formation (ciliogenesis), primary cilia length, and stability. The effects on cell proliferation were evaluated by flow cytometry. All SLs disrupted primary cilia formation in the early stages of ciliogenesis, irrespective of starvation conditions or cytochalasin-D treatment, with no effect on cilia length or cell cycle progression. Interestingly, grosheimin stabilized and promoted primary cilia formation under cilia homeostasis and elongation treatment conditions. Thus, SLs have potential as novel drugs for ciliopathies and tumor treatment.
Rational Design of AAV-rh74, AAV3B, and AAV8 with Limited Liver Targeting
Authors: Chan Christopher , Harris Kathryn K. , Zolotukhin Sergei , Keeler Geoffrey D.
Published: October 2023

Recombinant adeno-associated viruses (rAAVs) have become one of the leading gene therapies for treating a variety of diseases. One factor contributing to rAAVs’ success is the fact that a wide variety of tissue types can be transduced by different serotypes. However, one commonality amongst most serotypes is the high propensity for liver transduction when rAAVs are administered peripherally. One of the few exceptions is the naturally occurring clade F AAV hematopoietic stem cell 16 (AAVHSC16). AAVHSC16 represents an interesting capsid in that it shows minimal liver transduction when injected peripherally. For capsids other than AAVHSC16, targeting non-liver tissues via peripheral AAV injection represents a challenge due to the high liver transduction. Thus, there is a demand for liver-de-targeted rAAV vectors. The rational design of rAAV capsids relies on current knowledge to design improved capsids and represents one means of developing capsids with reduced liver transduction. Here, we utilized data from the AAVHSC16 capsid to rationally design four non-clade F rAAV capsids that result in reduced liver transduction following peripheral injection.
Identification, Sequencing, and Molecular Analysis of RNA2 of Artichoke Italian Latent Virus Isolates from Known Hosts and a New Host Plant Species
Authors: Elbeaino Toufic , Ben Slimen Amani , Belgacem Imen , Mnari-Hattab Monia , Spanò Roberta , Digiaro Michele , Abdelkhalek Ahmed
Published: October 2023

Despite its first description in 1977 and numerous reports of its presence in various plant species in many countries, the molecular information available in GenBank for artichoke Italian latent virus (AILV) is still limited to a single complete genome sequence (RNA1 and 2) of a grapevine isolate (AILV-V) and a partial portion of the RNA2 sequence from an isolate of unknown origin and host. Here, we report the results of molecular analyses conducted on the RNA2 of some AILV isolates, sequenced for the first time in this study, together with the first-time identification of AILV in a new host plant species, namely chard (Beta vulgaris subsp. vulgaris), associated with vein clearing and mottling symptoms on leaves. The different AILV isolates sequenced were from artichoke (AILV-C), gladiolus (AILV-G), Sonchus (AILV-S), and chard (AILV-B). At the molecular level, the sequencing results of the RNA2 segments showed that AILV-C, AILV-G, AILV-S, and AILV-B had a length of 4629 nt (excluding the 3′ terminal polyA tail), which is one nt shorter than that of the AILV-V reported in GenBank. A comparison of the RNA2 coding region sequences of all the isolates showed that AILV-V was the most divergent isolate, with the lowest sequence identities of 83.2% at the nucleotide level and 84.7% at the amino acid level. Putative intra-species sequence recombination sites were predicted among the AILV isolates, mainly involving the genomes of AILV-V, AILV-C, and AILV-B. This study adds insights into the variability of AILV and the occurrence of recombination that may condition plant infection.
HIV-1 Remission: Accelerating the Path to Permanent HIV-1 Silencing
Authors: Lyons Danielle E. , Kumar Priti , Roan Nadia R. , Defechereux Patricia A. , Feschotte Cedric , Lange Ulrike C. , Murthy Niren , Sameshima Pauline , Verdin Eric , Ake Julie A. , Parsons Matthew S. , Nath Avindra , Gianella Sara , Smith Davey M. , Kallas Esper G. , Villa Thomas J. , Strange Richard , Mwesigwa Betty , Furler O’Brien Robert L. , Nixon Douglas F. , Ndhlovu Lishomwa C. , Valente Susana T. , Ott Melanie
Published: October 2023

Despite remarkable progress, a cure for HIV-1 infection remains elusive. Rebound competent latent and transcriptionally active reservoir cells persevere despite antiretroviral therapy and rekindle infection due to inefficient proviral silencing. We propose a novel “block-lock-stop” approach, entailing long term durable silencing of viral expression towards an irreversible transcriptionally inactive latent provirus to achieve long term antiretroviral free control of the virus. A graded transformation of remnant HIV-1 in PLWH from persistent into silent to permanently defective proviruses is proposed, emulating and accelerating the natural path that human endogenous retroviruses (HERVs) take over millions of years. This hypothesis was based on research into delineating the mechanisms of HIV-1 latency, lessons from latency reversing agents and advances of Tat inhibitors, as well as expertise in the biology of HERVs. Insights from elite controllers and the availability of advanced genome engineering technologies for the direct excision of remnant virus set the stage for a rapid path to an HIV-1 cure.
Circularized Nanodiscs for Multivalent Mosaic Display of SARS-CoV-2 Spike Protein Antigens
Authors: Mabrouk Moustafa T. , Zidan Asmaa A. , Aly Nihal , Mohammed Mostafa T. , Ghantous Fadi , Seaman Michael S. , Lovell Jonathan F. , Nasr Mahmoud L.
Published: October 2023

The emergence of vaccine-evading SARS-CoV-2 variants urges the need for vaccines that elicit broadly neutralizing antibodies (bnAbs). Here, we assess covalently circularized nanodiscs decorated with recombinant SARS-CoV-2 spike glycoproteins from several variants for eliciting bnAbs with vaccination. Cobalt porphyrin–phospholipid (CoPoP) was incorporated into the nanodisc to allow for anchoring and functional orientation of spike trimers on the nanodisc surface through their His-tag. Monophosphoryl-lipid (MPLA) and QS-21 were incorporated as immunostimulatory adjuvants to enhance vaccine responses. Following optimization of nanodisc assembly, spike proteins were effectively displayed on the surface of the nanodiscs and maintained their conformational capacity for binding with human angiotensin-converting enzyme 2 (hACE2) as verified using electron microscopy and slot blot assay, respectively. Six different formulations were prepared where they contained mono antigens; four from the year 2020 (WT, Beta, Lambda, and Delta) and two from the year 2021 (Omicron BA.1 and BA.2). Additionally, we prepared a mosaic nanodisc displaying the four spike proteins from year 2020. Intramuscular vaccination of CD-1 female mice with the mosaic nanodisc induced antibody responses that not only neutralized matched pseudo-typed viruses, but also neutralized mismatched pseudo-typed viruses corresponding to later variants from year 2021 (Omicron BA.1 and BA.2). Interestingly, sera from mosaic-immunized mice did not effectively inhibit Omicron spike binding to human ACE-2, suggesting that some of the elicited antibodies were directed towards conserved neutralizing epitopes outside the receptor binding domain. Our results show that mosaic nanodisc vaccine displaying spike proteins from 2020 can elicit broadly neutralizing antibodies that can neutralize mismatched viruses from a following year, thus decreasing immune evasion of new emerging variants and enhancing healthcare preparedness.
Advancing Vaccine Strategies against Candida Infections: Exploring New Frontiers
Authors: Kaur Gurpreet , Chawla Sonam , Kumar Piyush , Singh Ritu
Published: October 2023

Candida albicans, along with several non-albicans Candida species, comprise a prominent fungal pathogen in humans, leading to candidiasis in various organs. The global impact of candidiasis in terms of disease burden, suffering, and fatalities is alarmingly high, making it a pressing global healthcare concern. Current treatment options rely on antifungal drugs such as azoles, polyenes, and echinocandins but are delimited due to the emergence of drug-resistant strains and associated adverse effects. The current review highlights the striking absence of a licensed antifungal vaccine for human use and the urgent need to shift our focus toward developing an anti-Candida vaccine. A number of factors affect the development of vaccines against fungal infections, including the host, intraspecies and interspecies antigenic variations, and hence, a lack of commercial interest. In addition, individuals with a high risk of fungal infection tend to be immunocompromised, so they are less likely to respond to inactivated or subunit whole organisms. Therefore, it is pertinent to discover newer and novel alternative strategies to develop safe and effective vaccines against fungal infections. This review article provides an overview of current vaccination strategies (live attenuated, whole-cell killed, subunit, conjugate, and oral vaccine), including their preclinical and clinical data on efficacy and safety. We also discuss the mechanisms of immune protection against candidiasis, including the role of innate and adaptive immunity and potential biomarkers of protection. Challenges, solutions, and future directions in vaccine development, namely, exploring novel adjuvants, harnessing the trained immunity, and utilizing immunoinformatics approaches for vaccine design and development, are also discussed. This review concludes with a summary of key findings, their implications for clinical practice and public health, and a call to action for continued investment in candidiasis vaccine research.
Antimicrobial Susceptibility Profiles of Acinetobacter baumannii Strains, Isolated from Clinical Cases of Companion Animals in Greece
Authors: Lysitsas Marios , Triantafillou Eleutherios , Chatzipanagiotidou Irene , Antoniou Konstantina , Valiakos George
Published: October 2023

Acinetobacter baumannii–calcoaceticus (Abc) Complex bacteria are troublesome nosocomial pathogens in human medicine, especially during the last 30 years. Recent research in veterinary medicine also supports its emergence as an animal pathogen. However, relevant data are limited. In this study, we obtained 41 A. baumannii isolates from clinical samples of canine and feline origin collected in veterinary clinics in Greece between 2020 and 2023. Biochemical identification, antimicrobial susceptibility testing, molecular identification and statistical analysis were performed. Most of the samples were of soft tissue and urine origin, while polymicrobial infections were recorded in 29 cases. Minocycline was the most effective in vitro antibiotic, whereas high resistance rates were detected for almost all the agents tested. Notably, 20 isolates were carbapenem resistant and 19 extensively drug resistant (XDR). This is the first report of canine and feline infections caused by Abc in Greece. The results create concerns regarding the capability of the respective bacteria to cause difficult-to-treat infections in pets and persist in veterinary facilities through hospitalized animals, contaminated equipment, and surfaces. Moreover, the prevalence of highly resistant strains in companion animals constitutes a public health issue since they could act as a reservoir, contributing to the spread of epidemic clones in a community.